2-One_layer_encode_with_random_impulse.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook shows that how random noise embedder, impulse embedder works with different number of samples when embedding dimension is large enough."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pylab import *\n",
    "from skimage.color import rgb2gray\n",
    "from skimage.transform import rescale,resize\n",
    "from torch.autograd import Variable\n",
    "import torch, time,os\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import scipy\n",
    "\n",
    "\n",
    "# Set the CUDA flag\n",
    "cuda_flag = True\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '0'\n",
    "criterion = torch.nn.MSELoss()\n",
    "epochs = 4000\n",
    "lr = 1e-4\n",
    "\n",
    "sample_N = 512\n",
    "sample_ds = [1,2,3,4,6,8,11,16]\n",
    "embedding_dimension = 10000\n",
    "\n",
    "Nd = len(sample_ds)\n",
    "N_test = 10\n",
    "N_repeat = 10\n",
    "\n",
    "#kk = torch.randint(512,(N_test,))\n",
    "kk = [158, 329,  47, 344, 257, 410, 426, 199, 426, 295]\n",
    "#kk = [ 88, 269, 409,  59, 245, 382, 277, 362,  23, 308, 504, 448, 411, 304, 365, 200,  92, 350, 405,  77]\n",
    "\n",
    "losses_ = np.zeros((Nd,N_test,N_repeat,epochs))\n",
    "sr_ = np.zeros((Nd,N_test,N_repeat))\n",
    "trn_psnr_ = np.zeros((Nd,N_test,N_repeat,epochs))\n",
    "tst_psnr_ = np.zeros((Nd,N_test,N_repeat,epochs))\n",
    "\n",
    "file_path = 'Final_2one/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "rgb = imread('4.2.07.tiff');im = rgb2gray(rgb);\n",
    "signal = torch.from_numpy(im).type(torch.FloatTensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "width = 256\n",
    "\n",
    "# Here define the MLP\n",
    "class SLP(nn.Module):\n",
    "    def __init__(self,input_dim):\n",
    "        super(SLP, self).__init__()\n",
    "        self.fco = nn.Linear(input_dim, 1)\n",
    "    def forward(self, x):\n",
    "        out = self.fco(x)\n",
    "        return out\n",
    "    def name(self):\n",
    "        return \"SLP\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def srank_func(X):\n",
    "    # Get the SVD\n",
    "    (u,s,v) = torch.svd(X)\n",
    "    sr = (s*s).sum()/s[0]/s[0]\n",
    "    return sr\n",
    "\n",
    "def psnr_func(x,y):\n",
    "    diff = x - y\n",
    "    err = (diff*diff).flatten().mean().log10()\n",
    "    return -10*err\n",
    "\n",
    "def impulse_embedding(x, b, s=1e-4):\n",
    "    emb = (abs(x-b)<s).type(torch.FloatTensor)\n",
    "    return emb/emb.norm(dim=1).max()\n",
    "\n",
    "def square_embedding(x, b, f=1):\n",
    "    emb = (torch.sin(2.*np.pi*f*(x-b))>0).type(torch.FloatTensor)\n",
    "    return emb/emb.norm(dim=1).max()\n",
    "\n",
    "def shift_embedding(x, c):\n",
    "    all_emb = scipy.linalg.circulant(c)\n",
    "    idx = (x*all_emb.shape[0]).int().squeeze()\n",
    "    emb = torch.from_numpy(all_emb[idx]).type(torch.FloatTensor)\n",
    "    return emb/emb.norm(dim=1).max()\n",
    "    \n",
    "\n",
    "def norm_func(x):\n",
    "    # Flatten the data \n",
    "    #x = x.flatten(1,3)\n",
    "    \n",
    "    # Normalize for gain and bias\n",
    "    y = x-x.mean(1).unsqueeze(-1)\n",
    "    y = x/x.norm(dim=1).unsqueeze(-1)    \n",
    "    return y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Impulse embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==>>> N: 0, F: 0, train psnr: 123.317--- , test psnr: 10.6905--- , time: 13.7508 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 123.284--- , test psnr: 10.6843--- , time: 20.6273 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 122.983--- , test psnr: 10.5398--- , time: 27.5644 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 123.185--- , test psnr: 10.7061--- , time: 34.5919 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 123.262--- , test psnr: 10.708--- , time: 41.4767 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 123.047--- , test psnr: 10.5113--- , time: 48.5085 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 123.319--- , test psnr: 10.6845--- , time: 55.4044 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 123.104--- , test psnr: 10.6111--- , time: 62.361 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 122.93--- , test psnr: 10.5241--- , time: 69.3007 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 122.931--- , test psnr: 10.5585--- , time: 76.2198 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 126.884--- , test psnr: 11.3291--- , time: 7.00134 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 126.813--- , test psnr: 11.2669--- , time: 17.3118 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 126.789--- , test psnr: 11.3537--- , time: 27.2858 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 126.797--- , test psnr: 11.1977--- , time: 37.5854 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 126.809--- , test psnr: 11.3295--- , time: 48.5938 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 126.566--- , test psnr: 11.1533--- , time: 58.6001 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 126.626--- , test psnr: 11.2009--- , time: 69.0974 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 126.811--- , test psnr: 11.1797--- , time: 80.2207 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 126.824--- , test psnr: 11.1778--- , time: 91.2214 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 126.583--- , test psnr: 11.2614--- , time: 102.633 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 124.474--- , test psnr: 10.6204--- , time: 11.0731 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 125.42--- , test psnr: 10.8328--- , time: 21.9368 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 124.887--- , test psnr: 10.7301--- , time: 33.1414 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 124.838--- , test psnr: 10.7895--- , time: 44.6489 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 125.03--- , test psnr: 10.7355--- , time: 55.5441 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 124.978--- , test psnr: 10.7416--- , time: 66.734 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 125.352--- , test psnr: 10.8554--- , time: 77.5935 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 124.835--- , test psnr: 10.7626--- , time: 88.2528 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 124.851--- , test psnr: 10.7609--- , time: 98.9955 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 125.104--- , test psnr: 10.8003--- , time: 110.287 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 127.785--- , test psnr: 11.9625--- , time: 10.7844 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 127.746--- , test psnr: 11.977--- , time: 21.4701 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 128.134--- , test psnr: 11.9562--- , time: 32.3492 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 127.953--- , test psnr: 11.8157--- , time: 43.2977 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 128.166--- , test psnr: 11.848--- , time: 54.49 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 128.06--- , test psnr: 11.9817--- , time: 65.4737 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 127.805--- , test psnr: 11.9037--- , time: 76.4191 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 127.679--- , test psnr: 11.7548--- , time: 87.1521 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 127.763--- , test psnr: 11.7571--- , time: 98.4905 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 127.761--- , test psnr: 11.7839--- , time: 109.625 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 125.944--- , test psnr: 11.0156--- , time: 11.1658 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 125.686--- , test psnr: 10.9414--- , time: 21.9838 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 126--- , test psnr: 11.0146--- , time: 32.4868 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 125.852--- , test psnr: 11.015--- , time: 44.0994 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 125.907--- , test psnr: 10.958--- , time: 55.2344 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 125.307--- , test psnr: 10.9017--- , time: 66.085 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 126.206--- , test psnr: 11.027--- , time: 77.0721 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 126.201--- , test psnr: 10.9655--- , time: 88.6239 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 125.918--- , test psnr: 11.0347--- , time: 99.5273 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 125.608--- , test psnr: 10.8829--- , time: 110.522 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 128.165--- , test psnr: 12.208--- , time: 10.2987 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 128.272--- , test psnr: 12.2334--- , time: 21.2571 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 128.361--- , test psnr: 12.2568--- , time: 32.5451 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 128.199--- , test psnr: 12.1783--- , time: 44.2087 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 128.096--- , test psnr: 12.2822--- , time: 55.4003 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 128.458--- , test psnr: 12.2872--- , time: 66.0441 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 128.387--- , test psnr: 12.3326--- , time: 76.8593 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 128.376--- , test psnr: 12.3164--- , time: 87.7318 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 128.189--- , test psnr: 12.2257--- , time: 98.6149 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 128.132--- , test psnr: 12.15--- , time: 109.417 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 127.285--- , test psnr: 11.8832--- , time: 9.58154 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 127.366--- , test psnr: 11.8534--- , time: 16.6702 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 127.619--- , test psnr: 11.9442--- , time: 24.2514 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 127.367--- , test psnr: 11.8723--- , time: 31.5425 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 127.639--- , test psnr: 11.8782--- , time: 38.6418 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 127.03--- , test psnr: 11.7891--- , time: 46.3101 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 127.264--- , test psnr: 11.7905--- , time: 53.8324 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 127.355--- , test psnr: 11.8985--- , time: 61.5436 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 127.308--- , test psnr: 11.851--- , time: 69.3573 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 127.266--- , test psnr: 11.7995--- , time: 77.036 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 124.232--- , test psnr: 10.5891--- , time: 7.74855 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 124.775--- , test psnr: 10.6784--- , time: 15.9069 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 124.602--- , test psnr: 10.6515--- , time: 24.1399 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 124.882--- , test psnr: 10.7202--- , time: 32.165 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 118.867--- , test psnr: 10.7107--- , time: 40.2484 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 124.516--- , test psnr: 10.6538--- , time: 48.2869 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 124.513--- , test psnr: 10.6603--- , time: 56.4072 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 124.363--- , test psnr: 10.6101--- , time: 64.5421 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 124.736--- , test psnr: 10.7344--- , time: 72.8021 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 124.501--- , test psnr: 10.6238--- , time: 81.0103 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 127.299--- , test psnr: 11.8457--- , time: 7.87086 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 127.284--- , test psnr: 11.7955--- , time: 15.9299 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 127.224--- , test psnr: 11.8419--- , time: 24.0591 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 127.348--- , test psnr: 11.936--- , time: 32.1667 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 127.299--- , test psnr: 11.7885--- , time: 40.1048 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 127.358--- , test psnr: 11.9517--- , time: 47.8143 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 127.378--- , test psnr: 11.9432--- , time: 55.3995 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 127.649--- , test psnr: 11.9338--- , time: 62.8283 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 127.3--- , test psnr: 11.8579--- , time: 70.2939 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 127.379--- , test psnr: 11.8866--- , time: 77.843 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 126.732--- , test psnr: 10.7588--- , time: 7.58392 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 126.54--- , test psnr: 10.6186--- , time: 15.099 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 126.447--- , test psnr: 10.7154--- , time: 22.5437 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 126.14--- , test psnr: 10.6386--- , time: 29.9175 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 126.829--- , test psnr: 10.8229--- , time: 37.3493 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 126.515--- , test psnr: 10.7251--- , time: 45.0629 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 126.657--- , test psnr: 10.759--- , time: 52.6144 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 126.457--- , test psnr: 10.7706--- , time: 60.0959 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 126.352--- , test psnr: 10.7305--- , time: 67.8398 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 126.776--- , test psnr: 10.8104--- , time: 75.4955 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 124.079--- , test psnr: 10.5355--- , time: 7.28696 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 124.024--- , test psnr: 10.5869--- , time: 14.713 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 124.11--- , test psnr: 10.6428--- , time: 22.1342 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 124.065--- , test psnr: 10.605--- , time: 29.7447 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 123.905--- , test psnr: 10.5237--- , time: 37.2002 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 124.101--- , test psnr: 10.5447--- , time: 44.1223 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 124.015--- , test psnr: 10.5972--- , time: 51.1493 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 124.009--- , test psnr: 10.5761--- , time: 58.0973 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 124.122--- , test psnr: 10.6631--- , time: 65.318 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 123.886--- , test psnr: 10.5375--- , time: 72.3009 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 128.165--- , test psnr: 11.3001--- , time: 6.86488 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 128.246--- , test psnr: 11.2271--- , time: 13.6861 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 128.297--- , test psnr: 11.2811--- , time: 20.2127 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 128.872--- , test psnr: 11.4065--- , time: 27.0072 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 128.545--- , test psnr: 11.3376--- , time: 33.3569 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 128.764--- , test psnr: 11.4032--- , time: 39.7633 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 128.476--- , test psnr: 11.2316--- , time: 46.3142 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 128.3--- , test psnr: 11.2665--- , time: 52.6828 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 128.407--- , test psnr: 11.2283--- , time: 58.9252 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 128.488--- , test psnr: 11.2857--- , time: 65.4971 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 125.747--- , test psnr: 10.8124--- , time: 6.61025 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 125.455--- , test psnr: 10.7336--- , time: 13.2401 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 125.983--- , test psnr: 10.8718--- , time: 20.1222 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 125.867--- , test psnr: 10.8415--- , time: 27.0522 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 125.532--- , test psnr: 10.7886--- , time: 33.9952 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 125.96--- , test psnr: 10.8847--- , time: 40.7505 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 125.475--- , test psnr: 10.7698--- , time: 47.4645 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 125.502--- , test psnr: 10.766--- , time: 54.2133 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 125.952--- , test psnr: 10.8934--- , time: 60.9811 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 125.95--- , test psnr: 10.8374--- , time: 67.8697 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 129.672--- , test psnr: 12.0203--- , time: 6.69047 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 129.625--- , test psnr: 11.8327--- , time: 13.3677 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 129.604--- , test psnr: 11.8315--- , time: 20.0359 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 130.375--- , test psnr: 12.0219--- , time: 26.7499 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 130.079--- , test psnr: 12.0182--- , time: 33.6626 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 129.666--- , test psnr: 11.7674--- , time: 40.5454 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 129.932--- , test psnr: 12.0345--- , time: 47.1949 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 129.643--- , test psnr: 11.9064--- , time: 54.0193 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 129.73--- , test psnr: 11.8861--- , time: 60.8782 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 129.684--- , test psnr: 11.7852--- , time: 67.6692 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 126.868--- , test psnr: 10.9949--- , time: 6.61382 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 127.009--- , test psnr: 10.9724--- , time: 13.2849 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 126.906--- , test psnr: 10.9767--- , time: 19.958 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 126.9--- , test psnr: 10.9779--- , time: 26.7013 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 126.824--- , test psnr: 10.987--- , time: 33.482 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 126.983--- , test psnr: 10.9266--- , time: 40.2002 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 126.265--- , test psnr: 10.8649--- , time: 47.1557 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 126.699--- , test psnr: 10.9194--- , time: 53.9365 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 126.704--- , test psnr: 10.9536--- , time: 60.541 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 126.803--- , test psnr: 11.0271--- , time: 67.2916 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 130.417--- , test psnr: 12.1735--- , time: 6.67512 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 130.055--- , test psnr: 12.2015--- , time: 13.3189 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 130.535--- , test psnr: 12.2534--- , time: 19.9748 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 130.476--- , test psnr: 12.2032--- , time: 26.6463 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 130.335--- , test psnr: 12.1003--- , time: 33.0669 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 130.318--- , test psnr: 12.2231--- , time: 39.6982 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 130.153--- , test psnr: 12.145--- , time: 46.335 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 130.39--- , test psnr: 12.148--- , time: 52.9737 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 130.179--- , test psnr: 12.1665--- , time: 59.718 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 130.528--- , test psnr: 12.1998--- , time: 66.3681 seconds ---\n",
      "==>>> N: 1, F: 6, train psnr: 129.438--- , test psnr: 11.832--- , time: 6.148 seconds ---\n",
      "==>>> N: 1, F: 6, train psnr: 129.289--- , test psnr: 11.8324--- , time: 12.3114 seconds ---\n",
      "==>>> N: 1, F: 6, train psnr: 129.288--- , test psnr: 11.8548--- , time: 18.5195 seconds ---\n",
      "==>>> N: 1, F: 6, train psnr: 129.433--- , test psnr: 11.8456--- , time: 24.7185 seconds ---\n",
      "==>>> N: 1, F: 6, train psnr: 129.354--- , test psnr: 11.9542--- , time: 30.8946 seconds ---\n",
      "==>>> N: 1, F: 6, train psnr: 129.586--- , test psnr: 11.9419--- , time: 37.3289 seconds ---\n",
      "==>>> N: 1, F: 6, train psnr: 129.345--- , test psnr: 11.8586--- , time: 43.8273 seconds ---\n",
      "==>>> N: 1, F: 6, train psnr: 129.476--- , test psnr: 11.9106--- , time: 50.017 seconds ---\n",
      "==>>> N: 1, F: 6, train psnr: 129.321--- , test psnr: 11.8365--- , time: 56.6178 seconds ---\n",
      "==>>> N: 1, F: 6, train psnr: 129.506--- , test psnr: 11.8705--- , time: 63.3173 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 125.96--- , test psnr: 10.6258--- , time: 6.69649 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 125.683--- , test psnr: 10.6245--- , time: 13.4508 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 125.935--- , test psnr: 10.6569--- , time: 20.3679 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 125.882--- , test psnr: 10.6597--- , time: 27.0411 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 126.005--- , test psnr: 10.6978--- , time: 33.6799 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 125.81--- , test psnr: 10.6506--- , time: 40.3853 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 126.034--- , test psnr: 10.7389--- , time: 47.1359 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 126.064--- , test psnr: 10.7024--- , time: 53.5409 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 125.864--- , test psnr: 10.6035--- , time: 60.0841 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 125.833--- , test psnr: 10.6222--- , time: 66.529 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 129.386--- , test psnr: 11.967--- , time: 6.5555 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 129.335--- , test psnr: 11.8592--- , time: 13.1425 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 129.81--- , test psnr: 11.9101--- , time: 19.6421 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 129.626--- , test psnr: 11.9083--- , time: 26.0958 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 129.527--- , test psnr: 11.9025--- , time: 32.6272 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 129.506--- , test psnr: 11.9328--- , time: 39.2638 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 129.386--- , test psnr: 11.8368--- , time: 45.8951 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 129.26--- , test psnr: 11.9072--- , time: 52.4961 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 129.374--- , test psnr: 11.8906--- , time: 59.0287 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 129.571--- , test psnr: 11.9758--- , time: 65.6134 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 128.067--- , test psnr: 10.6154--- , time: 6.52175 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 128.143--- , test psnr: 10.7127--- , time: 13.1688 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 128.316--- , test psnr: 10.7938--- , time: 19.8498 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 127.958--- , test psnr: 10.6863--- , time: 26.6084 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 128.174--- , test psnr: 10.7156--- , time: 33.328 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 127.795--- , test psnr: 10.6029--- , time: 40.109 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 127.966--- , test psnr: 10.6542--- , time: 46.7174 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 128.171--- , test psnr: 10.8037--- , time: 53.35 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 128.157--- , test psnr: 10.7396--- , time: 60.0023 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 127.979--- , test psnr: 10.6789--- , time: 66.5549 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 124.588--- , test psnr: 10.4844--- , time: 6.55773 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 124.834--- , test psnr: 10.6364--- , time: 13.2127 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 125.056--- , test psnr: 10.5993--- , time: 19.862 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 124.572--- , test psnr: 10.5286--- , time: 26.3735 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 124.663--- , test psnr: 10.5152--- , time: 32.5069 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 124.746--- , test psnr: 10.6104--- , time: 38.9989 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 124.792--- , test psnr: 10.4976--- , time: 45.6553 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 124.853--- , test psnr: 10.6091--- , time: 52.2179 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 124.991--- , test psnr: 10.6192--- , time: 58.8485 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 124.856--- , test psnr: 10.6032--- , time: 66.0098 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 128.98--- , test psnr: 11.453--- , time: 7.00533 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 129.191--- , test psnr: 11.4477--- , time: 13.7658 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 128.718--- , test psnr: 11.2311--- , time: 20.4294 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 128.734--- , test psnr: 11.2343--- , time: 27.0468 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 128.981--- , test psnr: 11.4502--- , time: 33.7357 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 128.761--- , test psnr: 11.2375--- , time: 40.3766 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 129.082--- , test psnr: 11.3282--- , time: 47.1263 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 128.938--- , test psnr: 11.3748--- , time: 53.9523 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 129.193--- , test psnr: 11.3837--- , time: 60.6188 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 129.111--- , test psnr: 11.4222--- , time: 67.5208 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 125.228--- , test psnr: 10.7458--- , time: 6.85576 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 125.808--- , test psnr: 10.8368--- , time: 13.6263 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 125.835--- , test psnr: 10.7609--- , time: 20.3513 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 125.228--- , test psnr: 10.7397--- , time: 27.0728 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 125.829--- , test psnr: 10.8248--- , time: 34.0246 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 125.906--- , test psnr: 10.8005--- , time: 40.8116 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 125.891--- , test psnr: 10.9451--- , time: 47.4835 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 125.866--- , test psnr: 10.9056--- , time: 54.2815 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 125.967--- , test psnr: 10.8916--- , time: 60.9549 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 125.848--- , test psnr: 10.8677--- , time: 67.6739 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 130.347--- , test psnr: 11.8371--- , time: 6.66567 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 130.071--- , test psnr: 11.8247--- , time: 13.3737 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 130.236--- , test psnr: 11.9255--- , time: 19.9149 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 130.199--- , test psnr: 11.7138--- , time: 26.5366 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 129.919--- , test psnr: 11.8589--- , time: 33.2043 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 129.945--- , test psnr: 11.6999--- , time: 39.8753 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 130.273--- , test psnr: 11.7659--- , time: 46.8213 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 130.322--- , test psnr: 11.7364--- , time: 54.6101 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 130.12--- , test psnr: 11.9198--- , time: 62.627 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 129.986--- , test psnr: 11.8355--- , time: 70.3551 seconds ---\n",
      "==>>> N: 2, F: 4, train psnr: 126.794--- , test psnr: 10.8731--- , time: 7.59992 seconds ---\n",
      "==>>> N: 2, F: 4, train psnr: 126.579--- , test psnr: 10.7995--- , time: 14.7846 seconds ---\n",
      "==>>> N: 2, F: 4, train psnr: 126.799--- , test psnr: 10.7874--- , time: 22.0496 seconds ---\n",
      "==>>> N: 2, F: 4, train psnr: 126.395--- , test psnr: 10.7847--- , time: 29.1469 seconds ---\n",
      "==>>> N: 2, F: 4, train psnr: 127.028--- , test psnr: 10.8715--- , time: 36.8841 seconds ---\n",
      "==>>> N: 2, F: 4, train psnr: 126.905--- , test psnr: 10.9026--- , time: 44.7284 seconds ---\n",
      "==>>> N: 2, F: 4, train psnr: 126.724--- , test psnr: 10.8052--- , time: 52.3087 seconds ---\n",
      "==>>> N: 2, F: 4, train psnr: 126.737--- , test psnr: 10.8559--- , time: 60.0966 seconds ---\n",
      "==>>> N: 2, F: 4, train psnr: 127.152--- , test psnr: 10.9182--- , time: 67.7565 seconds ---\n",
      "==>>> N: 2, F: 4, train psnr: 126.986--- , test psnr: 10.9054--- , time: 75.4334 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 130.84--- , test psnr: 12.3735--- , time: 7.63829 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 130.542--- , test psnr: 12.266--- , time: 15.3961 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 130.517--- , test psnr: 12.2692--- , time: 22.7147 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 130.501--- , test psnr: 12.2807--- , time: 30.0079 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 130.776--- , test psnr: 12.3016--- , time: 37.3681 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 131.293--- , test psnr: 12.4014--- , time: 44.503 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 130.685--- , test psnr: 12.3422--- , time: 51.4808 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 130.54--- , test psnr: 12.3246--- , time: 58.3469 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 131.214--- , test psnr: 12.4241--- , time: 65.2337 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 130.602--- , test psnr: 12.3176--- , time: 72.1865 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 129.878--- , test psnr: 11.8538--- , time: 7.12831 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 129.825--- , test psnr: 11.8215--- , time: 14.179 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 130.451--- , test psnr: 11.963--- , time: 21.1562 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 130.117--- , test psnr: 11.9508--- , time: 28.1442 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 130.057--- , test psnr: 11.9306--- , time: 35.3134 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 129.833--- , test psnr: 11.8008--- , time: 42.4309 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 129.835--- , test psnr: 11.8285--- , time: 49.554 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 129.938--- , test psnr: 11.8472--- , time: 56.7195 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 129.844--- , test psnr: 11.8536--- , time: 63.8274 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 129.942--- , test psnr: 11.8826--- , time: 70.9758 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 126.413--- , test psnr: 10.6143--- , time: 6.60917 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 126.382--- , test psnr: 10.6949--- , time: 13.2161 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 126.192--- , test psnr: 10.6532--- , time: 20.2833 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 126.281--- , test psnr: 10.6486--- , time: 27.4104 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 126.115--- , test psnr: 10.5693--- , time: 34.3478 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 126.457--- , test psnr: 10.6322--- , time: 41.1671 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 126.364--- , test psnr: 10.6783--- , time: 48.0735 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 126.174--- , test psnr: 10.6097--- , time: 55.0339 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 126.457--- , test psnr: 10.7026--- , time: 61.9878 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 126.517--- , test psnr: 10.6889--- , time: 68.8135 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 130.099--- , test psnr: 11.7919--- , time: 6.96196 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 130.125--- , test psnr: 11.945--- , time: 13.9176 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 130.104--- , test psnr: 11.909--- , time: 20.8429 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 130.046--- , test psnr: 11.9122--- , time: 27.7618 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 130.138--- , test psnr: 11.8482--- , time: 34.851 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 129.996--- , test psnr: 11.8852--- , time: 41.9558 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 130.058--- , test psnr: 11.8972--- , time: 49.0369 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 129.937--- , test psnr: 11.8051--- , time: 55.8555 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 130.014--- , test psnr: 11.8021--- , time: 62.0001 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 122.313--- , test psnr: 11.933--- , time: 67.8424 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 127.76--- , test psnr: 10.7637--- , time: 5.84012 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 127.846--- , test psnr: 10.7664--- , time: 12.1626 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 128.164--- , test psnr: 10.8666--- , time: 19.0036 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 128.086--- , test psnr: 10.8112--- , time: 25.8799 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 128.076--- , test psnr: 10.8132--- , time: 32.6891 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 128.064--- , test psnr: 10.7861--- , time: 39.0794 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 128.025--- , test psnr: 10.9197--- , time: 45.4478 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 128.085--- , test psnr: 10.7982--- , time: 52.2476 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 128.157--- , test psnr: 10.8258--- , time: 59.4694 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 128.046--- , test psnr: 10.8607--- , time: 66.771 seconds ---\n",
      "==>>> N: 3, F: 0, train psnr: 124.01--- , test psnr: 10.8913--- , time: 7.25265 seconds ---\n",
      "==>>> N: 3, F: 0, train psnr: 124.127--- , test psnr: 10.9655--- , time: 14.1764 seconds ---\n",
      "==>>> N: 3, F: 0, train psnr: 124.019--- , test psnr: 10.9281--- , time: 21.2972 seconds ---\n",
      "==>>> N: 3, F: 0, train psnr: 123.87--- , test psnr: 10.7647--- , time: 27.8983 seconds ---\n",
      "==>>> N: 3, F: 0, train psnr: 123.959--- , test psnr: 10.7705--- , time: 35.0226 seconds ---\n",
      "==>>> N: 3, F: 0, train psnr: 123.841--- , test psnr: 10.7944--- , time: 42.2034 seconds ---\n",
      "==>>> N: 3, F: 0, train psnr: 123.94--- , test psnr: 10.8507--- , time: 49.2335 seconds ---\n",
      "==>>> N: 3, F: 0, train psnr: 124.104--- , test psnr: 10.8823--- , time: 56.1414 seconds ---\n",
      "==>>> N: 3, F: 0, train psnr: 123.953--- , test psnr: 10.8284--- , time: 62.86 seconds ---\n",
      "==>>> N: 3, F: 0, train psnr: 124.146--- , test psnr: 10.9737--- , time: 69.9509 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 129.224--- , test psnr: 11.1239--- , time: 7.11213 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 128.778--- , test psnr: 11.1366--- , time: 14.1223 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 129.108--- , test psnr: 11.1632--- , time: 21.1841 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 128.878--- , test psnr: 11.1196--- , time: 28.4972 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 128.924--- , test psnr: 11.0391--- , time: 35.6814 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 128.86--- , test psnr: 11.0953--- , time: 42.6133 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 129.051--- , test psnr: 11.1666--- , time: 49.4947 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 128.732--- , test psnr: 11.0156--- , time: 56.5032 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 129.022--- , test psnr: 11.1331--- , time: 63.2621 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 129.219--- , test psnr: 11.0971--- , time: 70.42 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 126.114--- , test psnr: 10.8612--- , time: 7.04038 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 125.943--- , test psnr: 10.8565--- , time: 14.1773 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 125.918--- , test psnr: 10.7505--- , time: 21.3212 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 126.041--- , test psnr: 10.7573--- , time: 28.2913 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 125.818--- , test psnr: 10.7761--- , time: 35.387 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 125.547--- , test psnr: 10.6697--- , time: 42.3423 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 125.743--- , test psnr: 10.7099--- , time: 49.3096 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 125.777--- , test psnr: 10.6535--- , time: 56.3444 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 125.967--- , test psnr: 10.8013--- , time: 63.3598 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 125.936--- , test psnr: 10.7547--- , time: 70.4069 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 130.118--- , test psnr: 11.6983--- , time: 7.06578 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 130.543--- , test psnr: 11.7118--- , time: 14.215 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 130.223--- , test psnr: 11.6924--- , time: 21.5121 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 129.837--- , test psnr: 11.5771--- , time: 28.8886 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 130.073--- , test psnr: 11.5632--- , time: 36.0266 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 130.089--- , test psnr: 11.6217--- , time: 43.1497 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 130.739--- , test psnr: 11.818--- , time: 50.3139 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 130.075--- , test psnr: 11.6872--- , time: 57.5763 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 129.975--- , test psnr: 11.6336--- , time: 64.5869 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 130.22--- , test psnr: 11.6634--- , time: 71.5502 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 126.048--- , test psnr: 11.3256--- , time: 7.24815 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 125.899--- , test psnr: 11.2417--- , time: 14.349 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 126.798--- , test psnr: 11.3934--- , time: 21.3953 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 126.054--- , test psnr: 11.2826--- , time: 28.4752 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 125.954--- , test psnr: 11.3215--- , time: 35.6224 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 126.733--- , test psnr: 11.3931--- , time: 42.7155 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 126.831--- , test psnr: 11.4414--- , time: 49.7255 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 126.834--- , test psnr: 11.3718--- , time: 56.7443 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 125.908--- , test psnr: 11.2446--- , time: 63.7153 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 126.691--- , test psnr: 11.4211--- , time: 70.8401 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 130.717--- , test psnr: 12.171--- , time: 7.11628 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 130.7--- , test psnr: 12.1944--- , time: 13.931 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 130.896--- , test psnr: 12.369--- , time: 20.8319 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 130.972--- , test psnr: 12.3631--- , time: 27.9314 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 130.669--- , test psnr: 12.2641--- , time: 35.0472 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 131.028--- , test psnr: 12.3051--- , time: 42.1472 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 130.825--- , test psnr: 12.2812--- , time: 49.4731 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 131.008--- , test psnr: 12.2947--- , time: 56.6735 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 130.918--- , test psnr: 12.3363--- , time: 63.7882 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 130.965--- , test psnr: 12.205--- , time: 70.692 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 130.021--- , test psnr: 11.8851--- , time: 7.09281 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 130.168--- , test psnr: 11.9432--- , time: 14.2122 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 130.086--- , test psnr: 11.9083--- , time: 21.1339 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 130.066--- , test psnr: 11.8876--- , time: 27.256 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 130.118--- , test psnr: 11.9212--- , time: 33.3471 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 129.929--- , test psnr: 11.865--- , time: 39.4502 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 130.06--- , test psnr: 11.8676--- , time: 45.5441 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 130.058--- , test psnr: 11.8878--- , time: 51.6176 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 130.082--- , test psnr: 11.9453--- , time: 58.669 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 130.087--- , test psnr: 11.8989--- , time: 66.2074 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 126.47--- , test psnr: 10.4091--- , time: 7.59422 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 126.655--- , test psnr: 10.4128--- , time: 15.1321 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 126.972--- , test psnr: 10.4918--- , time: 21.8506 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 126.674--- , test psnr: 10.4021--- , time: 29.5153 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 126.888--- , test psnr: 10.512--- , time: 37.429 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 126.614--- , test psnr: 10.4616--- , time: 44.956 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 126.747--- , test psnr: 10.4742--- , time: 52.4606 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 126.78--- , test psnr: 10.5026--- , time: 59.5405 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 126.682--- , test psnr: 10.4504--- , time: 66.3585 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 126.614--- , test psnr: 10.4291--- , time: 72.9922 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 129.901--- , test psnr: 11.7983--- , time: 6.50024 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 129.959--- , test psnr: 11.8708--- , time: 12.8486 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 130.05--- , test psnr: 11.9164--- , time: 20.0771 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 129.847--- , test psnr: 11.7848--- , time: 27.453 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 130.171--- , test psnr: 11.9378--- , time: 34.6418 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 130.096--- , test psnr: 11.9464--- , time: 41.7225 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 129.835--- , test psnr: 11.7681--- , time: 51.9361 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 129.93--- , test psnr: 11.8149--- , time: 62.9281 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 129.752--- , test psnr: 11.7974--- , time: 73.9151 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 129.809--- , test psnr: 11.7844--- , time: 85.162 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 128.172--- , test psnr: 10.5962--- , time: 10.0449 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 128.588--- , test psnr: 10.746--- , time: 20.6712 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 128.189--- , test psnr: 10.6287--- , time: 31.4338 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 128.429--- , test psnr: 10.6976--- , time: 41.6444 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 128.486--- , test psnr: 10.7561--- , time: 52.2793 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 128.427--- , test psnr: 10.6352--- , time: 62.7604 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 128.413--- , test psnr: 10.6328--- , time: 73.3015 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 128.517--- , test psnr: 10.7545--- , time: 84.1319 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 128.199--- , test psnr: 10.5878--- , time: 94.7546 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 128.335--- , test psnr: 10.7172--- , time: 105.672 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 125.673--- , test psnr: 10.3021--- , time: 10.4922 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 125.813--- , test psnr: 10.319--- , time: 20.3462 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 125.861--- , test psnr: 10.3862--- , time: 30.9668 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 125.617--- , test psnr: 10.2883--- , time: 41.7184 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 125.476--- , test psnr: 10.2154--- , time: 51.9556 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 125.56--- , test psnr: 10.2387--- , time: 62.2185 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 125.886--- , test psnr: 10.3621--- , time: 72.5931 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 125.622--- , test psnr: 10.2982--- , time: 82.9885 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 125.805--- , test psnr: 10.4262--- , time: 93.3207 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 125.762--- , test psnr: 10.3223--- , time: 103.61 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 129.259--- , test psnr: 11.1237--- , time: 10.1143 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 128.862--- , test psnr: 11.1787--- , time: 20.3319 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 128.82--- , test psnr: 11.0992--- , time: 30.1583 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 128.944--- , test psnr: 11.1951--- , time: 39.9536 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 129.14--- , test psnr: 11.1447--- , time: 50.2496 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 129.17--- , test psnr: 11.2124--- , time: 60.5038 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 129.304--- , test psnr: 11.2537--- , time: 70.7932 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 129.259--- , test psnr: 11.2663--- , time: 81.1488 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 129.608--- , test psnr: 11.2578--- , time: 91.4691 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 129.094--- , test psnr: 11.1983--- , time: 101.559 seconds ---\n",
      "==>>> N: 4, F: 2, train psnr: 125.928--- , test psnr: 10.4204--- , time: 10.6067 seconds ---\n",
      "==>>> N: 4, F: 2, train psnr: 125.735--- , test psnr: 10.3009--- , time: 20.7818 seconds ---\n",
      "==>>> N: 4, F: 2, train psnr: 126.029--- , test psnr: 10.4201--- , time: 31.0409 seconds ---\n",
      "==>>> N: 4, F: 2, train psnr: 125.795--- , test psnr: 10.2828--- , time: 41.1658 seconds ---\n",
      "==>>> N: 4, F: 2, train psnr: 125.768--- , test psnr: 10.3301--- , time: 51.4578 seconds ---\n",
      "==>>> N: 4, F: 2, train psnr: 126.152--- , test psnr: 10.5082--- , time: 61.4007 seconds ---\n",
      "==>>> N: 4, F: 2, train psnr: 126.254--- , test psnr: 10.5112--- , time: 71.4447 seconds ---\n",
      "==>>> N: 4, F: 2, train psnr: 125.673--- , test psnr: 10.2943--- , time: 81.5918 seconds ---\n",
      "==>>> N: 4, F: 2, train psnr: 125.924--- , test psnr: 10.4315--- , time: 91.9824 seconds ---\n",
      "==>>> N: 4, F: 2, train psnr: 126.023--- , test psnr: 10.5069--- , time: 102.492 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 130.565--- , test psnr: 12.2271--- , time: 10.2094 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 129.829--- , test psnr: 11.9322--- , time: 21.0481 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 129.985--- , test psnr: 12.0279--- , time: 32.493 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 129.99--- , test psnr: 12.061--- , time: 43.973 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 130.061--- , test psnr: 12.1215--- , time: 55.3987 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 130.469--- , test psnr: 12.1907--- , time: 66.94 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 130.07--- , test psnr: 11.9744--- , time: 77.9364 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 130.423--- , test psnr: 12.1366--- , time: 88.0591 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 130.023--- , test psnr: 12.0453--- , time: 99.8348 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 130.022--- , test psnr: 12.0739--- , time: 110.875 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 127.46--- , test psnr: 10.9091--- , time: 10.2948 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 127.5--- , test psnr: 10.8147--- , time: 20.4968 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 127.224--- , test psnr: 10.7765--- , time: 30.7029 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 127.322--- , test psnr: 10.8935--- , time: 40.7664 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 127.602--- , test psnr: 10.8999--- , time: 50.8731 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 127.67--- , test psnr: 10.9274--- , time: 60.9495 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 127.175--- , test psnr: 10.7653--- , time: 71.224 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 127.514--- , test psnr: 10.859--- , time: 80.3819 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 127.156--- , test psnr: 10.8138--- , time: 89.3398 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 127.6--- , test psnr: 10.8887--- , time: 99.5875 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 130.918--- , test psnr: 12.2659--- , time: 10.3055 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 130.816--- , test psnr: 12.2978--- , time: 20.5697 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 130.828--- , test psnr: 12.2727--- , time: 30.9309 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 130.834--- , test psnr: 12.3036--- , time: 41.6441 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 130.795--- , test psnr: 12.2582--- , time: 52.2859 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 130.594--- , test psnr: 12.1595--- , time: 62.8888 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 130.792--- , test psnr: 12.2453--- , time: 73.5079 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 130.76--- , test psnr: 12.2505--- , time: 83.932 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 130.957--- , test psnr: 12.3462--- , time: 94.6494 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 130.933--- , test psnr: 12.2981--- , time: 105.289 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 130.685--- , test psnr: 12.0644--- , time: 10.6815 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 130.435--- , test psnr: 11.9424--- , time: 21.4337 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 130.432--- , test psnr: 11.9711--- , time: 32.2552 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 130.471--- , test psnr: 11.9878--- , time: 42.8587 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 130.438--- , test psnr: 11.9509--- , time: 53.4637 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 130.376--- , test psnr: 11.9425--- , time: 64.2329 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 130.575--- , test psnr: 12.0481--- , time: 74.9715 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 130.44--- , test psnr: 12.0498--- , time: 85.4355 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 130.612--- , test psnr: 12.0554--- , time: 96.5402 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 130.157--- , test psnr: 11.898--- , time: 107.412 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 127.224--- , test psnr: 10.3406--- , time: 10.3503 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 127.153--- , test psnr: 10.2833--- , time: 20.7823 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 127.303--- , test psnr: 10.3249--- , time: 31.2796 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 127.288--- , test psnr: 10.3993--- , time: 41.9516 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 127.085--- , test psnr: 10.332--- , time: 53.5462 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 127.188--- , test psnr: 10.2874--- , time: 64.6487 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 127.583--- , test psnr: 10.3763--- , time: 76.4337 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 126.915--- , test psnr: 10.2747--- , time: 87.5657 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 127.605--- , test psnr: 10.3572--- , time: 98.8536 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 126.197--- , test psnr: 10.2607--- , time: 110.166 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 130.525--- , test psnr: 12.0096--- , time: 11.1467 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 130.589--- , test psnr: 12.0204--- , time: 22.4122 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 130.151--- , test psnr: 11.9072--- , time: 33.3858 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 130.489--- , test psnr: 12.001--- , time: 44.665 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 130.521--- , test psnr: 12.0275--- , time: 56.0618 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 130.034--- , test psnr: 11.889--- , time: 67.1899 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 130.448--- , test psnr: 11.9689--- , time: 78.8305 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 130.124--- , test psnr: 11.872--- , time: 90.0072 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 130.526--- , test psnr: 12.0137--- , time: 101.027 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 130.076--- , test psnr: 11.894--- , time: 112.671 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 128.052--- , test psnr: 10.621--- , time: 11.185 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 128.043--- , test psnr: 10.689--- , time: 22.2993 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 127.811--- , test psnr: 10.6638--- , time: 33.4784 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 127.983--- , test psnr: 10.7102--- , time: 44.748 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 127.859--- , test psnr: 10.6357--- , time: 55.9345 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 128.139--- , test psnr: 10.6706--- , time: 66.9197 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 128.361--- , test psnr: 10.7739--- , time: 77.7332 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 127.965--- , test psnr: 10.6734--- , time: 88.5665 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 128.052--- , test psnr: 10.6404--- , time: 99.4558 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 127.852--- , test psnr: 10.6412--- , time: 110.454 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 123.328--- , test psnr: 10.6914--- , time: 10.8891 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 123.083--- , test psnr: 10.697--- , time: 21.8571 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 123.289--- , test psnr: 10.7668--- , time: 32.4038 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 123.285--- , test psnr: 10.7176--- , time: 43.211 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 124.149--- , test psnr: 10.8884--- , time: 54.0176 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 123.151--- , test psnr: 10.8085--- , time: 64.8378 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 123.31--- , test psnr: 10.7309--- , time: 75.721 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 123.362--- , test psnr: 10.7405--- , time: 86.731 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 123.077--- , test psnr: 10.6326--- , time: 97.7542 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 123.202--- , test psnr: 10.6382--- , time: 108.823 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 129.174--- , test psnr: 11.1289--- , time: 11.4691 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 129.943--- , test psnr: 11.2334--- , time: 22.2104 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 129.313--- , test psnr: 11.2735--- , time: 33.0153 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 129.517--- , test psnr: 11.1017--- , time: 43.5647 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 129.547--- , test psnr: 11.2141--- , time: 54.6134 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 129.285--- , test psnr: 11.225--- , time: 65.0939 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 129.594--- , test psnr: 11.1822--- , time: 76.4704 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 129.441--- , test psnr: 11.2125--- , time: 87.2452 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 129.313--- , test psnr: 11.1328--- , time: 97.9184 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 129.803--- , test psnr: 11.1841--- , time: 108.749 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 125.188--- , test psnr: 10.942--- , time: 10.6584 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 124.843--- , test psnr: 10.876--- , time: 21.4489 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 125.217--- , test psnr: 11.0669--- , time: 32.2915 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 125.377--- , test psnr: 11.0058--- , time: 43.4297 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 125.02--- , test psnr: 11.0611--- , time: 54.1468 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 125.254--- , test psnr: 10.9426--- , time: 65.5889 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 124.811--- , test psnr: 10.8728--- , time: 76.6055 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 125.19--- , test psnr: 10.9989--- , time: 87.8728 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 125.18--- , test psnr: 11.082--- , time: 99.381 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 125.187--- , test psnr: 11.1088--- , time: 110.028 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 130.277--- , test psnr: 12.1042--- , time: 11.6001 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 130.274--- , test psnr: 12.1151--- , time: 22.427 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 130.298--- , test psnr: 12.1447--- , time: 33.1554 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 129.968--- , test psnr: 12.07--- , time: 44.257 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 130.047--- , test psnr: 11.9687--- , time: 55.4592 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 130.301--- , test psnr: 12.2198--- , time: 66.9609 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 130.279--- , test psnr: 12.1511--- , time: 78.4299 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 130.242--- , test psnr: 12.1165--- , time: 89.7663 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 130.107--- , test psnr: 12.0654--- , time: 100.945 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 130.16--- , test psnr: 12.0677--- , time: 112.295 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 126.018--- , test psnr: 10.302--- , time: 11.1031 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 126.15--- , test psnr: 10.3774--- , time: 22.1972 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 125.815--- , test psnr: 10.3226--- , time: 33.6335 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 125.876--- , test psnr: 10.3509--- , time: 44.815 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 125.662--- , test psnr: 10.3537--- , time: 55.9111 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 125.798--- , test psnr: 10.2873--- , time: 67.1277 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 126.008--- , test psnr: 10.2921--- , time: 78.3436 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 125.882--- , test psnr: 10.3538--- , time: 89.4 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 125.873--- , test psnr: 10.3656--- , time: 100.772 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 125.583--- , test psnr: 10.2431--- , time: 112.249 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 131.327--- , test psnr: 11.6348--- , time: 10.8474 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 131.234--- , test psnr: 11.6417--- , time: 21.8597 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 131.202--- , test psnr: 11.6519--- , time: 33.0451 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 131.22--- , test psnr: 11.5859--- , time: 43.9536 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 131.605--- , test psnr: 11.7139--- , time: 54.8248 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 131.528--- , test psnr: 11.7236--- , time: 65.8942 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 131.259--- , test psnr: 11.7065--- , time: 77.2946 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 131.17--- , test psnr: 11.6615--- , time: 88.4555 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 131.298--- , test psnr: 11.7045--- , time: 99.6231 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 131.579--- , test psnr: 11.7356--- , time: 110.744 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 130.564--- , test psnr: 11.7074--- , time: 11.435 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 130.178--- , test psnr: 11.5368--- , time: 22.2455 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 130.201--- , test psnr: 11.533--- , time: 33.2187 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 131.108--- , test psnr: 11.6496--- , time: 44.3227 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 130.506--- , test psnr: 11.6563--- , time: 55.3275 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 130.593--- , test psnr: 11.7109--- , time: 66.327 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 130.482--- , test psnr: 11.6608--- , time: 77.4695 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 130.343--- , test psnr: 11.6072--- , time: 88.5898 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 130.626--- , test psnr: 11.6478--- , time: 99.5814 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 130.548--- , test psnr: 11.6769--- , time: 109.355 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 126.726--- , test psnr: 10.8106--- , time: 10.4418 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 126.511--- , test psnr: 10.7093--- , time: 21.2952 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 126.639--- , test psnr: 10.8157--- , time: 32.1438 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 126.852--- , test psnr: 10.8642--- , time: 43.2383 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 126.623--- , test psnr: 10.8855--- , time: 54.496 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 126.775--- , test psnr: 10.8388--- , time: 65.4871 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 126.66--- , test psnr: 10.7501--- , time: 77.0576 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 126.829--- , test psnr: 10.8451--- , time: 87.889 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 126.59--- , test psnr: 10.7592--- , time: 99.1416 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 126.531--- , test psnr: 10.788--- , time: 110.154 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 130.401--- , test psnr: 11.6563--- , time: 11.1455 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 130.893--- , test psnr: 11.5239--- , time: 22.6613 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 130.374--- , test psnr: 11.5935--- , time: 34.1218 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 130.702--- , test psnr: 11.6584--- , time: 45.2594 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 130.205--- , test psnr: 11.5689--- , time: 56.8445 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 130.589--- , test psnr: 11.5798--- , time: 68.41 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 130.315--- , test psnr: 11.6415--- , time: 79.3663 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 130.596--- , test psnr: 11.6777--- , time: 90.2835 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 130.428--- , test psnr: 11.6413--- , time: 101.085 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 130.743--- , test psnr: 11.651--- , time: 112.547 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 128.122--- , test psnr: 11.0128--- , time: 11.4395 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 128.152--- , test psnr: 11.0579--- , time: 22.3005 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 127.823--- , test psnr: 10.888--- , time: 33.7178 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 127.942--- , test psnr: 10.9933--- , time: 44.2844 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 128.083--- , test psnr: 11.011--- , time: 55.0029 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 127.932--- , test psnr: 10.9696--- , time: 65.5601 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 128.345--- , test psnr: 10.9719--- , time: 75.065 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 127.826--- , test psnr: 10.9235--- , time: 85.9146 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 127.843--- , test psnr: 10.9303--- , time: 96.4602 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 127.942--- , test psnr: 10.9644--- , time: 106.724 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 123.342--- , test psnr: 10.7872--- , time: 10.5004 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 123.367--- , test psnr: 10.5949--- , time: 20.9023 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 123.428--- , test psnr: 10.7317--- , time: 31.4011 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 123.615--- , test psnr: 10.7946--- , time: 42.0323 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 123.446--- , test psnr: 10.7749--- , time: 53.1017 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 123.502--- , test psnr: 10.7494--- , time: 63.6879 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 123.238--- , test psnr: 10.6429--- , time: 74.2107 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 123.291--- , test psnr: 10.5991--- , time: 84.6769 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 123.461--- , test psnr: 10.7985--- , time: 95.2272 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 123.483--- , test psnr: 10.8276--- , time: 106.143 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 129.844--- , test psnr: 10.9972--- , time: 10.4737 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 129.978--- , test psnr: 11.0556--- , time: 20.9897 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 129.787--- , test psnr: 10.992--- , time: 32.047 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 129.638--- , test psnr: 11.0198--- , time: 42.7888 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 129.549--- , test psnr: 11.0344--- , time: 53.5301 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 129.331--- , test psnr: 10.8575--- , time: 64.173 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 129.878--- , test psnr: 10.9681--- , time: 74.8985 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 129.628--- , test psnr: 10.8384--- , time: 85.2916 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 129.715--- , test psnr: 11.0419--- , time: 95.7731 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 129.556--- , test psnr: 10.9922--- , time: 106.8 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 127.877--- , test psnr: 9.86161--- , time: 10.9862 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 127.94--- , test psnr: 9.90356--- , time: 22.1487 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 127.88--- , test psnr: 9.86564--- , time: 33.4153 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 128.036--- , test psnr: 9.85248--- , time: 44.5005 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 128.487--- , test psnr: 9.93552--- , time: 55.4691 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 127.624--- , test psnr: 9.77986--- , time: 65.9846 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 127.923--- , test psnr: 9.92122--- , time: 76.2919 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 127.912--- , test psnr: 9.86912--- , time: 86.5374 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 127.861--- , test psnr: 9.8837--- , time: 96.8914 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 127.993--- , test psnr: 9.83489--- , time: 107.223 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 130.325--- , test psnr: 11.675--- , time: 10.9236 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 130.55--- , test psnr: 11.7895--- , time: 21.3641 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 131.077--- , test psnr: 11.847--- , time: 31.8089 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 130.497--- , test psnr: 11.7807--- , time: 42.23 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 130.507--- , test psnr: 11.8146--- , time: 52.4999 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 130.487--- , test psnr: 11.7797--- , time: 62.9262 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 130.518--- , test psnr: 11.7956--- , time: 73.2508 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 130.678--- , test psnr: 11.8464--- , time: 83.6773 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 130.736--- , test psnr: 11.8996--- , time: 93.9076 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 130.456--- , test psnr: 11.7595--- , time: 104.242 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 128.093--- , test psnr: 11.2416--- , time: 10.3996 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 126.233--- , test psnr: 11.158--- , time: 20.7436 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 128.245--- , test psnr: 11.2899--- , time: 31.2609 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 128.796--- , test psnr: 11.3147--- , time: 41.7457 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 128.435--- , test psnr: 11.3121--- , time: 52.3129 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 126.295--- , test psnr: 11.1459--- , time: 62.8346 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 128.346--- , test psnr: 11.3098--- , time: 73.5166 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 128.844--- , test psnr: 11.2385--- , time: 84.3786 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 128.171--- , test psnr: 11.2044--- , time: 95.0335 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 128.636--- , test psnr: 11.2349--- , time: 106.059 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 131.415--- , test psnr: 11.715--- , time: 11.0587 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 131.395--- , test psnr: 11.7344--- , time: 21.5889 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 131.052--- , test psnr: 11.764--- , time: 32.2117 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 131.327--- , test psnr: 11.7919--- , time: 42.4865 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 131.001--- , test psnr: 11.6758--- , time: 52.9759 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 131.33--- , test psnr: 11.794--- , time: 63.5741 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 131.487--- , test psnr: 11.8386--- , time: 74.2639 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 131.131--- , test psnr: 11.6715--- , time: 85.0554 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 131.142--- , test psnr: 11.6856--- , time: 96.0082 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 131.144--- , test psnr: 11.7224--- , time: 105.598 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 130.87--- , test psnr: 11.8868--- , time: 10.097 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 131.07--- , test psnr: 11.8437--- , time: 20.9115 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 130.916--- , test psnr: 11.8037--- , time: 31.8412 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 130.847--- , test psnr: 11.8365--- , time: 42.2152 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 131.258--- , test psnr: 11.9387--- , time: 52.448 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 131.394--- , test psnr: 11.9163--- , time: 63.061 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 130.709--- , test psnr: 11.7538--- , time: 74.1505 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 130.576--- , test psnr: 11.7632--- , time: 84.5007 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 131.129--- , test psnr: 11.9116--- , time: 94.8271 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 131.065--- , test psnr: 11.8755--- , time: 105.188 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 124.861--- , test psnr: 10.7223--- , time: 10.1481 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 125.009--- , test psnr: 10.7429--- , time: 20.6312 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 124.805--- , test psnr: 10.6743--- , time: 31.7278 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 124.829--- , test psnr: 10.6773--- , time: 42.1783 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 124.871--- , test psnr: 10.6478--- , time: 52.878 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 125.036--- , test psnr: 10.7201--- , time: 63.3768 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 125.206--- , test psnr: 10.7859--- , time: 73.8727 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 125.518--- , test psnr: 10.6651--- , time: 84.3175 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 125.144--- , test psnr: 10.7871--- , time: 95.6429 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 124.828--- , test psnr: 10.6606--- , time: 106.289 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 130.925--- , test psnr: 11.8296--- , time: 10.6679 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 130.914--- , test psnr: 11.8273--- , time: 21.8354 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 131.019--- , test psnr: 11.8017--- , time: 32.2585 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 131.024--- , test psnr: 11.8119--- , time: 42.9487 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 130.714--- , test psnr: 11.7657--- , time: 53.7015 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 131.19--- , test psnr: 11.8661--- , time: 64.148 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 131.174--- , test psnr: 11.836--- , time: 74.7521 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 130.938--- , test psnr: 11.8605--- , time: 84.6791 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 130.85--- , test psnr: 11.8191--- , time: 94.7049 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 131.081--- , test psnr: 11.7968--- , time: 104.937 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 128.445--- , test psnr: 10.5436--- , time: 9.72625 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 128.284--- , test psnr: 10.4513--- , time: 19.6931 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 128.108--- , test psnr: 10.3778--- , time: 30.6082 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 127.95--- , test psnr: 10.4076--- , time: 41.6333 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 128.192--- , test psnr: 10.4071--- , time: 52.2961 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 128.047--- , test psnr: 10.3861--- , time: 63.2561 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 128.375--- , test psnr: 10.5674--- , time: 74.0104 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 128.425--- , test psnr: 10.5407--- , time: 84.6475 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 128.178--- , test psnr: 10.3682--- , time: 95.1897 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 128.295--- , test psnr: 10.4541--- , time: 105.06 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 123.356--- , test psnr: 10.2783--- , time: 10.2037 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 123.603--- , test psnr: 10.4115--- , time: 20.2585 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 125.135--- , test psnr: 10.4995--- , time: 30.7889 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 123.346--- , test psnr: 10.3403--- , time: 41.4355 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 123.426--- , test psnr: 10.4572--- , time: 52.1976 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 123.802--- , test psnr: 10.4497--- , time: 62.5091 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 123.466--- , test psnr: 10.3072--- , time: 72.9385 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 123.564--- , test psnr: 10.3498--- , time: 83.8331 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 123.633--- , test psnr: 10.4453--- , time: 94.6804 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 123.666--- , test psnr: 10.442--- , time: 105.136 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 129.925--- , test psnr: 10.5748--- , time: 10.3163 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 129.721--- , test psnr: 10.4348--- , time: 20.3875 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 130.001--- , test psnr: 10.4963--- , time: 29.8324 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 130.255--- , test psnr: 10.5654--- , time: 38.8607 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 129.881--- , test psnr: 10.5739--- , time: 47.8869 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 130.237--- , test psnr: 10.6032--- , time: 57.0484 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 130.056--- , test psnr: 10.5911--- , time: 67.4663 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 130.176--- , test psnr: 10.5778--- , time: 78.0384 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 129.952--- , test psnr: 10.569--- , time: 88.6071 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 130.115--- , test psnr: 10.6393--- , time: 99.5709 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 124.862--- , test psnr: 11.5446--- , time: 10.5488 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 124.912--- , test psnr: 11.5736--- , time: 21.0516 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 124.683--- , test psnr: 11.4678--- , time: 31.7951 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 124.766--- , test psnr: 11.4015--- , time: 42.2605 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 125.282--- , test psnr: 11.5736--- , time: 52.4847 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 125.317--- , test psnr: 11.624--- , time: 62.8829 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 125.113--- , test psnr: 11.5042--- , time: 73.0217 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 124.856--- , test psnr: 11.6012--- , time: 83.406 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 124.977--- , test psnr: 11.6223--- , time: 93.9831 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 124.725--- , test psnr: 11.4531--- , time: 104.213 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 130.565--- , test psnr: 11.8481--- , time: 10.0817 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 130.854--- , test psnr: 11.7902--- , time: 20.6042 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 130.145--- , test psnr: 11.6257--- , time: 30.9105 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 130.515--- , test psnr: 11.7361--- , time: 41.3445 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 130.432--- , test psnr: 11.7279--- , time: 51.8186 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 130.908--- , test psnr: 11.7768--- , time: 62.0646 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 131.185--- , test psnr: 11.6832--- , time: 72.3233 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 130.449--- , test psnr: 11.6701--- , time: 82.6857 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 130.334--- , test psnr: 11.6867--- , time: 93.0349 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 130.226--- , test psnr: 11.6313--- , time: 104.035 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 129.255--- , test psnr: 10.4771--- , time: 10.547 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 129.279--- , test psnr: 10.3923--- , time: 21.7395 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 129.061--- , test psnr: 10.3057--- , time: 32.0218 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 129.192--- , test psnr: 10.4317--- , time: 42.4461 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 129.195--- , test psnr: 10.3442--- , time: 53.3049 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 129.139--- , test psnr: 10.3144--- , time: 63.8001 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 129.328--- , test psnr: 10.393--- , time: 74.9844 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 129.332--- , test psnr: 10.4141--- , time: 85.8423 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 129.34--- , test psnr: 10.3946--- , time: 96.8383 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 129.158--- , test psnr: 10.4312--- , time: 107.844 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 131.778--- , test psnr: 12.1054--- , time: 10.9571 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 131.823--- , test psnr: 12.0704--- , time: 21.8397 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 131.805--- , test psnr: 12.0514--- , time: 32.6515 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 132.311--- , test psnr: 12.1459--- , time: 43.6274 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 131.547--- , test psnr: 11.9715--- , time: 54.899 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 131.825--- , test psnr: 11.9742--- , time: 65.5937 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 132.041--- , test psnr: 12.049--- , time: 76.1558 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 131.61--- , test psnr: 11.9622--- , time: 87.0708 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 132.226--- , test psnr: 12.1519--- , time: 97.7752 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 132.927--- , test psnr: 11.9809--- , time: 109.067 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 131.591--- , test psnr: 11.9792--- , time: 10.7353 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 131.545--- , test psnr: 11.9419--- , time: 21.7059 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 131.743--- , test psnr: 11.9402--- , time: 32.2812 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 131.459--- , test psnr: 11.8005--- , time: 42.461 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 131.324--- , test psnr: 11.889--- , time: 52.9957 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 131.606--- , test psnr: 11.9348--- , time: 63.7851 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 131.224--- , test psnr: 11.8498--- , time: 74.5596 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 131.614--- , test psnr: 11.973--- , time: 81.7716 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 131.612--- , test psnr: 11.8976--- , time: 88.7841 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 131.605--- , test psnr: 11.9009--- , time: 95.9568 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 128.364--- , test psnr: 10.9874--- , time: 7.32159 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 128.103--- , test psnr: 10.8912--- , time: 14.8849 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 128.349--- , test psnr: 11.0003--- , time: 22.4094 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 128.051--- , test psnr: 10.8765--- , time: 30.5203 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 127.885--- , test psnr: 10.8949--- , time: 41.2557 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 128.41--- , test psnr: 11.0364--- , time: 48.1575 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 128.375--- , test psnr: 10.968--- , time: 55.4502 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 128.231--- , test psnr: 11.0006--- , time: 61.8888 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 128.268--- , test psnr: 10.9399--- , time: 68.8518 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 128.512--- , test psnr: 10.9989--- , time: 76.1136 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 131.57--- , test psnr: 11.9878--- , time: 8.06661 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 131.609--- , test psnr: 11.9825--- , time: 17.0314 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 131.528--- , test psnr: 11.874--- , time: 24.2731 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 131.612--- , test psnr: 11.835--- , time: 31.5192 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 131.622--- , test psnr: 11.9967--- , time: 38.6257 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 131.63--- , test psnr: 11.9775--- , time: 45.5366 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 131.606--- , test psnr: 11.9792--- , time: 52.3392 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 131.289--- , test psnr: 11.8285--- , time: 59.7006 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 131.538--- , test psnr: 11.8399--- , time: 66.9925 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 131.547--- , test psnr: 11.8917--- , time: 74.084 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 127.428--- , test psnr: 10.5735--- , time: 9.29109 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 127.267--- , test psnr: 10.5821--- , time: 19.8338 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 127.473--- , test psnr: 10.5974--- , time: 26.8 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 127.463--- , test psnr: 10.6417--- , time: 33.8016 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 127.851--- , test psnr: 10.6255--- , time: 41.1889 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 128.01--- , test psnr: 10.7332--- , time: 48.3076 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 127.505--- , test psnr: 10.5965--- , time: 55.9643 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 127.608--- , test psnr: 10.6645--- , time: 63.2893 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 127.551--- , test psnr: 10.715--- , time: 70.4259 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 127.541--- , test psnr: 10.7423--- , time: 77.9221 seconds ---\n"
     ]
    }
   ],
   "source": [
    "all_data = torch.from_numpy(np.linspace(0, 1, sample_N+1)[:-1].reshape(-1,1)).type(torch.FloatTensor)\n",
    "\n",
    "c = np.zeros((embedding_dimension,))\n",
    "c[0] = 1\n",
    "c = c/np.linalg.norm(c)\n",
    "\n",
    "for i in range(Nd):\n",
    "    sample_d = sample_ds[i]\n",
    "\n",
    "    train_data = shift_embedding(all_data[::2*sample_d],c)\n",
    "    test_data = shift_embedding(all_data[sample_d::2*sample_d],c)\n",
    "\n",
    "    # Calculate the input stable rank\n",
    "    sr_[i] = srank_func(train_data)\n",
    "\n",
    "\n",
    "    for j in range(N_test):\n",
    "        start_time = time.time()\n",
    "        \n",
    "        \n",
    "        # Prepare the data\n",
    "        all_target = signal[kk[j]].reshape(-1,1)\n",
    "        train_label = all_target[::2*sample_d]\n",
    "        test_label = all_target[sample_d::2*sample_d]\n",
    "        \n",
    "        xt, yt = train_data.cuda(),train_label.cuda()\n",
    "        xt, yt = Variable(xt), Variable(yt)\n",
    "\n",
    "        xts, yts = test_data.cuda(),test_label.cuda()\n",
    "        \n",
    "        \n",
    "        for k in range(N_repeat):\n",
    "            # Initialize classification model to learn\n",
    "            model = SLP(embedding_dimension).cuda()\n",
    "            # Set the optimization\n",
    "            optimizer = optim.Adam(model.parameters(), 1e-3, betas=(0.9, 0.999),weight_decay=1e-8)\n",
    "            \n",
    "\n",
    "            for epoch in range(epochs):\n",
    "                model.train()\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                out = model(xt)\n",
    "                loss = criterion(out, yt)\n",
    "\n",
    "                loss.backward()\n",
    "                losses_[i,j,k,epoch] = loss.item()\n",
    "                optimizer.step()\n",
    "\n",
    "                model.eval()\n",
    "                trn_psnr_[i,j,k,epoch] = psnr_func(model(xt),yt)\n",
    "                tst_psnr_[i,j,k,epoch] = psnr_func(model(xts),yts)\n",
    "\n",
    "\n",
    "            print(\"==>>> N: %g, F: %g, train psnr: %g--- , test psnr: %g--- , time: %g seconds ---\" \n",
    "                    % (i,j, np.mean(trn_psnr_[i,j,k,-100:]),np.mean(tst_psnr_[i,j,k,-100:]),time.time() - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f4333dda358>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure()\n",
    "for i in range(Nd):\n",
    "    plot(np.mean(trn_psnr_[i,:,:],(0,1)))\n",
    "    plot(np.mean(tst_psnr_[i,:,:],(0,1)),'--')\n",
    "\n",
    "figure()\n",
    "plot(sample_ds,np.mean(sr_,(1,2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_name = 'impulse'\n",
    "np.save(file_path+file_name+'_losses',losses_)\n",
    "np.save(file_path+file_name+'_sr',sr_)\n",
    "np.save(file_path+file_name+'_trn',trn_psnr_)\n",
    "np.save(file_path+file_name+'_tst',tst_psnr_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# random noise embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==>>> N: 0, F: 0, train psnr: 112.112--- , test psnr: 5.68134--- , time: 8.35212 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 113.256--- , test psnr: 7.89045--- , time: 16.8279 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 112.069--- , test psnr: 5.35961--- , time: 25.2656 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 112.57--- , test psnr: 5.98385--- , time: 33.7491 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 112.522--- , test psnr: 7.04619--- , time: 42.245 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 112.739--- , test psnr: 5.90551--- , time: 50.8406 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 112.048--- , test psnr: 8.65034--- , time: 59.5991 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 112.96--- , test psnr: 8.33021--- , time: 68.4682 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 112.618--- , test psnr: 5.43367--- , time: 77.0771 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 112.901--- , test psnr: 5.07905--- , time: 86.0902 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 113.661--- , test psnr: 5.38549--- , time: 8.78616 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 113.931--- , test psnr: 6.3962--- , time: 17.7615 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 113.805--- , test psnr: 5.81889--- , time: 25.8745 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 113.772--- , test psnr: 5.69593--- , time: 34.8562 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 113.729--- , test psnr: 9.32934--- , time: 43.7218 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 113.695--- , test psnr: 5.98564--- , time: 52.2857 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 113.955--- , test psnr: 9.6505--- , time: 60.9195 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 113.679--- , test psnr: 8.13852--- , time: 69.7008 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 113.832--- , test psnr: 9.23286--- , time: 78.5501 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 113.45--- , test psnr: 7.66821--- , time: 87.2264 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 113.392--- , test psnr: 6.85034--- , time: 8.8004 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 113.223--- , test psnr: 7.23732--- , time: 17.6903 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 112.482--- , test psnr: 7.53372--- , time: 26.5678 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 112.971--- , test psnr: 5.78217--- , time: 35.6233 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 112.332--- , test psnr: 7.80151--- , time: 44.1745 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 112.812--- , test psnr: 8.60714--- , time: 52.7795 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 112.578--- , test psnr: 5.91476--- , time: 61.1147 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 112.379--- , test psnr: 7.27263--- , time: 69.7723 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 113.057--- , test psnr: 5.50948--- , time: 78.585 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 112.901--- , test psnr: 5.62493--- , time: 87.4887 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 115.04--- , test psnr: 8.99966--- , time: 8.72674 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 114.734--- , test psnr: 5.95605--- , time: 17.4568 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 115.164--- , test psnr: 6.71318--- , time: 26.4641 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 114.702--- , test psnr: 7.77995--- , time: 35.4256 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 114.69--- , test psnr: 6.29279--- , time: 44.3495 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 115.056--- , test psnr: 5.03017--- , time: 53.3758 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 114.958--- , test psnr: 7.13479--- , time: 62.2 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 114.828--- , test psnr: 6.70125--- , time: 70.6965 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 114.49--- , test psnr: 8.41641--- , time: 79.6065 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 114.844--- , test psnr: 8.32714--- , time: 87.9451 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 113.547--- , test psnr: 8.06457--- , time: 8.85112 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 113.761--- , test psnr: 7.34067--- , time: 17.0993 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 113.38--- , test psnr: 8.36082--- , time: 26.0365 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 113.7--- , test psnr: 6.52241--- , time: 34.9651 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 113.298--- , test psnr: 6.29908--- , time: 43.9521 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 114.053--- , test psnr: 5.60365--- , time: 52.6745 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 113.628--- , test psnr: 6.39826--- , time: 61.5171 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 113.386--- , test psnr: 4.88716--- , time: 70.4044 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 113.903--- , test psnr: 5.73917--- , time: 79.5396 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 113.33--- , test psnr: 5.80933--- , time: 88.4267 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 116.193--- , test psnr: 7.87146--- , time: 8.89838 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 116.351--- , test psnr: 7.67845--- , time: 18.2356 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 116.008--- , test psnr: 7.6235--- , time: 26.9069 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 115.342--- , test psnr: 8.25143--- , time: 35.5406 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 115.92--- , test psnr: 8.57933--- , time: 44.4862 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 115.888--- , test psnr: 7.93985--- , time: 52.7872 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 116.144--- , test psnr: 9.26126--- , time: 61.9629 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 115.891--- , test psnr: 8.14635--- , time: 70.8522 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 116.149--- , test psnr: 7.05857--- , time: 79.6884 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 115.678--- , test psnr: 7.70247--- , time: 88.4547 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 115.759--- , test psnr: 9.3052--- , time: 8.63989 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 117.129--- , test psnr: 8.77256--- , time: 17.5835 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 115.532--- , test psnr: 11.6543--- , time: 25.9329 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 115.592--- , test psnr: 6.58574--- , time: 34.8337 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 115.516--- , test psnr: 7.95385--- , time: 43.5893 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 115.408--- , test psnr: 7.20692--- , time: 52.4179 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 115.657--- , test psnr: 8.81399--- , time: 61.3439 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 115.042--- , test psnr: 7.65384--- , time: 70.1035 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 116.075--- , test psnr: 6.39476--- , time: 78.9466 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 115.416--- , test psnr: 7.40348--- , time: 87.4496 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 113.326--- , test psnr: 7.06739--- , time: 8.86003 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 113.717--- , test psnr: 7.27906--- , time: 17.3979 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 113.709--- , test psnr: 6.53306--- , time: 25.5094 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 113.701--- , test psnr: 5.74688--- , time: 34.5425 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 113.037--- , test psnr: 5.80537--- , time: 43.3282 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 113.476--- , test psnr: 6.92349--- , time: 52.2049 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 113.284--- , test psnr: 8.54157--- , time: 60.7426 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 113.821--- , test psnr: 5.82694--- , time: 69.7065 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 113.707--- , test psnr: 8.23912--- , time: 78.7035 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 113.945--- , test psnr: 5.66291--- , time: 87.4444 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 115.361--- , test psnr: 7.34865--- , time: 8.96132 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 115.785--- , test psnr: 10.199--- , time: 18.0427 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 117.576--- , test psnr: 8.03381--- , time: 26.9467 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 115.699--- , test psnr: 8.01012--- , time: 35.7052 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 115.533--- , test psnr: 6.19083--- , time: 44.4173 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 115.863--- , test psnr: 7.94949--- , time: 53.2288 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 115.696--- , test psnr: 9.77771--- , time: 62.0139 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 115.644--- , test psnr: 6.2749--- , time: 70.7829 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 115.299--- , test psnr: 8.94538--- , time: 79.704 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 115.92--- , test psnr: 8.36316--- , time: 88.5618 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 113.142--- , test psnr: 7.27302--- , time: 8.79385 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 112.123--- , test psnr: 6.74964--- , time: 17.5871 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 113.139--- , test psnr: 7.85252--- , time: 26.5451 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 113.117--- , test psnr: 6.56177--- , time: 35.4467 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 113.343--- , test psnr: 7.80277--- , time: 43.9643 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 113.287--- , test psnr: 5.31676--- , time: 52.667 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 113.424--- , test psnr: 8.34406--- , time: 61.6603 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 113.239--- , test psnr: 6.90444--- , time: 70.3813 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 113.124--- , test psnr: 5.29567--- , time: 78.7129 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 112.873--- , test psnr: 7.53547--- , time: 87.1761 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 117.433--- , test psnr: 6.29578--- , time: 8.67639 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 115.313--- , test psnr: 6.11366--- , time: 17.4289 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 114.132--- , test psnr: 5.16779--- , time: 26.1923 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 118.079--- , test psnr: 6.41749--- , time: 34.8887 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 114.466--- , test psnr: 6.43391--- , time: 43.427 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 114.314--- , test psnr: 5.27963--- , time: 52.248 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 114.964--- , test psnr: 6.61584--- , time: 60.8534 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 114.544--- , test psnr: 6.26651--- , time: 69.3074 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 115.717--- , test psnr: 6.23586--- , time: 77.9984 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 119.784--- , test psnr: 6.07385--- , time: 86.4984 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 121.437--- , test psnr: 5.88094--- , time: 8.66913 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 121.168--- , test psnr: 6.12421--- , time: 17.3737 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 117.924--- , test psnr: 6.6134--- , time: 26.2215 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 120.409--- , test psnr: 5.00078--- , time: 34.1703 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 115.487--- , test psnr: 5.75087--- , time: 42.8648 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 120.607--- , test psnr: 6.17616--- , time: 51.4073 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 120.44--- , test psnr: 5.9442--- , time: 60.2387 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 121.219--- , test psnr: 5.94309--- , time: 68.9657 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 121.919--- , test psnr: 6.20721--- , time: 77.8726 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 121.414--- , test psnr: 8.46471--- , time: 86.6079 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 119.2--- , test psnr: 7.0458--- , time: 8.60723 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 114.713--- , test psnr: 6.61131--- , time: 17.2302 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 119.364--- , test psnr: 5.09689--- , time: 25.8225 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 120.321--- , test psnr: 8.27455--- , time: 34.676 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 119.609--- , test psnr: 3.79024--- , time: 43.6097 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 114.48--- , test psnr: 8.49286--- , time: 52.3079 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 118.664--- , test psnr: 7.89592--- , time: 61.2421 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 114.731--- , test psnr: 7.06803--- , time: 69.8848 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 114.657--- , test psnr: 5.64542--- , time: 78.4226 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 118.627--- , test psnr: 6.30119--- , time: 87.3247 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 120.759--- , test psnr: 8.0506--- , time: 8.53807 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 121.822--- , test psnr: 7.96152--- , time: 17.1465 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 122.523--- , test psnr: 6.04338--- , time: 25.0899 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 120.744--- , test psnr: 8.49593--- , time: 33.8893 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 122.446--- , test psnr: 6.83642--- , time: 42.4248 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 121.832--- , test psnr: 6.19122--- , time: 51.202 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 122.366--- , test psnr: 7.68681--- , time: 60.1174 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 122.478--- , test psnr: 6.35979--- , time: 68.508 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 122.66--- , test psnr: 7.70542--- , time: 76.3571 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 122.342--- , test psnr: 5.99017--- , time: 85.0819 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 121.23--- , test psnr: 7.5376--- , time: 8.70486 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 115.443--- , test psnr: 6.36767--- , time: 17.211 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 121.255--- , test psnr: 6.60991--- , time: 25.6915 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 121.185--- , test psnr: 6.73779--- , time: 34.5264 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 121.071--- , test psnr: 6.25542--- , time: 43.2789 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 121.306--- , test psnr: 5.89983--- , time: 51.5941 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 121.48--- , test psnr: 5.6701--- , time: 60.0121 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 120.96--- , test psnr: 5.74939--- , time: 68.5776 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 121.196--- , test psnr: 6.2468--- , time: 77.0819 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 121.563--- , test psnr: 7.66626--- , time: 85.8359 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 122.675--- , test psnr: 8.15611--- , time: 8.71322 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 123.65--- , test psnr: 8.94178--- , time: 17.4043 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 123.657--- , test psnr: 9.08008--- , time: 26.1062 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 123.373--- , test psnr: 6.73958--- , time: 34.7898 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 123.365--- , test psnr: 7.95353--- , time: 43.61 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 123.498--- , test psnr: 8.34695--- , time: 52.3222 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 123.801--- , test psnr: 7.54177--- , time: 61.0094 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 123.742--- , test psnr: 8.4339--- , time: 69.6195 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 123.638--- , test psnr: 7.82697--- , time: 78.2143 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 123.127--- , test psnr: 7.87424--- , time: 86.9111 seconds ---\n",
      "==>>> N: 1, F: 6, train psnr: 122.625--- , test psnr: 9.26624--- , time: 8.20523 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 121.131--- , test psnr: 6.09177--- , time: 17.0205 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 119.324--- , test psnr: 5.89209--- , time: 25.5947 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 120.948--- , test psnr: 6.46987--- , time: 33.3582 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 121.048--- , test psnr: 6.05576--- , time: 42.1911 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 121.048--- , test psnr: 5.8372--- , time: 50.5939 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 119.847--- , test psnr: 5.22798--- , time: 58.8604 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 121.279--- , test psnr: 7.81263--- , time: 67.4497 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 120.932--- , test psnr: 5.77726--- , time: 76.0699 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 119.047--- , test psnr: 7.78786--- , time: 84.7427 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 122.978--- , test psnr: 7.75266--- , time: 8.90173 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 122.946--- , test psnr: 8.03672--- , time: 17.7726 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 123.108--- , test psnr: 6.69322--- , time: 26.4524 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 123.279--- , test psnr: 7.81995--- , time: 34.6529 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 122.468--- , test psnr: 8.0728--- , time: 43.404 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 123.337--- , test psnr: 7.9631--- , time: 51.7653 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 123.101--- , test psnr: 7.85083--- , time: 60.1185 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 123.208--- , test psnr: 8.22254--- , time: 68.5406 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 123.003--- , test psnr: 7.48259--- , time: 77.0581 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 122.823--- , test psnr: 7.91105--- , time: 85.9458 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 120.772--- , test psnr: 6.75933--- , time: 8.52623 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 119.954--- , test psnr: 5.89298--- , time: 17.1625 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 119.416--- , test psnr: 6.60162--- , time: 25.4975 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 119.971--- , test psnr: 5.64028--- , time: 33.8696 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 117.734--- , test psnr: 5.82678--- , time: 42.0273 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 114.868--- , test psnr: 7.42475--- , time: 51.0067 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 120.241--- , test psnr: 6.36006--- , time: 59.5827 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 120.161--- , test psnr: 5.91973--- , time: 68.5097 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 115.131--- , test psnr: 5.66545--- , time: 76.9739 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 119.438--- , test psnr: 5.63757--- , time: 85.6206 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 121.128--- , test psnr: 6.06257--- , time: 8.66323 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 121.51--- , test psnr: 5.28907--- , time: 17.4831 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 121.236--- , test psnr: 5.16538--- , time: 26.257 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 121.553--- , test psnr: 5.43793--- , time: 34.9869 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 121.359--- , test psnr: 7.36509--- , time: 43.4841 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 121.452--- , test psnr: 5.71332--- , time: 52.1152 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 121.383--- , test psnr: 5.85324--- , time: 60.7642 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 121.498--- , test psnr: 5.43626--- , time: 69.5732 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 121.334--- , test psnr: 5.71209--- , time: 78.346 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 121.563--- , test psnr: 5.13501--- , time: 87.1146 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 121.427--- , test psnr: 6.16479--- , time: 8.81919 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 122.737--- , test psnr: 6.45047--- , time: 17.4465 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 121.989--- , test psnr: 7.24246--- , time: 26.0209 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 122.875--- , test psnr: 6.76194--- , time: 34.2546 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 122.682--- , test psnr: 7.04447--- , time: 42.8014 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 122.593--- , test psnr: 6.68665--- , time: 51.6574 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 122.601--- , test psnr: 6.79761--- , time: 60.2229 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 122.76--- , test psnr: 6.59707--- , time: 68.9511 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 122.756--- , test psnr: 7.17513--- , time: 77.7289 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 122.704--- , test psnr: 7.02433--- , time: 86.2909 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 121.503--- , test psnr: 6.22976--- , time: 8.66108 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 121.516--- , test psnr: 5.52871--- , time: 17.1161 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 121.47--- , test psnr: 5.80042--- , time: 25.5594 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 121.609--- , test psnr: 5.1745--- , time: 34.408 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 121.553--- , test psnr: 5.40983--- , time: 42.9758 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 121.512--- , test psnr: 5.23391--- , time: 51.8659 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 122.046--- , test psnr: 5.01135--- , time: 60.6076 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 121.535--- , test psnr: 5.21058--- , time: 69.1288 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 121.784--- , test psnr: 3.95104--- , time: 77.7069 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 121.178--- , test psnr: 5.88926--- , time: 86.2284 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 123.479--- , test psnr: 7.54239--- , time: 8.8899 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 123.81--- , test psnr: 6.74095--- , time: 17.5157 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 123.267--- , test psnr: 6.42321--- , time: 26.2779 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 123.861--- , test psnr: 8.04323--- , time: 34.9269 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 124.024--- , test psnr: 7.28374--- , time: 43.6375 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 123.868--- , test psnr: 7.15552--- , time: 51.7635 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 124.004--- , test psnr: 6.32104--- , time: 60.3911 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 123.606--- , test psnr: 7.10712--- , time: 68.9644 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 123.479--- , test psnr: 6.9473--- , time: 76.7683 seconds ---\n",
      "==>>> N: 2, F: 4, train psnr: 122.561--- , test psnr: 7.10545--- , time: 85.9026 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 124.622--- , test psnr: 7.47072--- , time: 8.72581 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 124.58--- , test psnr: 7.243--- , time: 17.3614 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 124.999--- , test psnr: 6.81822--- , time: 26.1426 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 124.648--- , test psnr: 7.8163--- , time: 34.8069 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 124.71--- , test psnr: 8.60096--- , time: 43.3628 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 124.62--- , test psnr: 7.92836--- , time: 51.8782 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 124.894--- , test psnr: 7.07147--- , time: 60.5466 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 124.903--- , test psnr: 7.40998--- , time: 69.2093 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 124.648--- , test psnr: 8.00559--- , time: 77.8828 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 124.542--- , test psnr: 8.16821--- , time: 86.4555 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 124.359--- , test psnr: 8.32667--- , time: 8.76856 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 124.158--- , test psnr: 6.97645--- , time: 17.5736 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 124.426--- , test psnr: 8.16289--- , time: 26.306 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 124.229--- , test psnr: 7.83858--- , time: 34.8961 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 124.531--- , test psnr: 7.24517--- , time: 43.7545 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 124.744--- , test psnr: 8.28397--- , time: 52.6307 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 124.745--- , test psnr: 7.79502--- , time: 61.3542 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 124.288--- , test psnr: 7.98366--- , time: 69.7439 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 124.856--- , test psnr: 7.6393--- , time: 78.5959 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 124.771--- , test psnr: 7.20416--- , time: 87.202 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 122.681--- , test psnr: 7.03105--- , time: 8.59534 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 122.432--- , test psnr: 6.87754--- , time: 17.3773 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 122.54--- , test psnr: 7.14915--- , time: 25.635 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 122.417--- , test psnr: 5.81658--- , time: 34.2396 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 122.69--- , test psnr: 6.79935--- , time: 42.8122 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 121.87--- , test psnr: 7.37253--- , time: 51.6668 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 122.645--- , test psnr: 7.12127--- , time: 60.2743 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 122.425--- , test psnr: 6.49843--- , time: 68.9361 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 122.396--- , test psnr: 6.08123--- , time: 77.4812 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 122.462--- , test psnr: 5.60819--- , time: 85.9517 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 124.705--- , test psnr: 6.29384--- , time: 8.64005 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 124.449--- , test psnr: 6.92887--- , time: 16.8841 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 124.445--- , test psnr: 6.95481--- , time: 25.4369 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 124.23--- , test psnr: 7.56695--- , time: 34.2341 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 124.545--- , test psnr: 8.43569--- , time: 42.751 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 124.415--- , test psnr: 7.74704--- , time: 51.6137 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 124.622--- , test psnr: 7.64773--- , time: 60.1313 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 124.3--- , test psnr: 7.12402--- , time: 68.7222 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 124.344--- , test psnr: 8.34977--- , time: 77.5047 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 124.866--- , test psnr: 7.79266--- , time: 86.2008 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 122.249--- , test psnr: 6.48355--- , time: 8.6897 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 121.677--- , test psnr: 4.96274--- , time: 17.1395 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 121.938--- , test psnr: 5.91538--- , time: 25.7569 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 122.335--- , test psnr: 7.36127--- , time: 34.6566 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 121.887--- , test psnr: 6.39887--- , time: 43.3646 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 122.266--- , test psnr: 6.94907--- , time: 51.9537 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 121.856--- , test psnr: 7.06753--- , time: 60.6729 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 121.875--- , test psnr: 6.21347--- , time: 69.483 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 122.134--- , test psnr: 5.88727--- , time: 78.094 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 122.023--- , test psnr: 5.73422--- , time: 86.6036 seconds ---\n",
      "==>>> N: 3, F: 0, train psnr: 122.227--- , test psnr: 5.17663--- , time: 8.73964 seconds ---\n",
      "==>>> N: 3, F: 0, train psnr: 122.436--- , test psnr: 5.69324--- , time: 17.3709 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 123.331--- , test psnr: 6.46293--- , time: 33.9942 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 123.606--- , test psnr: 6.46958--- , time: 42.2925 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 123.663--- , test psnr: 6.35785--- , time: 50.7955 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 123.31--- , test psnr: 5.96676--- , time: 59.2681 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 123.79--- , test psnr: 5.96786--- , time: 67.9446 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 123.481--- , test psnr: 6.34394--- , time: 76.6391 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 123.477--- , test psnr: 5.91822--- , time: 85.393 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 122.799--- , test psnr: 5.84643--- , time: 8.72444 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 122.467--- , test psnr: 5.50826--- , time: 17.3767 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 122.473--- , test psnr: 6.32712--- , time: 26.0009 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 122.693--- , test psnr: 5.78166--- , time: 34.466 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 122.851--- , test psnr: 6.2108--- , time: 43.2639 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 122.757--- , test psnr: 6.61343--- , time: 51.7461 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 122.856--- , test psnr: 6.2812--- , time: 60.4384 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 122.362--- , test psnr: 5.70761--- , time: 69.227 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 122.765--- , test psnr: 4.51751--- , time: 77.8356 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 122.518--- , test psnr: 5.89519--- , time: 86.5303 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 124.739--- , test psnr: 6.85217--- , time: 8.71213 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 124.433--- , test psnr: 7.01465--- , time: 17.1433 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 124.568--- , test psnr: 7.15862--- , time: 25.7123 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 124.234--- , test psnr: 7.18997--- , time: 34.0005 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 124.574--- , test psnr: 7.35606--- , time: 42.6988 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 124.63--- , test psnr: 7.28488--- , time: 50.9181 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 124.687--- , test psnr: 8.34478--- , time: 59.6154 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 124.601--- , test psnr: 7.08268--- , time: 68.2288 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 124.327--- , test psnr: 8.27249--- , time: 76.8428 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 124.724--- , test psnr: 6.98642--- , time: 85.6322 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 122.879--- , test psnr: 7.60563--- , time: 8.50502 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 123.17--- , test psnr: 6.15085--- , time: 16.6875 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 123.45--- , test psnr: 6.7599--- , time: 24.8669 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 123.238--- , test psnr: 6.65194--- , time: 33.4339 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 123.127--- , test psnr: 6.22609--- , time: 42.0122 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 123.325--- , test psnr: 5.80986--- , time: 51.001 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 123.245--- , test psnr: 6.29973--- , time: 59.7993 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 123.157--- , test psnr: 8.1698--- , time: 68.3986 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 123.289--- , test psnr: 6.07325--- , time: 76.8424 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 123.111--- , test psnr: 7.25948--- , time: 85.5425 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 125.214--- , test psnr: 7.60534--- , time: 8.91825 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 125.577--- , test psnr: 7.86697--- , time: 17.127 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 128.418--- , test psnr: 7.65482--- , time: 25.6238 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 127.655--- , test psnr: 7.6347--- , time: 34.365 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 125.304--- , test psnr: 8.08762--- , time: 43.0321 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 125.452--- , test psnr: 8.12802--- , time: 51.6959 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 125.529--- , test psnr: 7.92844--- , time: 60.2857 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 125.646--- , test psnr: 6.47201--- , time: 69.0834 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 125.568--- , test psnr: 8.35194--- , time: 77.9022 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 125.674--- , test psnr: 7.90163--- , time: 86.6991 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 125.119--- , test psnr: 6.7512--- , time: 8.46006 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 125.503--- , test psnr: 7.59369--- , time: 17.3919 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 125.542--- , test psnr: 6.39791--- , time: 26.1446 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 125.611--- , test psnr: 6.67171--- , time: 34.4928 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 125.412--- , test psnr: 7.22033--- , time: 43.0015 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 125.312--- , test psnr: 6.45145--- , time: 51.2794 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 125.462--- , test psnr: 8.13595--- , time: 59.7388 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 125.408--- , test psnr: 7.44761--- , time: 68.4054 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 128.215--- , test psnr: 7.08011--- , time: 76.4993 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 125.179--- , test psnr: 6.6563--- , time: 85.2413 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 123.513--- , test psnr: 5.76436--- , time: 8.67917 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 123.642--- , test psnr: 5.99299--- , time: 17.4777 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 123.615--- , test psnr: 7.10873--- , time: 26.1131 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 123.432--- , test psnr: 6.20398--- , time: 34.9253 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 123.944--- , test psnr: 7.07431--- , time: 44.1792 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 123.748--- , test psnr: 5.93775--- , time: 52.8674 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 123.659--- , test psnr: 6.06233--- , time: 61.4559 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 123.617--- , test psnr: 5.77179--- , time: 71.464 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 123.701--- , test psnr: 7.24274--- , time: 83.2596 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 123.689--- , test psnr: 5.9297--- , time: 91.7391 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 125.032--- , test psnr: 7.66228--- , time: 8.48658 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 125.474--- , test psnr: 7.08812--- , time: 16.949 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 125.404--- , test psnr: 6.81616--- , time: 25.1807 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 125.388--- , test psnr: 7.05199--- , time: 33.8745 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 125.509--- , test psnr: 9.20718--- , time: 42.6869 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 128.665--- , test psnr: 7.00346--- , time: 51.1977 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 126.052--- , test psnr: 7.08609--- , time: 60.1476 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 125.114--- , test psnr: 8.43153--- , time: 68.8779 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 125.652--- , test psnr: 7.91449--- , time: 77.6116 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 125.331--- , test psnr: 7.6828--- , time: 86.4851 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 123.168--- , test psnr: 5.50563--- , time: 8.55621 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 123.089--- , test psnr: 5.59262--- , time: 17.2253 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 123.109--- , test psnr: 5.45051--- , time: 25.9181 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 122.627--- , test psnr: 6.34718--- , time: 34.4757 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 122.55--- , test psnr: 6.78465--- , time: 43.0792 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 122.895--- , test psnr: 6.03825--- , time: 51.7076 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 123.082--- , test psnr: 4.85268--- , time: 60.6058 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 123.253--- , test psnr: 6.14322--- , time: 70.9328 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 122.832--- , test psnr: 5.03686--- , time: 83.068 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 123.104--- , test psnr: 6.17687--- , time: 95.3301 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 123.836--- , test psnr: 5.08649--- , time: 12.0294 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 123.511--- , test psnr: 4.96405--- , time: 24.0461 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 123.816--- , test psnr: 4.59287--- , time: 35.9269 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 124.078--- , test psnr: 5.651--- , time: 48.2593 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 123.681--- , test psnr: 5.52556--- , time: 59.7057 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 123.7--- , test psnr: 5.4242--- , time: 71.4373 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 123.814--- , test psnr: 5.03897--- , time: 82.8308 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 123.692--- , test psnr: 4.93776--- , time: 94.7365 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 123.229--- , test psnr: 4.7945--- , time: 106.655 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 123.639--- , test psnr: 5.28071--- , time: 119.286 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 128.715--- , test psnr: 6.27499--- , time: 11.6984 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 127.697--- , test psnr: 6.61411--- , time: 23.7737 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 124.324--- , test psnr: 5.85134--- , time: 35.5777 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 129.626--- , test psnr: 6.66091--- , time: 47.6407 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 127.406--- , test psnr: 6.21434--- , time: 59.1838 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 128.341--- , test psnr: 5.20629--- , time: 67.5376 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 128.379--- , test psnr: 6.49319--- , time: 76.2917 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 127.027--- , test psnr: 6.68463--- , time: 84.0919 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 128.11--- , test psnr: 6.13915--- , time: 93.0662 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 124.528--- , test psnr: 6.22442--- , time: 101.761 seconds ---\n",
      "==>>> N: 4, F: 2, train psnr: 124.124--- , test psnr: 4.98381--- , time: 8.68675 seconds ---\n",
      "==>>> N: 4, F: 2, train psnr: 123.962--- , test psnr: 5.83474--- , time: 17.0068 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 130.572--- , test psnr: 6.5581--- , time: 60.3786 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 130.193--- , test psnr: 7.44377--- , time: 69.0481 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 130.474--- , test psnr: 6.89456--- , time: 77.9304 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 130.994--- , test psnr: 6.17199--- , time: 86.7389 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 130.622--- , test psnr: 5.61206--- , time: 8.60203 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 129.652--- , test psnr: 5.81183--- , time: 17.3167 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 124.769--- , test psnr: 5.84841--- , time: 25.7378 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 128.969--- , test psnr: 5.90837--- , time: 34.609 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 129.812--- , test psnr: 5.96554--- , time: 43.4507 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 130.015--- , test psnr: 5.70221--- , time: 51.9636 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 128.911--- , test psnr: 5.77274--- , time: 60.4099 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 129.09--- , test psnr: 5.73998--- , time: 69.175 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 129.332--- , test psnr: 6.71288--- , time: 78.0399 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 129.008--- , test psnr: 6.17434--- , time: 86.8819 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 131.516--- , test psnr: 7.32001--- , time: 8.78061 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 131.948--- , test psnr: 7.72799--- , time: 17.5345 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 132.035--- , test psnr: 7.64321--- , time: 26.3568 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 131.808--- , test psnr: 7.76569--- , time: 35.0604 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 131.468--- , test psnr: 8.13227--- , time: 43.6576 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 132.034--- , test psnr: 7.87428--- , time: 52.4889 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 131.354--- , test psnr: 7.61121--- , time: 60.6222 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 131.782--- , test psnr: 6.67551--- , time: 69.258 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 132.142--- , test psnr: 7.96964--- , time: 77.9955 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 131.452--- , test psnr: 7.26689--- , time: 86.2582 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 131.353--- , test psnr: 6.97545--- , time: 8.27753 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 131.688--- , test psnr: 7.94232--- , time: 16.6995 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 131.455--- , test psnr: 6.96047--- , time: 25.4033 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 131.556--- , test psnr: 6.8118--- , time: 33.7687 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 132.069--- , test psnr: 7.03353--- , time: 42.4448 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 131.544--- , test psnr: 7.36954--- , time: 51.128 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 131.787--- , test psnr: 7.45817--- , time: 59.563 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 132.052--- , test psnr: 7.5051--- , time: 68.4497 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 131.953--- , test psnr: 7.08385--- , time: 77.027 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 131.825--- , test psnr: 6.71291--- , time: 85.9105 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 130.209--- , test psnr: 5.70857--- , time: 8.55279 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 129.628--- , test psnr: 5.83041--- , time: 17.463 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 128.975--- , test psnr: 6.33903--- , time: 26.2213 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 129.914--- , test psnr: 6.34149--- , time: 34.822 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 128.766--- , test psnr: 5.62575--- , time: 43.4784 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 129.535--- , test psnr: 5.40067--- , time: 52.1489 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 129.307--- , test psnr: 5.97853--- , time: 60.606 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 129.478--- , test psnr: 5.32425--- , time: 69.1864 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 129.233--- , test psnr: 5.699--- , time: 76.9527 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 124.776--- , test psnr: 5.28175--- , time: 85.7404 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 132.146--- , test psnr: 7.237--- , time: 8.73829 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 131.554--- , test psnr: 6.92006--- , time: 17.5011 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 131.291--- , test psnr: 7.15626--- , time: 26.3242 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 131.482--- , test psnr: 7.62062--- , time: 34.8089 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 131.411--- , test psnr: 6.94625--- , time: 43.2619 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 132.052--- , test psnr: 6.77026--- , time: 51.6509 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 131.369--- , test psnr: 6.52088--- , time: 60.2734 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 131.532--- , test psnr: 6.81595--- , time: 69.2929 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 131.814--- , test psnr: 6.60243--- , time: 77.9869 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 131.49--- , test psnr: 7.57594--- , time: 86.7828 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 124.028--- , test psnr: 5.63563--- , time: 8.84084 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 123.904--- , test psnr: 6.27254--- , time: 17.5358 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 123.898--- , test psnr: 6.57249--- , time: 25.9785 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 123.957--- , test psnr: 5.8996--- , time: 34.4457 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 129.882--- , test psnr: 6.26328--- , time: 43.0374 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 124.052--- , test psnr: 6.0819--- , time: 51.8443 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 124.047--- , test psnr: 5.80187--- , time: 60.59 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 124.16--- , test psnr: 5.09774--- , time: 68.8416 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 124.095--- , test psnr: 5.61692--- , time: 77.6424 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 127.146--- , test psnr: 6.32268--- , time: 86.3142 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 128.676--- , test psnr: 5.62136--- , time: 8.65546 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 128.65--- , test psnr: 5.56187--- , time: 16.4943 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 126.926--- , test psnr: 5.68769--- , time: 24.9622 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 129.628--- , test psnr: 5.07285--- , time: 33.6007 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 129.639--- , test psnr: 6.09778--- , time: 42.2866 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 129.105--- , test psnr: 5.24209--- , time: 50.8373 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 128.667--- , test psnr: 4.70315--- , time: 59.257 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 128.407--- , test psnr: 4.88863--- , time: 68.1699 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 128.487--- , test psnr: 4.68509--- , time: 76.7125 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 129.154--- , test psnr: 4.9235--- , time: 85.4315 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 131.695--- , test psnr: 5.81405--- , time: 8.54226 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 130.999--- , test psnr: 6.32455--- , time: 17.3955 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 130.988--- , test psnr: 7.20382--- , time: 26.1259 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 131.104--- , test psnr: 6.41328--- , time: 35.0963 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 130.821--- , test psnr: 6.02272--- , time: 43.8226 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 130.952--- , test psnr: 5.88173--- , time: 52.6989 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 131.032--- , test psnr: 6.67047--- , time: 61.064 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 130.618--- , test psnr: 6.6857--- , time: 69.7074 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 130.905--- , test psnr: 6.47533--- , time: 78.5172 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 131.249--- , test psnr: 6.30919--- , time: 87.3963 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 129.828--- , test psnr: 5.39075--- , time: 8.85273 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 129.442--- , test psnr: 6.63009--- , time: 17.5351 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 129.864--- , test psnr: 5.41736--- , time: 26.3322 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 129.664--- , test psnr: 4.85351--- , time: 34.9505 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 129.319--- , test psnr: 5.75076--- , time: 43.3495 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 129.668--- , test psnr: 5.94193--- , time: 52.0541 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 129.861--- , test psnr: 5.23929--- , time: 60.8698 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 129.783--- , test psnr: 5.71241--- , time: 69.5726 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 130.038--- , test psnr: 6.30653--- , time: 78.3482 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 129.566--- , test psnr: 5.24262--- , time: 87.0192 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 132.215--- , test psnr: 6.36685--- , time: 9.03662 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 132.485--- , test psnr: 7.12113--- , time: 17.9808 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 132.34--- , test psnr: 6.60437--- , time: 26.7028 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 132.044--- , test psnr: 7.33437--- , time: 35.3744 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 132.002--- , test psnr: 6.52843--- , time: 44.0012 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 131.692--- , test psnr: 7.37927--- , time: 52.5509 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 131.946--- , test psnr: 6.36969--- , time: 61.4633 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 131.538--- , test psnr: 8.21432--- , time: 69.9489 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 132.354--- , test psnr: 7.66232--- , time: 78.74 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 132.211--- , test psnr: 6.75962--- , time: 87.1532 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 130.969--- , test psnr: 5.42364--- , time: 8.6016 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 130.868--- , test psnr: 6.44588--- , time: 17.3082 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 130.993--- , test psnr: 5.85461--- , time: 25.3217 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 131.174--- , test psnr: 5.23237--- , time: 33.9133 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 133.765--- , test psnr: 6.78137--- , time: 52.2422 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 133.747--- , test psnr: 7.5527--- , time: 60.6803 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 133.797--- , test psnr: 6.4455--- , time: 69.2402 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 133.832--- , test psnr: 6.40795--- , time: 77.645 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 133.873--- , test psnr: 7.00821--- , time: 86.2581 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 132.681--- , test psnr: 7.77847--- , time: 8.55912 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 133.547--- , test psnr: 8.25136--- , time: 17.2475 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 133.118--- , test psnr: 6.60159--- , time: 25.9072 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 133.356--- , test psnr: 7.03435--- , time: 33.5509 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 132.829--- , test psnr: 6.75848--- , time: 42.1713 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 133.268--- , test psnr: 7.34609--- , time: 50.7718 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 132.78--- , test psnr: 7.10726--- , time: 59.5168 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 133.041--- , test psnr: 7.12709--- , time: 68.0002 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 133.003--- , test psnr: 7.02273--- , time: 76.4886 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 133.056--- , test psnr: 6.99029--- , time: 85.1135 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 130.826--- , test psnr: 5.89907--- , time: 8.65889 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 130.989--- , test psnr: 6.50686--- , time: 17.0355 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 131.657--- , test psnr: 5.72711--- , time: 25.591 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 131.321--- , test psnr: 5.82278--- , time: 33.6858 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 131.276--- , test psnr: 6.10002--- , time: 42.6025 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 131.383--- , test psnr: 5.72175--- , time: 51.2193 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 130.813--- , test psnr: 6.44501--- , time: 59.487 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 131.348--- , test psnr: 6.57614--- , time: 68.3056 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 131.209--- , test psnr: 5.35119--- , time: 76.94 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 131.739--- , test psnr: 5.83909--- , time: 85.4262 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 132.785--- , test psnr: 6.8858--- , time: 8.44611 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 133.104--- , test psnr: 7.12896--- , time: 17.2745 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 132.847--- , test psnr: 6.88122--- , time: 25.9289 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 133.031--- , test psnr: 7.0794--- , time: 34.7467 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 133.017--- , test psnr: 6.73779--- , time: 43.5204 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 132.991--- , test psnr: 7.23916--- , time: 51.9483 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 132.7--- , test psnr: 8.0398--- , time: 60.3556 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 132.949--- , test psnr: 7.46226--- , time: 68.6433 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 133.132--- , test psnr: 8.06732--- , time: 77.1327 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 133.152--- , test psnr: 7.993--- , time: 85.7537 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 129.53--- , test psnr: 5.64961--- , time: 8.63972 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 129.632--- , test psnr: 6.28908--- , time: 16.1917 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 130.271--- , test psnr: 5.07362--- , time: 24.7478 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 130.324--- , test psnr: 6.49335--- , time: 33.3186 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 129.44--- , test psnr: 5.15936--- , time: 42.2988 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 129.963--- , test psnr: 6.64403--- , time: 50.8927 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 130.153--- , test psnr: 6.61152--- , time: 59.8381 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 130.093--- , test psnr: 5.44682--- , time: 68.6105 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 130.125--- , test psnr: 5.29233--- , time: 77.4014 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 130.227--- , test psnr: 5.47015--- , time: 85.8392 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 130.951--- , test psnr: 5.43012--- , time: 8.5486 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 131.259--- , test psnr: 5.84382--- , time: 16.3157 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 131.015--- , test psnr: 5.18034--- , time: 25.2605 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 131.327--- , test psnr: 5.61372--- , time: 34.2736 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 131.046--- , test psnr: 5.1566--- , time: 42.8138 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 131.264--- , test psnr: 4.8178--- , time: 51.3692 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 131.162--- , test psnr: 5.59871--- , time: 60.0553 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 131.293--- , test psnr: 4.83946--- , time: 68.3084 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 130.946--- , test psnr: 5.49165--- , time: 77.0951 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 131.083--- , test psnr: 6.1152--- , time: 85.8366 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 132.056--- , test psnr: 6.26087--- , time: 9.10961 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 132.613--- , test psnr: 5.52914--- , time: 17.7641 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 132.218--- , test psnr: 6.09406--- , time: 26.4734 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 132.279--- , test psnr: 6.04436--- , time: 34.2099 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 132.169--- , test psnr: 6.4308--- , time: 42.7147 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 132.367--- , test psnr: 6.25737--- , time: 51.0255 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 132.576--- , test psnr: 5.92273--- , time: 59.1846 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 131.948--- , test psnr: 5.07267--- , time: 67.8912 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 132.002--- , test psnr: 5.77892--- , time: 76.4209 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 132.3--- , test psnr: 5.79526--- , time: 85.1131 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 131.902--- , test psnr: 4.67767--- , time: 8.75351 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 131.407--- , test psnr: 5.03847--- , time: 17.3381 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 131.298--- , test psnr: 4.84887--- , time: 25.7923 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 131.467--- , test psnr: 4.52441--- , time: 34.3417 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 131.38--- , test psnr: 5.12402--- , time: 43.2186 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 131.048--- , test psnr: 4.87489--- , time: 51.741 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 131.176--- , test psnr: 5.22855--- , time: 60.2523 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 131.513--- , test psnr: 4.89093--- , time: 69.196 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 131.318--- , test psnr: 4.95476--- , time: 77.7861 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 131.105--- , test psnr: 4.91837--- , time: 86.0652 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 132.797--- , test psnr: 5.87756--- , time: 8.56449 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 133.236--- , test psnr: 7.02356--- , time: 16.191 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 133.264--- , test psnr: 6.31071--- , time: 24.7577 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 133.368--- , test psnr: 6.78843--- , time: 33.4877 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 133.044--- , test psnr: 7.09888--- , time: 41.9066 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 133.013--- , test psnr: 6.95583--- , time: 50.0392 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 133.128--- , test psnr: 6.97854--- , time: 58.722 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 132.901--- , test psnr: 7.44816--- , time: 67.5756 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 132.942--- , test psnr: 6.39356--- , time: 76.037 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 133.091--- , test psnr: 6.82142--- , time: 84.7113 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 132.211--- , test psnr: 6.38385--- , time: 8.55512 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 131.882--- , test psnr: 5.83401--- , time: 17.0088 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 132.103--- , test psnr: 5.15134--- , time: 25.9029 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 131.672--- , test psnr: 6.29368--- , time: 34.4974 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 131.714--- , test psnr: 5.4782--- , time: 43.555 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 132.401--- , test psnr: 6.45424--- , time: 52.239 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 132.144--- , test psnr: 5.51461--- , time: 61.0844 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 131.831--- , test psnr: 6.2639--- , time: 69.5177 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 132.181--- , test psnr: 6.16859--- , time: 78.2941 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 131.784--- , test psnr: 5.78578--- , time: 85.923 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 135.659--- , test psnr: 7.49572--- , time: 8.29038 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 134.761--- , test psnr: 6.9172--- , time: 16.492 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 136.371--- , test psnr: 7.53254--- , time: 25.295 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 135.171--- , test psnr: 6.78247--- , time: 33.7834 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 136.615--- , test psnr: 7.15709--- , time: 42.4037 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 136.016--- , test psnr: 7.15322--- , time: 51.0692 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 135.975--- , test psnr: 7.18105--- , time: 59.4866 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 135.1--- , test psnr: 7.13182--- , time: 68.0818 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 135.021--- , test psnr: 7.34707--- , time: 76.5746 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 138.03--- , test psnr: 7.05073--- , time: 84.9913 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 134.051--- , test psnr: 7.78682--- , time: 8.6006 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 134.375--- , test psnr: 7.46353--- , time: 17.1433 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 134.654--- , test psnr: 7.55577--- , time: 25.6497 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 134.402--- , test psnr: 7.66696--- , time: 34.4567 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 134.482--- , test psnr: 7.91399--- , time: 42.9143 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 134.278--- , test psnr: 7.70244--- , time: 51.5563 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 134.736--- , test psnr: 7.1249--- , time: 60.0087 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 134.443--- , test psnr: 7.69248--- , time: 68.6197 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 134.005--- , test psnr: 7.56263--- , time: 77.2485 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 134.734--- , test psnr: 7.89807--- , time: 85.9272 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 131.626--- , test psnr: 6.13251--- , time: 8.72386 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 131.912--- , test psnr: 5.80202--- , time: 17.2385 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 131.886--- , test psnr: 6.55938--- , time: 25.7495 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 131.712--- , test psnr: 5.60486--- , time: 34.3984 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 132.169--- , test psnr: 6.28391--- , time: 43.2453 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 131.979--- , test psnr: 6.55218--- , time: 51.9113 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 131.869--- , test psnr: 5.69178--- , time: 60.6265 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 131.929--- , test psnr: 6.52466--- , time: 69.0402 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 131.557--- , test psnr: 6.12885--- , time: 77.8073 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 132.004--- , test psnr: 6.29743--- , time: 86.4626 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 134.591--- , test psnr: 7.59452--- , time: 8.82154 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 134.97--- , test psnr: 7.6967--- , time: 17.5384 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 134.137--- , test psnr: 6.89981--- , time: 26.283 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 134.605--- , test psnr: 7.47123--- , time: 35.1406 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 134.251--- , test psnr: 8.04048--- , time: 43.6678 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 134.086--- , test psnr: 7.45058--- , time: 52.5687 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 134.518--- , test psnr: 7.09653--- , time: 60.4502 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 134.722--- , test psnr: 7.99061--- , time: 69.1108 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 134.39--- , test psnr: 7.0939--- , time: 77.5725 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 134.253--- , test psnr: 7.29367--- , time: 86.1977 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 131.469--- , test psnr: 5.78321--- , time: 8.63623 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 131.889--- , test psnr: 5.72326--- , time: 17.4394 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 131.932--- , test psnr: 4.89069--- , time: 25.9264 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 132.056--- , test psnr: 5.15757--- , time: 34.7103 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 131.811--- , test psnr: 6.20173--- , time: 43.2487 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 131.441--- , test psnr: 5.33904--- , time: 51.5578 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 132.142--- , test psnr: 5.29168--- , time: 60.1762 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 131.462--- , test psnr: 5.23137--- , time: 68.9301 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 131.549--- , test psnr: 5.23995--- , time: 77.7877 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 132.002--- , test psnr: 5.47776--- , time: 86.5159 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 131.89--- , test psnr: 5.22073--- , time: 8.10865 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 131.786--- , test psnr: 4.74216--- , time: 16.7602 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 131.597--- , test psnr: 5.2332--- , time: 25.495 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 132.518--- , test psnr: 5.05857--- , time: 33.9347 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 132.894--- , test psnr: 5.0199--- , time: 42.539 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 131.965--- , test psnr: 4.74145--- , time: 50.8712 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 131.718--- , test psnr: 5.1489--- , time: 59.1279 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 131.886--- , test psnr: 5.30242--- , time: 67.7726 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 131.686--- , test psnr: 4.47628--- , time: 76.1903 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 131.856--- , test psnr: 4.65455--- , time: 84.9314 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 138.391--- , test psnr: 6.55747--- , time: 8.47493 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 135.413--- , test psnr: 5.89058--- , time: 17.0426 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 136.548--- , test psnr: 5.1923--- , time: 25.7708 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 138.018--- , test psnr: 6.2707--- , time: 33.9465 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 138.658--- , test psnr: 5.39643--- , time: 42.8571 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 138.247--- , test psnr: 5.65657--- , time: 51.5577 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 138.026--- , test psnr: 5.85455--- , time: 60.3196 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 137.236--- , test psnr: 5.80097--- , time: 68.9978 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 138.405--- , test psnr: 5.8242--- , time: 77.7411 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 138.43--- , test psnr: 6.21086--- , time: 86.3385 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 132.113--- , test psnr: 5.90266--- , time: 8.23554 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 132.323--- , test psnr: 5.64531--- , time: 16.4843 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 132.004--- , test psnr: 5.56303--- , time: 24.3205 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 131.984--- , test psnr: 5.9007--- , time: 32.714 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 132.172--- , test psnr: 5.41165--- , time: 41.0312 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 131.937--- , test psnr: 5.72862--- , time: 49.4162 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 132.186--- , test psnr: 5.05221--- , time: 57.2499 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 132.108--- , test psnr: 5.7121--- , time: 66.1064 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 132.446--- , test psnr: 6.08203--- , time: 74.6307 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 132.418--- , test psnr: 5.80525--- , time: 82.7487 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 139.385--- , test psnr: 6.3162--- , time: 8.41413 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 139.223--- , test psnr: 6.52277--- , time: 17.0289 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 138.862--- , test psnr: 6.86277--- , time: 25.5605 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 138.728--- , test psnr: 7.04832--- , time: 34.1499 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 138.6--- , test psnr: 7.28295--- , time: 42.8324 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 138.587--- , test psnr: 6.78769--- , time: 51.3149 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 138.92--- , test psnr: 7.26677--- , time: 59.7544 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 139.354--- , test psnr: 6.19471--- , time: 68.5012 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 138.96--- , test psnr: 7.17216--- , time: 77.1892 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 139.368--- , test psnr: 6.78095--- , time: 85.9234 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 137.103--- , test psnr: 5.60497--- , time: 8.54731 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 138.236--- , test psnr: 5.67633--- , time: 17.3527 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 138.047--- , test psnr: 5.82298--- , time: 25.8787 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 138.016--- , test psnr: 6.19481--- , time: 34.5611 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 138.187--- , test psnr: 5.31054--- , time: 43.1503 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 138.263--- , test psnr: 5.54696--- , time: 51.4819 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 138.159--- , test psnr: 5.23609--- , time: 60.2129 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 138.496--- , test psnr: 5.67532--- , time: 68.8075 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 138.924--- , test psnr: 5.82333--- , time: 77.5768 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 138.817--- , test psnr: 5.62777--- , time: 85.9826 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 141.741--- , test psnr: 7.3241--- , time: 8.03555 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 141.2--- , test psnr: 7.68382--- , time: 16.3179 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 141.085--- , test psnr: 7.2539--- , time: 25.1059 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 141.989--- , test psnr: 7.57708--- , time: 33.598 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 141.75--- , test psnr: 7.5467--- , time: 42.0366 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 140.676--- , test psnr: 7.18227--- , time: 50.6336 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 141.719--- , test psnr: 7.34367--- , time: 59.2277 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 141.663--- , test psnr: 7.53266--- , time: 67.691 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 141.337--- , test psnr: 7.1202--- , time: 76.5026 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 140.929--- , test psnr: 7.78028--- , time: 85.5147 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 140.112--- , test psnr: 7.30073--- , time: 8.52549 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 140.427--- , test psnr: 6.64361--- , time: 16.893 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 141.109--- , test psnr: 7.20499--- , time: 25.6295 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 140.411--- , test psnr: 6.84172--- , time: 34.4728 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 140.988--- , test psnr: 6.74214--- , time: 43.0257 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 140.393--- , test psnr: 7.04444--- , time: 51.7506 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 140.978--- , test psnr: 6.97164--- , time: 60.329 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 140.777--- , test psnr: 7.16806--- , time: 69.0805 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 140.912--- , test psnr: 6.75--- , time: 77.5317 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 140.21--- , test psnr: 7.23594--- , time: 86.3202 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 138.122--- , test psnr: 6.04235--- , time: 8.62879 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 137.206--- , test psnr: 6.47572--- , time: 17.3033 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 137.231--- , test psnr: 5.83311--- , time: 25.9627 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 137.876--- , test psnr: 5.63262--- , time: 34.4823 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 137.341--- , test psnr: 6.09844--- , time: 42.7247 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 138.061--- , test psnr: 5.93676--- , time: 51.2504 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 137.656--- , test psnr: 5.96505--- , time: 59.813 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 137.891--- , test psnr: 6.21868--- , time: 68.4421 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 137.274--- , test psnr: 6.39826--- , time: 76.9173 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 138.25--- , test psnr: 6.06056--- , time: 85.4907 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 140.377--- , test psnr: 6.72735--- , time: 8.32112 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 140.747--- , test psnr: 6.96088--- , time: 17.2258 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 140.742--- , test psnr: 6.61057--- , time: 25.7583 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 141.021--- , test psnr: 6.61372--- , time: 34.3599 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 140.729--- , test psnr: 6.68665--- , time: 43.0305 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 140.778--- , test psnr: 7.11463--- , time: 50.7622 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 140.439--- , test psnr: 7.1159--- , time: 59.2008 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 140.623--- , test psnr: 7.36656--- , time: 67.6758 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 140.91--- , test psnr: 6.73--- , time: 76.1631 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 140.574--- , test psnr: 7.15136--- , time: 84.6018 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 133.082--- , test psnr: 6.24495--- , time: 8.59421 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 132.38--- , test psnr: 5.57686--- , time: 17.1715 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 132.229--- , test psnr: 6.12137--- , time: 25.728 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 132.15--- , test psnr: 5.59258--- , time: 34.3241 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 131.871--- , test psnr: 6.31356--- , time: 43.0782 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 132.491--- , test psnr: 5.84294--- , time: 50.5757 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 132.491--- , test psnr: 5.83381--- , time: 58.77 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 132.771--- , test psnr: 5.3254--- , time: 67.3395 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 131.953--- , test psnr: 5.80682--- , time: 75.5595 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 132.453--- , test psnr: 5.50699--- , time: 83.6813 seconds ---\n"
     ]
    }
   ],
   "source": [
    "all_data = torch.from_numpy(np.linspace(0, 1, sample_N+1)[:-1].reshape(-1,1)).type(torch.FloatTensor)\n",
    "\n",
    "\n",
    "\n",
    "for i in range(Nd):\n",
    "    sample_d = sample_ds[i]\n",
    "\n",
    "    for j in range(N_test):\n",
    "        start_time = time.time()\n",
    "        \n",
    "        \n",
    "        # Prepare the data\n",
    "        all_target = signal[kk[j]].reshape(-1,1)\n",
    "        train_label = all_target[::2*sample_d]\n",
    "        test_label = all_target[sample_d::2*sample_d]\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        for k in range(N_repeat):\n",
    "            \n",
    "            c = np.random.randn(embedding_dimension)\n",
    "            c = c/np.linalg.norm(c)\n",
    "            \n",
    "            train_data = shift_embedding(all_data[::2*sample_d],c)\n",
    "            test_data = shift_embedding(all_data[sample_d::2*sample_d],c)\n",
    "            \n",
    "            xt, yt = train_data.cuda(),train_label.cuda()\n",
    "            xt, yt = Variable(xt), Variable(yt)\n",
    "\n",
    "            xts, yts = test_data.cuda(),test_label.cuda()\n",
    "\n",
    "            # Calculate the input stable rank\n",
    "            sr_[i,j,k] = srank_func(train_data)\n",
    "\n",
    "            \n",
    "            # Initialize classification model to learn\n",
    "            model = SLP(embedding_dimension).cuda()\n",
    "            # Set the optimization\n",
    "            optimizer = optim.Adam(model.parameters(), 1e-4, betas=(0.9, 0.999),weight_decay=1e-8)\n",
    "            \n",
    "\n",
    "            for epoch in range(epochs):\n",
    "                model.train()\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                out = model(xt)\n",
    "                loss = criterion(out, yt)\n",
    "\n",
    "                loss.backward()\n",
    "                losses_[i,j,k,epoch] = loss.item()\n",
    "                optimizer.step()\n",
    "\n",
    "                model.eval()\n",
    "                trn_psnr_[i,j,k,epoch] = psnr_func(model(xt),yt)\n",
    "                tst_psnr_[i,j,k,epoch] = psnr_func(model(xts),yts)\n",
    "\n",
    "\n",
    "            print(\"==>>> N: %g, F: %g, train psnr: %g--- , test psnr: %g--- , time: %g seconds ---\" \n",
    "                    % (i,j, np.mean(trn_psnr_[i,j,k,-100:]),np.mean(tst_psnr_[i,j,k,-100:]),time.time() - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f43302d1518>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SQ8hSCDkfH7asI2Sz/chyhHw+dpDZdBiN7mGL2YFZrMVhn4PZ3IDeYEWvs2Ay+TEa3ej1FkSxBqPRjU4nTsjvcrg48j8dGkckiqISHUwT7ksVNySHNybTcYl4IEMmIaOMoRZGsx6zzYjFbsTps9A0x4vDax61ti1OEw6vGaN4ZFvciqoQzASJZCOjAh3NRQmkA0RyEQbTg3THuwlmgsjK+91FI5uJVZYqmh3NLPIvosZWMxoR4jV7cYtuvGYvFoNlVDRHIj+KFnWOzmCOd5I5gok977G6g0mJSPr9UTxQDN0bEedGj5Xjmt2jr33DQu5ziPgd4oREgqiqQj4f28fNMSLYQSQ5RF5OFN0i+VjRBZJPUCiM7fcXBD0GgxODwYXJ6MHhmIvJWDUq3AaDA6PRg9HoxmSqwmTyodfbEISpt1cyXjRx1ygZVVXpXBdk3XPdBHqT5PdJZTeYdKNRI01zvNg8Iu4aKzaXOJrSbjTrD1pG9UhgxNLek9wz6suO5CIEM0H6kn0MpoqukUAmMKZoi3oRt+imxlbDcdXHUWOrodpSjd/qx2/xj/40G8yjY3L5QtGyHrai+/tzbExKBBIxgsmhUbEOJnMHTHixDm8o+uwmWqpsLG3x4rOL+O1FK9vvGBZuh4jNpB+3YOfzKSRpaB/fdAw5Hy0Kdi6AJAeRcsFh/3UIVR277EFRqB2YTH5MJh9220wMRhcGvQOTqWrYJVI1LNRVGAyuY1KoK0ETd42SiAczvPrH7XRtDOGptTL35Dp8TQ58jXYc3qmxKSkXZAbSA8UY7MzQqJtkKD3EQGqAgVTxvbH82Wa9mQZ7AzW2GlpdrfisPupt9VRZqvBZfFSZq3Cb3diNdnSCbtSHHRgO5YtEJNb05BiMJxhKBAkkcvRGMkRSEokDhPLZRUPRiraLtPntLJvufa9Q28Wiy8Rhwmoa/0e5UEjvtaj3ta6HXSK53CCyHCWXG6RQSI45hyAYMJl8xYdYjd0xF9Hkw2iqKvqvje6975uq0OlM4173kYqqqqhSASWVR0nLFJIySlJGScso6TyFpISSlLHM92FbWjPh19fEXeOQ9O2I8OTPN6ACp1zVxsKzGo+4kMBsPstQeoiueBdD6aH3+LYHUgOjVvj+Pm2jzlj0Z1trWORfRLW1mmprNY32RnwWHx6z532ukVQuz0A8y2Asy0Aoy/ZYloFYnIH4EKFkjj3RDMHkgTcdq50iVTaRxU1uquwmvNaiG2TUJTIs4OZDVFE8FKqqUCikkOUYkjRELjc07AoJI0thsrn+94i4ooydXGQwuDCZvJhM1dhs7Xi9J2MW6zANi7bJ6B12h7iPastaVVXUXGFYpCWU1D6CnZIppGTUbB4lk6cQlygkJCgcYMNWJyCY9Qh2I7p4DtskrFcTd42D0rs1zFO/3IDDa+aSzy3CWWX5wNdQUApF6zo9QFesi8F0MXJkMDXIUHqIwfQg0Vz0fePcopsaaw31tnoW+BZQY62h1lZLtXWvm8QtukdFu6CohJK50QzHdXuyDMSTDMRCo5mPg7HsmJa222qkxmGmym7itHY/tU4z1c4Ry7oYHVJlM+GyGMd9h1MoZIpCLQeRpfCoOOekIJIUIJcbJJvtRZLCwNgx4waDC1GsQTT5sbqWFEV62JoetayH3SFHq3WtKipKJo+aGRbkVFG09xdsZdjCLqTkA4q1YNYjWAxg0qMadBRcIrLPgqSo5BSVrKSSlAokUnkSKYlkbGQ/JMOSVg/LJ+H308Rd44D0bA7z1F0bcPktXH7bcVidE/chLygFwtkw2UK2GPaXjRDKhghmgoQyofcIeDATpLCfz9Zr9lJjraHOVsfi6sWj1nejo5F6Wz0+iw+j3jh6/kgizZ5oht5Ihq2dGXojffRHdxVFO55lKJF7n7Wt1wlUO0RqnGba/HZObfNR4zRT6yoeq3WaqXdbJsDKVkfD+HJSgFxugGy2j1xusBjSN5wMI0nBA7pEDAY3oujHZPLjqzobk+gvxlMbXJhE/3DctX84EsQ45hxTmRGxVlLyqBgrqb3CPCLYoy6SlHzASEjBqEOwGsGsRxX15KssyH4LkgKZvEJGVkiNiHUmT2Ywe8B4foNJh8VuwuoyYfFbaJrhwuo0YXEYEa1GqhomJ3TykOIuCMJ9wCXAkKqq84eP3QFcCkhAB3C9qqrR4fe+CqwACsDnVVV9elJWrjGp9O2I8NRdG3BXW7n8tsVYHJULu6qqrB5czarBVWwKbqIz3klfsm/MDUkAi8FCjbWGGlsNy+qWUWMrWtw11hpana3U2GpGK/bti5RX6AqlWN+VZHeom+5wmu5wmt5Ihv5Yhux+IZc2k546t4Vap5kZM3zUukRqneZh8S4Kd5VdRD+O4k6qqhYzGXOD5HIDyHIYSQqRkwLDVvYQ2WwfkjSIMlbsut4+KtgOxzxEU/G5KPpHXSLFzcijz8Iu+qyVojU9Is5JmcKIYI+I9Yi1nZYPdKOCYDEgWAyooh7FqKfgMyBVW5FUyMnDYp3Nk8oUiKfypGI5UMcu9ibaDFgdJkSrEXuDnSqrEYvDiMVhwmwzIloNmO3GYQE3YTxMuRaCeqCvm5ETBOF0IAk8uI+4nw+8oKpqXhCE7wOoqvoVQRDmAg8DJwL1wHPATPVAW+XDLF26VF21atW4fxmNiSHcl+LPd6zG5jLx4S8tqVjY80qev+78Kw9seoCueBcCAi2uFtrd7TQ4Gqi31WM2mHGL7qJvW/RSZanCarQecm5VVekIpHinK8ya3RE29cXZMZRA3ue22WUxMq3KSpPHSr3bTJ3LQr3bQoPbQoPHgsc6PhfJSMRIUbiHRtPNs7n+YcEOIklDY4q2TmcuWtKiH7O5YdRFYjR6MInViKZqzOYGDIbJ8MYePtS88p7Nxfe4PUZcIcOvlZSMOkYOBIAg6hGsBjAbUEx6CnoBWRCQUMnkVdK5AqlcgWS6aFlnk/kxQ3MRGA3NtThMWOxGzMM/RyzrkaqdI2Jdaes/VVVJ59NEshGiuejoz1ZXK/N98yuaUxCE1aqqLh3rvUNa7qqqviIIQst+x57Z5+VK4Krh55cDf1BVNQd0CoKwk6LQv1nBujUOA5mExFO/XI/eqOOSzy2qWNiDmSBfeulLrBlaw7yqefzXqf/F2U1nYzeN7xZ0c1+cP7zTzdObBhiMFy0rr83E/AYXp8/0M6fOwXSfnWk+62h2ZDkU3SOxomWdC+wTMRIsirg0IuRjR4zodGbM5jpEsQ63aykm0Yco1iKKtZjFGozGKkwmL3q9/YiPLiqF0YiQEXFODFvZib3WdiEhUYhLKAnpgGKNXkBnM4LFgGrSU3Cbkb1mZASyikJaVkjnFBKZPImkRCqYQzlACWCTxYDNVRRie4MZn82IaDMWw3KH3SEW+7BbxGasqOzuiFBHc1Gi2SiRXIRINjKaWTxybP/XeeX9+zWfnvfpisX9YEyEz/0G4I/Dzxsoiv0IvcPH3ocgCDcDNwM0NzdPwDI0xktBVvj7rzaSiklc8cXjKt487Yn3cP3T1xPLxfjuqd/lkumXjEvIFEXl6U0D/O/LHazvjSEadJw9u5rTZ/o5qdVLq89W8vyyHCOT2U063UU6s5vcsIU94iaRpBCq+n53kSAYi9a1WIPdPosq72mIYjUmsQbRVI0oViOKtUdM6vl4UBW1GK6XLFrRhfiwOKf2HlMy+UNb1xbDXuvaJSJXWZBVlVxBJZNXSEkKqUyeeEommcwjh95/hwNgEPVYh90elioLjS1OLI5i0pt52MI224xYnSIWh7Fsy1pVVTL5TNGa3l+Y97Gy9xfqA7kVdYIOl8mF2+zGI3posjexwLegeIcqenCb3bhF9+gdq8/iK2u9pTIucRcE4etAHvjdyKExThvT76Oq6t3A3VB0y4xnHRrjR1VVXvztVvp3xjj/xnnUtroqmieQDnDTszeRK+R46OKHmO2dPa51vbB1kB/8YxtbBxJM99n4j0vmcuWSBtzW0u4oksltBALPEom8STK1HVnetzOOMBwdUo3JVIXd1l70X4vFhBpxOLGmWEvENaUtbbWg7nV5JIo+6kJi2B0SzxWfp0Y2HPNjf2oNOnR2I5gNqEbdqHUtAdkCpOW9rpBYUiY9kBlzHkEn7HWDOIz4amw0O0xYnCPHhrOVhwW93GzlvJIfFeRINjJqVY+UeBgR6H3FfKzcBgABAZfoGhXiBnsD833zcYmuolAPH99XrPcvA3G4qFjcBUH4FMWN1nPUvY77XqBpn9Magb7Kl6fxQbH6H7vZ9tYAJ17aSnuFCRVpOc0tz91COBvm3vPvHZewZ+UC3/vbFh54czfTfTZ+8rHFXLqovqTNTVVVCQSfoavrFyQSmwBw2Ofh852DzTodq7UFi6UFi2Uaev3UrR+iKsOCPSLSSQklMfL6vQKupMeODBGMOnQOE6rFgGIzUnCIyAJklWHftVTcaIyl8yTjElJw7E1Go1k/LMgmLNVWPG2m0dejrpBh0RathrJaAI64QEbqyo/UmQ9nw6OPkTyGcDZMLBc7YJNqp8mJx+zBI3qos9cxp2rOqDU9ItYjlrVHLAq1Xndkl8I4EBWJuyAIFwJfAc5QVTW9z1uPA78XBOFHFDdU24G3x71KjUll5+oh3npsFzNPrGHpxS0Vz/O9t7/HzshO7jr3Lhb4F1Q8T2cwxb/8fg2b+uLceGort184u+Ryr9ncAFu3fp1Q6CWs1jZmtn+D6ppLEE2Tc+s70aiqipotFP3UIyKdGPZhD1vcI88PFMonGHUIw/5rRdSTtxqRRgRbLrpDir5rmWREphAYW7D3RnwY8VZZaHQWa/9YnXvrAFmdxc3HcqtvqqpKSk4Vm35kggSzxRDYkVDY/YX7QC32nCYnXrMXr9nLDPcMlopL8Vq8eMRi8pnH7BlNRHOJLoxHYQjogSglFPJh4EzAJwhCL/BN4KuACDw7fKu6UlXVz6iqukkQhEeAzRTdNZ89VKSMxuElGcnywkNbqJ3u5KzrZlfseljZv5K/7vwrNy24iVMaTql4Pe/uifGJX78FwK8/uZRz55Z+FxEMvsjmLbdTKGRob/s6jY2fRKc7/Kkco5mNIxb2/pZ2cu/xQlKC/BiKrRfQ203oHEb0bhGh2ooEpGWFZLZQ9Ftn8sQSMokDCLZOL+y1pF0idY2OoqXt3Gtdj0SEWOzGirOQk1JyNLlsKD00+nxUyIcFfCzB1gt6vOZi1JTX7GWacxpes7dY4mGfGvNec1HA981l0HgvhwyF/CDQQiEPD6qq8re7NtK7JczV/3ESLn9lG6gFpcBHn/woKTnFY1c8NtrUoVw298X5+K9XYjMZePimZTRXHTokEopNFzo6fkh3z73Y7bOZP+9n2GzTK1pDpShpGWlPkvxQmnwoSz6S3WtlJ2UYq7uQADq7sSjaNiN6hwmdw4TeYULvMBbdJaKeUChLf0+CcH+a6FCa6GCavLR3PoNRh9NvweYWR61qq8s0Gr5ndRaLt01E679oNkpvsne0vvyIeO8r5ul8+n3jnCZnsa681YfP4sNn9o0Kts/iG324RNcR4a+eKowrFFLj6GXn6iG6NgQ5+SNtFQs7wGMdj7E9sp07zrijYmHf0h/nE79eicWoL0vY8/kE69ffRDT2Dg0N19Le9rUPzI9eSEikNwTIrAsg9eztdiSY9Bi8ZnQOI0afqyjYduPoT73DhM5uRGc1jul7jgcz7FgfZNfzvQx0xIrx2QK4fBbctVYaZnpw11hxV1tGK2+W48MuhaSUZGt4K1vCW9gS2kJnrJPuRDdx6b1diAyCAb/VT7W1mnZPO6c2nDpan2cka9hv9WMxfPBlK451NHE/RpGyeV59ZAf+ZgeLzm6seJ6UnOLONXey2L+YC6ZdUNEc2wYSfOLXbyEa9Pzh5tKFXZLCrFt/PcnkVubN/TG1tZdVdP1yKSQlEi/0kHyrHwoqxnobzvOnYWp2YKyxobOXnxylqiq9WyKseWY3vVsjAHjrbSw+r5n6dje1M1yIk9ScRC7IrAusY0Ngw6iYdye6R9/3WXy0udu4qPUimhxNNDmaRmv0eM1ezdI+QtHE/Rhl40u9ZOISF92yYFwVHu/deC+hbIg7z76zolv+7YMJPn7PSox6gULx2lUAACAASURBVIdvXsa0qtIyMmU5ztp1nyKd7mDhgv/F5zur7GuXi6qoJN/oI/7sblS5gG1pLfZTGzBWl/ZldCAGdsV489EO+nZEsbpMLLtiOm3HV+Pyj2/egyEVJF7oeYFnu57ljb43SMrFhKwGewNzvHO4vO1y5njnMKdqzqTFYR+tqKpKMpcnmCw2AA8mJUKpHKGkNNrKMJySRh/XLpvG589pn/B1aOJ+DCJl8qx9tpvmeVXUzagsnh2gP9nPg5sf5OLWi1noX1j2+D3RDB+/5y30OoGHb1pGq680YS8U0qzfsIJUageLFt5NVdXpZV+7XOShNJE/bUfqTmCe5cH1oenjFvVEOMtr/7eDXWsDWBxGTvvYTOadWo/eOHmW8K7YLv649Y881fkUsVwMv8XPBS0XcHrj6RxfczwusfK/h2MBVVWJZWT6olkG4hn6oln6Yxn6Y1n6o9nRiqIZeew4EofZQJWt2BS80WNlUaObWbWOSVmrJu7HIOtf6CGXynPSZa3jmucna34CwG1Lbit7bFrKc9MDq8jJBf5y68lM95eW2amqCps2f4lYbB0L5v9s0oVdLagkXukl/txudKIe78dmYVnsH9fGZKGgsP65Ht55qhOAEy9tZdE5TZjMk/dx3BzazK83/prndj+HUWfknOZzuKL9Ck6qPWnKxnFPBvGsTF80Q380S18sw0AsOyrk/dEs/WMIt14nUOMQqXNbmFvv5JzZ1VQ79zZU8dpMoz9LDemdCDRxP8bIpmTWPddD6yIf1dOcFc+zIbCBv3X+jZsW3ESdva7s8d9+cjNbBuLc9+kTaK8p3XLp7LyTQOAZ2tv/H9XVF5Z93XIopGTCD28ltzOKZYEP92Uz0I+jOibA0O44zz+whXBfitZFPk79aPuk1sgPZoL8ePWPebzjcRxGBzcuuJFr516L1+ydtGtOBQKJHJv6YmwdSNAZSLErmKQzmCKYfG+mqk5gtEronDonZ8+uptZVLPNc6zJT77Lgd4yvcuhkoYn7Mca7L/ciZfKceGnloYKqqnLHO3fgs/hYsWBF2eNf2DrIw2/3cMsZ0zlrVnXJ4waH/kZn18+oq7uKpsZPl33dcshHcwTv3Ug+nMVzVTu2pbXjmk9VVNY8s5u3H+/E6jJx8a0LaV04eb7svJLnkW2P8PO1PydTyHDjghtZMX/FuAu3TUUG41lW746wqS/Gpr44m/riBBJ78wB8dhPTfXbOmV1Dq99Go8cyXEHUjN8uYjjCuo6ViibuxxCFvMLGl/bQPM+Lr7HyD/nTu59mXWAd/3nyf2IzlleSNpySuP1PG5ld6+CL580seVwq1cHmzbfjci1h9qz/b1LrvMjBDMFfb0TJ5PHfuACxwjo7o/PlCjz/m810rA3Qdnw1Z3x8Fmbb5CXfrBtax3+99V9sDW9led1yvnbS12hxtUza9Y40uoIp3u4M83ZXmLc7w3SHi3H3ep1Ae7Wd09p9zK1zMq/exdw6Jy7r0ZkIpYn7McTO1UOk4xKLzm469MkHIFfI8ZPVP2GmZyaXz7i8rLGqqvL1RzcSy0g8eMOJiIbSfL2KkuPdTbeh11tYMP/n6HSTF8eeD2YI/GoDKCr+mxdiGmeXnGxS5vE71xHsSXDyR9pYfG7TpH0xZfNZfrT6Rzy89WGqrdX88Iwfcv6086d0wbNSeXdPjCfW9/Hs5kF2BVMAeKxGTmjx8snl01ja4mV2rWPcHbOmEpq4HyOoqsr653vw1Fppmlu5v/V3W37HnuQe7jn/nrI34h5f38ff3x3gKxfOZm596f7+jo7/IZnczMIFv0IUJ75L/Aj5cJbAPRtBUfDfvBBjzfgaZWSTMo/9dC2R/jQX//NCWibRDdMd7+a2l25jR2QHn5jzCT533OfKvquaamSkAk9u6OO3b3WzvieKQSewfEYVn1w+jVPafMzw2yuq1X60oIn7MUL/zhiB7gRnfHxWxZZcKBPi7g13c2bjmSyrW1be2GSObz2+ieOa3dx8eun+/lD4Nbp77qWh4RP4/eeWu+SSycdyBH69ESVXwH/zgvELe2pfYV9A87yqCVrp+3mz702+/PKXEQSBX57zS05rPG3SrnUkkMrleWjlbu5+ZRfhlERbtZ1vXjqXK49rPGpdLJWgifsxwvoXehBtBmYtq3xj8Jfrfkkun+OLS79Y9tjvPLWFZC7Pf1+5sOTIAlmOsnnzv2GztdPe9tWyr1kqhZRM8J6NKCkZ/40LMNWP0xWTknnsJ0Vhv2gShV1VVX6/9ffc8c4dtLpaufPsO2lyVO5yO9LZX9TPmOnnM2fMYNl07zHheioXTdyPAeLBDJ3rAhx3wbSKm/V2RDv4044/cfWsq2l1lRcf//L2AI+u3cPnz24rK2Fj587vI8shFi+6F71+csIF1bxC6KHN5KPZorA3jS+hJJuSefyn6wj3p7j4nxcybZKEXSpIfGfld3h056Oc1XQW3zvte0etGyZfUHho5W5+9sLOUVH/wrntLGn2HO6lHdFo4n4MsOGlXgRBYMEZY3Y8LIlfrPsFFoOFzyz6TFnj0lKerz+6kel+G7ee1VbyuGh0FX39j9DcfBMOx9xyl1sSqqoS+csOpK443qtnIbaMLyomly4Ke6gvyUW3LJg0YY9LcT7/wudZPbiamxfezGcXf/aore/yZkeIbz2+iW2DCU5pq+KL583i+GmaqJeCJu5HOVI2z5bX+phxfDV2j7miObaFt/Hs7me5ZeEteMzlfbB+8twOeiMZHrllecmRCooisXXb/8Ms1jO99fOVLLkkEi/1kl4zhPPcZqyLS4+3H4v9hb1lweRsng6mBvnMc5+hK97Ff5/233xo+ocm5TqHm2ha4j8e28Tj6/tocFv432uP54J5NZr7pQw0cT/K2fpmP1K2MK7wx1+u+yUOo4Pr5l5X1ridQ0nue62Tjy5t5MTW0iN0unvuJ5XawcKFd6PXT07xrMyWEPFnurAs8uM4Z3wN2nOZPI//dB3B3skV9t5ELyueXkFMinHXuXeVvak9VXijI8iXHllPIJHj8+e0c+uZM46pEMaJQhP3oxhVVdn40h5qWp3UtFZWamBTaBMv9LzArYtvLbuo1Hee2ozFqOf2C0vvpZrJ9NDZeSd+//n4feeUu9ySkIMZwn/chrHOhveq9nFZg/sK+4W3LJi0cMc9yT2seHoFSTnJfRfcx9yqyXFVHU6kvMKPnt3Or17poLXKxl9uPZmFje7DvawpiybuRzG9WyNEB9Oce33lQnDXurtwmpxcN6c8q/2FrYO8tC3A//vQHHz20pOOtu/4DoKgZ2b7N8pdakkoUoHQQ5sRdAJV181FGIdFmMvkeWI4QenCm+dPWjmBvmQfN/zjBpJyknvOv+eoFPaOQJIv/GEt7+6Jc82JzXzjkjlYTZo8jQftX+8o5t2X92C2G5mxxF/R+I2Bjbzc+zJfWPKFsmqSSHmFbz+5hel+G59c3lLyuEjkLYLB55gx/UuYzfUVrPjQRB/vID+UxnfDfAwV7kFAsWzyE3euI7A7wQU3z6d1UWX/xoeiL9nHDU/fQEJOHLXC/vSmAb70yHqMeoFfXXc8F8wbXx0fjSKauB+lJCNZOtcHOO78ZgwVWqe/WP8LPKKHa2ZfU9a437zRSWcwxf3Xn1ByiVNVVdix83uIYi1NTTdUstxDkl43RHrVII6zmjC3Vx5xkZcKPPnz9aPCPn3x5Ah7f7KfG56+gXguzj3n38O8qnmTcp3DhaKo/OS57dz5wk4WNbq469rjqXdr7fgmCk3cj1I2vdqHCsw7rbLwx3VD63h9z+t88fgvlhU/HUzmuPP5nZw9u7q8io+DT5JIbGTunB+i11duUR+IfChD5NGdmKY5cZ5b+Qaqqqg8e/9m+nfFuODGyRP2gdTAqLDfff7dzPMdXcIez8r86x/W8fzWIa46vpHvXDFf2zSdYA5pVgmCcJ8gCEOCILy7zzGvIAjPCoKwY/inZ/i4IAjCnYIg7BQEYYMgCEsmc/EaY6MUFDa/3se0+VU4fZVZQr9Y9wu8Zi8fm/Wx8sa9uJO0lOdrF88peUyhkKOj4w4c9nnU1pZXjKwU1IJC6OGtIAh4r56FMI4Srm/8ZSe71gY49ap22o4fX/jkgQhmgqx4egXRXJRfnfcr5vvmT8p1Dhc7hxJc8fPXeXl7gG9fPo87rlqoCfskUMpf+W+A/bsi/DvwvKqq7cDzw68BLgLahx83A3dNzDI1yqF7U5h0TGLuKZX5rVcPrmZl/0pWzF+B1Vh6KGJPOM3vVnbz0aVNtFWX7qPv7f0N2VwfbW3/jjAJyTiJF3uQe5N4PtI2Lj/7plf3sO65Hhac1cjCcTQVPxhJKcmtz93KUHqIu869iwX+BZNyncPF05sGuPznrxPPyvz+pmVct7xFi12fJA75SVJV9RUgvN/hy4EHhp8/AFyxz/EH1SIrAbcgCOW36dEYF1ve6MfiMDJtQWUZkve9ex9es5d/mvVPZY378XPbEQT4wrmlN/uVpDCdXb/EV3U2Xu/J5S710PP3Joi/0IN1sR/rgspdKAO7Yrzyh+00z6vi1H8aX/jkgZAKEre9eBvbI9v5nzP/h8XViyf8GocLRVH50TPbuOWh1bRV23nic6eWlfugUT6Vmkk1qqr2Awz/HLk/bQB69jmvd/jY+xAE4WZBEFYJgrAqEAhUuAyN/UnHJbo2BJm1rA59Be6HjmgHr/S+wtWzr8ZiKN2ls20gwaNr9/Dpk1uoc5U+rrPrZyhKhra2r5S91kOhygrhR7ajsxtxXzaj4nlSsRz/+NVG7B6R826YOyllZAtKga+++lXeGniLb5/ybU5vnPym3x8U8azMjQ+u4s4XdvJPxzfyx1uWl/U3olEZE72hOtZfvTrWiaqq3g3cDbB06dIxz9Eon20rB1AUlbmnVHbD9ODmBxH1IlfPurqscT99fjt2k4F/PrN0EU2nO9mz5/fU138Mm630ujOlEnt2dzHs8fp56CosBVsoKDx9z7vkMnk+8rmlk9JBSVVVvv/O93lm9zN8eemXuXTGpRN+jcPF7lCKFQ+soiuY4tuXz+PaZdM0N8wHRKXiPigIQp2qqv3Dbpeh4eO9wL557o1A33gWqFEe298ZoKbViae2/AqBoUyIJzqe4Mr2K8uqIbMrkOTv7w5w65kzcFtLbyC9s+OH6HQira1fKHuthyLXFSP5ai+2E2sxz6r89v/NRzvo3xnjvBVzx9Wa8GD8dstveXjrw3xq7qf41LxPTco1Dgdv7Qrxmd+uRgUeWnESy2dMXk17jfdTqVvmcWDkr/BTwGP7HP/kcNTMMiA24r7RmHxigQzBniQzllQWxfHozkeRFZmPz/l4WePufmUXJr2OT59ceingVGongcA/aGr6NKJpYjM7lVyB8CPb0XvMuD5UXnnifendGmb9cz0sOKOBmSdMTmLNSz0vccc7d3Bu87kV1ck/UnlkVQ/X3vsWHpuJv956iibsh4FDWu6CIDwMnAn4BEHoBb4J/DfwiCAIK4BuYGTn7W/AxcBOIA1cPwlr1jgAu9YV9y5mHFf+xmFBKfCn7X/ixNoTme4qvVPSQCzLn9f0cvUJzfgdpZcZ2N19DzqdmabGibdU4890UYhk8d+0EJ1Y2c1pLi3z/ANbcNdYWf6RiXcZQbHa5u2v3M7cqrl897TvHhVlewuKyvf/sZW7X9nFqW0+fvHxJVp3pMPEIf/yVVU9UHri+6o6qaqqAp8d76I0KmPX2iF8TfaKYtvf7H+TPck93Hb8bWWNu+/1TgqKyk2nlf6FkM32MTDwGA0N12AyTaxFJ/UlSb7Rh+3EWsTplddnf/WRHaRiEh/5t+MrbnByMGK5GLe9eBsOo4Ofnf2zsjavj1QyUoHPPbyW57YMct2yafzHpXMxjiOnQGN8aBmqRwmpaI6BXXFOuqwyN8RfdvwFj+jhnKbSKzHG0jK/W7mbSxbW01xVejx8d8/9gEJz040VrPTAqKpK9LEOdFYDrgtaKp6nY+0Q21YOsPRDLRVX0zwYiqrwtde+xkB6gPsvuB+/dXKyXD9IIimJGx54h3U9Ub516Vw+fUrl7jCNiUET96OEEZfM9OPK97eHs2Fe7HmRa2Zfg1Ff+i30Qyu7SEkFPnNG6REyshyhr+8P1NRcisVSeWeosUivGULaHcfzkfaKo2NSsRwv/XYb/mYHSy9umdD1jfCrDb/ild5X+PpJXz8qYtn7ohmuu/cteiIZ7vrEEi6cr6W2HAlo4n6U0LE2gKfWireu/CiZp3Y9RV7J8+G2D5c8JisXuP/1Ls6c5WdufenWbU/PgxQKaaY131z2Og+GkskT+3snpmYH1uNrKppDVVVe+u1W5FyBc6+fW1GewKF4tfdV7lp3F5dOv7Ts0g5HIn3RDFffvZJISuKhG07kpOnaxumRguYQOwrIJCX6dkSZXsFGqqqqPLrzUeZXzafdU3pm6Z/X9BJKSWVZ7fl8kp7eB/D5zsVun1X2Wg9G/LndKCkZ9+VtCBUmGW15o5+ujSGWf3hGRV+Sh2JPcg///uq/M9Mzk28s/8aUj/d+j7DfeJIm7EcYmrgfBXSuD6IqKjMqcMlsi2xjR2QHV7RdceiTh1EUlfte62R+g5OTykgh37Pn9+TzMVpabi17nQdDHkqTfLMf24m1mBoqi0VPhLO89sgOGma5WXjWxNeNkRWZ21++HUVV+PGZP57yG6j9sQzX3LNX2Bc3aR2TjjQ0cT8K2LUugKPKjK+pfGF7vONxDDoDF7buXxvuwLyyI0BHIMUNp7SWbH0WCjm6e+7F6zkFl3NR2es8GLGndiGYdDjPm1bReFVVefnhbaiqytnXzanY8j8YP1/7czYEN/Ctk79Fk7PyfrZHAv2xosUeTmrCfiSjifsUR8rk6dkSZvpx/rJv8/NKnqd2PcWZjWeW1R/13tc6qXaIXLKw9KqT/f1/QpKCTGv557LWeCgy28Jkt0VwntOM3l56duy+7Fw9xO6NIU66bHrFJZIPxht73uC+d+/jqplXcUHLBRM+/wdJfyzDNcPC/uCKEzVhP4LRxH2K0/VuECWvMqOCphFv9L1BOBsuq5bJ9sEEr+4I8snl00rusqQoMru7f4XLeRwe97Ky13kg1IJC7MldGHwW7MsrK2+cTcq8+sftVE9zsPDsibeog5kgX33tq7S527j9hNsnfP4PkoFYlmvuXkkwKfHAihM5rrnyblYak48m7lOcXWsCWJ0maitI2Hmi4wncopvTGk4recx9r3UiGnR8/KTSXSCDg0+Qze6hpeXWCd1ETL7ZTz6QwfWhVoQSv2j25/U/7yCXynPWdbMnvNqjoip87dWvkZbT3HH6HVPazx5I5LjmnqKwP7jiRJZown7Eo4n7FEaWCuzeFGL6Yn/ZfuKElODFnhe5sOXCkmPbQ8kcf1m7hyuXNOK1leYCUVWFrt3/i90+m6qqs8pa48EopGTiz3Ujtrsxz66sMFjPljBb3xxg8fnN+BodE7a2Ee5/937e7H+Tr5z4Fdo8k1PC4IMgkZX59P1vMxDL8sANJ2jCPkXQxH0K07M5TF5SmL6kfJfMs7ufJVfIcdmMy0oe87u3upHyCitObSl5TCDwDOl0By3T/nlCrfb4s7tRpTzuS6ZXNK8sFXjpd1txVVs4YRKSldYNreNna3/GBS0X8JH2j0z4/B8UWbnAzQ+uZttAgruuXcLx07QGG1MFTdynMB1rhxBtBurby9/UerzjcVqcLSX358zlCzz45m7OmOmnrbo0K1dVVbp2/xKLZRrV1ReVvcYDIQ+kSL3Vj+2kOow1lcWjv/NEJ/FglrOunY1hgmvHxKU4X3nlK9Taavnm8m9O2Xj2gqJy2x/W8eauED/8p0WcWUbDc43DjybuU5RCXqFrQ4jWRf6yMyl7E72sHlzNZTMuK1l4nlzfTzCZY8WppdcMCYdfJZHYRMu0zyAIEyOgqqoSfXIXgtmA89zKQh8D3QnWPdfN3FPraZg5sS4GVVX51hvfYig9xA9O/wEO08S7ez4IVFXlG4+9yz82DfCNS+ZyxXETWypCY/LRxH2K0rstgpTJVxQl8+SuJwG4ZPolJZ2vqir3vtZJe7Wd09pLr73e1fVLRLGW2trSE6QORXZLmNzOKK5zm9FX0BVJKSi88NAWLA4TJ19Zeeu9A/F/2/+PZ3c/y+eXfJ6F/oUTPv8HxY+f3c7v3+rm1jNnlPWFrnHkoIn7FGXX2gBGs57GOeVZnqqq8njH45xQewJ19tIKPK3cFWZzf5wbTi09aSkSfYdo7B2mNd+ETldZ/Pn+qHmF2FO7MFRbsC2rrDjVuud6CPYkOf3qmYgTXGd8e2Q7P3jnB5xSf8qU7qj0wBtd3PnCTj62tIl/u2Biy0RofHBo4j4FURSVzvUBWuZXYTCW5+5YNbiKnkRPWeUG7n2tE6/NxIfLuDXfvfsujEYv9fUTVxwr+UYf+VAW9yUzECoo6hUZSPH2E51MP85fcbeqA5GW0/zby/+G3WjnO6d+Z8o23nhifR/femIT582t4b8+PH/K7hdoaOI+JenfGSWTkCsq7/vojkexG+2cN+28ks7vDKZ4fusgnzipGXOJXyTx+AZCoZdpbroevX5iYrsLSYn4892YZ3sxV+AnVxWVF3+7FYNJx+lXz5yQNe3LD975AZ2xTr532vfwWSa2beAHxas7AnzxkXWcMM3Lz645DoPWaGNKo/3vTUE61gbQG3U0zysvLC0hJXh297Nc1HpRyQk197/eiVGn47rlpW9e7uq8E4PBTWPjdWWt72DEn9mNKisV90R995U99O+MccpV7dhcpbcDLIW/d/6dP+/4MzfMv4Hl9csndO4PivU9UW55aDUz/Hbu+dTSkr/INY5cNHGfYqiKyq61AZrnejGZyyvH//fOv5MtZLmy/cqSzo+lZf5vVS+XLa6n2mEuaUzRan+Rac0rMBgmJlJE6k2QemcA+8n1GP2ld3waXVMow5uPdtA018vs5RPb6Lo73s1/vvmfLPYv5l+O+5cJnfuDoiOQ5PrfvIPXZuLBG07EZdF6nh4NaOI+xRjanSAVzVXUBPsvO/5Cu6edeVXzSjr/9293k5EL3FBGy7RdnT+dUKtdVYZb59mMOM9tLn+8qvLy77ehAmd+fNaE+pClgsSXX/4yekHPD07/AQbd1Ot9MxDL8sl730YAHlpxEtXO0r7ENY58NHGfYnSsHUKnE5i2oDy/7rbwNjaFNnFl25UlCZxcUHjgjS5OaasqudNSLL6eUOglpjXfOGFWe3r1IFJPAtfFrejKvFMB2PbWAN2bwiy/YuIrPv5o9Y/YEt7Cd075TsmRR0cS0bTEJ+97i1hG5oEbTqTVN/ENSjQOH+MSd0EQ/lUQhE2CILwrCMLDgiCYBUFoFQThLUEQdgiC8EdBECYmDk4DVS26ZBpnezCXGeP96M5HMeqMJce2/21jPwPxbFkxzp2dP8Vo9EyY1a6kZWL/6MLU4sRaweZxOi7x2iM7qJvhYsEZE9uA4/nu5/ndlt9x7ZxrOat54mrmfFBkpAIrHlhFVzDN3dcdz/yG8gvPaRzZVCzugiA0AJ8HlqqqOh/QA1cD3wd+rKpqOxABVkzEQjUgtCdFLJApu52eVJB4cteTnN18Nm7zoUsVjCQtTffbOHNmaaIai60bjpC5EYOhsm5I+xN/rhslLeO+bEZF7pRX/rANWSpw1nWzJ7QBR1+yj2+8/g3mVs3lX4//1wmb94NCLih89vdrWNMd4SdXL+bktqkZ3aNxcMbrljEAFkEQDIAV6AfOBv40/P4DwMSlJx7j7Fo7BAK0LipP3F/ofoFYLsaVbaVtpL7TFWFDb4wbTmktuQxuZ9edw1b7tWWt7UBIfUmSb/ZhW1aHqb78L4uOtUN0rAlwwoda8dROnLtBVmRuf6XYLu+Hp/8Qk35q3Zgqisq//3kjL2wd4tuXz+fiBVPPnaRRGhWLu6qqe4AfAt0URT0GrAaiqqrmh0/rBcbMfBEE4WZBEFYJgrAqEAhUuoxjio61Aerb3Fid5QnKX3b8hTpbHcvqS2uUce9ru3BbjXxkSWmujFhs7YRa7aqqEn28A53VgKuC1nnZlMwrD2/H12TnuPPL34Q9GL9Y+wvWB9bzzeXfnHLt8lRV5dtPbebPa3r513Nncu2yymrzaEwNxuOW8QCXA61APWADxir9p441XlXVu1VVXaqq6lK/v/zIj2ONyECKcF+K6WXWkulL9rGyfyVXtF1RUtbk7lCKZzYXk5YsJVZL3DXBvvb0ugBSVxzXha3oKigR8MrD28gmZc6+bk7ZRdUOOm/vK9z77r1cNfMqLmqduCqXHxR3Pr+T+1/v4vpTWvj8OVO3vrxGaYznL/9coFNV1YCqqjLwF+BkwD3spgFoBPrGuUYNoGNN8e5mRpm12/+6868AJZcbuOfVXRh1Oj61vKWk8yORlYTDrzJt2i0YDON3fyhpmdhTuzA2ObAeX1P2+B2rBtmxaogTLmnB3zxxFRl7E7189dWvMts7m6+c8JUJm/eD4v7XO/nxc9u56vhGvvGhuVpZgWOA8Yh7N7BMEASrUPxLOQfYDLwIXDV8zqeAx8a3RA0o+pBrpzuxe0qPQ84ref6848+cXH8y9fZD9xgNJHI8sqqXjxzfUFK8s6qqdOz6H0RTDY0NE2O1x57uQknLeD7cVvYmaCqW4+WHt1Hd4mTJBRPncsjms3zxpS+iovKjM3+E2TC1YsH/vLqX/3xiM+fPreG/r1ww4e0ENY5MxuNzf4vixukaYOPwXHcDXwG+KAjCTqAKuHcC1nlMEx1KE+xJll3s6uXelxlKD/HRWR8t6fzfvNGJXFC4+fTSSuGGQi8Ri62hpfVf0OvHL3i53XFSbw1gP7mh7E1UVS3WjslLCud+eg66CXTHfPet77IlvIXvnfo9mhxTy8/+zKYBbv/zBk6eUcWdWr2YY4pxpdSpqvpN4Jv7YWuORgAAIABJREFUHd4FnDieeTXeS8eaIYCyxf3//n/2zjM8qqprw/dJ7703QnojgYAU6SCgqIAVFMRCsTewv/b26icgNkSaIFXKC6KidJAOAUJ67z2TMqnTz/cjWGLapABBz31dXIHsPedsJpNn9qy91rNSt+Fq4coor1Edzq1VqPnuVC63RLjpVcwiijoysxZjbu6Dh/s9nVpXq9fT6qjemY6hrQk2XThETT5ZTG58BSPuCezR7JgdaTvYmbGT+ZHzGe09useuezU4mSnjqc0XifC0ZcVsyS/m34b0Nn4dkHmhHBdfG6wd9N8d59fkc6LoBHcF3aVXWfymM3nUKjQ8Nlq/XXtp2c/U1SXj1/d5DAy670VSd7wIdUkDdlP8MTDtnAjVyBo5vjUdz2A7Isf2XLFSYkUiH575kGHuw3gi6okeu+7VICankrnrYvB1tGDdwzdgZXr9WSNIdA9J3Hs5NbJGyvNqCejsrj19G4aCoV657UqNltXHsxke4EikV8dFTjqdmqysT7GyDMbVVb+K1/bQVCmoOZCLWagD5uGdK6gRdSKHvksGAcbNDu2xYiW5Us6CwwtwMHfg41EfY2hw/ex6L+ZV8dC353CzMWPD3CHYWVxfufgSPYMk7r2cjD9CMvpnyai0Knal72KM9xhcLTvOONl5oZCyWiWPj9YvPa64eAeNjbn4+S9E6GZTit9z2gHspna+7V3c4QIK06oZcU8gNo494x2jE3W8cuwVyhvLWTJ6CfZmPdtn9UqSUChn9pqzOFiasGneUL3dPCX+eUji3svJvFCOs491p0yv9ufup0pZpddBqlYnsuK3LCI8bRge4NjxfK2S7JwvsLEZgJPjOL3X1BaKxAoUyZXYTOiDkV3nhKiyqJ5TuzLx7edI6I09V2n59aWvOV54nFcGv0I/5349dt0rTXJxDbNWn8HGzJhN84bgZisJ+78ZSdx7MTUVjZTl1BAwsHMhma2pW/G29maoe8cVqfsSS8iS1fP46AC9cp8LCr9DqSzB339ht3OldUoN1T9mYuxmidXwjlM1/4pGrWXf6kRMzAwZMyukx/K2D+QeYPml5Uz1n8o9Qd0/KL5apJfWMmvVGcyMDNk8byhe9p33vZf4ZyGJey/m98KlzhiFpVelc6HsAvcG3dthRaooiiw/momvowU3R3TcxEKtriYnZxmOjqNxsO9+xyH5Lzloa1TY3RnQ6Z6op3ZmUlFYx7jZoT3WWSmtKo3Xjr9GpFMkbwx747op9Mkqr+P+VWcwMBDYNG8IPo6SsEtI4t6rST9Xiksfa+xc9P9l3Zq6FRMDE6YGTO1w7m/pMi4VyHl0tD+GehxEZud8hUZTR4B/9ys0lVly6k8XY3WjB6Y++vnF/05uQgVxhwqIHOuFbyd97duiWlHNM4eewcrYik/HfoqpYc+24rtS5FU0cP/KM+h0IpvmDsHPuWccOSWufyRx76VUlzZQnldL4A36l+DXqerYnbmbib4TOzwEFEWRzw6k4WFrppdBWENDLgUF6/Fwvxsrq2C919TqvdVaqv6XjqGDGTaTfDv12IYaFQfXJeHoacmwOzt/ANsaGp2GF357gbKGMj4d+ykuFp33jr8W5MjqmbHiFAqNlg1zhxDo2nN2CxLXP5K491LSzpWCAAGd8Ff5IfMHGjQNzAyd2eHck5kVXMir5vGxAZgYdfwyyMxahCAY4ef3nN7raQv5/jw0skbs7wzEQE9zMmh6Qzq4LhmVQsuEOeEY9VBRzpLzSzhTfIY3hr5BlHNUj1zzSpNVXsf0FadoVGvZNHcooe6d+/Qj8c9HEvdeiCiKpJ8rxSPADit7/cIDOlHHpuRNRDlHEeEU0eH8zw6m42Zjxr2DOt61y+WxlJXtoY/PXExNO2/m9VdU+bXUHSvAcrAbZgEd59T/lbjDBeQlVjD8rgAcu+Dx3ho/ZPzA+qT1zAqdxR2Bd/TINa80GWW1TF9xGo1WZPP8oXq3QZT4dyGJey9Ell9HdWlDp0IyxwuPk1ebp9eu/XRWBWezK3lstB+mRu3vfkVRJD3jv5iYOOHjM0/v9bR6LY2Oyu1pGFqbYDtZ//Z9ALKCOk7+LwPfSCciRrfaIqDTXCy7yDun3mGI2xAWDlrYI9e80qSW1DJjxWlEEbbMH0qImyTsEq0jiXsvJP1cKQYGQqeqUjcmb8TF3IWb+tzU4dzPD6bjZGXKjMEdN7KQyfYjl8fQt++z3bb0rTmcj6a0Abs7AjrV7Fql0LBvVQJmFsaMe6Bn0h7za/N59tCzeFh5sHjMYr0sGq41SUU13LfyNAaCwJb5Q6UYu0S7SOLeyxB1IukxpXiHO2BmpZ9nS1Z1FieLTjI9ZDrGHfi8nMup5GRmBY+N9uvQSEqnU5OR+X9YWATg4a6fs2RbqEvqqT2cj3l/Z8xDOy6W+h1RFDm6KZXq0gYmzAnH3Lr7pfQ1qhqePPgkOnR8Nf4rbE17f3PohEI59686jamRAd8/OowAFykrRqJ9JHHvZRRnyamrUhI4SP+QzKaUTZgYmHB30N0dzv38YDqOlibcP6TjXXtB4QYaGrIJDHgZg27sbEWtjsptaRiYG2F3e+cyXJJPFJN2tpQbbuuLV3D3bQDUOjULjiwgvzafT8d8Sh+b3t9qLja/mvtXnsbSxIjv5w/Ty7VTQkIS915G+rlSjIwN6BulX/52jaqG3Zm7uaXvLTiYObQ792JeFcfSZcwb5YeFSftirVJVkJ39GQ4OI3F0HKv3+luj9nA+6sI67KYFYGipv4OkrKCO375PwzvUnoG3+HZrDdD0KeCD0x9wpvgMbw97mxvcbuj2Na80JzNkzFx5GlsLY7bMHyoVKEnoTe8PNP6L0Gp1ZJwvwzfSCRM9Y9I703fSqGnU6yB1yf407C2M9WqMnJm1BK22kaDA17sV41YV1FJzKA+LAS5YdKLgSKXQsHdlAmYWRtz0cHiPdA/6Luk7dqTvYF6/eXoVeV1r9iWW8NTmi/RxsGDD3CG46tEdS0Lid6Sdey8iP6kSRZ1a7ywZrU7L5pTNRLtEE+oY2u7c01kVHEuX8fgY/w69vWtrkygq+h4vrwewtOx6I2VRraXy+1QMrUywm6J/OEYURY5sSEFe1sDEueFY2HQ/zn4w7yCLYxYzoc8EnhrwVLevd6X534UCHt94gVA3a7Y+OkwSdolOI4l7LyL1TAlmlsb0idDvwPFg3kEK6wqZFTar3XmiKLJ4Xyou1qbM7qDxtSiKpKW/h7GxPX19n9F36a0i/zUHTXkj9vcEYWCu/4fExGNFpMeUMWSqHx6B3Y+zx5fH8+qxV4lwiuDDER926LlzrVl7IpsFWy8x2NeBjfOGYm8p+bFLdJ7e/Sr/F6FsUJMdKyPwBlcM9agYFUWRtYlr8bH2YZx3+9a7R9PKOZdTxdPjAjrMkCkr20N19Vn8/RZgbNz1HGpFZjV1J4qwHOaOWScEujyvluNb0/EJdyB6YvcPO3Nrcnny4JM4mjny+bjPe3Vza1EU+fxgOm//mMSEMFe+lTooSXQDSdx7CRnny9BqdIQM69idEeBC2QXiZfHMDpvdbpegpl17Gl725ky/of0MGa22kYyMj7CyCsPDo+upjzqFhqptaRg5mWN7i/7FSo11KvYsj8Pc2pibHgrrdlclWaOMR/c/CsDyCctxMu8Zk7ErgU4n8sHPySzZn8adAzz5ema01PNUoltI4t5LSDlVgr27Jc4++hWmrE1Yi72pPVMCprQ7b29iCfGFcp4dH9ihh0xu3koUyiKCAt9AELouLNU/ZqGVK7G/N0hv7xidVse+VYk01qi55bF+3c5nr1fX88SBJ6hUVPLV+K96dcqjSqPj+a2xrDqezUM3+rLoniiMOmmBLCHxd7r1ChIEwU4QhO2CIKQIgpAsCMIwQRAcBEHYLwhC+uWv10+PsmtEdVkDJVlyQoa66ZWZklWdxZGCI8wImYG5UdsdmrS6pl27n7Mldwxov2S/sTGP3NzluLhMxt5+cKf/D7/TEF9Ow/lSrMd4d8rK9/SuLApSqhh9fzAufbpXUq/WqVl4ZCFpVWksGr2oV3dTqlGoeXjtWX6ILeLFScG8dXtYj2QGSUh0d3vwGfCrKIohQBSQDLwCHBRFMRA4ePnfEu2QeroEQYCgwfqFZNYlrcPU0JQZITPanbf7UiHpZXUsmBDU7k5QFEVS095BEIwIDPxPp9b+VzTVCqp2ZGDsbY3NTR0XSf1OekwpF/fnETHas9vt8kRR5O2Tb3Oi6ARvDXuLUV6junW9K0lpjYJ7l5/iTFYli++J4smx+nXDkpDQhy6LuyAINsAoYDWAKIoqURSrganAusvT1gHTurvIfzKiTiT1dAleoQ56OUCWN5TzY+aPTAuY1m7RklqrY+mBdELdbZgc0b5glsv2UVFxBL++z2Jmqt8bzN8RtSKVW1JBFHGcEax3Z6WKwjoOrU/Bzc+WEfcEdunef+Xzi5+zO3M3T/Z/sle7PKaX1nLnspPkVzaw5qEbuGtgx+6cEhKdoTs7dz+gHPhWEISLgiCsEgTBEnAVRbEY4PLXVt2vBEGYLwhCjCAIMeXl5d1YxvVNUXo1tZUKQobqJ6qbUzaj0WmYHTa73XmbzuSRW9HAi5OC2v2Yr9HUk5b2HlZWIXh5Pdiptf+V2sN5qHJqsJsWgJGjfs28lQ1qflkej4mpITfPj9ArS6g91iasZVX8Ku4OuptHIx/t1rWuJGezK7nr65OotDq+f3QYo4L0b6MoIaEv3fltMgKiga9FURwA1NOJEIwoiitEURwkiuIgZ+d/74s75UwJxmaG9O3f8XPQoG7g+9TvuanPTfjYtB32qFWo+exgOkP9HBgb3L6zZE7OlyiVxQQHvdNl/xhljpyag01VqJYD9HOy1OlE9q9JorZCwc3zI7C0615bu+1p21l8fjGTfCfx+pDuVdVeSfbEFzNr9RmcrE353+M3EuHZ+03LJK5PuiPuBUCBKIpnLv97O01iXyoIgjvA5a9l3VviPxeVQkPm+TICBrpgrEdWyba0bdSoango/KF2531zNIvKehWvTQ5tV+Tq6lLJy1+Dh/u92NkN6uzyAdA1qKnckoqhgxl20/SvQj31vwxyEyoYOSMI90427fg7v2b/yrun3mWE5wj+O+K/7aaGXitEUeSrwxk8sfEC/Txt2fHYjXg7SD4xEleOLou7KIolQL4gCL831BwPJAG7gd8/3z8I/NCtFf6DyYgpQ63UEjbco8O5Co2CtYlrGeo+lEjnyDbnlcgVrDqexZQoDyK92hZNURRJTX0LQ0Mr/P1f7NL6RVGkamcG2hoVjjNCMNCz4CbpRBGxB/LpN8aLiFHda7zxW8FvvHrsVaJdo1kyZgnGhvobk10tlBotC7dd4pO9qUzt78HGuUOkqlOJK053y9+eBjYKgmACZAEP0/SGsVUQhDlAHnBPN+/xjyXxeBEOHpa49u049W9nxk5kjTL+b9T/tTtvyf5UdDp4cVL7TaxLSv5HtfwcoSH/xcSkfTfJtqg/W0JjvAybm30x8dYvP78ovYqjm1LxDrVnxD1d960BOFdyjgVHFhDkEMSX475sNy30WlFZr+LR9TGcy6ni+ZuCeGa8lBEjcXXolriLohgLtPZ5fnx3rvtvQFZQR1lODSPuCezwl12tVbMmYQ0DXAYwyLXt8ElKSQ3bzxfwyPC+7X7kV6lkpKV/iK1tNO7uHXvAt3qNwjqqd2diGmSP9Sj9Mj3k5Y38sjwBGydzJs6NwKAbhTqJskSePvQ0nlaeLL9pOVYmva95RUZZLY+sjaGkRsHn9w1gSlTHn9AkJHoKqQzuGpF0vAgDI4HgIR1nyfyU9RMl9SXMj5zf7hvBR7+kYGVqxFPj2t8Rp6W9h1bbQEjIhwhdMNHSNWqo2JiMoZUxDtOD9bIJUDVq+HlZHKIocusTkZh1wtf976RWpvLogUexM7VjxYQV2Jv1vjq5Y+nl3LHsJA0qDVvmD5WEXeKqI4n7NUCj0pJ2tgT/AS4dttLT6DSsil9FmGMYwz2GtznvRIaMI6nlPDUuADuLtuO5MtkhSst+wtf3CawsO59XLooildvS0FYrcbg/VK/mGzqdyL7ViVSXNnDz/AjsXLt+kJhamcrcfXMxNzJn5cSVuFrq37HqaiCKIutO5vDQt+fwtDNn15PDifbpfW8+Ev98JMu5a0DmhTKUDRrCRnS8m9ubs5e82jyWjlna5q5do9Xx3k9JeNqZt2vpq9HUkpL6BpaWQfj26VoeeN2xQhRJFdje5oepHjYBoihyYls6uQkVjL4vCK+QrsX3AdKr0pm3bx4mhiasmbgGb2vvLl/rSqBQa3ljVwLbzhcwPsSFpTP6Y23W+w54Jf4dSOJ+DUg8XoStszmeQe2nAOpEHSvjVhJgF8BYn7Zb3W0+l09KSW2HToIZmZ+gVJbSL+IrDAw6n62hzJEj/zUb8whHrPTI8AG4dDCfuMMFRI33JmJ016swM6szmbtvLsYGxqyZtAZvm94l7MXyRh5bf55LBXKeGRfAcze1XzwmIXGlkcT9KlNVUk9xhpxhd/h3eJB6MO8gmfJMPh75cZsNJqobVCzel8pQPwdujmg7fl9VfY7Cwo14ez+MrW3/Tq9bW6eiclMKRvZm2N8dpFfGR8b5Mk5sz8A/2pnhd3U9MyZLnsWcvXMwEAxYNWlVr3N4PJdTyeMbztOo0vLNAwOZFN41CwcJiZ5EEverTNLxIgwMBEKGte/3otVpWRa7jL62fZnkO6nNeUsPpFPTqOat28PbFFytVklKymuYmXnh77eg02sWtSKVm1LQNqhxeaI/Bnr0dy3KqObAt0m4+9ty08Nd92bPlmczZ+8cAFZPWk1fW/394a80oiiy4XQu7/yYhLeDBZvnDSXQVb+UUAmJK40k7lcRtUpL8sli+vZ36rAv6N6cvWRUZ/DJqE/arLhMLall/elc7h/iQ6h72/Hv7OzPaGjIon/UWgwNO3+YKd+ThTJLjv09QZh4dJxyWFVSz55lcVg7mjH58UiMuth0IrM6k3n75qETdayZtAY/W78uXedKoFBreeuHRL6PyWdssDNLZwzA1lyKr0v0HiRxv4qknytF2aAhcmz7sWeNTsPXl74m0D6Qib4TW50jiiLv/pSIlakRCye0XbBULT9Pbt5KPDym4+g4stNrrr9QSt2JIqyGe2A5sOPMlIYaFT99eQkDQ4HbnorqMBuoLVIqU5i/bz6GBoasnrgafzv9rQ2uNHkVDTyx6TwJhTU8PS6A56X4ukQvRBL3q4QoisQfKcDR07JDL5Wfsn4ipyaHpWOWthlr35dUyomMCt6+PazNUnattoGkpBcxM/MgMOC1Tq9ZVVBL1f/SMfWzxXZyx+EQlULDT19eoqFGxbQF0dg6d61iNK48jscOPIalsSWrJvauGPuvCSW8uP0SArBy9iAmhPWuVEwJid+RxP0qUZJVgyy/jtH3B7d7GKnWqVl+aTmhDqGM82m98bVCreX9n5MIcrVi1tC2hS8j4/9obMwlesAmjIw6V8GprVVRsT4JQysTHO4P6dCfXaPWsufreGQFdUx+rB+uvl3rphRTEsOTB5/EwcyB1ZNW42HVO4p/1FodH/+Swqrj2UR62fLV/dGS8ZdEr0YS96tE/JECTMyNCBrc/k5vV8YuCusKeW38a22+CXx1OIP8ykY2zR3SZoelysoTFBSux9v7Yezth3RqraJWR8WmZHQNGpwfi8LQqv3zgd/7nxamVnHTw2H4RnatEfXJopM8e+hZ3K3cWTmh9xQoFVU38tSmC1zIq+bBYX147dZQTI16n/OkhMRfkcT9KlAvV5J5oYx+o70waSfTRKVVsSJuBZHOkYz0bD0+nllex/KjmUzr78GNAa2LqEZTS1Lyy1hY+OHv90Kn11v9Uxaq7BocZgRj4tn+jl8URQ5vTCX7koyR0wP1slNojSP5R1hwZAF9bfuyYsIKHM0du3SdnuZIahnPfx+LSqPji/sGcLtkIyBxnSCJ+1Ug6XgROq1IxOj27W23pm6lpL6Ed298t9VduyiKvLErATNjQ/5za1ib10lLew+lspRBg7ZjaGjWqbXWnSyi/lQxVqM8sejffuMNURQ5uSODlJPF3HCrL5Fju1ZYtDtzN2+eeJNQh1CWT1iOrem1b2Ch0uhYtC+VFb9lEeJmzbKZ0fg59z5zMgmJtpDE/Qqj1epI/K0QnzCHdj1ValW1fBP3DUPdhzLUfWirc36ILeJkZgXvTYvA2br1zkWlZb9QXLID3z5PYGsT1am1NqZUUv1jJmahDtje3PEB6oW9uX/4st9wW9fyz79N+JYl55cwxH0In439DEtjyy5dpyfJKq/jmS0XSSisYeYQH16/NQxzPZqpSEj0JiRxv8JkXiijXq5izMz20x/XJKyhWlnN8wOfb3XXLm9U8/7PSUR52XL/4NZb7CkURaSkvIaNdSR9+z7TqXWqS+qp3JyCsbslDjNCOiw6ij9SwOldWQQNdmXkvR3bFv8dnajj0/OfsjZxLZN8J/HhiA8xMby2DSxEUWRbTAFv7U7E1NhAqjaVuK6RxP0KIooisfvzsXO1oE9E2zHk0vpSNiRt4Fa/WwlzbD3csmhvKpX1KtY+PBjDVoRXFLUkJi1EFLWEh3+KgYH++eXaWhWytYkIpoY4PRiOgWn7u9Sk40X8tiUN30gnxj0Y2unqU7VOzdsn32Z35m7uC7mPVwa/0mbK59VC3qDmtV3x/BxXzDA/Rz6d3h83286FtCQkehOSuF9BijOqKc+rbUp/bEcAl11ahlbU8vSAp1sdv5hXxYYzuTw4zLfNhso5Ocuorj5LWOgnWFj46r1GUa1F9l0Sunp1U2aMbfuNqlNOFXN4Ywo+4Y7cPC8Cw0423GhQN/DC0Rc4VniMJ/s/yaORj17zzkRnsyt5/vtYSmsUvHRzMI+O8m/1DVRC4npCEvcryMX9+ZhZGRMytO2P9hlVGezK2MXM0Jl4WrU8cFVqtLy0PQ43GzMWTgxq9RrV8vNk53yBq+sU3Nzu0Ht9ok6kcmsa6oJaHGeFdpgZk3qmhIPfJeMdYs8tj0VgaNw5Ya9UVPL0oadJkCXw5rA3uSfo2nZgVKi1fHogjZW/ZeHtYMH2x2+kv3f3mnVLSPQWJHG/QlSXNpATL2PQZF+M2jmMW3phKZZGlszvN7/V8a8OZ5JeVse3D93Qqje4RlNLYuICTE3dCQluPcumNURRRP5zFo3xMmwn98U8vP3c9IzzZRxcm4RnkB23dMEvJkuexZMHnqS8sZwlo5cwvs+17cSYUChnwdZY0krruG+wN/+5NQwrPRt8S0hcD0iv5itE7MF8DA0N6NeOh/m5knMcLTjKc9HPYWfWcseYXFzDssMZ3DHAk7EhLdMSRVEkOeU/KJXFDIzegpGR/o6Edb8V/uEZYzWy/RTNrIvl7FudiJu/Lbc+EYVxJzNHzpWc49nDz/7hxR7pHNmpx/ckaq2OZYcz+eJQOg6WJnz78A2MDW4/5VNC4npEEvcrQGOdipRTxQQPcW3T/VGr0/Lx2Y9xt3RnZujMFuMarY6XtsdhZ2HMm7e1fshaWLiRsrKf8fdbiK1ttN7rq79QivyXbMwjnbC91a/d3X7mhTL2rUrE1dea256KwriDw9a/sztzN2+dfAsfax+W3bSs1dDT1SK9tJaF2y4RVyBnWn8P3p4S3m5LQgmJ65lui7sgCIZADFAoiuJtgiD0BbYADsAF4AFRFFXdvc/1RMLRQrRqHVE3tZ6yCLAjfQepVaksGr0IM6OWWRmrjmcTXyjnq/ujWzUGq6mJIy39fRwdx9Cnz2N6r02RVkXV9iYzMId72z/oTTtbwoG1ybj62nDb01HtVtf+HVEUWXZpGcsvLWeI2xCWjF2CjUnX/Ga6i1Ynsvp4Fov2pWFlasSymdFM7te+n76ExPVOT+zcnwWSgd9/cz8GPhVFcYsgCMuBOcDXPXCf6wK1UkvcoQL69HPEwb31ghy5Us4XF79gkOsgJvZpaembVV7Hp/vTmBTuyuR+LQ9j1epq4hOewtTEmfCwRQh6phGqCmqp2JCEsasFjrPDEIzaflzSiSIOb0jBM8iOyY9HdkrYlVolb518i5+zfmZawDTeHPomxobXxus8ubiGV3bEcalAzoQwVz68o1+bBWASEv8kuiXugiB4AbcCHwALhKbP9+OA+y9PWQe8zb9I3BOPFaKoVzPoFt825yy/tJwaVQ0vD365RUhEqxN5cXscpkYGvDc1osW4KOpITHoBpbKMgQO/x9jYXq91qcsbkK1NxMDCGKeHI9rtphR/pIDftqThE+bALY/1a/dA+O+U1pfy3OHnSKhI4JkBzzC339xrkuqoUGv58lAGy49mYmtuzBf3DeC2SPdrnnYpIXG16O7OfSnwEvD7SZ4jUC2KoubyvwuAaxdkvcpo1Tpi9+fhGWyHm1/r+eiZ1ZlsTtnMXYF3EeIQ0mJ8+dFMzudW8dmM/rjYtAzX5OauoKLiMEFBb+ltL6CpVCBbFQ8iOM2JwLCdLlCxB/I4sT0D30inpjz2TqQ7Xiq/xHOHn6NB3cBnYz9r07L4SnM2u5JX/hdHVnk9d0V78fqtoW163ktI/FPpsrgLgnAbUCaK4nlBEMb8/u1WpoptPH4+MB/Ax6ft2PT1RPKpYurlKsY/3PoBqCiKfHz2YyyMLXhqwFMtxhMK5Xy6P43bIt2Z0or7YGXVKTKzFuPiciteng/otSZtjZLy1fHolDqc5/fD2Ll1fxtRFInZk8PZH7Pxj3ZmwiPhGLYTtvk7O9N38t7p93CzdGPlhJUE2He9IXZXqVWo+fjXFDaczsPL3pz1cwYzMtD5qq9DQqI30J2d+3BgiiAIkwEzmmLuSwE7QRCMLu/evYCi1h4siuIKYAXAoEGDWn0DuJ7QanVc2JuLa18bvIJbD5Ucyj/EqeJTvHzDyziYOTQbU6i1PP9SfR3wAAAgAElEQVR9LI5WJrw/rWU4prGxgISEp7Gw6EtoyId6hRe09WrKVyWgq1XjNDeizf6nok7k2NZ04o8UEDzEjXGzQzDQs/JUo9OwOGYxG5I3MNR9KItGL7rqro6iKPJLQgnv/phEWa2CuSP6smBiEBYmUjKYxL+XLr/6RVF8FXgV4PLO/QVRFGcKgrANuJumjJkHgR96YJ29nvRzpdRWKBg5PahV4W1QN/DR2Y8IsAtgesj0FuOf7E0lvayOdY8MbpGep9U2EBf/KKKoJSryG726KukaNchWx6OpVOD0cDimPq1nqmg1Og6uTSI9poyom7wZfmeA3l4xlYpKXvrtJc4Un2FW6CwWDlqIkcHVFdRsWT1v/pDAsXQZYe42LH9goFRlKiHBlclzfxnYIgjC+8BFYPUVuEevQtSJXPg1F0dPK3z7tW4Qtix2GSX1Jay/ZT3GfzP1OpkhY/XxbGYP68PooOZhBFEUSUp+mbq6NPpHrcLComNrXZ1Si2xtIurSBhwfCMPMv3WxUyk0/PpNPPnJVQy7w58BE330PnCMLYtl4dGFVCuqeW/4e0wLmKbX43qKRpWWZUcy+OZoFqZGBrwzJZxZQ/tInjASEpfpEXEXRfEIcOTy37OAwT1x3euF9POlVJU0MHFueKvimFqZyobkDdwVeBf9Xfo3G6uqV7Fw2yX8nCx59ZbQFo/Nzf2asrI9BPi/jKPj6A7XolNqkX2bgCqvBof7QzAPcWh1XmOtip++vER5fh3jZocQeqN+HYZEUWRD8gaWxCzBzdKNDZM3EOrYct1XkgNJpbz9YyIFVY3cMcCTVyeH4GItOThKSPwVKSjZTXRaHWd/zMbR05KA6JZl7DpRx7un38XW1JbnBz7fbEwUm9IeZXVKdjx+Y4uGEOWyg2RmLcHVdQo+PvM6XotSg+zbxCZhnxGCRb/WDxPl5Y389OUlaisV3PJoBH2j9Dt0rFPV8ebJN9mfu5+x3mN5f8T7V7UwKbeinvd+SuZAcimBLlZsnjeUYf69ox2fhERvQxL3bpJ6pgR5WSO3PNav1Vj19rTtxJXH8eGID1scNH57IocDyaW8cVsYkV7NQyd1dakkJi7A2jqM0JD/dhgu0SkuC3v+ZWGPbF2wS7Lk7Pk6Dp1WZMoz/fEI1C8+nVaVxsIjC8mvzWfBwAU8FP7QVcsZr1Wo+fJwBt8ez8HIUODVW0J4ZERfjDtpNywh8W9CEvduoNXoOPdTDi59rOkb1dJVUdYoY+mFpQx2G8xtfrc1G4svkPPfX5K5KdSFR4b7NhtTKkuJvTQHI0NLIvst77APqk6hQbYmAVVBHQ73hWLRr3WHx4zzZRxYm4SlrQm3PRWFvVvHLe1EUWRH+g4+PvsxViZWrJq4ikFugzp8XE+g1Ylsjcln8b5UZHUq7h7oxYuTgnFtJf9fQkKiOZK4d4PkE0XUVioYMzO4lUpSkfdPv49So+T1oa83G69VqHlq8wWcrEz55O6oZmMaTT2X4uah0cgZGL0FM7P2Y+E6hQbZ6gRUhXU43h+CeURLYRdFkYv78ji1MxM3P1smP94Pc+uOi3rkSjnvnHqH/bn7GeI+hI9GfoSTefvWwD3FyUwZ7/6YREpJLTf42rPmoRtafLqRkJBoG0ncu4hGpSVmTw7uAbZ4h7U8tNybs5eDeQdZMHABfW3/zHARRZH/7Ewgv7KBLfOHNaucbGqV9zy1tclERa7A2jq83TVo61TIvk1EXVyP48yQVj3ZtVodv21KJelEMQGDXBj/YKheXuznS8/zyrFXkDXIWDBwAQ+GP3hVWuFly+r5755k9iWV4mlnzlf3RzO5n5tkGyAh0Ukkce8i8UcKqZermDCnZYZMRWMFH5z5gEinSGaHzW429t2pXHZfKuKFiUEM7vvnm4IoiqSlv4dMdpDgoHdwchrb7v011QpkqxPQVitxnB3WalaMok7N3lUJFKRUMfCWPgy53a/DHHaNTsM3cd+wIm4FXlZebJi8gXCn9t9keoKyGgWfHUzn+3P5mBgZ8OKkYOaM6ItZJ5uCSEhINCGJexdQ1Kk5/2sOPuEOeAa1rEb98MyH1KvreXf4uxga/ClOMTmVvPdTEuNDXHhiTPPy/Pz8bykoWI+P9xy8vGa1e391eQOyVQnolBqc5kRg6tuyIlRWUMcvy+Ooq1YybnYooTd2bHFbUFvAq8deJbY8lin+U3htyGtYGnccl+8ONQo13xzNZM3xHNRaHfcN9uHp8QFSaqOERDeRxL0LxOzJQdWo4cY7W/qn7M3Zy77cfTwb/Sz+dv5/fL+sRsHjGy/gZW/Okun9MfjLDrq4eCfpGR/g7HwzAQGvtHtvVWEdsjXxIAg4z49s1VIg43wZB9clYWJuxB0Lo3Hr274dgCiKbE/fzqJzixAEgY9GfsStfrd29DR0C4Vay4bTuXx5OIPqBjW3R3mwcEIQvk5X9s1EQuLfgiTunaS6rIH4owWEDvfA8W8NpWWNMj488yHhjuE8FP7QH99XaXQ8sfECdQoNG+YMwdb8zwrVctlBklNext5+GOFhS9r1ZldmVSNbl4SBuRFOcyJamICJOpEzP2Zx/pcmj5tbHuuHpW373uWl9aW8dfItThSdYIjbEN4b/h7uVleukYVGq2PnxUI+3Z9GkVzByEAnXr45hAjPq+tHIyHxT0cS905yelcmBkYGDL69uQ2AKIq8eeJN6tX1vD/8/WYeKx/8nERMbhVf3DeAYLc/+5xWVZ8jIeFprKzCLqc8ti3EDbFlVG5Lw8jBDKe5/TD6m2grG9Qc+DaJnPgKQoe7M3pGcLt2vaIo8lPWT/z37H/R6DS8NuQ1pgdPv2KHphqtjh9ii/jiUDo5FQ1Eedmy6J4obgy4Otk3EhL/NiRx7wTFmXIyL5Qz+Pa+LXbEW1K3cKzwGK8OfrWZ3e3Wc/msO5XLvJF9uf0vNr61tcnExc3DzMyT/lGr2zQDE0WR2qMF1Pyag0lfG5weCMPAork3TXleLb+uiKeuUsnI6UH0G+PZbnZJRWMF751+j4N5B+nv3J/3R7xPH5s+XXlKOkSj1bH7UhFfHMogW1ZPqLsN3zwwkIlhrlIGjITEFUQSdz0RdSLHt6VjYWtC/7/1Rs2oymBxzGJGeo7kvpD7/vj+qcwKXtsZ/0fo4XcaGrKJvfQQhoaWDOi/DhOT1kvoRa1I9e4M6s+UYB7ljMM9Qc1a44miSOKxIo5tTcPC2oRpC6Nx9287vCGKIrsydrEoZhGNmkYWDlzIA2EPNDv07Sm0OpHdlwr54mAGWbJ6QtysWT6rSdQNJHMvCYkrjiTuepJ8spiynBpuejgMY9M/xVClVfHysZexNLbk3eHv/rEbzZbV89iG8/R1suSrmdEYXS6Vb2jI4cKFmYiijugB69osUtIptVRuSkaRWoX1GC9sJvo2S2NUKTQc2ZhK+rlSfMIduOnhMMyt2i5MyqvJ493T73Km+AwDXAbw9rC38bPz64mnphlqrY4fLxXx5eEMssp/F/VoJoa5SaIuIXEVkcRdDxT1ak7tzMQ9wJagwa7NxpZeWEpaVRpfjf/qj+rN6gYVc9aew9BAYPWDN2Bj1hRGaWjI5cLFmehENdEDNmBp2Xq3Ik21gop1SahL6rG7IwCrIc0POCsK6/h1RQLysgaGTPVj4KQ+beavq3Vqvkv8jq8vfY2xgTFvDH2Du4Pu7vHYeqNKy9aYfFb8lkVhdSMhbtZ8PTOaSeGSqEtIXAskcdeDMz9koWzUMGpGc5uBg7kHWZ+0nvtC7mOU1yigaef6+IYLFFQ1snHeEHwcmzJaGhvzuXBxJlqtgujojVhZBbd6L2WOnIr1yYgaHY4PhWMe3LzQKeFoISd2ZGBqbsTU5wbg2UbXJ4AEWQJvn3yb1KpUxvuM59XBr+Jq6drm/K4gb1Dz3akcvj2ZQ2W9ikF97HlvWjhjg12kmLqExDVEEvcOKMutIeFYIZFjvHDy+vPQM78mn9dPvE6EYwQvDHoBAJ1O5KXtcZzKqmDJvVHc4NskzI2NhVy4OAuttoHoAeuxtmrZGBug7mwx1T9kYmRvhuPsMIxd/kx1bKhRcWh9MrnxFfiEOzL+wVAs2mh0Xa2o5rOLn7EjbQfO5s4sHbOU8X3G99RTAkBpjYLVx7PZeDqXepWWcSEuPD7G/4//s4SExLVFEvd20OlEftuShrmVcbPUR4VGwYKjCzAQDFg8ZjEmhk0i+9GvKey8WMgLE4O4M9oLaDo8vXhxNhptLQP6r2/VL0bU6qj+KYv6U8WYBtnjeF8IBuZ//mhy4mUc+i4ZlULbbjaMVqdlR/oOPr/4OXWqOmaFzeLxqMexNrFuMberJBbJ+fZEDrtji9DodNwe5cFjo/0Jdb96vu4SEhIdI4l7O8QfKaA0u+kQ1fQv6Ycfnf2IlMoUvhr/FR5WTQeiK37LZMVvWTw4rA9Pjm2KpdfVpXIxdjaiqGPAgA3YWEe0uIe2VkXl5hSUWXKsRnpie0vfP+LnGpWWkzsyiD9aiKOnFVOfD8OxjSbXsWWxfHjmQ5IrkxnsNrhFSmZ30OpEDiaXsuZENqezKrEwMWTGYG/mjvD7I+wkISHRu5DEvQ1qZI2c3pWJT7hjs0PUnek72ZG+g3n95v0RZ//fhQI+3JPCrZHuvHl7k5GYvOYSsbEPY2hgRnT0xlYPT5VZ1VRsTkFUaLG/NwjL6D/vU5xRzaH1KVSXNtD/Jm+GTvVvtSipvKGczy9+zq6MXbhYuPDJqE+Y5DupR+LddUoNW8/ls/ZkDnmVDXjamfPa5BCmD/LB9m+59hISEr0LSdxbQRRFjmxMQRCEZl7tF8su8u7pdxnqPpQn+j8BwMHkUl7aHseN/o4suTcKQwOBqqozXIqbh7GxA9ED1mNu7t38+rrLhUn7cjByMsdxTj+MLzfOUCu1nP4hk7jDBVg7mDH1uf54teL42KBuYF3iOr5N/Ba1Ts0jEY/waOSjWBh3fyedLatn/alctsXkU6vUMLCPPa/cEsLEMNc/UjolJCR6N5K4t0Lq6RLyk6sYNSMIa4cmd8KiuiKeO/wcnlaeLBq9CCMDI46mlfP4hguEeTRVXZoaGVJWvpfExOcxM/NmwIB1mJm6Nbu2rkFN5dY0FCmVmEc6YX9XIAamTT+GwrQqDq1Poaa8kX5jvBg6zQ8Ts+Y/Iq1Oyw+ZP/DlxS8pbyxnYp+JPBf9HN42zd9AOotaq+NAUikbzuRyIqMCIwOByf3ceWREX/p7S00yJCSuN7os7oIgeAPfAW6ADlghiuJngiA4AN8DvkAOcK8oilXdX+rVoa5KyfFt6bj72xIxyhNo2iU/c+gZ1Fo1n4/7HFtTW05kyJj/XQwBLlZ898hgrM2Myc9fS1r6+9jYRBEVuRITk+Y7bmW2nMrvU9HWqrCb6o/lUHcEQUCl0HB6ZybxRwuxcTJj2oIBrVoJnyg8weLzi0mvSifKOYolY5bQ36V/t/6/RdWNbDmbx5Zz+ZTVKvG0M+eFiUHcO8gbF6mdnYTEdUt3du4aYKEoihcEQbAGzguCsB94CDgoiuJHgiC8ArwCvNz9pV55RJ3IwXVJaDU6xs0ORTAQ0Ik6/nP8P6RXp7Ns/DL8bP04nVXBnHXn8HW0ZMPcIdiaG5Ge/iF5+atxdppAePinGBqa/3ldrY6aA3nUHsnH0MEMl8eiMPG2RhRFMs6XcXxbOvVyJZHjvBg61b9ZBSzApfJLfHHxC84Un8HLyovFoxczoc+ELsfVtTqRY+nlbDidx6GUUkRgTJAzHw7pw9gQFwyloiMJieueLou7KIrFQPHlv9cKgpAMeAJTgTGXp60DjnCdiHvc4QIKUqoYMzMYO1cLRFHkk3OfcCDvAC/d8BLDPYdzNruSR9aew8vego3zhmBrJpKQ+CxlZXvw8ppNUODrCMKf4qyWNVK5JQV1QR0WA12xm+KHgakR8vJGftuSRl5iBU7eVtz8aEQL3/WUyhS+vPglRwuO4mDmwEs3vMT04Ol/pF52lhxZPdvPF7DjQgHFcgVOViY8Ntqf+wb74O0gZb1ISPyT6JGYuyAIvsAA4Azgeln4EUWxWBAEl564x5WmorCOUzsz8Y10ImxEU3rjd0nfsSF5A7NCZzErdBZH08p5dH0MHnbmbJo7BGtjORcuPkFNzUUCAl7Fx3vOH7tpURRpiCml+sdMMDTAYWYIFv2c0ap1xOzJIeaXHAwMBEbcE0i/MZ4Y/OWgMqs6i69iv2Jf7j6sTax5NvpZ7g+5v0uHpfVKDT/HF7M9poCzOZUYCDAqyJnXbw1jQpgrJkbSAamExD+Rbou7IAhWwA7gOVEUa/QNFQiCMB+YD+Dj49PB7CuLRq1l/5pETCyMGPdACIIg8HPWzyyKWcQk30m8eMOL7E0s4enNFwlwsWb9nMEY61I4d+5x1Joa+kV8hYvLzX9er1pJ9c50FKlVmPrZYj89GEMbE3LiZZzYnkF1aQP+0c6MuCcIK/s/rYOz5dmsjFvJz9k/Y2ZoxqORjzI7fDY2Jp0rEBJFkXM5VWyNyWdPfDENKi1+Tpa8dHMwdw7wws1WiqVLSPzT6Za4C4JgTJOwbxRF8X+Xv10qCIL75V27O1DW2mNFUVwBrAAYNGiQ2J11dJdj36dTUVjPbU9FYW5twqmiU7x+4nUGuQ7igxEfsOtiES9ujyPSy5a1Dw2mQf4T8amvYWLiwqBB2/+wExBFkfqzJcj3ZINOxPZ2P6yGeVBZUs/JdcnkJVVi52rBbU9F0SfiT5vflMoUVsatZH/ufkwNTZkdNptHIh7B3qxt35jWSCut5YfYQnZfKiK/shErUyOmRHlwzyAvon3sJa8XCYl/Ed3JlhGA1UCyKIpL/jK0G3gQ+Ojy1x+6tcIrTMqpYpKOFxF9cx/6RDhyvvQ8zx5+lr62ffls3GdsOl3EOz8mcaO/I9/M6k9JwSfk5a/G3m4oERFf/JERo6lUULUjDWWmvGm3flcgalNDjn2fRsKxIkzMDBlxTyARoz0xvBwKiS2LZUXcCo4VHsPK2Io5/eYwK3QWjuat+7u3Rn5lAz/GFbE7toiUkloMDQSGBzjx/E1B3BzhhoWJlO0qIfFvpDu/+cOBB4B4QRBiL3/vNZpEfasgCHOAPOCe7i3xyiErqOPIplQ8g+0YcntfLpVf4okDT+Bm6cby8StYureANSeymRjmyid3uJOa9CByeQxeXg8QGPAfDAyMEbU66k4WUbM/FwQBuzsCMO3vTMKxoqZG2gotESM9uOH2vphbmSCKIicLT7IyfiUxpTHYmdrx9ICnmREyQ+/wi6xOyZ74YnbHFhGT25RlOrCPPe9ODWdyP3ecrNrvmyohIfHPpzvZMseBtj7n96wF4RVA2aDm12/iMbUwYuKcCJKrk3l8/+M4mTvx1dhvePN/ufyaWMJDN/ryxLByLsVOQ6dTEh72KW5uU5qukSOnamcGmtIGzILtsZniT0ZKFefePkNdlRLvMAeG3x2Ao4cVSq2Snek7+S7pOzKqM3Axd+GlG17irsC79DooLatVsDexlF8TijmdVYlWJxLsas2Lk4KZEuUhZbtISEg041/5mV2n1bF3ZQK1lQqmPj+AbHU68/fNx8bUhkUjlvHMxixi86t5/dZgxnjsJD5+OVZWIUSEf4GlpR/aOhXyX3JoOF+Koa0pDjNDKFLq2Pv5JapLG3DxtWHcg6F4hzhQ0VjB17Hr2ZK6hUpFJUH2Qbw3/D0m953cYUpjYXUjvyaU8GtCMTG5VYgi+Dlb8thoP26P8iDETXJilJCQaJ1/pbgf25pOfnIVYx8Iodg6kyf3PomtiS0vRX3K3DWZyOqULL3bGw/xFXJzL+LhMYOgwDcwwJS6U0XI9+UiKrVYjfZC7m7FTz/mUJ5Xi727Jbc82o++/Z1Ir07nzRNL+TnrZ1Q6FaO9RvNA2AMMdhvc7sFmjqyeXy4L+qUCOQAhbtY8Nz6IW/q5EehiJR2MSkhIdMi/TtzjDheQcLSQARN8qPTN4vn9z+Nu5c6d7u/w+NocHC1N+OouOYbyF2kwMCY8fCmuLrehSK1CvicBTVkjJn621AU7sP9kMWU/ZGPtYMb4B0PpM8ieA3n7efvXbVwsu4iZoRl3BN7BzNCZ9LXt2+p6NFodF/KqOZhcysGUMjLK6gCI8rLl5ZtDuDnCjb5OllfzKZKQkPgH8K8S9+w4Gce3puEb6UTtgCxeOfQy/rb+BLOQt3cWMdjXmicGbEZbtQ87h5GEhn6EQYUVstUJKDOqMXQ0Q3mjB8djZVRcSMPGyYyxs0KwCFOzI2sTu3bsolpZTR+bPrww6AWmBUzD1tS2xTrkDWqOppdzKLmUI2nlVDeoMTYUGNLXkfsH+zAx3BUveymGLiEh0XX+NeJemFbF3hUJOPtYUz4sliXHFhHq0A9N0SNszK7k3v4aJrktRGxsICjobdws76H2xzwazqcjmBmh7OfE2bRqKvfkYudqwZjZQRS5p7A0/W1O/XgKI8GIsT5juTf4Xoa4DWkWOhFFkczyeg6nlHEwpZRzOVVodSIOliaMD3FlfKgLIwOdsDaTPNIlJCR6hn+FuJfn1fLzsjisncxIG3aQLXEbibQfSULsrahUKp4dcopI283YWg8k2Ps9tKeNKD0TA0C9pzXn8muRHyvGwdOS8Bm2nDLdx/LcPVSnV+Nm6cZT/Z/izsA7cbZw/uOe1Q0qjmfIOJYm41h6OUVyBdAUP39stB/jQlzp720nmXRJSEhcEf7x4l5ZXM+PX8RiYm7ImehtHM7dR5Dp7Zw4OYxAJxWPRH+Ku5UcP++3sE65kZpdpYgaHTUO5pwrqKMuoRLXICuUo4vYpv6S9Nx0TAxMGOczjqkBUxnmPgxDA0PUWh1nsys5ll7Ob+ky4gqqEUWwNjNiRIATT41zZlSQkxRukZCQuCoIonhNK/+BJvuBmJiYHr9uRWEdPyy9iFbUcWjAGpK0l7Cpu4f8vP7c4h/L1L7f4e44Fo+yuahOqNCptVRbGBNT0oACActQDZfcD3Owfg9aUUukUyRTA6YyyXcS1sY2pJfVcSpTxonMCk5lVlCn1GBoINDf246RgU6MDHQmystW6l4kISFxRRAE4bwoioNaG/vH7txlBbX8sDQWDSp2BH9Kpa6KxryHMdB48cyA5QzxlOMh/w/GO/xQaBopNxCIl6tpVKipDMlkr9UWKg3KcNG68GD4g0zxm4JW5cLprApejcngdFYllfUqALwdzJnS34NRgU4M83fC1lyKnUtISFxb/pHiXpZbw+7PY1HQwObAj2k0sKAq9QmGumRyf/B6/JW3Y/XLCAStCXlqLWmNWqocqzkXuJ8ku9NYm1sxoc8EIu3GIK/y5GyKnHv3ZFFRnwKAp505Y4NdGOrnwFA/R6k6VEJCotfxjwvL5MTL+HVFPPVGNewIWUKVwh/jqnE8GLKN0eaOOF6YhoHSnmyFlnStiiTni1x0OoTCroYBjiNxFAdTWubFxbxaZHVNO3MPWzOG+jsy1M+RYX6OeNmbS4VEEhIS15x/TVgm6XgRhzcmI7Mo4uegNVRWTGSYdQOzwnbilzkNocqfDKWOGOMcYt1PkOeaiKNZOGY1UylK9uIXdVMHpT6OjYwKdGaInwPD/JzwdpDEXEJC4vriHyHuOp3Ib/9LIvFAKXl2yfzidQjrqsn8xzuWG0uGozg7gYuaBg7a7yPTLwk5rlSWB6NNuQWZoTHhHrbMHmLPIF97ovvY42ItNbOQ6AHa+lT8+0ZBpwNamWNwuU2jVtPKuACGl39tterL9xCbjxtd9izSqFo+XjAAw8tnQhplK2vr7rhh0/pEEbSqro/rdKBTtxw3MGp6fro9rgWdppVxYzAw6P64VgOituW4oUnTz/+P8b/8vHqY617cG+tUrP/8MOo8YxJdTvGbTRlTjb2YbmxA7cVH2GWWxlG3TWRihrouFOuyEQzycWTgSHsG+tgT5W2HmbFhxzfqKlpN04tMq256Meg0iIIhgmWTZ7syMwZdQz2iSoFOrURUKjGyd8U8chQAlbu/RqNqBI0anVqNTqvBzCcEh3EzQBTJ/XIBOq0Ona7pDzot1iEDcZsyD02dnJTFzyKKIog6RK0ORBHn6GG43/U0ypJckhcvQNQ1fR+x6avr8Il43PM0jZlxJC99CUTx8jWahMTrpmm43vkUtZeOkrbsbX4XkCYtE/G5fSYut8+n+uRu0tcsujws/iF2ftPn4zhpNhW/riPj+xV/PleXrx/00ELsx9xN6Y7Pyf5hM38PHYY+9TZ2Q26maP175O7b/bfHQ7/XvsAqbBj537xMwbEDLX4k/T/YgHmfUHKXPk1RzMkW44O++AVjexcyP55DWXxsi/HB685iaGhI+jv3I0tLbTYmGBgydP1ZAFJfv5vKnJxm48YmJgz69hQASS9PQV5U3GzcxNKCgSuOARC/YDJ1sopm4+a2NvRfdhiA2Gcm0lhd02zc0tGRyM/3AXDhiXEo6xubjdt4uBP+yU8AxMwbjVrVXADtfX0J+WAHAGceGYlO21ygnIKCCXxrEwCnHryxxXPjEtEf/1dXo6mRce6Jm1uMuw8ciu+Cr1Dkp3Dx1Vktxj1HjMPn8U+oTzlN3AdPtxj3vul2vB5+G3nMryR99kaLcd/b78N9xgtUHfmelNWLWowHzJiH8+2PIduzkvTNK1uMBz+yEIfx91O6YylZuza1GA99+l3sht56+bX3Y4vxfq99gVX4jeSvfI2CYwdbjPf/8PJr7/PnKIo5haW9HZFftpzXE1zX4h6bmMq+FUmYKi045fYLZhYKPqq3pi7bjsUudcS6HOaukmrurlJhLigw5DgGuiMIxQbc9dBmAHY+NQOlvOayuF3eBRkaMuO7PQBsfug2BKUKURQv+xuLYGTIjA17AdgycyKCStM09rsImRpx78b9AGybdTOom/+C6MxMmH758bv/8xKCRtdsXGtpyvR1TeJ+eP33iK5BDkgAAAoiSURBVDqxmbeyxvoM08fNACDmyIUWz4smOY/pU+Yhy80gNe7/2zv3GKmqO45/fvNkX8AuiKKiCGnCw1qKYm19xKipKFgbSy31jxrbhLQ+0to0LcbU2D+atDZNTROjsdaiba2ifaRRG7tBKQvLm12QN8uyuMtrF1hAlt3Zx5z+cc46r7tDVufe2R1+n2Qy557fnXu+93fO/c0958w9cyDHvvPoeyz+xmPsb1jH/ubjOfa9tSv41jcfY9v/3qftcHeOfX/tSu6/71E2rqil83gyx96yYiWL7llCfe0KujsHlafOoHXFB9x353eoW1nHwJncJtjx/koW3rqIujUbkK7cu5quulXc8aX5rF7/IeFzuZPZA2vWcNOsL7OmYR/R7soce3nDFr5w5UzW7vzI036kuZkrrp3Exr3HPO39iR7C5RVsaTlFpCfLnlZRW9vOEs629wqDA6Tbj/bm2vtDXOuSu48nCWXZTVKY49JNnSEkkWlPHk9yjUsfOBODvswbl9Yjvcx26YNdZTCQufZ/a+tZZrh027nynN7HR82dfM6lD2drBw7uO8p04MTRo972Xa1MBQ7s3u1p/6hxP1cAOzZv8bS3btrJ/Q/B5vr1dHrYD61rYNFiWLt6Pd0e9iN1G7nvnu+zun4zAx72E6vWsvD2B6hbuxXxsHetrueOGxawetPu3LoD+uvruXn2V1jT2ETUw17W2MCcK2eydlcr0Z5Kkiflk/oqNKM6uP/xneVMO3mK/t49zLP/WcEeIJRM8vjS15g5uYrl93+V9mgZnWmfi/WnulMfH+qgI1aWcdzy7tTdTM/pbk5m2au6Ut3Rs+cMp2OZAWZ8d8p+IhGiK5o5zFOTZm/vi9ETyayGiWnHbzNl9EcyL9CLup1+EVrClaluvuOSXntBRqqqaYlUkc2lA/Z4sYmTPe2XY7vdsUmXetqnRuz5xCd4f35azC50Fq++iF0e9hnldr2d+NgadnrYrx5n/90qXj6OvZFTOfZ54+2TwNFoOc0en59SY/+TPRyKeeqbUT0RAElGPO03VNmL0vSFPO1J11Pp6xXasuyhZOrLrichdGTZY2l3wud6hBNZ9rK0tnkmEeJUlr0y7U67MxHmTCSzbY5LpIY6OvqidEUy22ZNIvX5owPx3LbXk7K3SRn94cy2N6k3dX6ebc99PFxR5em7y/rtMx+xmos97VNcSIpNuMS77YXtl1F8ord9+mDbqxmi7VXYZbLjVd5t75qx9q8t4+VV7PWwzxtv2040OuY8bS8+RNuzPfbBtjc24TE0VSBG9a9lVm1opOmlp6g6/TGExE56hgTCIRa9Yrum7/x8CYnjxyASQSIRQrE44bIKFj79PADr/rWMnrNniFaMI1I5lnC8jHh5BZ+/7mYADjbvpre3D4mNIew+H42P4dJqG4BO9yQwQFiEkIgtHiEWtY00mfQYdwNCblw1mRxAPP7zREL2IjDJZM4FBOgEr6IoeX8tM6qDu6IoyoVMvuCuz8UriqKUIBrcFUVRShAN7oqiKCWIb8FdROaLyB4RaRKRpX6VoyiKouTiS3AXkTDwHHAXMAv4tojM8qMsRVEUJRe/7tyvB5qMMc3GmF7gdeBen8pSFEVRsvAruF8GtKZtt7k8RVEUJQD8Cu5eT9hk/KBeRJaIyCYR2dTR0eGTDEVRlAsTv5YfaAOmpG1fDhxO38EY8yLwIoCIdIjIwU9Z1kQgd4GU4jNSdcHI1aa6hofqGh6lqOvKoQy+PKEqIhFgL3A7cAjYCDxgjNnhQ1mbhnpCq5iMVF0wcrWpruGhuobHhabLlzt3Y0y/iDwKvAeEgZf9COyKoiiKN76tCmmMeRd416/jK4qiKENTCk+ovnj+XYrCSNUFI1eb6hoeqmt4XFC6RsSqkIqiKEphKYU7d0VRFCULDe6KoiglyKgO7sVenExEWkTkQxFpFJFNLq9GRGpFZJ97r3b5IiK/d1q3icjcAup4WUTaRWR7Wt6wdYjIg27/fSLyoE+6nhaRQ85njSJyd5rtCadrj4jcmZZf0HoWkSki8oGI7BKRHSLyQ5dfVJ/l0VVUn4nIGBHZICJbna5fuPyrRGS9O/c3RCTm8uNuu8nZp55Pb4F1LRORA2n+muPyA2v77phhEWkQkbfddrD+Mu6f7UfbC/sTy/3ANCAGbAVmBayhBZiYlfcMsNSllwK/dum7gf9gn969AVhfQB23AHOB7Z9WB1ADNLv3apeu9kHX08BPPPad5eowDlzl6jbsRz0Dk4G5Ll2FfSZjVrF9lkdXUX3mzrvSpaPAeueH5cBil/8C8AOXfhh4waUXA2/k0+uDrmXAIo/9A2v77rg/Bl4D3nbbgfprNN+5j9TFye4FXnHpV4Cvp+W/aizrgPEiMrkQBRpjVgEnP6OOO4FaY8xJY0wnUAvM90HXUNwLvG6MSRhjDgBN2DoueD0bY44YY7a49MfALuzaR0X1WR5dQxGIz9x5n3WbUfcywG3AWy4/21+DfnwLuF1EJI/eQusaisDavohcDiwAXnLbQsD+Gs3BfSQsTmaA/4rIZhFZ4vIuNsYcAXuxApNcftB6h6sjSH2Pum7xy4NDH8XS5brAX8Te9Y0Yn2XpgiL7zA0xNALt2OC3HzhljOn3KOOT8p39NDAhCF3GmEF//dL563ciEs/WlVW+H/X4LPBTIOm2JxCwv0ZzcD/v4mQBcKMxZi523fpHROSWPPuOBL0wtI6g9D0PTAfmAEeA3xZLl4hUAn8HfmSMOZNv1yC1eegqus+MMQPGmDnYdaKuB2bmKaNoukTkauAJYAYwDzvU8rMgdYnIQqDdGLM5PTtPGb7oGs3B/byLk/mNMeawe28H/olt9McGh1vce7vbPWi9w9URiD5jzDF3QSaBP5DqZgaqS0Si2AD6V2PMP1x20X3mpWuk+MxpOQWsxI5Zjxe7jlR2GZ+U7+zjsMNzQeia74a3jDEmAfyJ4P11I/A1EWnBDondhr2TD9Zfn3XSoFgv7NIJzdiJhsFJo9kBll8BVKWl67HjdL8hc1LuGZdeQOZkzoYC65lK5sTlsHRg73AOYCeUql26xgddk9PSj2PHFAFmkzl51IydGCx4PbtzfxV4Niu/qD7Lo6uoPgMuAsa7dBlQBywE3iRzgvBhl36EzAnC5fn0+qBrcpo/nwV+VYy27459K6kJ1UD9VbDgUowXdvZ7L3b878mAy57mHL8V2DFYPnasbAWwz73XpDW055zWD4HrCqjlb9jueh/22/57n0YH8F3spE0T8JBPuv7syt0G/JvMwPWk07UHuMuvegZuwnZvtwGN7nV3sX2WR1dRfQZcAzS48rcDT6VdAxvcub8JxF3+GLfd5OzTzqe3wLred/7aDvyF1C9qAmv7ace9lVRwD9RfuvyAoihKCTKax9wVRVGUIdDgriiKUoJocFcURSlBNLgriqKUIBrcFUVRShAN7oqiKCWIBndFUZQS5P8KLTup0efmQAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure()\n",
    "for i in range(Nd):\n",
    "    plot(np.mean(trn_psnr_[i,:,:],(0,1)))\n",
    "    plot(np.mean(tst_psnr_[i,:,:],(0,1)),'--')\n",
    "\n",
    "figure()\n",
    "plot(sample_ds,np.mean(sr_,(1,2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_name = 'random'\n",
    "np.save(file_path+file_name+'_losses',losses_)\n",
    "np.save(file_path+file_name+'_sr',sr_)\n",
    "np.save(file_path+file_name+'_trn',trn_psnr_)\n",
    "np.save(file_path+file_name+'_tst',tst_psnr_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "signal = torch.randn((512,512)).type(torch.FloatTensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==>>> N: 0, F: 0, train psnr: 111.072--- , test psnr: -0.726219--- , time: 6.86672 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 114.558--- , test psnr: -0.726306--- , time: 13.5811 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 110.99--- , test psnr: -0.727415--- , time: 20.2065 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 89.6616--- , test psnr: -0.72711--- , time: 26.8519 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 111.245--- , test psnr: -0.727188--- , time: 33.4582 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 111.009--- , test psnr: -0.725744--- , time: 40.0203 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 111.043--- , test psnr: -0.726535--- , time: 46.9051 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 103.112--- , test psnr: -0.72755--- , time: 53.6459 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 94.2303--- , test psnr: -0.725982--- , time: 60.3201 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 111.022--- , test psnr: -0.727869--- , time: 67.0524 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 111.219--- , test psnr: 0.490585--- , time: 6.75171 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 111.13--- , test psnr: 0.490339--- , time: 13.4505 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 111.22--- , test psnr: 0.489726--- , time: 20.0866 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 111.211--- , test psnr: 0.489901--- , time: 26.7463 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 111.236--- , test psnr: 0.490547--- , time: 33.5162 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 111.222--- , test psnr: 0.49075--- , time: 40.4506 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 111.224--- , test psnr: 0.490579--- , time: 47.3496 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 111.2--- , test psnr: 0.489881--- , time: 54.1592 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 111.192--- , test psnr: 0.490628--- , time: 61.0186 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 111.202--- , test psnr: 0.490154--- , time: 67.8568 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 112.937--- , test psnr: 0.178917--- , time: 6.74681 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 109.853--- , test psnr: 0.179129--- , time: 13.4322 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 112.938--- , test psnr: 0.17855--- , time: 20.4131 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 112.943--- , test psnr: 0.178771--- , time: 27.2535 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 110.483--- , test psnr: 0.177563--- , time: 34.1668 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 112.941--- , test psnr: 0.178424--- , time: 41.0252 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 112.942--- , test psnr: 0.179028--- , time: 48.0031 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 112.943--- , test psnr: 0.17927--- , time: 54.8511 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 112.953--- , test psnr: 0.17802--- , time: 61.6168 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 112.946--- , test psnr: 0.178047--- , time: 68.3023 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 108.108--- , test psnr: -0.564737--- , time: 6.55999 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 111.97--- , test psnr: -0.566099--- , time: 13.4364 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 110.186--- , test psnr: -0.565697--- , time: 20.0891 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 111.979--- , test psnr: -0.567204--- , time: 26.6762 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 109.173--- , test psnr: -0.565512--- , time: 33.4849 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 107.354--- , test psnr: -0.565314--- , time: 40.3456 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 111.94--- , test psnr: -0.565726--- , time: 47.1875 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 105.413--- , test psnr: -0.565214--- , time: 53.9089 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 111.204--- , test psnr: -0.565119--- , time: 60.6383 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 112.008--- , test psnr: -0.565842--- , time: 67.6847 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 110.212--- , test psnr: -0.70304--- , time: 6.94007 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 94.4395--- , test psnr: -0.703494--- , time: 14.1531 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 110.243--- , test psnr: -0.703699--- , time: 20.9491 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 110.584--- , test psnr: -0.702916--- , time: 27.9528 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 110.201--- , test psnr: -0.703148--- , time: 34.5491 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 110.278--- , test psnr: -0.70327--- , time: 41.3744 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 110.257--- , test psnr: -0.702933--- , time: 48.3079 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 110.278--- , test psnr: -0.702984--- , time: 55.1618 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 110.219--- , test psnr: -0.703107--- , time: 61.8773 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 106.624--- , test psnr: -0.703332--- , time: 68.405 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 111.358--- , test psnr: 0.843835--- , time: 6.78284 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 111.359--- , test psnr: 0.843899--- , time: 13.4854 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 106.697--- , test psnr: 0.843793--- , time: 20.1761 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 111.358--- , test psnr: 0.843934--- , time: 27.0514 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 111.353--- , test psnr: 0.843886--- , time: 33.7193 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 111.307--- , test psnr: 0.843852--- , time: 40.6773 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 111.378--- , test psnr: 0.843932--- , time: 47.3818 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 111.358--- , test psnr: 0.84393--- , time: 54.1242 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 108.661--- , test psnr: 0.843763--- , time: 60.9227 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 106.868--- , test psnr: 0.843692--- , time: 67.8148 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 112.268--- , test psnr: -0.164983--- , time: 6.80575 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 112.444--- , test psnr: -0.162948--- , time: 13.5986 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 112.38--- , test psnr: -0.167832--- , time: 20.2458 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 112.118--- , test psnr: -0.162541--- , time: 27.0314 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 112.277--- , test psnr: -0.164686--- , time: 33.8925 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 112.393--- , test psnr: -0.166266--- , time: 40.7649 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 112.086--- , test psnr: -0.168451--- , time: 47.5421 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 112.012--- , test psnr: -0.166755--- , time: 54.301 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 108.631--- , test psnr: -0.168026--- , time: 61.1657 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 112.025--- , test psnr: -0.168024--- , time: 67.8144 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 112.614--- , test psnr: -0.253898--- , time: 6.73336 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 100.321--- , test psnr: -0.254863--- , time: 13.6132 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 101.687--- , test psnr: -0.254188--- , time: 20.4125 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 90.3206--- , test psnr: -0.252302--- , time: 27.195 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 93.9849--- , test psnr: -0.253552--- , time: 33.9619 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 113.717--- , test psnr: -0.254008--- , time: 40.7936 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 115.86--- , test psnr: -0.254531--- , time: 47.5508 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 112.836--- , test psnr: -0.253367--- , time: 54.1992 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 110.853--- , test psnr: -0.25433--- , time: 60.8537 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 108.052--- , test psnr: -0.251971--- , time: 67.2982 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 112.402--- , test psnr: -0.162087--- , time: 6.65471 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 112.264--- , test psnr: -0.166345--- , time: 13.5153 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 112.26--- , test psnr: -0.164545--- , time: 20.103 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 112.467--- , test psnr: -0.164792--- , time: 26.4719 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 107.256--- , test psnr: -0.169102--- , time: 33.3124 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 112.176--- , test psnr: -0.164221--- , time: 40.1586 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 111.947--- , test psnr: -0.167343--- , time: 46.9356 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 112.416--- , test psnr: -0.162881--- , time: 53.7305 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 108.44--- , test psnr: -0.164146--- , time: 60.6203 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 112.302--- , test psnr: -0.167271--- , time: 67.4195 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 110.136--- , test psnr: 0.347878--- , time: 6.84765 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 110.093--- , test psnr: 0.34712--- , time: 13.634 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 110.155--- , test psnr: 0.348441--- , time: 20.3133 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 110.14--- , test psnr: 0.348629--- , time: 27.2151 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 110.011--- , test psnr: 0.34789--- , time: 34.0668 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 110.22--- , test psnr: 0.347671--- , time: 40.9788 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 110.137--- , test psnr: 0.347481--- , time: 47.7747 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 110.107--- , test psnr: 0.348361--- , time: 54.6321 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 110.185--- , test psnr: 0.34736--- , time: 61.4333 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 110.167--- , test psnr: 0.348226--- , time: 68.0102 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 113.496--- , test psnr: -0.297714--- , time: 6.80556 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 113.819--- , test psnr: -0.295894--- , time: 13.5141 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 106.194--- , test psnr: -0.295676--- , time: 20.2939 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 106.685--- , test psnr: -0.293374--- , time: 27.1499 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 113.306--- , test psnr: -0.298197--- , time: 33.9381 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 113.619--- , test psnr: -0.295729--- , time: 40.5749 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 113.832--- , test psnr: -0.294544--- , time: 47.3218 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 113.813--- , test psnr: -0.299082--- , time: 53.784 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 113.776--- , test psnr: -0.297306--- , time: 59.6012 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 113.816--- , test psnr: -0.292746--- , time: 65.419 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 115.843--- , test psnr: -1.29306--- , time: 5.76972 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 115.815--- , test psnr: -1.2929--- , time: 11.6095 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 115.856--- , test psnr: -1.29317--- , time: 17.4061 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 115.847--- , test psnr: -1.29347--- , time: 23.2185 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 115.833--- , test psnr: -1.29311--- , time: 29.0259 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 115.838--- , test psnr: -1.29284--- , time: 34.7951 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 115.834--- , test psnr: -1.29295--- , time: 40.5763 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 115.788--- , test psnr: -1.29292--- , time: 46.3565 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 115.832--- , test psnr: -1.29351--- , time: 52.1998 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 115.836--- , test psnr: -1.29294--- , time: 58.0148 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 117.386--- , test psnr: -0.454075--- , time: 5.97874 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 117.342--- , test psnr: -0.455189--- , time: 12.661 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 112.936--- , test psnr: -0.454547--- , time: 19.5419 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 117.415--- , test psnr: -0.454493--- , time: 26.2513 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 117.408--- , test psnr: -0.454291--- , time: 32.9151 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 117.435--- , test psnr: -0.453289--- , time: 39.6531 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 103.942--- , test psnr: -0.454142--- , time: 46.5031 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 117.376--- , test psnr: -0.454993--- , time: 52.9863 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 117.419--- , test psnr: -0.455085--- , time: 59.6192 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 107.94--- , test psnr: -0.454037--- , time: 66.3123 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 112.811--- , test psnr: -0.182769--- , time: 6.6829 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 112.775--- , test psnr: -0.18271--- , time: 13.3745 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 112.823--- , test psnr: -0.180436--- , time: 20.0836 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 112.846--- , test psnr: -0.178811--- , time: 26.8483 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 112.835--- , test psnr: -0.182622--- , time: 33.6236 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 112.832--- , test psnr: -0.18369--- , time: 40.3411 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 112.841--- , test psnr: -0.181087--- , time: 47.1999 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 112.845--- , test psnr: -0.183373--- , time: 53.8847 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 112.819--- , test psnr: -0.182671--- , time: 60.5956 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 112.844--- , test psnr: -0.179184--- , time: 67.3799 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 102.89--- , test psnr: 0.35689--- , time: 6.67089 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 111.125--- , test psnr: 0.357136--- , time: 13.2705 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 118.345--- , test psnr: 0.357935--- , time: 19.947 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 113.468--- , test psnr: 0.357528--- , time: 26.5453 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 111.155--- , test psnr: 0.356887--- , time: 33.2721 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 105.529--- , test psnr: 0.358823--- , time: 40.2378 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 117.459--- , test psnr: 0.361776--- , time: 47.2687 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 92.1914--- , test psnr: 0.359359--- , time: 53.907 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 110.972--- , test psnr: 0.355848--- , time: 60.6013 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 111.067--- , test psnr: 0.355351--- , time: 67.192 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 115.667--- , test psnr: -0.175617--- , time: 6.68591 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 115.713--- , test psnr: -0.175559--- , time: 13.3863 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 115.679--- , test psnr: -0.175482--- , time: 20.0862 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 115.659--- , test psnr: -0.175678--- , time: 26.9353 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 115.652--- , test psnr: -0.175788--- , time: 33.6009 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 106.825--- , test psnr: -0.175512--- , time: 40.1426 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 107.569--- , test psnr: -0.175533--- , time: 46.8452 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 115.768--- , test psnr: -0.175638--- , time: 53.2294 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 115.578--- , test psnr: -0.175542--- , time: 59.578 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 115.632--- , test psnr: -0.175481--- , time: 66.0043 seconds ---\n",
      "==>>> N: 1, F: 6, train psnr: 112.581--- , test psnr: 0.0777828--- , time: 6.72258 seconds ---\n",
      "==>>> N: 1, F: 6, train psnr: 112.099--- , test psnr: 0.0802992--- , time: 13.3336 seconds ---\n",
      "==>>> N: 1, F: 6, train psnr: 112.484--- , test psnr: 0.0800635--- , time: 20.0654 seconds ---\n",
      "==>>> N: 1, F: 6, train psnr: 112.729--- , test psnr: 0.0809658--- , time: 26.4207 seconds ---\n",
      "==>>> N: 1, F: 6, train psnr: 112.827--- , test psnr: 0.0839362--- , time: 32.6981 seconds ---\n",
      "==>>> N: 1, F: 6, train psnr: 108.627--- , test psnr: 0.0810175--- , time: 39.3182 seconds ---\n",
      "==>>> N: 1, F: 6, train psnr: 112.518--- , test psnr: 0.0788947--- , time: 45.9477 seconds ---\n",
      "==>>> N: 1, F: 6, train psnr: 112.647--- , test psnr: 0.0815669--- , time: 52.4488 seconds ---\n",
      "==>>> N: 1, F: 6, train psnr: 112.538--- , test psnr: 0.081649--- , time: 58.9166 seconds ---\n",
      "==>>> N: 1, F: 6, train psnr: 112.696--- , test psnr: 0.079563--- , time: 65.4001 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 115.52--- , test psnr: 0.00060019--- , time: 6.54395 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 112.679--- , test psnr: 0.000598921--- , time: 13.303 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 115.231--- , test psnr: 0.00058391--- , time: 20.0012 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 114.181--- , test psnr: 0.000598564--- , time: 26.7823 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 115.144--- , test psnr: 0.000565386--- , time: 33.2802 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 115.297--- , test psnr: 0.000596195--- , time: 40.0761 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 115.381--- , test psnr: 0.000591794--- , time: 46.5802 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 115.526--- , test psnr: 0.000592042--- , time: 53.2097 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 106.721--- , test psnr: 0.000597803--- , time: 60.0739 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 103.332--- , test psnr: 0.000569788--- , time: 66.7573 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 112.513--- , test psnr: 0.0801146--- , time: 6.57674 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 112.811--- , test psnr: 0.0821863--- , time: 12.9106 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 112.714--- , test psnr: 0.0813871--- , time: 19.4074 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 112.873--- , test psnr: 0.0827301--- , time: 25.9064 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 112.812--- , test psnr: 0.0805605--- , time: 32.4558 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 112.814--- , test psnr: 0.083097--- , time: 38.946 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 112.604--- , test psnr: 0.0833261--- , time: 45.4286 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 106.004--- , test psnr: 0.0795721--- , time: 51.9634 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 112.625--- , test psnr: 0.0787232--- , time: 58.4066 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 112.907--- , test psnr: 0.0812466--- , time: 64.8936 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 110.048--- , test psnr: -0.529246--- , time: 6.56043 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 109.612--- , test psnr: -0.529869--- , time: 13.0246 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 109.964--- , test psnr: -0.529933--- , time: 19.4726 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 110.003--- , test psnr: -0.530138--- , time: 25.8095 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 109.494--- , test psnr: -0.527329--- , time: 32.3616 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 110.079--- , test psnr: -0.528306--- , time: 38.8127 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 110.024--- , test psnr: -0.52835--- , time: 45.2065 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 110.037--- , test psnr: -0.527881--- , time: 51.5792 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 109.959--- , test psnr: -0.529844--- , time: 57.9658 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 110.08--- , test psnr: -0.52812--- , time: 64.502 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 111.849--- , test psnr: -0.5606--- , time: 6.6096 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 111.841--- , test psnr: -0.559473--- , time: 13.1702 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 111.847--- , test psnr: -0.559405--- , time: 20.1314 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 111.769--- , test psnr: -0.560248--- , time: 26.8349 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 111.812--- , test psnr: -0.559944--- , time: 33.4511 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 111.802--- , test psnr: -0.559771--- , time: 40.0836 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 111.834--- , test psnr: -0.561085--- , time: 46.6669 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 109.957--- , test psnr: -0.560667--- , time: 53.1836 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 111.78--- , test psnr: -0.559547--- , time: 59.9429 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 111.811--- , test psnr: -0.562224--- , time: 66.483 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 110.287--- , test psnr: 0.377223--- , time: 6.48914 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 110.459--- , test psnr: 0.375845--- , time: 13.0086 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 110.574--- , test psnr: 0.378981--- , time: 19.5173 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 110.419--- , test psnr: 0.378239--- , time: 26.0006 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 104.608--- , test psnr: 0.378737--- , time: 32.5275 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 110.457--- , test psnr: 0.377951--- , time: 39.0091 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 103.728--- , test psnr: 0.380775--- , time: 45.5068 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 108.149--- , test psnr: 0.377598--- , time: 51.9933 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 110.31--- , test psnr: 0.378217--- , time: 58.43 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 110.641--- , test psnr: 0.374875--- , time: 64.8918 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 114.083--- , test psnr: 0.934819--- , time: 6.5009 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 114.134--- , test psnr: 0.937983--- , time: 12.9795 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 114.105--- , test psnr: 0.93392--- , time: 19.4979 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 114.135--- , test psnr: 0.934674--- , time: 25.9983 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 114.062--- , test psnr: 0.933897--- , time: 32.5026 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 114.121--- , test psnr: 0.931033--- , time: 39.0416 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 114.123--- , test psnr: 0.931328--- , time: 45.5288 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 114.079--- , test psnr: 0.931548--- , time: 52.0132 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 114.097--- , test psnr: 0.930595--- , time: 57.9448 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 114.107--- , test psnr: 0.934915--- , time: 63.8934 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 112.272--- , test psnr: -0.224255--- , time: 5.91946 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 109.208--- , test psnr: -0.221318--- , time: 11.8261 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 112.303--- , test psnr: -0.220541--- , time: 17.758 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 112.272--- , test psnr: -0.222249--- , time: 23.8679 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 112.296--- , test psnr: -0.217832--- , time: 30.4614 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 112.286--- , test psnr: -0.222076--- , time: 37.3066 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 108.063--- , test psnr: -0.22235--- , time: 44.0423 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 101.976--- , test psnr: -0.221455--- , time: 50.6288 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 100.682--- , test psnr: -0.224179--- , time: 57.2474 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 111.858--- , test psnr: -0.218706--- , time: 63.9234 seconds ---\n",
      "==>>> N: 2, F: 4, train psnr: 117.685--- , test psnr: -1.88612--- , time: 6.61143 seconds ---\n",
      "==>>> N: 2, F: 4, train psnr: 117.699--- , test psnr: -1.88498--- , time: 13.082 seconds ---\n",
      "==>>> N: 2, F: 4, train psnr: 117.695--- , test psnr: -1.88566--- , time: 19.5349 seconds ---\n",
      "==>>> N: 2, F: 4, train psnr: 117.683--- , test psnr: -1.88571--- , time: 26.0934 seconds ---\n",
      "==>>> N: 2, F: 4, train psnr: 113.125--- , test psnr: -1.88409--- , time: 33.559 seconds ---\n",
      "==>>> N: 2, F: 4, train psnr: 117.719--- , test psnr: -1.88582--- , time: 41.2897 seconds ---\n",
      "==>>> N: 2, F: 4, train psnr: 117.686--- , test psnr: -1.88194--- , time: 49.0601 seconds ---\n",
      "==>>> N: 2, F: 4, train psnr: 117.691--- , test psnr: -1.88191--- , time: 56.9412 seconds ---\n",
      "==>>> N: 2, F: 4, train psnr: 117.67--- , test psnr: -1.88652--- , time: 64.6164 seconds ---\n",
      "==>>> N: 2, F: 4, train psnr: 117.698--- , test psnr: -1.88395--- , time: 71.5799 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 116.089--- , test psnr: -0.34727--- , time: 6.41516 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 116.102--- , test psnr: -0.353261--- , time: 12.835 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 116.087--- , test psnr: -0.344536--- , time: 19.6804 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 116.081--- , test psnr: -0.358205--- , time: 27.5476 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 116.144--- , test psnr: -0.347937--- , time: 35.4238 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 116.103--- , test psnr: -0.358508--- , time: 43.2142 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 115.436--- , test psnr: -0.342184--- , time: 50.9773 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 116.081--- , test psnr: -0.351997--- , time: 58.7937 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 116.106--- , test psnr: -0.357691--- , time: 66.6943 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 116.129--- , test psnr: -0.347363--- , time: 74.5251 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 115.019--- , test psnr: 0.32924--- , time: 7.99108 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 114.365--- , test psnr: 0.343057--- , time: 14.6434 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 115.012--- , test psnr: 0.332856--- , time: 21.2188 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 114.969--- , test psnr: 0.334095--- , time: 27.8479 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 114.989--- , test psnr: 0.340043--- , time: 34.4562 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 115.003--- , test psnr: 0.33898--- , time: 41.0641 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 115.022--- , test psnr: 0.330901--- , time: 47.7083 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 115.022--- , test psnr: 0.340976--- , time: 54.4678 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 115.028--- , test psnr: 0.334797--- , time: 61.0819 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 114.992--- , test psnr: 0.336212--- , time: 67.9807 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 114.808--- , test psnr: -0.920045--- , time: 6.55122 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 114.843--- , test psnr: -0.919316--- , time: 13.0872 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 114.859--- , test psnr: -0.918861--- , time: 19.6498 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 114.848--- , test psnr: -0.920012--- , time: 26.1632 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 114.87--- , test psnr: -0.921105--- , time: 32.6886 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 114.833--- , test psnr: -0.919745--- , time: 39.1949 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 114.828--- , test psnr: -0.918442--- , time: 45.7291 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 114.852--- , test psnr: -0.918098--- , time: 52.2997 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 114.867--- , test psnr: -0.921778--- , time: 58.8099 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 114.845--- , test psnr: -0.91911--- , time: 65.3071 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 115.031--- , test psnr: 0.331685--- , time: 6.56523 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 115.006--- , test psnr: 0.334893--- , time: 13.0784 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 114.988--- , test psnr: 0.340608--- , time: 19.579 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 115.004--- , test psnr: 0.331695--- , time: 26.0821 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 114.975--- , test psnr: 0.337099--- , time: 32.5921 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 114.99--- , test psnr: 0.326792--- , time: 39.1299 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 114.987--- , test psnr: 0.335666--- , time: 45.6353 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 114.979--- , test psnr: 0.334429--- , time: 52.1268 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 115.031--- , test psnr: 0.338025--- , time: 58.6359 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 114.979--- , test psnr: 0.336213--- , time: 65.1553 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 110.297--- , test psnr: 1.0331--- , time: 6.59747 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 110.744--- , test psnr: 1.04025--- , time: 12.889 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 110.689--- , test psnr: 1.04281--- , time: 19.4119 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 110.638--- , test psnr: 1.03823--- , time: 25.94 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 110.713--- , test psnr: 1.03933--- , time: 32.4792 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 110.645--- , test psnr: 1.04698--- , time: 38.9971 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 110.627--- , test psnr: 1.03637--- , time: 45.4147 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 110.715--- , test psnr: 1.04169--- , time: 51.8479 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 110.616--- , test psnr: 1.04386--- , time: 58.2908 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 110.734--- , test psnr: 1.0439--- , time: 64.7141 seconds ---\n",
      "==>>> N: 3, F: 0, train psnr: 115.199--- , test psnr: 0.480995--- , time: 6.42299 seconds ---\n",
      "==>>> N: 3, F: 0, train psnr: 115.161--- , test psnr: 0.482639--- , time: 12.8858 seconds ---\n",
      "==>>> N: 3, F: 0, train psnr: 115.185--- , test psnr: 0.483386--- , time: 19.3181 seconds ---\n",
      "==>>> N: 3, F: 0, train psnr: 115.169--- , test psnr: 0.480284--- , time: 25.7424 seconds ---\n",
      "==>>> N: 3, F: 0, train psnr: 115.2--- , test psnr: 0.483009--- , time: 31.7183 seconds ---\n",
      "==>>> N: 3, F: 0, train psnr: 115.188--- , test psnr: 0.482053--- , time: 37.493 seconds ---\n",
      "==>>> N: 3, F: 0, train psnr: 115.213--- , test psnr: 0.483191--- , time: 43.2838 seconds ---\n",
      "==>>> N: 3, F: 0, train psnr: 115.182--- , test psnr: 0.470435--- , time: 49.9237 seconds ---\n",
      "==>>> N: 3, F: 0, train psnr: 115.161--- , test psnr: 0.478902--- , time: 56.3771 seconds ---\n",
      "==>>> N: 3, F: 0, train psnr: 108.243--- , test psnr: 0.474929--- , time: 63.4529 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 105.731--- , test psnr: 0.30377--- , time: 7.33072 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 118.587--- , test psnr: 0.304359--- , time: 14.7529 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 118.603--- , test psnr: 0.303648--- , time: 20.9997 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 89.8794--- , test psnr: 0.307174--- , time: 27.5677 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 127.956--- , test psnr: 0.306713--- , time: 34.1514 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 100.647--- , test psnr: 0.306279--- , time: 40.7104 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 118.629--- , test psnr: 0.303735--- , time: 47.2832 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 89.9605--- , test psnr: 0.304151--- , time: 53.9868 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 108.588--- , test psnr: 0.306191--- , time: 60.7299 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 116.191--- , test psnr: 0.307173--- , time: 67.4649 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 111.427--- , test psnr: 1.50797--- , time: 6.80635 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 105.583--- , test psnr: 1.50957--- , time: 13.8874 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 116.3--- , test psnr: 1.51146--- , time: 20.4446 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 115.344--- , test psnr: 1.50849--- , time: 27.511 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 116.28--- , test psnr: 1.51014--- , time: 34.4295 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 116.276--- , test psnr: 1.51019--- , time: 40.9159 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 115.372--- , test psnr: 1.50587--- , time: 47.4876 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 116.289--- , test psnr: 1.51035--- , time: 54.5846 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 112.162--- , test psnr: 1.50955--- , time: 61.7263 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 115.281--- , test psnr: 1.50836--- , time: 68.8502 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 97.9803--- , test psnr: 0.45093--- , time: 7.13114 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 124.643--- , test psnr: 0.450194--- , time: 14.2769 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 115.61--- , test psnr: 0.450968--- , time: 21.2098 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 111.491--- , test psnr: 0.451274--- , time: 27.7996 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 114.194--- , test psnr: 0.452336--- , time: 35.1629 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 113.528--- , test psnr: 0.451319--- , time: 42.611 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 96.9577--- , test psnr: 0.450813--- , time: 49.8378 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 124.509--- , test psnr: 0.450517--- , time: 56.2142 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 109.86--- , test psnr: 0.450973--- , time: 62.7576 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 83.6199--- , test psnr: 0.450628--- , time: 69.3191 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 111.806--- , test psnr: -1.44688--- , time: 6.57946 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 111.796--- , test psnr: -1.43967--- , time: 13.1247 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 111.956--- , test psnr: -1.44752--- , time: 19.81 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 111.8--- , test psnr: -1.44745--- , time: 26.3335 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 111.783--- , test psnr: -1.44528--- , time: 32.8687 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 111.864--- , test psnr: -1.4455--- , time: 40.4193 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 111.823--- , test psnr: -1.45191--- , time: 47.8745 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 111.794--- , test psnr: -1.45322--- , time: 54.1436 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 111.804--- , test psnr: -1.4552--- , time: 60.6574 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 111.757--- , test psnr: -1.4374--- , time: 67.3425 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 113.13--- , test psnr: 2.20535--- , time: 6.69646 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 113.115--- , test psnr: 2.20362--- , time: 13.4761 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 113.12--- , test psnr: 2.20451--- , time: 20.2371 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 113.108--- , test psnr: 2.20419--- , time: 27.0151 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 113.108--- , test psnr: 2.20503--- , time: 33.7125 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 113.146--- , test psnr: 2.20354--- , time: 40.2306 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 113.131--- , test psnr: 2.204--- , time: 46.6681 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 113.127--- , test psnr: 2.20359--- , time: 53.1858 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 113.11--- , test psnr: 2.20353--- , time: 59.5063 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 113.121--- , test psnr: 2.20466--- , time: 65.7975 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 114.763--- , test psnr: -0.689395--- , time: 6.35288 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 114.766--- , test psnr: -0.688535--- , time: 12.7331 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 112.533--- , test psnr: -0.687316--- , time: 19.1031 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 114.75--- , test psnr: -0.688248--- , time: 25.4115 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 114.765--- , test psnr: -0.688348--- , time: 31.1992 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 114.768--- , test psnr: -0.688865--- , time: 37.2064 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 114.738--- , test psnr: -0.68937--- , time: 43.8272 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 114.76--- , test psnr: -0.688973--- , time: 50.3232 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 114.737--- , test psnr: -0.688805--- , time: 56.3202 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 114.396--- , test psnr: -0.688199--- , time: 62.7212 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 122.463--- , test psnr: -0.329691--- , time: 6.60149 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 122.398--- , test psnr: -0.32941--- , time: 13.2385 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 122.355--- , test psnr: -0.329659--- , time: 19.9515 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 122.373--- , test psnr: -0.329565--- , time: 26.7113 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 96.6449--- , test psnr: -0.329444--- , time: 33.2696 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 122.388--- , test psnr: -0.329219--- , time: 39.3763 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 124.202--- , test psnr: -0.329632--- , time: 46.8294 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 122.4--- , test psnr: -0.32935--- , time: 54.0636 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 122.325--- , test psnr: -0.329864--- , time: 60.9613 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 89.9518--- , test psnr: -0.32926--- , time: 67.453 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 114.725--- , test psnr: -0.68788--- , time: 6.41938 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 114.746--- , test psnr: -0.688522--- , time: 12.9911 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 114.728--- , test psnr: -0.688295--- , time: 19.5372 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 114.716--- , test psnr: -0.687645--- , time: 26.0274 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 105.303--- , test psnr: -0.687302--- , time: 32.547 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 106.372--- , test psnr: -0.687388--- , time: 39.0608 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 114.713--- , test psnr: -0.687734--- , time: 45.5435 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 114.718--- , test psnr: -0.688284--- , time: 52.0225 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 114.76--- , test psnr: -0.687836--- , time: 58.5023 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 110.29--- , test psnr: -0.688856--- , time: 64.9918 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 113.428--- , test psnr: -1.92715--- , time: 6.48223 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 113.448--- , test psnr: -1.92777--- , time: 12.9772 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 113.402--- , test psnr: -1.92715--- , time: 19.4534 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 113.453--- , test psnr: -1.92731--- , time: 25.9553 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 113.44--- , test psnr: -1.92725--- , time: 32.474 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 113.454--- , test psnr: -1.92782--- , time: 38.9653 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 113.414--- , test psnr: -1.92791--- , time: 45.4562 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 113.425--- , test psnr: -1.92737--- , time: 51.6514 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 113.435--- , test psnr: -1.92816--- , time: 57.7874 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 113.428--- , test psnr: -1.9272--- , time: 64.2326 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 111.17--- , test psnr: -1.58247--- , time: 7.70731 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 111.059--- , test psnr: -1.58551--- , time: 15.1271 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 111.185--- , test psnr: -1.59318--- , time: 22.5029 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 111.102--- , test psnr: -1.5695--- , time: 29.0985 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 111.081--- , test psnr: -1.5839--- , time: 35.7012 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 111.162--- , test psnr: -1.58648--- , time: 42.2817 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 111.181--- , test psnr: -1.5774--- , time: 48.7895 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 111.089--- , test psnr: -1.5735--- , time: 55.3544 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 111.137--- , test psnr: -1.57336--- , time: 61.8976 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 111.136--- , test psnr: -1.57863--- , time: 68.4325 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 116.907--- , test psnr: -2.1429--- , time: 6.52025 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 116.882--- , test psnr: -2.14356--- , time: 13.037 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 116.887--- , test psnr: -2.14278--- , time: 19.6032 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 116.807--- , test psnr: -2.14282--- , time: 26.142 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 116.823--- , test psnr: -2.14494--- , time: 32.6945 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 116.834--- , test psnr: -2.14768--- , time: 39.2407 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 116.861--- , test psnr: -2.14408--- , time: 45.779 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 116.808--- , test psnr: -2.15156--- , time: 52.1398 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 116.844--- , test psnr: -2.14089--- , time: 58.8986 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 116.852--- , test psnr: -2.14442--- , time: 65.7536 seconds ---\n",
      "==>>> N: 4, F: 2, train psnr: 115.882--- , test psnr: -0.472411--- , time: 6.56616 seconds ---\n",
      "==>>> N: 4, F: 2, train psnr: 96.3615--- , test psnr: -0.471131--- , time: 13.1207 seconds ---\n",
      "==>>> N: 4, F: 2, train psnr: 105.24--- , test psnr: -0.461645--- , time: 19.677 seconds ---\n",
      "==>>> N: 4, F: 2, train psnr: 90.2295--- , test psnr: -0.476445--- , time: 26.2258 seconds ---\n",
      "==>>> N: 4, F: 2, train psnr: 112.78--- , test psnr: -0.461985--- , time: 32.7869 seconds ---\n",
      "==>>> N: 4, F: 2, train psnr: 115.849--- , test psnr: -0.460301--- , time: 39.3286 seconds ---\n",
      "==>>> N: 4, F: 2, train psnr: 104.27--- , test psnr: -0.454739--- , time: 45.9274 seconds ---\n",
      "==>>> N: 4, F: 2, train psnr: 125.246--- , test psnr: -0.466839--- , time: 52.465 seconds ---\n",
      "==>>> N: 4, F: 2, train psnr: 114.245--- , test psnr: -0.467179--- , time: 58.9834 seconds ---\n",
      "==>>> N: 4, F: 2, train psnr: 89.9212--- , test psnr: -0.463801--- , time: 65.552 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 114.438--- , test psnr: -0.856276--- , time: 6.59945 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 114.48--- , test psnr: -0.85335--- , time: 13.1583 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 114.5--- , test psnr: -0.855313--- , time: 19.7262 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 114.551--- , test psnr: -0.857844--- , time: 26.233 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 114.468--- , test psnr: -0.854462--- , time: 32.6399 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 114.542--- , test psnr: -0.856152--- , time: 39.0798 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 114.481--- , test psnr: -0.847114--- , time: 45.5217 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 114.521--- , test psnr: -0.852697--- , time: 51.9523 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 106.154--- , test psnr: -0.859886--- , time: 58.3946 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 110.301--- , test psnr: -0.860524--- , time: 65.1237 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 115.532--- , test psnr: 0.712517--- , time: 8.16351 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 115.55--- , test psnr: 0.710826--- , time: 15.9117 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 115.548--- , test psnr: 0.71176--- , time: 22.5928 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 115.536--- , test psnr: 0.7159--- , time: 29.0954 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 115.548--- , test psnr: 0.711456--- , time: 35.5843 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 115.557--- , test psnr: 0.716136--- , time: 42.1345 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 115.533--- , test psnr: 0.711289--- , time: 48.7214 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 103.778--- , test psnr: 0.711294--- , time: 55.305 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 115.621--- , test psnr: 0.725211--- , time: 61.8647 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 115.622--- , test psnr: 0.712233--- , time: 68.4275 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 119.776--- , test psnr: -0.212279--- , time: 6.57896 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 119.771--- , test psnr: -0.218268--- , time: 13.1838 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 119.762--- , test psnr: -0.206444--- , time: 19.7235 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 113.721--- , test psnr: -0.220201--- , time: 26.2548 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 119.732--- , test psnr: -0.211173--- , time: 32.6057 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 119.808--- , test psnr: -0.209242--- , time: 39.1091 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 108.565--- , test psnr: -0.218851--- , time: 45.6694 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 119.8--- , test psnr: -0.213068--- , time: 52.2433 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 119.785--- , test psnr: -0.209717--- , time: 59.798 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 119.766--- , test psnr: -0.220426--- , time: 67.4419 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 90.2254--- , test psnr: 0.545716--- , time: 7.347 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 113.66--- , test psnr: 0.546035--- , time: 13.9715 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 107.631--- , test psnr: 0.541884--- , time: 20.5298 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 89.3034--- , test psnr: 0.55118--- , time: 27.0759 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 109.701--- , test psnr: 0.545413--- , time: 33.6374 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 123.39--- , test psnr: 0.536441--- , time: 40.2122 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 109.142--- , test psnr: 0.54566--- , time: 46.8216 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 113.727--- , test psnr: 0.550426--- , time: 53.5023 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 113.682--- , test psnr: 0.535625--- , time: 60.1081 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 119.65--- , test psnr: 0.537428--- , time: 66.9769 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 116.107--- , test psnr: 1.04065--- , time: 6.74786 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 116.161--- , test psnr: 1.03949--- , time: 13.411 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 116.148--- , test psnr: 1.04001--- , time: 20.0455 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 116.091--- , test psnr: 1.03945--- , time: 26.6964 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 116.207--- , test psnr: 1.04006--- , time: 33.2149 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 116.121--- , test psnr: 1.03952--- , time: 40.1902 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 116.14--- , test psnr: 1.04079--- , time: 46.7127 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 116.081--- , test psnr: 1.04032--- , time: 53.5394 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 116.155--- , test psnr: 1.03942--- , time: 60.3733 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 116.141--- , test psnr: 1.03972--- , time: 67.1834 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 113.683--- , test psnr: 0.551854--- , time: 6.72656 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 113.72--- , test psnr: 0.541469--- , time: 13.5432 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 113.675--- , test psnr: 0.539003--- , time: 20.3356 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 122.606--- , test psnr: 0.544324--- , time: 27.2279 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 112.986--- , test psnr: 0.549007--- , time: 33.9337 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 89.8073--- , test psnr: 0.544888--- , time: 40.7563 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 122.712--- , test psnr: 0.550721--- , time: 47.414 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 99.1671--- , test psnr: 0.538666--- , time: 53.7523 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 120.339--- , test psnr: 0.545347--- , time: 60.6078 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 113.714--- , test psnr: 0.552318--- , time: 67.518 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 109.387--- , test psnr: -0.474745--- , time: 7.09395 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 109.401--- , test psnr: -0.481732--- , time: 13.9673 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 109.468--- , test psnr: -0.45655--- , time: 20.5858 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 109.443--- , test psnr: -0.468755--- , time: 27.1482 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 109.423--- , test psnr: -0.457339--- , time: 33.8045 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 109.436--- , test psnr: -0.474489--- , time: 40.7382 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 109.442--- , test psnr: -0.475957--- , time: 47.6191 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 109.43--- , test psnr: -0.480317--- , time: 54.3827 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 109.398--- , test psnr: -0.480437--- , time: 61.0648 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 109.404--- , test psnr: -0.470725--- , time: 67.6021 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 120.52--- , test psnr: 0.0408901--- , time: 6.58654 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 120.727--- , test psnr: 0.0397198--- , time: 13.3875 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 119.276--- , test psnr: 0.0476464--- , time: 20.3281 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 119.379--- , test psnr: 0.0346396--- , time: 27.2415 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 119.779--- , test psnr: 0.041166--- , time: 33.9009 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 120.508--- , test psnr: 0.0489992--- , time: 40.3955 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 120.741--- , test psnr: 0.0406154--- , time: 47.0927 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 120.427--- , test psnr: 0.0450798--- , time: 53.9653 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 120.829--- , test psnr: 0.0391561--- , time: 60.7742 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 120.396--- , test psnr: 0.0347571--- , time: 67.6946 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 122.054--- , test psnr: -0.649865--- , time: 6.60724 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 122.263--- , test psnr: -0.636817--- , time: 12.8192 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 121.858--- , test psnr: -0.648542--- , time: 19.9203 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 121.844--- , test psnr: -0.639682--- , time: 26.5905 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 122.326--- , test psnr: -0.650536--- , time: 33.0798 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 121.777--- , test psnr: -0.62824--- , time: 39.8226 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 122.266--- , test psnr: -0.623557--- , time: 46.4941 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 122.254--- , test psnr: -0.631679--- , time: 53.1661 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 121.861--- , test psnr: -0.644104--- , time: 60.1018 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 121.864--- , test psnr: -0.666885--- , time: 67.4375 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 118.179--- , test psnr: -0.727936--- , time: 7.42652 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 118.125--- , test psnr: -0.730057--- , time: 14.6866 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 118.223--- , test psnr: -0.723768--- , time: 21.2696 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 118.166--- , test psnr: -0.728008--- , time: 28.0946 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 118.187--- , test psnr: -0.72307--- , time: 34.8064 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 114.939--- , test psnr: -0.723129--- , time: 41.603 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 118.137--- , test psnr: -0.718122--- , time: 48.4477 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 110.258--- , test psnr: -0.721772--- , time: 55.3619 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 118.161--- , test psnr: -0.726081--- , time: 62.1816 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 118.165--- , test psnr: -0.71456--- , time: 68.9928 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 116.068--- , test psnr: -1.59156--- , time: 6.7251 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 116.115--- , test psnr: -1.59367--- , time: 13.6413 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 116.124--- , test psnr: -1.586--- , time: 20.4925 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 116.129--- , test psnr: -1.58334--- , time: 26.7284 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 116.02--- , test psnr: -1.59516--- , time: 33.4313 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 116.049--- , test psnr: -1.6068--- , time: 40.3145 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 116.102--- , test psnr: -1.57938--- , time: 47.1791 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 102.784--- , test psnr: -1.59162--- , time: 54.0984 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 116.092--- , test psnr: -1.594--- , time: 60.9996 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 116.092--- , test psnr: -1.59081--- , time: 67.8707 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 112.533--- , test psnr: -0.736253--- , time: 6.857 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 112.526--- , test psnr: -0.735307--- , time: 12.8109 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 112.604--- , test psnr: -0.733663--- , time: 18.6443 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 112.628--- , test psnr: -0.738651--- , time: 24.4681 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 112.547--- , test psnr: -0.729531--- , time: 31.2688 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 112.593--- , test psnr: -0.727191--- , time: 39.5362 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 112.577--- , test psnr: -0.726877--- , time: 47.159 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 112.524--- , test psnr: -0.732573--- , time: 53.5537 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 112.563--- , test psnr: -0.743633--- , time: 60.3714 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 112.54--- , test psnr: -0.738945--- , time: 67.2145 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 114.012--- , test psnr: -0.681901--- , time: 6.96839 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 113.994--- , test psnr: -0.677572--- , time: 14.0148 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 114.039--- , test psnr: -0.688705--- , time: 20.9193 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 114.047--- , test psnr: -0.672195--- , time: 27.6783 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 114.023--- , test psnr: -0.673367--- , time: 34.3923 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 114.027--- , test psnr: -0.674937--- , time: 41.4666 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 114.004--- , test psnr: -0.677316--- , time: 48.3257 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 114.011--- , test psnr: -0.687002--- , time: 55.8288 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 114.061--- , test psnr: -0.681986--- , time: 63.9738 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 113.996--- , test psnr: -0.67746--- , time: 71.5156 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 119.008--- , test psnr: 0.16971--- , time: 7.09324 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 119.099--- , test psnr: 0.188913--- , time: 13.9213 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 119.117--- , test psnr: 0.198804--- , time: 20.7635 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 119.144--- , test psnr: 0.20812--- , time: 27.5931 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 119.056--- , test psnr: 0.194562--- , time: 34.2723 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 119.067--- , test psnr: 0.20044--- , time: 40.8851 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 119.124--- , test psnr: 0.208489--- , time: 47.7553 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 119.122--- , test psnr: 0.207559--- , time: 54.6376 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 119.046--- , test psnr: 0.170727--- , time: 61.5788 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 119.075--- , test psnr: 0.196206--- , time: 68.5206 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 122.132--- , test psnr: 0.394673--- , time: 6.95381 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 122.174--- , test psnr: 0.394159--- , time: 13.7633 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 122.422--- , test psnr: 0.393844--- , time: 20.6088 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 122.211--- , test psnr: 0.392408--- , time: 27.4213 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 122.189--- , test psnr: 0.384313--- , time: 34.1628 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 122.108--- , test psnr: 0.392292--- , time: 40.7837 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 122.198--- , test psnr: 0.380517--- , time: 47.5314 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 122.179--- , test psnr: 0.385188--- , time: 54.391 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 122.199--- , test psnr: 0.382565--- , time: 61.2364 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 122.216--- , test psnr: 0.379588--- , time: 68.0845 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 119.152--- , test psnr: 0.204248--- , time: 6.82356 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 119.039--- , test psnr: 0.197849--- , time: 13.5344 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 119.103--- , test psnr: 0.193523--- , time: 20.2774 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 118.988--- , test psnr: 0.184409--- , time: 27.0469 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 119.071--- , test psnr: 0.1997--- , time: 33.8249 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 119.057--- , test psnr: 0.186756--- , time: 40.6655 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 119.053--- , test psnr: 0.191403--- , time: 47.5178 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 118.983--- , test psnr: 0.204167--- , time: 54.4669 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 119.116--- , test psnr: 0.188817--- , time: 61.9258 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 119.045--- , test psnr: 0.180107--- , time: 69.1861 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 113.344--- , test psnr: -0.229592--- , time: 6.80719 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 113.388--- , test psnr: -0.210297--- , time: 13.5328 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 113.384--- , test psnr: -0.212182--- , time: 20.0655 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 113.41--- , test psnr: -0.224102--- , time: 26.6974 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 113.38--- , test psnr: -0.224725--- , time: 33.6154 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 113.365--- , test psnr: -0.210848--- , time: 40.5087 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 113.396--- , test psnr: -0.226505--- , time: 47.1543 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 113.351--- , test psnr: -0.223066--- , time: 53.845 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 113.306--- , test psnr: -0.21986--- , time: 60.4044 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 113.358--- , test psnr: -0.234044--- , time: 67.0252 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 111.35--- , test psnr: -1.77028--- , time: 6.48023 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 111.357--- , test psnr: -1.76343--- , time: 13.1604 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 111.312--- , test psnr: -1.76189--- , time: 19.5471 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 111.343--- , test psnr: -1.76229--- , time: 26.4639 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 111.312--- , test psnr: -1.76285--- , time: 33.2984 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 111.343--- , test psnr: -1.76607--- , time: 40.1475 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 111.346--- , test psnr: -1.76904--- , time: 46.917 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 111.312--- , test psnr: -1.7604--- , time: 53.519 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 111.3--- , test psnr: -1.76778--- , time: 60.2437 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 111.305--- , test psnr: -1.7644--- , time: 66.9494 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 111.584--- , test psnr: 0.989401--- , time: 6.77734 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 111.606--- , test psnr: 0.99224--- , time: 13.2736 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 111.616--- , test psnr: 0.991084--- , time: 19.9849 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 111.601--- , test psnr: 0.99164--- , time: 26.6855 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 111.603--- , test psnr: 0.99217--- , time: 33.5084 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 111.585--- , test psnr: 0.991288--- , time: 40.0931 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 111.58--- , test psnr: 0.991825--- , time: 46.8171 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 111.6--- , test psnr: 0.989493--- , time: 53.3568 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 111.617--- , test psnr: 0.992288--- , time: 60.0706 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 111.559--- , test psnr: 0.992245--- , time: 66.548 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 122.606--- , test psnr: 1.08258--- , time: 6.55841 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 123.38--- , test psnr: 1.09769--- , time: 13.0626 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 122.532--- , test psnr: 1.1016--- , time: 19.5922 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 122.591--- , test psnr: 1.09704--- , time: 26.1099 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 122.51--- , test psnr: 1.11115--- , time: 32.631 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 122.476--- , test psnr: 1.09454--- , time: 39.1593 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 122.525--- , test psnr: 1.09212--- , time: 45.6817 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 122.545--- , test psnr: 1.08889--- , time: 52.202 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 122.581--- , test psnr: 1.09808--- , time: 58.7133 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 122.498--- , test psnr: 1.07265--- , time: 65.2234 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 120.759--- , test psnr: -1.97381--- , time: 6.5186 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 120.82--- , test psnr: -1.95618--- , time: 13.0669 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 120.556--- , test psnr: -1.95641--- , time: 19.5788 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 120.813--- , test psnr: -1.96143--- , time: 26.2629 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 120.5--- , test psnr: -1.9692--- , time: 33.116 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 120.529--- , test psnr: -1.95735--- , time: 40.003 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 120.806--- , test psnr: -1.9588--- , time: 46.9722 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 120.547--- , test psnr: -1.97544--- , time: 53.9512 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 120.507--- , test psnr: -1.97055--- , time: 60.9215 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 120.572--- , test psnr: -1.96618--- , time: 67.8872 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 112.99--- , test psnr: -0.842758--- , time: 6.97488 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 113.051--- , test psnr: -0.842431--- , time: 13.9339 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 112.99--- , test psnr: -0.832304--- , time: 20.9488 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 113.061--- , test psnr: -0.833797--- , time: 27.8337 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 113.058--- , test psnr: -0.833986--- , time: 34.7065 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 112.716--- , test psnr: -0.827581--- , time: 41.6168 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 113.004--- , test psnr: -0.833571--- , time: 48.5548 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 113.097--- , test psnr: -0.84794--- , time: 55.5277 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 113.028--- , test psnr: -0.830789--- , time: 62.5279 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 113.026--- , test psnr: -0.858896--- , time: 69.4975 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 114.726--- , test psnr: 1.11559--- , time: 6.88549 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 114.736--- , test psnr: 1.12112--- , time: 13.7542 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 114.719--- , test psnr: 1.11383--- , time: 20.4999 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 114.734--- , test psnr: 1.11582--- , time: 27.2613 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 114.721--- , test psnr: 1.12069--- , time: 33.9721 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 114.753--- , test psnr: 1.12358--- , time: 40.1924 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 114.708--- , test psnr: 1.12363--- , time: 46.9669 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 114.69--- , test psnr: 1.10689--- , time: 53.5691 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 114.749--- , test psnr: 1.11525--- , time: 60.3592 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 114.717--- , test psnr: 1.1103--- , time: 67.1332 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 115.768--- , test psnr: 1.95564--- , time: 6.80917 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 115.832--- , test psnr: 1.94475--- , time: 13.5853 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 115.814--- , test psnr: 1.95615--- , time: 20.4842 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 115.812--- , test psnr: 1.95008--- , time: 27.5672 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 115.858--- , test psnr: 1.96055--- , time: 34.6491 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 115.863--- , test psnr: 1.95517--- , time: 41.7165 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 115.804--- , test psnr: 1.94965--- , time: 48.7826 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 115.852--- , test psnr: 1.95747--- , time: 55.8567 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 115.765--- , test psnr: 1.94743--- , time: 62.9294 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 115.754--- , test psnr: 1.95333--- , time: 70.0021 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 114.476--- , test psnr: 0.813149--- , time: 7.07651 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 114.533--- , test psnr: 0.81402--- , time: 14.0931 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 114.5--- , test psnr: 0.812168--- , time: 21.1821 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 114.492--- , test psnr: 0.811111--- , time: 28.2553 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 114.505--- , test psnr: 0.814173--- , time: 35.3307 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 114.468--- , test psnr: 0.813827--- , time: 42.3957 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 114.521--- , test psnr: 0.813639--- , time: 49.4684 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 114.52--- , test psnr: 0.813653--- , time: 56.4157 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 114.48--- , test psnr: 0.811924--- , time: 63.2296 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 114.49--- , test psnr: 0.813638--- , time: 70.089 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 115.826--- , test psnr: 1.95028--- , time: 6.74243 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 115.828--- , test psnr: 1.9539--- , time: 13.5654 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 115.761--- , test psnr: 1.94799--- , time: 20.3992 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 115.84--- , test psnr: 1.94735--- , time: 27.2386 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 115.796--- , test psnr: 1.94612--- , time: 34.0559 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 115.846--- , test psnr: 1.96198--- , time: 40.9018 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 115.772--- , test psnr: 1.94629--- , time: 47.7065 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 115.748--- , test psnr: 1.95156--- , time: 54.5413 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 115.837--- , test psnr: 1.95904--- , time: 61.3637 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 115.848--- , test psnr: 1.95341--- , time: 68.1989 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 109.225--- , test psnr: 0.158167--- , time: 6.87466 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 109.256--- , test psnr: 0.165414--- , time: 13.8693 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 109.207--- , test psnr: 0.157009--- , time: 20.8226 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 109.226--- , test psnr: 0.165361--- , time: 27.7612 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 109.243--- , test psnr: 0.171986--- , time: 34.6711 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 109.225--- , test psnr: 0.174046--- , time: 41.6177 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 109.202--- , test psnr: 0.148703--- , time: 48.5368 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 109.203--- , test psnr: 0.163032--- , time: 55.4824 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 109.18--- , test psnr: 0.167299--- , time: 62.4139 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 109.189--- , test psnr: 0.163147--- , time: 69.4894 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 119.889--- , test psnr: -0.235772--- , time: 6.83166 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 119.736--- , test psnr: -0.233148--- , time: 13.6845 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 120.823--- , test psnr: -0.209846--- , time: 20.5129 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 120.892--- , test psnr: -0.24868--- , time: 27.4266 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 119.851--- , test psnr: -0.209015--- , time: 34.3839 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 119.663--- , test psnr: -0.233726--- , time: 41.2886 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 119.809--- , test psnr: -0.211861--- , time: 48.3125 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 119.729--- , test psnr: -0.228627--- , time: 55.2772 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 120.058--- , test psnr: -0.238177--- , time: 62.161 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 119.743--- , test psnr: -0.225845--- , time: 68.9712 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 123.658--- , test psnr: 1.57777--- , time: 6.8936 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 123.51--- , test psnr: 1.57682--- , time: 13.765 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 123.585--- , test psnr: 1.5776--- , time: 20.648 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 123.572--- , test psnr: 1.57811--- , time: 27.6419 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 123.555--- , test psnr: 1.57679--- , time: 34.6149 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 123.599--- , test psnr: 1.57584--- , time: 41.4346 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 123.719--- , test psnr: 1.57753--- , time: 48.1963 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 123.55--- , test psnr: 1.57717--- , time: 55.0406 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 123.705--- , test psnr: 1.57681--- , time: 62.0317 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 123.694--- , test psnr: 1.57842--- , time: 68.9268 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 117.706--- , test psnr: -0.385588--- , time: 6.90794 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 117.783--- , test psnr: -0.386878--- , time: 13.8089 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 117.69--- , test psnr: -0.383129--- , time: 20.7163 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 117.757--- , test psnr: -0.381317--- , time: 27.6575 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 117.761--- , test psnr: -0.386485--- , time: 34.5946 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 117.766--- , test psnr: -0.387597--- , time: 41.4822 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 117.825--- , test psnr: -0.388588--- , time: 48.1764 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 117.839--- , test psnr: -0.386254--- , time: 55.4355 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 106.554--- , test psnr: -0.38798--- , time: 61.7112 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 117.803--- , test psnr: -0.385762--- , time: 67.592 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 118.914--- , test psnr: 0.313204--- , time: 5.89002 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 118.981--- , test psnr: 0.300856--- , time: 11.7586 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 118.842--- , test psnr: 0.288894--- , time: 17.6352 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 118.937--- , test psnr: 0.292291--- , time: 24.407 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 118.899--- , test psnr: 0.278607--- , time: 31.4934 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 118.921--- , test psnr: 0.304629--- , time: 38.3678 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 118.85--- , test psnr: 0.286533--- , time: 45.2437 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 119.027--- , test psnr: 0.33088--- , time: 52.1275 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 119.034--- , test psnr: 0.313457--- , time: 58.972 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 119.112--- , test psnr: 0.340285--- , time: 65.8431 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 120.867--- , test psnr: -1.1929--- , time: 6.36997 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 120.754--- , test psnr: -1.18976--- , time: 12.7238 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 120.798--- , test psnr: -1.19627--- , time: 19.276 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 120.917--- , test psnr: -1.18746--- , time: 26.0792 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 120.819--- , test psnr: -1.19263--- , time: 32.8663 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 120.894--- , test psnr: -1.1892--- , time: 39.6401 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 120.905--- , test psnr: -1.19416--- , time: 46.4964 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 120.79--- , test psnr: -1.18629--- , time: 53.3508 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 120.88--- , test psnr: -1.18858--- , time: 60.235 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 120.856--- , test psnr: -1.19115--- , time: 67.2081 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 120.161--- , test psnr: -0.499901--- , time: 6.97145 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 120.272--- , test psnr: -0.489028--- , time: 13.8682 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 120.151--- , test psnr: -0.51088--- , time: 20.757 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 120.137--- , test psnr: -0.473914--- , time: 27.9244 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 120.253--- , test psnr: -0.49125--- , time: 34.8441 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 120.232--- , test psnr: -0.475862--- , time: 41.7604 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 120.174--- , test psnr: -0.4842--- , time: 48.5548 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 120.147--- , test psnr: -0.48993--- , time: 54.4548 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 120.141--- , test psnr: -0.500237--- , time: 60.3271 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 120.155--- , test psnr: -0.50136--- , time: 66.1935 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 118.508--- , test psnr: -0.161177--- , time: 6.39996 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 118.519--- , test psnr: -0.157499--- , time: 13.2437 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 118.498--- , test psnr: -0.163456--- , time: 20.0804 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 118.773--- , test psnr: -0.158251--- , time: 26.9677 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 118.429--- , test psnr: -0.160013--- , time: 33.8716 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 118.657--- , test psnr: -0.164161--- , time: 40.7858 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 118.476--- , test psnr: -0.164516--- , time: 47.6898 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 118.449--- , test psnr: -0.159168--- , time: 54.1986 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 118.471--- , test psnr: -0.160832--- , time: 60.8172 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 118.477--- , test psnr: -0.158815--- , time: 67.7644 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 121.759--- , test psnr: 2.8081--- , time: 6.88755 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 121.945--- , test psnr: 2.7945--- , time: 13.7615 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 121.747--- , test psnr: 2.81126--- , time: 20.641 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 121.864--- , test psnr: 2.78151--- , time: 27.6671 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 121.785--- , test psnr: 2.79349--- , time: 34.547 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 121.873--- , test psnr: 2.77213--- , time: 41.4386 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 122.107--- , test psnr: 2.76094--- , time: 48.3461 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 121.819--- , test psnr: 2.81925--- , time: 55.2523 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 121.833--- , test psnr: 2.77698--- , time: 62.1691 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 121.728--- , test psnr: 2.81482--- , time: 69.0549 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 118.489--- , test psnr: -0.159687--- , time: 6.89058 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 118.495--- , test psnr: -0.166015--- , time: 13.8817 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 118.413--- , test psnr: -0.16357--- , time: 20.7508 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 118.631--- , test psnr: -0.163916--- , time: 27.6218 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 118.616--- , test psnr: -0.166667--- , time: 34.4687 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 118.557--- , test psnr: -0.158423--- , time: 41.3254 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 118.601--- , test psnr: -0.164213--- , time: 47.9375 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 118.581--- , test psnr: -0.157933--- , time: 53.8533 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 118.494--- , test psnr: -0.161981--- , time: 59.7605 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 118.612--- , test psnr: -0.154563--- , time: 65.6675 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 115.217--- , test psnr: -2.54213--- , time: 5.91457 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 115.193--- , test psnr: -2.49231--- , time: 11.8154 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 115.255--- , test psnr: -2.53433--- , time: 17.6883 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 115.276--- , test psnr: -2.51291--- , time: 23.5734 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 115.253--- , test psnr: -2.525--- , time: 29.4374 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 115.172--- , test psnr: -2.5362--- , time: 35.3051 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 115.174--- , test psnr: -2.50127--- , time: 41.1794 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 115.184--- , test psnr: -2.54148--- , time: 47.73 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 115.148--- , test psnr: -2.52668--- , time: 54.6001 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 115.2--- , test psnr: -2.50776--- , time: 61.513 seconds ---\n"
     ]
    }
   ],
   "source": [
    "all_data = torch.from_numpy(np.linspace(0, 1, sample_N+1)[:-1].reshape(-1,1)).type(torch.FloatTensor)\n",
    "\n",
    "c = np.zeros((embedding_dimension,))\n",
    "c[0] = 1\n",
    "c = c/np.linalg.norm(c)\n",
    "\n",
    "for i in range(Nd):\n",
    "    sample_d = sample_ds[i]\n",
    "\n",
    "    train_data = shift_embedding(all_data[::2*sample_d],c)\n",
    "    test_data = shift_embedding(all_data[sample_d::2*sample_d],c)\n",
    "\n",
    "    # Calculate the input stable rank\n",
    "    sr_[i] = srank_func(train_data)\n",
    "\n",
    "\n",
    "    for j in range(N_test):\n",
    "        start_time = time.time()\n",
    "        \n",
    "        \n",
    "        # Prepare the data\n",
    "        all_target = signal[kk[j]].reshape(-1,1)\n",
    "        train_label = all_target[::2*sample_d]\n",
    "        test_label = all_target[sample_d::2*sample_d]\n",
    "        \n",
    "        xt, yt = train_data.cuda(),train_label.cuda()\n",
    "        xt, yt = Variable(xt), Variable(yt)\n",
    "\n",
    "        xts, yts = test_data.cuda(),test_label.cuda()\n",
    "        \n",
    "        \n",
    "        for k in range(N_repeat):\n",
    "            # Initialize classification model to learn\n",
    "            model = SLP(embedding_dimension).cuda()\n",
    "            # Set the optimization\n",
    "            optimizer = optim.Adam(model.parameters(), 3e-3, betas=(0.9, 0.999),weight_decay=1e-8)\n",
    "            \n",
    "\n",
    "            for epoch in range(epochs):\n",
    "                model.train()\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                out = model(xt)\n",
    "                loss = criterion(out, yt)\n",
    "\n",
    "                loss.backward()\n",
    "                losses_[i,j,k,epoch] = loss.item()\n",
    "                optimizer.step()\n",
    "\n",
    "                model.eval()\n",
    "                trn_psnr_[i,j,k,epoch] = psnr_func(model(xt),yt)\n",
    "                tst_psnr_[i,j,k,epoch] = psnr_func(model(xts),yts)\n",
    "\n",
    "\n",
    "            print(\"==>>> N: %g, F: %g, train psnr: %g--- , test psnr: %g--- , time: %g seconds ---\" \n",
    "                    % (i,j, np.mean(trn_psnr_[i,j,k,-100:]),np.mean(tst_psnr_[i,j,k,-100:]),time.time() - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f433866a358>]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure()\n",
    "for i in range(Nd):\n",
    "    plot(np.mean(trn_psnr_[i,:,:],(0,1)))\n",
    "    plot(np.mean(tst_psnr_[i,:,:],(0,1)),'--')\n",
    "\n",
    "figure()\n",
    "plot(sample_ds,np.mean(sr_,(1,2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_name = 'impulse_random'\n",
    "np.save(file_path+file_name+'_losses',losses_)\n",
    "np.save(file_path+file_name+'_sr',sr_)\n",
    "np.save(file_path+file_name+'_trn',trn_psnr_)\n",
    "np.save(file_path+file_name+'_tst',tst_psnr_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==>>> N: 0, F: 0, train psnr: 106.883--- , test psnr: -0.68656--- , time: 7.81973 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 106.932--- , test psnr: -0.992907--- , time: 15.5915 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 107.099--- , test psnr: -0.809556--- , time: 23.3582 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 107.077--- , test psnr: -0.657367--- , time: 31.1497 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 106.859--- , test psnr: -0.693728--- , time: 38.826 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 107.006--- , test psnr: -0.804995--- , time: 46.5 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 107.115--- , test psnr: -0.76968--- , time: 54.1829 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 106.786--- , test psnr: -0.831906--- , time: 62.4368 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 107.078--- , test psnr: -0.605835--- , time: 70.2031 seconds ---\n",
      "==>>> N: 0, F: 0, train psnr: 107.079--- , test psnr: -0.991752--- , time: 77.7424 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 105.991--- , test psnr: 0.283128--- , time: 7.17688 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 105.911--- , test psnr: 0.153398--- , time: 14.6954 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 106.097--- , test psnr: 0.454441--- , time: 22.4761 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 106.058--- , test psnr: 0.183406--- , time: 30.1013 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 106.123--- , test psnr: 0.255618--- , time: 37.6912 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 105.913--- , test psnr: 0.0487942--- , time: 45.3619 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 106.051--- , test psnr: 0.536698--- , time: 53.1089 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 106.021--- , test psnr: 0.0793013--- , time: 60.9009 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 105.885--- , test psnr: 0.378263--- , time: 68.4721 seconds ---\n",
      "==>>> N: 0, F: 1, train psnr: 105.912--- , test psnr: 0.173957--- , time: 76.245 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 106.57--- , test psnr: -0.0129263--- , time: 7.87607 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 106.544--- , test psnr: 0.120045--- , time: 15.6053 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 106.554--- , test psnr: 0.0162459--- , time: 22.8076 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 106.574--- , test psnr: 0.0942609--- , time: 30.5503 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 106.527--- , test psnr: -0.0621341--- , time: 38.1637 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 106.583--- , test psnr: 0.277057--- , time: 45.7142 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 106.706--- , test psnr: 0.0430898--- , time: 53.2397 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 106.669--- , test psnr: -0.00959004--- , time: 60.7782 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 106.658--- , test psnr: -0.0665273--- , time: 68.719 seconds ---\n",
      "==>>> N: 0, F: 2, train psnr: 106.572--- , test psnr: -0.0511812--- , time: 76.6495 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 106.834--- , test psnr: -0.660805--- , time: 7.41118 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 106.846--- , test psnr: -0.465149--- , time: 15.254 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 106.819--- , test psnr: -0.594881--- , time: 23.1729 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 106.733--- , test psnr: -0.493625--- , time: 30.8905 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 106.897--- , test psnr: -0.54112--- , time: 38.3548 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 106.97--- , test psnr: -0.660817--- , time: 46.2223 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 106.787--- , test psnr: -0.614125--- , time: 53.9548 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 106.912--- , test psnr: -0.701947--- , time: 61.7477 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 106.911--- , test psnr: -0.511083--- , time: 69.3753 seconds ---\n",
      "==>>> N: 0, F: 3, train psnr: 106.939--- , test psnr: -0.741384--- , time: 77.0737 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 106.178--- , test psnr: -0.877659--- , time: 7.61758 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 106.207--- , test psnr: -0.661632--- , time: 15.2642 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 106.175--- , test psnr: -0.845044--- , time: 23.1403 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 106.228--- , test psnr: -1.00809--- , time: 31.2457 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 106.14--- , test psnr: -0.977881--- , time: 39.0571 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 106.244--- , test psnr: -0.780647--- , time: 46.9474 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 106.21--- , test psnr: -0.956026--- , time: 55.2241 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 106.227--- , test psnr: -0.943046--- , time: 62.7086 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 106.057--- , test psnr: -0.978039--- , time: 70.4304 seconds ---\n",
      "==>>> N: 0, F: 4, train psnr: 106.172--- , test psnr: -0.761934--- , time: 78.2086 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 107.35--- , test psnr: 0.384433--- , time: 7.79594 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 107.377--- , test psnr: 0.585787--- , time: 15.4541 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 107.415--- , test psnr: 0.65317--- , time: 23.1808 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 107.374--- , test psnr: 0.818862--- , time: 31.039 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 107.41--- , test psnr: 0.623682--- , time: 38.7137 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 107.488--- , test psnr: 0.590987--- , time: 46.3583 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 107.335--- , test psnr: 0.724289--- , time: 54.1703 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 107.57--- , test psnr: 0.481751--- , time: 62.0134 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 107.421--- , test psnr: 0.778059--- , time: 69.694 seconds ---\n",
      "==>>> N: 0, F: 5, train psnr: 107.412--- , test psnr: 0.706966--- , time: 77.2863 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 106.711--- , test psnr: -0.3232--- , time: 7.69531 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 106.83--- , test psnr: -0.266072--- , time: 15.3961 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 106.847--- , test psnr: -0.165542--- , time: 23.0394 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 106.767--- , test psnr: -0.294574--- , time: 30.7637 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 106.796--- , test psnr: -0.40676--- , time: 38.7585 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 106.8--- , test psnr: -0.0120289--- , time: 46.3736 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 106.856--- , test psnr: -0.297059--- , time: 54.4013 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 106.788--- , test psnr: -0.0934459--- , time: 61.954 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 106.833--- , test psnr: -0.23408--- , time: 69.7911 seconds ---\n",
      "==>>> N: 0, F: 6, train psnr: 106.714--- , test psnr: -0.154485--- , time: 77.688 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 107.184--- , test psnr: -0.426048--- , time: 7.78699 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 107.011--- , test psnr: -0.338702--- , time: 15.6136 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 106.845--- , test psnr: -0.522609--- , time: 23.3574 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 107.028--- , test psnr: -0.45738--- , time: 31.1644 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 107.225--- , test psnr: -0.439448--- , time: 38.5935 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 107.204--- , test psnr: -0.280202--- , time: 46.3788 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 107.08--- , test psnr: -0.382338--- , time: 54.2366 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 107.303--- , test psnr: -0.273074--- , time: 62.1525 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 106.991--- , test psnr: -0.242144--- , time: 69.9508 seconds ---\n",
      "==>>> N: 0, F: 7, train psnr: 107.09--- , test psnr: -0.493066--- , time: 77.8355 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 106.787--- , test psnr: -0.199988--- , time: 7.7676 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 106.634--- , test psnr: -0.335759--- , time: 15.6232 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 106.764--- , test psnr: -0.188872--- , time: 22.3888 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 106.758--- , test psnr: -0.189569--- , time: 29.245 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 106.852--- , test psnr: -0.223072--- , time: 36.9625 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 106.894--- , test psnr: -0.148848--- , time: 44.7022 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 106.837--- , test psnr: -0.343977--- , time: 52.5129 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 106.787--- , test psnr: -0.0359293--- , time: 60.3338 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 106.93--- , test psnr: -0.195115--- , time: 68.0105 seconds ---\n",
      "==>>> N: 0, F: 8, train psnr: 106.812--- , test psnr: -0.127986--- , time: 75.6059 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 105.361--- , test psnr: 0.160528--- , time: 7.60182 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 105.361--- , test psnr: -0.0605981--- , time: 15.4088 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 105.261--- , test psnr: 0.160036--- , time: 23.3557 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 105.343--- , test psnr: 0.0397896--- , time: 31.1162 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 105.306--- , test psnr: 0.0143531--- , time: 38.9021 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 105.108--- , test psnr: 0.337764--- , time: 46.5568 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 104.302--- , test psnr: 0.0842093--- , time: 54.2518 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 105.114--- , test psnr: 0.0597026--- , time: 62.2921 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 105.167--- , test psnr: 0.0743249--- , time: 70.121 seconds ---\n",
      "==>>> N: 0, F: 9, train psnr: 103.932--- , test psnr: 0.0134331--- , time: 77.921 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 113.861--- , test psnr: -0.550872--- , time: 7.7959 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 114.826--- , test psnr: -0.473864--- , time: 15.5431 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 114.221--- , test psnr: -0.344183--- , time: 22.8268 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 114.134--- , test psnr: -0.561689--- , time: 30.5373 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 114.771--- , test psnr: -0.441495--- , time: 38.2897 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 114.602--- , test psnr: -0.46157--- , time: 45.4682 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 113.283--- , test psnr: -0.460934--- , time: 53.1103 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 114.624--- , test psnr: -0.552023--- , time: 60.7892 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 114.495--- , test psnr: -0.613635--- , time: 68.3753 seconds ---\n",
      "==>>> N: 1, F: 0, train psnr: 114.903--- , test psnr: -0.540387--- , time: 75.863 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 114.298--- , test psnr: -1.32555--- , time: 7.79121 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 114.09--- , test psnr: -1.45957--- , time: 15.9872 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 114.429--- , test psnr: -1.34783--- , time: 23.7609 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 113.236--- , test psnr: -1.27938--- , time: 31.3043 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 114.568--- , test psnr: -1.31888--- , time: 38.5616 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 114.677--- , test psnr: -1.41487--- , time: 46.2949 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 114.573--- , test psnr: -1.47722--- , time: 53.9466 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 114.56--- , test psnr: -1.35267--- , time: 61.6754 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 114.49--- , test psnr: -1.33728--- , time: 68.8837 seconds ---\n",
      "==>>> N: 1, F: 1, train psnr: 114.505--- , test psnr: -1.36484--- , time: 76.2167 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 110.136--- , test psnr: -0.589034--- , time: 7.4941 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 110.237--- , test psnr: -0.389054--- , time: 14.9723 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 110.127--- , test psnr: -0.551747--- , time: 22.4691 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 109.924--- , test psnr: -0.38004--- , time: 30.0843 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 110.303--- , test psnr: -0.562835--- , time: 37.71 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 111.156--- , test psnr: -0.479835--- , time: 45.2536 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 109.895--- , test psnr: -0.397823--- , time: 52.781 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 110.331--- , test psnr: -0.59959--- , time: 60.2791 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 110.012--- , test psnr: -0.323847--- , time: 67.9244 seconds ---\n",
      "==>>> N: 1, F: 2, train psnr: 110.223--- , test psnr: -0.68292--- , time: 75.6198 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 109.665--- , test psnr: -0.158499--- , time: 7.77959 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 109.878--- , test psnr: -0.413083--- , time: 15.6332 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 109.905--- , test psnr: -0.163492--- , time: 23.6325 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 109.939--- , test psnr: -0.125933--- , time: 31.5418 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 109.773--- , test psnr: -0.154555--- , time: 39.1671 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 109.954--- , test psnr: -0.331203--- , time: 46.8157 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 110.199--- , test psnr: -0.118679--- , time: 54.5086 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 109.764--- , test psnr: -0.297444--- , time: 62.1782 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 109.816--- , test psnr: -0.235914--- , time: 69.4622 seconds ---\n",
      "==>>> N: 1, F: 3, train psnr: 109.685--- , test psnr: -0.205596--- , time: 77.269 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 107.848--- , test psnr: 0.630293--- , time: 7.72667 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 107.901--- , test psnr: 0.319541--- , time: 15.4408 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 107.933--- , test psnr: 0.341202--- , time: 23.2667 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 107.835--- , test psnr: 0.365816--- , time: 30.9383 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 107.85--- , test psnr: 0.231423--- , time: 38.4514 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 107.824--- , test psnr: 0.324127--- , time: 46.3484 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 107.882--- , test psnr: 0.457564--- , time: 54.0276 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 104.875--- , test psnr: 0.131746--- , time: 61.2497 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 107.757--- , test psnr: 0.279864--- , time: 68.232 seconds ---\n",
      "==>>> N: 1, F: 4, train psnr: 107.88--- , test psnr: 0.389841--- , time: 75.7621 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 115.656--- , test psnr: -0.181952--- , time: 7.52199 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 115.494--- , test psnr: -0.227111--- , time: 14.6339 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 115.637--- , test psnr: -0.303313--- , time: 22.0456 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 115.483--- , test psnr: -0.211289--- , time: 29.1393 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 115.876--- , test psnr: -0.279637--- , time: 36.2563 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 115.812--- , test psnr: -0.262675--- , time: 43.8537 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 115.642--- , test psnr: -0.312737--- , time: 50.9606 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 115.683--- , test psnr: -0.263938--- , time: 58.6536 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 115.658--- , test psnr: -0.197963--- , time: 66.4436 seconds ---\n",
      "==>>> N: 1, F: 5, train psnr: 115.988--- , test psnr: -0.223917--- , time: 74.1643 seconds ---\n",
      "==>>> N: 1, F: 6, train psnr: 109.204--- , test psnr: 0.10002--- , time: 7.66995 seconds ---\n",
      "==>>> N: 1, F: 6, train psnr: 109.28--- , test psnr: 0.008032--- , time: 15.3115 seconds ---\n",
      "==>>> N: 1, F: 6, train psnr: 109.227--- , test psnr: 0.187181--- , time: 23.0748 seconds ---\n",
      "==>>> N: 1, F: 6, train psnr: 109.137--- , test psnr: 0.0151556--- , time: 30.4533 seconds ---\n",
      "==>>> N: 1, F: 6, train psnr: 109.167--- , test psnr: -0.00156276--- , time: 38.1398 seconds ---\n",
      "==>>> N: 1, F: 6, train psnr: 109.262--- , test psnr: 0.106077--- , time: 45.7535 seconds ---\n",
      "==>>> N: 1, F: 6, train psnr: 109.223--- , test psnr: -0.0616855--- , time: 53.5012 seconds ---\n",
      "==>>> N: 1, F: 6, train psnr: 109.22--- , test psnr: 0.212538--- , time: 61.166 seconds ---\n",
      "==>>> N: 1, F: 6, train psnr: 109.209--- , test psnr: 0.23631--- , time: 68.7286 seconds ---\n",
      "==>>> N: 1, F: 6, train psnr: 109.099--- , test psnr: 0.0662686--- , time: 76.3874 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 112.674--- , test psnr: 0.037672--- , time: 7.7284 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 110.321--- , test psnr: -0.0435526--- , time: 14.4607 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 111.594--- , test psnr: 0.0954013--- , time: 21.163 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 110.346--- , test psnr: 0.0813599--- , time: 29.1035 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 111.282--- , test psnr: -0.076064--- , time: 36.6705 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 111.12--- , test psnr: 0.151231--- , time: 44.2221 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 110.533--- , test psnr: 0.0297154--- , time: 51.8959 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 112.394--- , test psnr: -0.116991--- , time: 59.6765 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 110.803--- , test psnr: -0.0295152--- , time: 67.2876 seconds ---\n",
      "==>>> N: 1, F: 7, train psnr: 111.683--- , test psnr: -0.13674--- , time: 74.9579 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 109.217--- , test psnr: -0.0193304--- , time: 6.68063 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 109.092--- , test psnr: 0.185501--- , time: 13.3729 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 109.156--- , test psnr: 0.0811591--- , time: 20.0708 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 109.204--- , test psnr: 0.173183--- , time: 26.742 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 109.288--- , test psnr: 0.187761--- , time: 34.4123 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 109.296--- , test psnr: 0.250233--- , time: 41.9758 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 109.216--- , test psnr: 0.136737--- , time: 49.5424 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 109.157--- , test psnr: -0.289623--- , time: 56.9873 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 109.192--- , test psnr: -0.101344--- , time: 64.7572 seconds ---\n",
      "==>>> N: 1, F: 8, train psnr: 109.138--- , test psnr: -0.184794--- , time: 72.6375 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 107.704--- , test psnr: -0.887534--- , time: 7.59097 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 107.699--- , test psnr: -0.719755--- , time: 15.3158 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 107.654--- , test psnr: -0.678801--- , time: 23.0111 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 107.661--- , test psnr: -0.714726--- , time: 30.6435 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 107.636--- , test psnr: -0.477158--- , time: 38.2124 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 107.56--- , test psnr: -0.445623--- , time: 45.7467 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 107.58--- , test psnr: -0.621234--- , time: 53.2907 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 107.489--- , test psnr: -0.5523--- , time: 60.8299 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 107.704--- , test psnr: -0.858893--- , time: 68.394 seconds ---\n",
      "==>>> N: 1, F: 9, train psnr: 107.648--- , test psnr: -0.751446--- , time: 75.9611 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 113.709--- , test psnr: -0.793728--- , time: 7.83463 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 114.417--- , test psnr: -0.757737--- , time: 15.2672 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 114.159--- , test psnr: -0.619982--- , time: 22.8112 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 114.154--- , test psnr: -0.928756--- , time: 30.293 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 114.127--- , test psnr: -0.851233--- , time: 36.8845 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 114.154--- , test psnr: -0.878013--- , time: 43.4693 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 114.48--- , test psnr: -0.538896--- , time: 50.0538 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 114.191--- , test psnr: -0.815365--- , time: 57.213 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 114.188--- , test psnr: -0.881231--- , time: 64.78 seconds ---\n",
      "==>>> N: 2, F: 0, train psnr: 113.881--- , test psnr: -0.695497--- , time: 72.2926 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 114.219--- , test psnr: 0.396936--- , time: 6.53387 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 113.681--- , test psnr: 0.261687--- , time: 13.0471 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 114.269--- , test psnr: 0.280192--- , time: 19.5672 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 114.073--- , test psnr: 0.398505--- , time: 26.8823 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 113.357--- , test psnr: 0.371184--- , time: 34.4277 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 114.208--- , test psnr: 0.395364--- , time: 41.8047 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 113.769--- , test psnr: 0.29052--- , time: 49.2902 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 113.546--- , test psnr: 0.426942--- , time: 56.5281 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 114.255--- , test psnr: 0.434215--- , time: 64.0738 seconds ---\n",
      "==>>> N: 2, F: 1, train psnr: 113.819--- , test psnr: 0.343177--- , time: 71.8482 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 116.658--- , test psnr: 1.15842--- , time: 7.72232 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 116.758--- , test psnr: 1.19838--- , time: 15.4685 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 116.772--- , test psnr: 1.09161--- , time: 22.9095 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 116.708--- , test psnr: 1.02992--- , time: 30.0563 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 116.663--- , test psnr: 1.06529--- , time: 37.811 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 116.71--- , test psnr: 0.877961--- , time: 45.7507 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 116.651--- , test psnr: 1.07199--- , time: 53.4453 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 116.736--- , test psnr: 1.06462--- , time: 61.183 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 116.543--- , test psnr: 1.1483--- , time: 68.3831 seconds ---\n",
      "==>>> N: 2, F: 2, train psnr: 116.693--- , test psnr: 0.751127--- , time: 75.8662 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 115.435--- , test psnr: -0.180192--- , time: 7.49014 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 115.262--- , test psnr: -0.0750226--- , time: 14.849 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 115.419--- , test psnr: -0.122794--- , time: 22.4534 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 115.327--- , test psnr: -0.125279--- , time: 29.9956 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 115.252--- , test psnr: -0.244125--- , time: 37.7991 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 115.408--- , test psnr: -0.332568--- , time: 46.0211 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 115.211--- , test psnr: -0.107998--- , time: 54.0345 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 115.378--- , test psnr: -0.152692--- , time: 61.436 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 115.477--- , test psnr: -0.278996--- , time: 68.9051 seconds ---\n",
      "==>>> N: 2, F: 3, train psnr: 115.217--- , test psnr: -0.107411--- , time: 76.5659 seconds ---\n",
      "==>>> N: 2, F: 4, train psnr: 117.02--- , test psnr: -1.78751--- , time: 7.57094 seconds ---\n",
      "==>>> N: 2, F: 4, train psnr: 117.105--- , test psnr: -1.86565--- , time: 15.3704 seconds ---\n",
      "==>>> N: 2, F: 4, train psnr: 117.049--- , test psnr: -1.87264--- , time: 22.9096 seconds ---\n",
      "==>>> N: 2, F: 4, train psnr: 117.097--- , test psnr: -2.02577--- , time: 30.8193 seconds ---\n",
      "==>>> N: 2, F: 4, train psnr: 117.075--- , test psnr: -1.91881--- , time: 38.4406 seconds ---\n",
      "==>>> N: 2, F: 4, train psnr: 117.075--- , test psnr: -1.84331--- , time: 45.8643 seconds ---\n",
      "==>>> N: 2, F: 4, train psnr: 117.085--- , test psnr: -1.81868--- , time: 53.4573 seconds ---\n",
      "==>>> N: 2, F: 4, train psnr: 117.159--- , test psnr: -1.70099--- , time: 60.5558 seconds ---\n",
      "==>>> N: 2, F: 4, train psnr: 117.075--- , test psnr: -1.58843--- , time: 67.8018 seconds ---\n",
      "==>>> N: 2, F: 4, train psnr: 116.97--- , test psnr: -1.88218--- , time: 75.1305 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 117.326--- , test psnr: 0.0766024--- , time: 7.64729 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 117.487--- , test psnr: 0.0832004--- , time: 15.315 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 117.38--- , test psnr: 0.17746--- , time: 22.9861 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 117.378--- , test psnr: 0.331466--- , time: 30.7593 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 117.429--- , test psnr: 0.318445--- , time: 38.4732 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 117.423--- , test psnr: 0.228431--- , time: 46.1865 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 117.394--- , test psnr: 0.348296--- , time: 53.736 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 117.399--- , test psnr: 0.201325--- , time: 61.3074 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 117.33--- , test psnr: 0.329114--- , time: 68.9662 seconds ---\n",
      "==>>> N: 2, F: 5, train psnr: 117.434--- , test psnr: 0.482537--- , time: 76.4627 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 116.223--- , test psnr: 0.389079--- , time: 7.48089 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 116.249--- , test psnr: 0.362543--- , time: 14.989 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 116.238--- , test psnr: 0.288379--- , time: 22.6219 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 116.113--- , test psnr: 0.241758--- , time: 30.282 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 116.141--- , test psnr: 0.449305--- , time: 37.9912 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 116.194--- , test psnr: 0.310893--- , time: 45.6276 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 116.203--- , test psnr: 0.337735--- , time: 53.1886 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 116.24--- , test psnr: 0.310197--- , time: 61.5617 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 116.26--- , test psnr: 0.422897--- , time: 68.1798 seconds ---\n",
      "==>>> N: 2, F: 6, train psnr: 116.054--- , test psnr: 0.519421--- , time: 74.7757 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 117.825--- , test psnr: -1.19416--- , time: 6.42371 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 117.785--- , test psnr: -1.05166--- , time: 14.0086 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 117.779--- , test psnr: -1.15025--- , time: 21.6022 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 117.778--- , test psnr: -1.13699--- , time: 29.204 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 117.838--- , test psnr: -1.05663--- , time: 36.8009 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 117.778--- , test psnr: -1.00712--- , time: 44.3271 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 117.915--- , test psnr: -0.97709--- , time: 51.2935 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 117.888--- , test psnr: -1.16978--- , time: 58.9599 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 117.729--- , test psnr: -0.953015--- , time: 66.3184 seconds ---\n",
      "==>>> N: 2, F: 7, train psnr: 117.848--- , test psnr: -1.05425--- , time: 74.321 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 116.271--- , test psnr: 0.451087--- , time: 7.82973 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 116.197--- , test psnr: 0.30002--- , time: 15.4913 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 116.159--- , test psnr: 0.225157--- , time: 23.192 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 116.221--- , test psnr: 0.171497--- , time: 31.0252 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 116.186--- , test psnr: 0.188273--- , time: 38.5574 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 116.217--- , test psnr: 0.414149--- , time: 46.0329 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 116.308--- , test psnr: 0.468821--- , time: 53.0327 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 116.205--- , test psnr: 0.585143--- , time: 60.6834 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 116.207--- , test psnr: 0.434649--- , time: 68.4961 seconds ---\n",
      "==>>> N: 2, F: 8, train psnr: 116.286--- , test psnr: 0.404104--- , time: 76.231 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 114.702--- , test psnr: 0.654429--- , time: 7.69239 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 114.65--- , test psnr: 0.446446--- , time: 15.2291 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 114.7--- , test psnr: 0.810332--- , time: 22.884 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 114.653--- , test psnr: 0.463275--- , time: 30.5586 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 114.882--- , test psnr: 0.723448--- , time: 38.0388 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 114.88--- , test psnr: 0.69223--- , time: 45.7364 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 114.523--- , test psnr: 0.841135--- , time: 53.3189 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 114.799--- , test psnr: 0.572584--- , time: 61.4553 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 114.879--- , test psnr: 0.598976--- , time: 69.2538 seconds ---\n",
      "==>>> N: 2, F: 9, train psnr: 114.717--- , test psnr: 0.55124--- , time: 76.7516 seconds ---\n",
      "==>>> N: 3, F: 0, train psnr: 118.333--- , test psnr: 0.675976--- , time: 7.70971 seconds ---\n",
      "==>>> N: 3, F: 0, train psnr: 118.239--- , test psnr: 1.02577--- , time: 15.3031 seconds ---\n",
      "==>>> N: 3, F: 0, train psnr: 118.378--- , test psnr: 0.867634--- , time: 22.9998 seconds ---\n",
      "==>>> N: 3, F: 0, train psnr: 118.328--- , test psnr: 0.88395--- , time: 30.7001 seconds ---\n",
      "==>>> N: 3, F: 0, train psnr: 118.323--- , test psnr: 0.764033--- , time: 38.351 seconds ---\n",
      "==>>> N: 3, F: 0, train psnr: 118.147--- , test psnr: 0.783113--- , time: 46.0178 seconds ---\n",
      "==>>> N: 3, F: 0, train psnr: 118.379--- , test psnr: 0.745105--- , time: 53.6115 seconds ---\n",
      "==>>> N: 3, F: 0, train psnr: 118.23--- , test psnr: 0.567854--- , time: 61.1703 seconds ---\n",
      "==>>> N: 3, F: 0, train psnr: 118.135--- , test psnr: 0.622275--- , time: 67.7093 seconds ---\n",
      "==>>> N: 3, F: 0, train psnr: 118.19--- , test psnr: 0.742433--- , time: 74.2576 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 119.041--- , test psnr: 0.132137--- , time: 6.60526 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 118.947--- , test psnr: 0.31403--- , time: 14.5023 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 119.037--- , test psnr: 0.120093--- , time: 22.5104 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 119.029--- , test psnr: 0.30007--- , time: 30.4656 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 118.963--- , test psnr: 0.303119--- , time: 38.1158 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 118.999--- , test psnr: 0.353767--- , time: 45.666 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 119.096--- , test psnr: 0.305548--- , time: 53.3598 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 118.925--- , test psnr: 0.319003--- , time: 60.2569 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 118.929--- , test psnr: 0.312849--- , time: 68.003 seconds ---\n",
      "==>>> N: 3, F: 1, train psnr: 118.886--- , test psnr: 0.110061--- , time: 75.733 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 116.264--- , test psnr: 1.51578--- , time: 7.40658 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 116.217--- , test psnr: 1.38959--- , time: 15.1521 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 116.245--- , test psnr: 1.36939--- , time: 23.0049 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 116.16--- , test psnr: 1.33875--- , time: 30.6852 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 116.275--- , test psnr: 1.39759--- , time: 38.2732 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 116.347--- , test psnr: 1.30245--- , time: 45.8415 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 116.053--- , test psnr: 1.34691--- , time: 53.5608 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 116.266--- , test psnr: 1.61028--- , time: 61.0749 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 116.129--- , test psnr: 1.80106--- , time: 68.521 seconds ---\n",
      "==>>> N: 3, F: 2, train psnr: 116.211--- , test psnr: 1.26052--- , time: 76.6443 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 117.175--- , test psnr: 0.15859--- , time: 7.87961 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 117.213--- , test psnr: 0.133149--- , time: 15.7219 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 110.789--- , test psnr: 0.064306--- , time: 23.5406 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 117.235--- , test psnr: 0.105362--- , time: 31.399 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 117.141--- , test psnr: 0.153473--- , time: 39.3304 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 117.248--- , test psnr: 0.197368--- , time: 46.9812 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 117.253--- , test psnr: 0.246128--- , time: 54.4678 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 117.291--- , test psnr: 0.245595--- , time: 62.1165 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 117.255--- , test psnr: 0.160955--- , time: 69.7572 seconds ---\n",
      "==>>> N: 3, F: 3, train psnr: 117.341--- , test psnr: 0.211306--- , time: 77.4629 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 116.295--- , test psnr: -1.30922--- , time: 7.80692 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 116.276--- , test psnr: -1.49661--- , time: 15.4951 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 116.237--- , test psnr: -1.47165--- , time: 23.0912 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 116.325--- , test psnr: -1.33269--- , time: 30.7509 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 116.399--- , test psnr: -1.40983--- , time: 39.0562 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 116.317--- , test psnr: -1.51223--- , time: 46.8166 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 116.341--- , test psnr: -1.40958--- , time: 54.6055 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 116.255--- , test psnr: -1.38418--- , time: 62.6551 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 116.28--- , test psnr: -1.61955--- , time: 70.3315 seconds ---\n",
      "==>>> N: 3, F: 4, train psnr: 116.327--- , test psnr: -1.56234--- , time: 77.8176 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 117.556--- , test psnr: 2.13839--- , time: 7.39671 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 117.584--- , test psnr: 2.27332--- , time: 14.7711 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 117.407--- , test psnr: 2.12912--- , time: 22.1509 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 117.612--- , test psnr: 2.12366--- , time: 29.3824 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 117.623--- , test psnr: 2.14138--- , time: 37.0552 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 117.422--- , test psnr: 2.05846--- , time: 44.6535 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 117.563--- , test psnr: 2.16388--- , time: 51.7523 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 117.599--- , test psnr: 2.17114--- , time: 58.779 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 117.576--- , test psnr: 2.25268--- , time: 65.5751 seconds ---\n",
      "==>>> N: 3, F: 5, train psnr: 117.562--- , test psnr: 2.03961--- , time: 72.2455 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 117.984--- , test psnr: -0.71568--- , time: 6.68847 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 118.009--- , test psnr: -0.807007--- , time: 13.3478 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 117.907--- , test psnr: -0.755255--- , time: 19.9903 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 117.94--- , test psnr: -0.756448--- , time: 26.6479 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 117.944--- , test psnr: -0.722138--- , time: 33.2306 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 117.946--- , test psnr: -0.820579--- , time: 41.4579 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 117.97--- , test psnr: -0.65656--- , time: 49.3033 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 117.865--- , test psnr: -0.603792--- , time: 57.1161 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 118.002--- , test psnr: -0.675124--- , time: 64.6867 seconds ---\n",
      "==>>> N: 3, F: 6, train psnr: 117.891--- , test psnr: -0.830652--- , time: 72.0997 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 119.476--- , test psnr: -0.524305--- , time: 8.13415 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 119.461--- , test psnr: -0.532014--- , time: 15.495 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 119.403--- , test psnr: -0.315094--- , time: 23.2327 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 119.437--- , test psnr: -0.245329--- , time: 30.8958 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 112.974--- , test psnr: -0.360116--- , time: 37.4867 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 119.405--- , test psnr: -0.400752--- , time: 44.0697 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 119.363--- , test psnr: -0.484968--- , time: 51.6675 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 119.471--- , test psnr: -0.374103--- , time: 59.349 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 119.266--- , test psnr: -0.468427--- , time: 67.3511 seconds ---\n",
      "==>>> N: 3, F: 7, train psnr: 119.403--- , test psnr: -0.415222--- , time: 75.0635 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 117.913--- , test psnr: -0.782567--- , time: 7.44803 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 117.86--- , test psnr: -0.615315--- , time: 14.8227 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 117.98--- , test psnr: -0.615712--- , time: 22.3509 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 117.902--- , test psnr: -0.679807--- , time: 29.6725 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 117.856--- , test psnr: -0.720003--- , time: 37.1633 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 117.994--- , test psnr: -0.749676--- , time: 44.9288 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 117.995--- , test psnr: -0.791468--- , time: 53.326 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 117.912--- , test psnr: -0.704537--- , time: 64.236 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 117.99--- , test psnr: -0.625495--- , time: 75.2542 seconds ---\n",
      "==>>> N: 3, F: 8, train psnr: 118.197--- , test psnr: -0.807914--- , time: 86.1944 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 117.134--- , test psnr: -1.9427--- , time: 11.4199 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 117.058--- , test psnr: -2.00859--- , time: 23.0595 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 117.127--- , test psnr: -1.91326--- , time: 33.1549 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 117.017--- , test psnr: -2.07322--- , time: 43.2074 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 117.114--- , test psnr: -2.13152--- , time: 52.9075 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 117.071--- , test psnr: -1.96105--- , time: 62.4318 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 117.089--- , test psnr: -2.04497--- , time: 72.4089 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 117.072--- , test psnr: -1.92091--- , time: 83.3219 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 117.107--- , test psnr: -1.96156--- , time: 94.3221 seconds ---\n",
      "==>>> N: 3, F: 9, train psnr: 116.976--- , test psnr: -1.85474--- , time: 105.257 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 117.637--- , test psnr: -1.41107--- , time: 10.8191 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 117.634--- , test psnr: -1.51654--- , time: 21.5337 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 117.61--- , test psnr: -1.34982--- , time: 32.4448 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 117.524--- , test psnr: -1.37415--- , time: 42.6648 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 117.594--- , test psnr: -1.47155--- , time: 53.3835 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 117.672--- , test psnr: -1.48043--- , time: 64.5117 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 117.547--- , test psnr: -1.56082--- , time: 75.5989 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 117.479--- , test psnr: -1.46473--- , time: 86.091 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 117.783--- , test psnr: -1.46336--- , time: 97.1603 seconds ---\n",
      "==>>> N: 4, F: 0, train psnr: 117.591--- , test psnr: -1.35708--- , time: 108.25 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 118.747--- , test psnr: -1.9991--- , time: 12.0687 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 118.833--- , test psnr: -1.9366--- , time: 24.3213 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 118.812--- , test psnr: -2.11488--- , time: 35.9844 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 118.82--- , test psnr: -2.08744--- , time: 47.6298 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 118.827--- , test psnr: -2.02268--- , time: 58.9207 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 118.703--- , test psnr: -1.93524--- , time: 69.8619 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 118.794--- , test psnr: -1.92641--- , time: 80.8667 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 118.834--- , test psnr: -2.02519--- , time: 92.2323 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 118.853--- , test psnr: -2.00631--- , time: 103.173 seconds ---\n",
      "==>>> N: 4, F: 1, train psnr: 118.722--- , test psnr: -1.98592--- , time: 114.182 seconds ---\n",
      "==>>> N: 4, F: 2, train psnr: 119.562--- , test psnr: 0.0221444--- , time: 10.8042 seconds ---\n",
      "==>>> N: 4, F: 2, train psnr: 119.56--- , test psnr: 0.00149191--- , time: 21.8249 seconds ---\n",
      "==>>> N: 4, F: 2, train psnr: 119.709--- , test psnr: 0.0654109--- , time: 32.9361 seconds ---\n",
      "==>>> N: 4, F: 2, train psnr: 119.572--- , test psnr: -0.10228--- , time: 42.6262 seconds ---\n",
      "==>>> N: 4, F: 2, train psnr: 119.597--- , test psnr: 0.214776--- , time: 52.2655 seconds ---\n",
      "==>>> N: 4, F: 2, train psnr: 119.484--- , test psnr: -0.128062--- , time: 62.0649 seconds ---\n",
      "==>>> N: 4, F: 2, train psnr: 112.425--- , test psnr: 0.145867--- , time: 72.9452 seconds ---\n",
      "==>>> N: 4, F: 2, train psnr: 119.574--- , test psnr: -0.100674--- , time: 83.699 seconds ---\n",
      "==>>> N: 4, F: 2, train psnr: 119.587--- , test psnr: 0.128678--- , time: 94.6712 seconds ---\n",
      "==>>> N: 4, F: 2, train psnr: 119.542--- , test psnr: 0.0499926--- , time: 105.766 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 118.229--- , test psnr: -0.946362--- , time: 11.775 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 118.206--- , test psnr: -0.926193--- , time: 23.0475 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 118.23--- , test psnr: -0.54793--- , time: 34.0087 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 118.267--- , test psnr: -0.624771--- , time: 45.0189 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 118.126--- , test psnr: -0.764664--- , time: 56.1008 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 118.37--- , test psnr: -0.64639--- , time: 66.9934 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 118.122--- , test psnr: -0.695729--- , time: 78.3132 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 118.313--- , test psnr: -0.813027--- , time: 89.858 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 118.152--- , test psnr: -0.926911--- , time: 100.796 seconds ---\n",
      "==>>> N: 4, F: 3, train psnr: 118.425--- , test psnr: -0.827148--- , time: 111.553 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 119.017--- , test psnr: 0.729917--- , time: 10.9068 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 118.955--- , test psnr: 0.932327--- , time: 21.694 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 118.98--- , test psnr: 1.01842--- , time: 33.0182 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 119.114--- , test psnr: 0.905975--- , time: 43.8683 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 119.018--- , test psnr: 0.832746--- , time: 54.6797 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 119.025--- , test psnr: 0.700474--- , time: 65.8285 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 118.934--- , test psnr: 1.02596--- , time: 76.8408 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 118.997--- , test psnr: 0.949235--- , time: 87.9157 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 118.983--- , test psnr: 0.948271--- , time: 98.9805 seconds ---\n",
      "==>>> N: 4, F: 4, train psnr: 119.007--- , test psnr: 0.823458--- , time: 110.002 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 120.243--- , test psnr: 0.587086--- , time: 10.9692 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 121.785--- , test psnr: 0.699448--- , time: 21.602 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 122.113--- , test psnr: 0.482023--- , time: 32.4881 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 122.649--- , test psnr: 0.390796--- , time: 43.5001 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 121.385--- , test psnr: 0.550163--- , time: 55.1286 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 120.28--- , test psnr: 0.40218--- , time: 66.1174 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 120.442--- , test psnr: 0.456487--- , time: 77.0265 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 121.104--- , test psnr: 0.422228--- , time: 86.4716 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 121.514--- , test psnr: 0.712608--- , time: 95.9043 seconds ---\n",
      "==>>> N: 4, F: 5, train psnr: 121.291--- , test psnr: 0.497082--- , time: 105.309 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 117.332--- , test psnr: 0.917237--- , time: 10.811 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 117.396--- , test psnr: 0.819019--- , time: 21.7263 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 117.323--- , test psnr: 0.928216--- , time: 32.1016 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 115.489--- , test psnr: 0.841428--- , time: 42.9654 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 111.057--- , test psnr: 0.73867--- , time: 54.0036 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 117.416--- , test psnr: 0.731571--- , time: 64.7215 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 117.365--- , test psnr: 0.841664--- , time: 75.6434 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 117.326--- , test psnr: 0.750104--- , time: 86.4962 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 117.335--- , test psnr: 0.902357--- , time: 97.3642 seconds ---\n",
      "==>>> N: 4, F: 6, train psnr: 117.397--- , test psnr: 0.823005--- , time: 108.314 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 123.868--- , test psnr: 1.04281--- , time: 11.6606 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 124.024--- , test psnr: 1.10181--- , time: 22.6588 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 122.336--- , test psnr: 0.903535--- , time: 33.3555 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 122.714--- , test psnr: 1.05052--- , time: 44.9435 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 123.704--- , test psnr: 1.04019--- , time: 56.2481 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 123.355--- , test psnr: 0.934197--- , time: 67.6382 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 123.702--- , test psnr: 0.998855--- , time: 79.3445 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 124.02--- , test psnr: 1.08182--- , time: 90.5014 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 123.177--- , test psnr: 1.04909--- , time: 101.511 seconds ---\n",
      "==>>> N: 4, F: 7, train psnr: 123.565--- , test psnr: 1.25994--- , time: 112.334 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 117.509--- , test psnr: 0.935666--- , time: 10.8908 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 117.41--- , test psnr: 0.918401--- , time: 22.1026 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 117.471--- , test psnr: 0.981734--- , time: 33.2106 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 117.279--- , test psnr: 0.838094--- , time: 44.4397 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 117.409--- , test psnr: 1.10133--- , time: 55.4533 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 117.463--- , test psnr: 0.964826--- , time: 66.3158 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 117.423--- , test psnr: 0.847796--- , time: 77.2078 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 111.042--- , test psnr: 0.948312--- , time: 88.063 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 117.457--- , test psnr: 0.754422--- , time: 99.868 seconds ---\n",
      "==>>> N: 4, F: 8, train psnr: 117.362--- , test psnr: 0.865214--- , time: 110.764 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 116.414--- , test psnr: -0.519201--- , time: 10.8147 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 116.467--- , test psnr: -0.26562--- , time: 22.5278 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 116.51--- , test psnr: -0.0457134--- , time: 33.4181 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 116.427--- , test psnr: -0.230477--- , time: 44.1121 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 116.526--- , test psnr: -0.172689--- , time: 55.0488 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 116.502--- , test psnr: 0.0307178--- , time: 66.4505 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 116.472--- , test psnr: -0.253859--- , time: 77.4932 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 116.43--- , test psnr: -0.32915--- , time: 88.4122 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 116.267--- , test psnr: -0.0333048--- , time: 99.53 seconds ---\n",
      "==>>> N: 4, F: 9, train psnr: 116.472--- , test psnr: -0.18898--- , time: 111.394 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 126.116--- , test psnr: 0.324647--- , time: 11.2581 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 126.221--- , test psnr: 0.3829--- , time: 21.9756 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 126.252--- , test psnr: 0.184745--- , time: 32.7829 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 126.23--- , test psnr: 0.413997--- , time: 43.572 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 126.143--- , test psnr: 0.105212--- , time: 54.6127 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 126.244--- , test psnr: 0.228345--- , time: 65.0179 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 126.13--- , test psnr: 0.36624--- , time: 76.0398 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 126.014--- , test psnr: 0.321126--- , time: 87.0425 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 126.041--- , test psnr: 0.170982--- , time: 98.34 seconds ---\n",
      "==>>> N: 5, F: 0, train psnr: 126.182--- , test psnr: 0.366415--- , time: 109.892 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 127.55--- , test psnr: 0.512808--- , time: 10.8975 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 127.532--- , test psnr: 0.609527--- , time: 21.5592 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 127.408--- , test psnr: 0.340818--- , time: 33.2854 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 127.463--- , test psnr: 0.466983--- , time: 43.0581 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 127.508--- , test psnr: 0.555943--- , time: 54.0358 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 127.526--- , test psnr: 0.369764--- , time: 64.9842 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 127.561--- , test psnr: 0.512083--- , time: 75.9824 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 127.52--- , test psnr: 0.399353--- , time: 87.2759 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 127.615--- , test psnr: 0.470359--- , time: 98.4253 seconds ---\n",
      "==>>> N: 5, F: 1, train psnr: 127.468--- , test psnr: 0.379433--- , time: 109.106 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 118.543--- , test psnr: -0.494015--- , time: 11.0733 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 118.301--- , test psnr: -0.451608--- , time: 22.4174 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 118.319--- , test psnr: -0.548728--- , time: 33.8706 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 118.444--- , test psnr: -0.705905--- , time: 45.0905 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 118.232--- , test psnr: -0.543509--- , time: 55.9993 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 118.404--- , test psnr: -0.613821--- , time: 67.1004 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 118.191--- , test psnr: -0.756103--- , time: 78.1453 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 118.323--- , test psnr: -0.705167--- , time: 89.0564 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 118.283--- , test psnr: -0.650646--- , time: 100.262 seconds ---\n",
      "==>>> N: 5, F: 2, train psnr: 118.442--- , test psnr: -0.530844--- , time: 111.989 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 125.758--- , test psnr: -0.630953--- , time: 11.742 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 125.825--- , test psnr: -0.669273--- , time: 23.1056 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 125.388--- , test psnr: -0.586207--- , time: 35.2406 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 125.789--- , test psnr: -0.723713--- , time: 46.6165 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 125.643--- , test psnr: -0.59683--- , time: 57.9035 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 125.503--- , test psnr: -0.676981--- , time: 68.9797 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 125.592--- , test psnr: -0.468015--- , time: 80.113 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 125.633--- , test psnr: -0.630886--- , time: 90.844 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 125.913--- , test psnr: -0.499801--- , time: 101.765 seconds ---\n",
      "==>>> N: 5, F: 3, train psnr: 125.706--- , test psnr: -0.620429--- , time: 112.607 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 117.9--- , test psnr: -0.230225--- , time: 10.9094 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 117.956--- , test psnr: -0.260374--- , time: 21.6563 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 117.871--- , test psnr: -0.177246--- , time: 32.3267 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 117.812--- , test psnr: -0.418196--- , time: 42.9632 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 117.832--- , test psnr: -0.312514--- , time: 53.6694 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 117.781--- , test psnr: -0.178654--- , time: 65.0254 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 117.892--- , test psnr: -0.117769--- , time: 76.0987 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 117.943--- , test psnr: -0.177161--- , time: 86.3646 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 117.881--- , test psnr: -0.291871--- , time: 97.5817 seconds ---\n",
      "==>>> N: 5, F: 4, train psnr: 117.946--- , test psnr: -0.364121--- , time: 109.301 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 126.639--- , test psnr: -0.72528--- , time: 10.8889 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 126.332--- , test psnr: -0.80087--- , time: 21.7943 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 126.641--- , test psnr: -0.774093--- , time: 33.1759 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 126.593--- , test psnr: -0.706239--- , time: 44.0717 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 126.324--- , test psnr: -0.73032--- , time: 54.9337 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 126.569--- , test psnr: -0.650316--- , time: 66.1586 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 126.703--- , test psnr: -0.662276--- , time: 77.0914 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 126.573--- , test psnr: -0.808565--- , time: 88.1122 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 126.72--- , test psnr: -0.72866--- , time: 99.0116 seconds ---\n",
      "==>>> N: 5, F: 5, train psnr: 126.672--- , test psnr: -0.806267--- , time: 109.903 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 122.752--- , test psnr: 1.07189--- , time: 10.6983 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 119.58--- , test psnr: 1.07539--- , time: 21.2929 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 123.124--- , test psnr: 1.13008--- , time: 32.0818 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 121.958--- , test psnr: 1.09868--- , time: 42.6081 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 123.594--- , test psnr: 1.15094--- , time: 53.4679 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 123.448--- , test psnr: 1.16281--- , time: 64.5802 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 123.405--- , test psnr: 1.14935--- , time: 75.6581 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 123.861--- , test psnr: 0.999796--- , time: 86.5797 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 123.513--- , test psnr: 1.16575--- , time: 97.4604 seconds ---\n",
      "==>>> N: 5, F: 6, train psnr: 122.391--- , test psnr: 1.11558--- , time: 108.166 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 128.883--- , test psnr: 0.381871--- , time: 11.1056 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 128.733--- , test psnr: 0.369924--- , time: 22.416 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 128.742--- , test psnr: 0.482322--- , time: 34.3083 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 128.764--- , test psnr: 0.460934--- , time: 45.0964 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 128.738--- , test psnr: 0.415343--- , time: 56.0172 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 128.738--- , test psnr: 0.406256--- , time: 66.712 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 128.72--- , test psnr: 0.517312--- , time: 77.63 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 128.802--- , test psnr: 0.428911--- , time: 88.6142 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 128.709--- , test psnr: 0.460788--- , time: 99.7013 seconds ---\n",
      "==>>> N: 5, F: 7, train psnr: 128.734--- , test psnr: 0.403899--- , time: 110.763 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 120.662--- , test psnr: 1.07631--- , time: 11.0773 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 122.981--- , test psnr: 1.04072--- , time: 22.1791 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 122.975--- , test psnr: 1.06533--- , time: 33.3377 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 123.801--- , test psnr: 1.09838--- , time: 45.0692 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 123.497--- , test psnr: 1.18454--- , time: 56.7242 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 119.351--- , test psnr: 1.01792--- , time: 68.0339 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 123.497--- , test psnr: 1.05032--- , time: 79.1807 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 121.148--- , test psnr: 1.10594--- , time: 91.0432 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 123.505--- , test psnr: 0.97343--- , time: 101.908 seconds ---\n",
      "==>>> N: 5, F: 8, train psnr: 123.569--- , test psnr: 1.11976--- , time: 112.939 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 118.7--- , test psnr: 0.325524--- , time: 10.0579 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 118.767--- , test psnr: 0.396211--- , time: 19.8207 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 118.744--- , test psnr: 0.278164--- , time: 30.6714 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 118.607--- , test psnr: 0.403817--- , time: 41.6728 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 118.696--- , test psnr: 0.440207--- , time: 52.4011 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 118.688--- , test psnr: 0.163341--- , time: 63.3418 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 118.663--- , test psnr: 0.170597--- , time: 74.3913 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 118.644--- , test psnr: 0.23479--- , time: 84.1499 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 118.651--- , test psnr: 0.30951--- , time: 95.0099 seconds ---\n",
      "==>>> N: 5, F: 9, train psnr: 118.735--- , test psnr: 0.397126--- , time: 106.065 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 124.893--- , test psnr: -1.96485--- , time: 11.1092 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 124.924--- , test psnr: -1.90743--- , time: 22.0847 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 124.837--- , test psnr: -2.15901--- , time: 33.035 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 124.998--- , test psnr: -2.09448--- , time: 44.4486 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 124.884--- , test psnr: -1.87103--- , time: 55.2409 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 124.717--- , test psnr: -1.96522--- , time: 66.3364 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 125.171--- , test psnr: -2.04914--- , time: 77.3018 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 125.019--- , test psnr: -2.14661--- , time: 87.6683 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 124.732--- , test psnr: -2.03375--- , time: 98.5011 seconds ---\n",
      "==>>> N: 6, F: 0, train psnr: 125.11--- , test psnr: -2.08252--- , time: 109.864 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 124.334--- , test psnr: 0.941266--- , time: 11.3017 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 124.73--- , test psnr: 0.88283--- , time: 23.0454 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 124.346--- , test psnr: 0.858877--- , time: 34.0047 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 124.505--- , test psnr: 1.03182--- , time: 45.0459 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 124.255--- , test psnr: 1.09301--- , time: 56.1309 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 124.403--- , test psnr: 1.00712--- , time: 67.3668 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 124.398--- , test psnr: 0.867152--- , time: 79.1272 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 124.266--- , test psnr: 0.994531--- , time: 90.8786 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 124.544--- , test psnr: 1.0554--- , time: 102.279 seconds ---\n",
      "==>>> N: 6, F: 1, train psnr: 124.674--- , test psnr: 0.895068--- , time: 113.902 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 129.953--- , test psnr: 0.865609--- , time: 11.4753 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 129.867--- , test psnr: 0.809008--- , time: 23.1305 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 129.875--- , test psnr: 0.767083--- , time: 34.8438 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 129.712--- , test psnr: 0.730725--- , time: 46.4375 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 129.982--- , test psnr: 0.917618--- , time: 57.3745 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 129.917--- , test psnr: 0.74943--- , time: 68.8849 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 129.644--- , test psnr: 0.779921--- , time: 80.5649 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 129.866--- , test psnr: 0.809312--- , time: 92.5534 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 129.74--- , test psnr: 0.878634--- , time: 104.554 seconds ---\n",
      "==>>> N: 6, F: 2, train psnr: 129.801--- , test psnr: 0.701767--- , time: 115.479 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 129.426--- , test psnr: -0.738552--- , time: 11.3505 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 129.405--- , test psnr: -0.718467--- , time: 22.3459 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 129.252--- , test psnr: -0.672411--- , time: 33.2452 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 129.316--- , test psnr: -0.729476--- , time: 44.3076 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 129.254--- , test psnr: -0.696403--- , time: 55.0791 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 129.242--- , test psnr: -0.773517--- , time: 65.9691 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 129.239--- , test psnr: -0.704734--- , time: 77.1961 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 129.495--- , test psnr: -0.782703--- , time: 88.0538 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 129.352--- , test psnr: -0.779132--- , time: 99.0005 seconds ---\n",
      "==>>> N: 6, F: 3, train psnr: 129.272--- , test psnr: -0.654844--- , time: 109.912 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 124.387--- , test psnr: -1.04717--- , time: 11.6547 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 124.371--- , test psnr: -0.810759--- , time: 22.7207 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 124.203--- , test psnr: -1.11314--- , time: 34.4794 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 124.142--- , test psnr: -1.19593--- , time: 45.6662 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 124.596--- , test psnr: -1.02995--- , time: 56.7303 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 124.295--- , test psnr: -0.884925--- , time: 68.488 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 124.488--- , test psnr: -1.05982--- , time: 78.4987 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 124.436--- , test psnr: -1.09558--- , time: 89.5929 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 124.199--- , test psnr: -0.824906--- , time: 100.544 seconds ---\n",
      "==>>> N: 6, F: 4, train psnr: 124.136--- , test psnr: -1.13296--- , time: 111.584 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 126.341--- , test psnr: 1.17616--- , time: 10.9662 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 125.954--- , test psnr: 1.21326--- , time: 21.8992 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 126.44--- , test psnr: 1.16896--- , time: 32.6317 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 126.322--- , test psnr: 1.41223--- , time: 43.5135 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 126.404--- , test psnr: 1.37--- , time: 54.5129 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 126.111--- , test psnr: 1.17297--- , time: 65.3353 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 126.102--- , test psnr: 1.33856--- , time: 76.3005 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 126.271--- , test psnr: 1.20118--- , time: 86.2871 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 126.107--- , test psnr: 1.08595--- , time: 97.1343 seconds ---\n",
      "==>>> N: 6, F: 5, train psnr: 126.214--- , test psnr: 1.20764--- , time: 108.015 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 128.017--- , test psnr: 1.91272--- , time: 10.8341 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 127.969--- , test psnr: 1.89959--- , time: 21.3474 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 128.211--- , test psnr: 1.89429--- , time: 31.9381 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 128.273--- , test psnr: 1.9877--- , time: 42.9501 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 128.223--- , test psnr: 1.97347--- , time: 53.7753 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 128.548--- , test psnr: 1.91938--- , time: 64.7105 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 128.326--- , test psnr: 1.71294--- , time: 75.6131 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 128.136--- , test psnr: 1.93659--- , time: 85.7458 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 128.152--- , test psnr: 2.02928--- , time: 96.6747 seconds ---\n",
      "==>>> N: 6, F: 6, train psnr: 128.046--- , test psnr: 1.70725--- , time: 107.435 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 125.695--- , test psnr: 0.796538--- , time: 11.4927 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 125.412--- , test psnr: 0.976315--- , time: 23.3126 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 125.666--- , test psnr: 0.761714--- , time: 34.9055 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 125.583--- , test psnr: 0.697722--- , time: 46.7344 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 125.614--- , test psnr: 0.842745--- , time: 58.1561 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 125.682--- , test psnr: 0.842388--- , time: 69.6017 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 125.538--- , test psnr: 0.925831--- , time: 80.8787 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 125.654--- , test psnr: 0.669348--- , time: 91.7887 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 125.443--- , test psnr: 0.619082--- , time: 102.637 seconds ---\n",
      "==>>> N: 6, F: 7, train psnr: 125.356--- , test psnr: 0.812673--- , time: 113.384 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 128.294--- , test psnr: 1.83781--- , time: 11.2519 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 128.258--- , test psnr: 1.90618--- , time: 22.2773 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 128.385--- , test psnr: 1.96078--- , time: 33.3053 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 128.194--- , test psnr: 2.08965--- , time: 44.4688 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 128.208--- , test psnr: 1.69964--- , time: 55.7105 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 128.118--- , test psnr: 1.93484--- , time: 67.8222 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 128.119--- , test psnr: 1.97658--- , time: 79.4938 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 127.928--- , test psnr: 1.82673--- , time: 90.9721 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 128.387--- , test psnr: 2.07948--- , time: 102.407 seconds ---\n",
      "==>>> N: 6, F: 8, train psnr: 128.155--- , test psnr: 1.79569--- , time: 113.643 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 117.189--- , test psnr: 0.384061--- , time: 11.2328 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 116.997--- , test psnr: 0.0988228--- , time: 22.185 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 116.908--- , test psnr: 0.241285--- , time: 33.0838 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 117.116--- , test psnr: 0.228102--- , time: 43.7865 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 117.057--- , test psnr: 0.35616--- , time: 53.3012 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 116.955--- , test psnr: 0.260726--- , time: 63.5546 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 117.004--- , test psnr: 0.176618--- , time: 73.8041 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 116.992--- , test psnr: 0.305498--- , time: 84.0398 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 117.051--- , test psnr: 0.233237--- , time: 94.6397 seconds ---\n",
      "==>>> N: 6, F: 9, train psnr: 117.026--- , test psnr: 0.285404--- , time: 106.063 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 129.205--- , test psnr: 0.619858--- , time: 11.0744 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 129.538--- , test psnr: 0.7513--- , time: 22.3308 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 129.45--- , test psnr: 0.564516--- , time: 33.3378 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 129.295--- , test psnr: 0.697846--- , time: 44.0741 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 129.603--- , test psnr: 0.606654--- , time: 54.7366 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 129.448--- , test psnr: 0.745248--- , time: 65.8641 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 129.201--- , test psnr: 0.695364--- , time: 76.937 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 129.767--- , test psnr: 0.686329--- , time: 88.0069 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 129.393--- , test psnr: 0.475673--- , time: 99.0172 seconds ---\n",
      "==>>> N: 7, F: 0, train psnr: 129.478--- , test psnr: 0.526949--- , time: 110.196 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 135.474--- , test psnr: 1.07016--- , time: 11.8193 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 135.684--- , test psnr: 1.38708--- , time: 22.9847 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 135.606--- , test psnr: 1.24527--- , time: 33.3407 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 135.485--- , test psnr: 1.04828--- , time: 43.9075 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 135.577--- , test psnr: 1.15627--- , time: 54.7715 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 135.179--- , test psnr: 1.12701--- , time: 65.5468 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 135.69--- , test psnr: 1.3251--- , time: 77.1531 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 135.96--- , test psnr: 1.26003--- , time: 88.7771 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 135.437--- , test psnr: 1.19136--- , time: 99.7595 seconds ---\n",
      "==>>> N: 7, F: 1, train psnr: 135.655--- , test psnr: 1.2802--- , time: 110.643 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 125.675--- , test psnr: -0.407312--- , time: 11.0067 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 125.454--- , test psnr: -0.49039--- , time: 21.7475 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 125.58--- , test psnr: -0.353614--- , time: 32.7079 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 125.841--- , test psnr: -0.516196--- , time: 43.6064 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 125.607--- , test psnr: -0.390822--- , time: 54.5863 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 125.708--- , test psnr: -0.199974--- , time: 65.3524 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 125.619--- , test psnr: -0.493052--- , time: 76.4842 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 125.7--- , test psnr: -0.505244--- , time: 87.3155 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 125.226--- , test psnr: -0.476746--- , time: 98.2543 seconds ---\n",
      "==>>> N: 7, F: 2, train psnr: 125.556--- , test psnr: -0.619329--- , time: 110.609 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 125.883--- , test psnr: 2.7646--- , time: 11.7758 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 126.077--- , test psnr: 2.47474--- , time: 23.405 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 126.517--- , test psnr: 2.93221--- , time: 34.4175 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 125.931--- , test psnr: 3.0281--- , time: 45.9917 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 127.347--- , test psnr: 2.99735--- , time: 57.5827 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 126.135--- , test psnr: 2.77032--- , time: 68.2336 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 125.934--- , test psnr: 3.00575--- , time: 80.0652 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 126.055--- , test psnr: 2.9264--- , time: 90.975 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 126.069--- , test psnr: 2.71941--- , time: 102.305 seconds ---\n",
      "==>>> N: 7, F: 3, train psnr: 126.087--- , test psnr: 2.64671--- , time: 113.429 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 126.36--- , test psnr: -1.39009--- , time: 11.1097 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 126.647--- , test psnr: -1.52579--- , time: 22.8628 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 126.27--- , test psnr: -1.22941--- , time: 33.7532 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 126.151--- , test psnr: -1.31961--- , time: 44.7675 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 126.855--- , test psnr: -1.48834--- , time: 55.6487 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 126.572--- , test psnr: -1.297--- , time: 67.3468 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 126.244--- , test psnr: -1.3235--- , time: 78.3938 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 126.303--- , test psnr: -1.47524--- , time: 89.4096 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 126.514--- , test psnr: -1.37155--- , time: 100.499 seconds ---\n",
      "==>>> N: 7, F: 4, train psnr: 126.137--- , test psnr: -1.24739--- , time: 111.59 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 128.702--- , test psnr: 1.40984--- , time: 11.1445 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 128.982--- , test psnr: 1.53954--- , time: 22.8197 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 128.864--- , test psnr: 1.43442--- , time: 34.3785 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 129.186--- , test psnr: 1.47818--- , time: 45.8302 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 128.952--- , test psnr: 1.41242--- , time: 56.6643 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 128.897--- , test psnr: 1.36983--- , time: 67.4114 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 128.77--- , test psnr: 1.20943--- , time: 78.3422 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 128.97--- , test psnr: 1.34946--- , time: 89.7165 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 129.856--- , test psnr: 1.24162--- , time: 100.142 seconds ---\n",
      "==>>> N: 7, F: 5, train psnr: 128.742--- , test psnr: 1.2454--- , time: 111.586 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 127.39--- , test psnr: -0.550672--- , time: 11.4381 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 127.493--- , test psnr: -0.553869--- , time: 22.2656 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 127.396--- , test psnr: -0.285584--- , time: 33.2013 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 127.583--- , test psnr: -0.520027--- , time: 44.7258 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 127.494--- , test psnr: -0.589642--- , time: 56.4295 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 127.56--- , test psnr: -0.478365--- , time: 67.1535 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 127.536--- , test psnr: -0.571344--- , time: 78.1235 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 127.473--- , test psnr: -0.494865--- , time: 89.0277 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 127.62--- , test psnr: -0.437499--- , time: 100.468 seconds ---\n",
      "==>>> N: 7, F: 6, train psnr: 127.895--- , test psnr: -0.636168--- , time: 111.815 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 132.233--- , test psnr: 3.73707--- , time: 11.3189 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 130.882--- , test psnr: 3.88613--- , time: 22.0295 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 130.112--- , test psnr: 3.82847--- , time: 34.0068 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 131.113--- , test psnr: 3.87947--- , time: 45.5732 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 132.266--- , test psnr: 3.85045--- , time: 56.3759 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 132.79--- , test psnr: 3.85904--- , time: 67.5753 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 130.993--- , test psnr: 3.70191--- , time: 78.7645 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 131.421--- , test psnr: 3.76928--- , time: 89.9189 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 129.981--- , test psnr: 4.02059--- , time: 100.165 seconds ---\n",
      "==>>> N: 7, F: 7, train psnr: 131.364--- , test psnr: 3.7454--- , time: 111.206 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 127.399--- , test psnr: -0.512467--- , time: 11.0777 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 127.349--- , test psnr: -0.326175--- , time: 22.1364 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 127.447--- , test psnr: -0.46182--- , time: 31.9044 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 127.711--- , test psnr: -0.567196--- , time: 41.6476 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 127.622--- , test psnr: -0.505435--- , time: 51.8259 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 127.587--- , test psnr: -0.572894--- , time: 62.1392 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 127.739--- , test psnr: -0.604577--- , time: 72.0142 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 127.479--- , test psnr: -0.524407--- , time: 81.5075 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 127.565--- , test psnr: -0.466901--- , time: 92.0714 seconds ---\n",
      "==>>> N: 7, F: 8, train psnr: 127.621--- , test psnr: -0.659712--- , time: 103.253 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 127.437--- , test psnr: -0.70911--- , time: 10.235 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 127.206--- , test psnr: -0.829765--- , time: 21.0756 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 126.941--- , test psnr: -0.775173--- , time: 32.0567 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 127.089--- , test psnr: -0.937918--- , time: 43.1147 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 126.862--- , test psnr: -0.721645--- , time: 54.111 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 126.7--- , test psnr: -0.785352--- , time: 65.1608 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 127.062--- , test psnr: -0.908987--- , time: 76.4638 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 127.221--- , test psnr: -0.869585--- , time: 87.5632 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 126.656--- , test psnr: -0.887279--- , time: 98.6621 seconds ---\n",
      "==>>> N: 7, F: 9, train psnr: 126.72--- , test psnr: -0.952313--- , time: 109.876 seconds ---\n"
     ]
    }
   ],
   "source": [
    "all_data = torch.from_numpy(np.linspace(0, 1, sample_N+1)[:-1].reshape(-1,1)).type(torch.FloatTensor)\n",
    "\n",
    "\n",
    "\n",
    "for i in range(Nd):\n",
    "    sample_d = sample_ds[i]\n",
    "\n",
    "    for j in range(N_test):\n",
    "        start_time = time.time()\n",
    "        \n",
    "        \n",
    "        # Prepare the data\n",
    "        all_target = signal[kk[j]].reshape(-1,1)\n",
    "        train_label = all_target[::2*sample_d]\n",
    "        test_label = all_target[sample_d::2*sample_d]\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        for k in range(N_repeat):\n",
    "            \n",
    "            c = np.random.randn(embedding_dimension)\n",
    "            c = c/np.linalg.norm(c)\n",
    "            \n",
    "            train_data = shift_embedding(all_data[::2*sample_d],c)\n",
    "            test_data = shift_embedding(all_data[sample_d::2*sample_d],c)\n",
    "            \n",
    "            xt, yt = train_data.cuda(),train_label.cuda()\n",
    "            xt, yt = Variable(xt), Variable(yt)\n",
    "\n",
    "            xts, yts = test_data.cuda(),test_label.cuda()\n",
    "\n",
    "            # Calculate the input stable rank\n",
    "            sr_[i,j,k] = srank_func(train_data)\n",
    "\n",
    "            \n",
    "            # Initialize classification model to learn\n",
    "            model = SLP(embedding_dimension).cuda()\n",
    "            # Set the optimization\n",
    "            optimizer = optim.Adam(model.parameters(), 2e-4, betas=(0.9, 0.999),weight_decay=1e-8)\n",
    "            \n",
    "\n",
    "            for epoch in range(epochs):\n",
    "                model.train()\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                out = model(xt)\n",
    "                loss = criterion(out, yt)\n",
    "\n",
    "                loss.backward()\n",
    "                losses_[i,j,k,epoch] = loss.item()\n",
    "                optimizer.step()\n",
    "\n",
    "                model.eval()\n",
    "                trn_psnr_[i,j,k,epoch] = psnr_func(model(xt),yt)\n",
    "                tst_psnr_[i,j,k,epoch] = psnr_func(model(xts),yts)\n",
    "\n",
    "\n",
    "            print(\"==>>> N: %g, F: %g, train psnr: %g--- , test psnr: %g--- , time: %g seconds ---\" \n",
    "                    % (i,j, np.mean(trn_psnr_[i,j,k,-100:]),np.mean(tst_psnr_[i,j,k,-100:]),time.time() - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f433856a198>]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure()\n",
    "for i in range(Nd):\n",
    "    plot(np.mean(trn_psnr_[i,:,:],(0,1)))\n",
    "    plot(np.mean(tst_psnr_[i,:,:],(0,1)),'--')\n",
    "\n",
    "figure()\n",
    "plot(sample_ds,np.mean(sr_,(1,2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_name = 'random_random'\n",
    "np.save(file_path+file_name+'_losses',losses_)\n",
    "np.save(file_path+file_name+'_sr',sr_)\n",
    "np.save(file_path+file_name+'_trn',trn_psnr_)\n",
    "np.save(file_path+file_name+'_tst',tst_psnr_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}