Notebooks/GAN_1D.ipynb

{
 "cells": [
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   "cell_type": "markdown",
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    "colab_type": "text",
    "id": "P5TEke7_J6wb"
   },
   "source": [
    "# GAN demo using simple algebraic function\n",
    "### Dr. Tirthajyoti Sarkar, Fremont, CA 94536\n",
    "\n",
    "We demonstrate building a Generative Adversarial Network (GAN) using Keras with a simple algebraic function\n",
    "\n",
    "### What is GAN?\n",
    "\n",
    "A generative adversarial network (GAN) is a class of machine learning systems invented by Ian Goodfellow and his colleagues in 2014.\n",
    "\n",
    "Two neural networks contest with each other in a game (in the sense of game theory, often but not always in the form of a **zero-sum game**). Given a training set, this technique **learns to generate new data with the same (or close enough) statistical distribution as that of the training set**. For example, a GAN trained on photographs can generate new photographs that look at least superficially authentic to human observers, having many realistic characteristics.\n",
    "\n",
    "Generative Adversarial Networks belong to the set of generative models. It means that they are able to produce / to generate (we’ll see how) new content.\n",
    "\n",
    "Here is an excellent article summarizing the concept: https://towardsdatascience.com/understanding-generative-adversarial-networks-gans-cd6e4651a29\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1956,
     "status": "ok",
     "timestamp": 1568075421237,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "QssjlDOTKOEV",
    "outputId": "10fb41f7-ff97-462f-c0d0-83f1fa7c5046"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.utils.vis_utils import plot_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Xy3_L6FNKcHp"
   },
   "source": [
    "## Simple function we want to emulate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "capvHI14KYOB"
   },
   "outputs": [],
   "source": [
    "def func(x):\n",
    "  from math import sin\n",
    "  return (x**2*np.sin(3*x)/(x**2+2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TdqKqCMaKqum"
   },
   "outputs": [],
   "source": [
    "x = np.linspace(-1,1,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1191,
     "status": "ok",
     "timestamp": 1568075423860,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "EkXybD01Kts-",
    "outputId": "803eff9e-9179-4d20-c4fc-971900501d16"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,func(x),color='k',lw=3)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RdRCOOUnLfJu"
   },
   "source": [
    "## The Discriminator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gjQSyOW5YtK-"
   },
   "source": [
    "### Define the discriminator model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8nDuzUzxLBEf"
   },
   "outputs": [],
   "source": [
    "def define_discriminator(n_inputs=2):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(100, activation='relu', kernel_initializer='he_uniform', input_dim=n_inputs))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    # compile model\n",
    "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 752
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1438,
     "status": "ok",
     "timestamp": 1568075426303,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "v42RfX90Lj4Z",
    "outputId": "90310a2a-2d4d-42f7-8153-869f131450bb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_25 (Dense)             (None, 100)               300       \n",
      "_________________________________________________________________\n",
      "dense_26 (Dense)             (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 401\n",
      "Trainable params: 401\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# define the discriminator model\n",
    "model_discriminator = define_discriminator()\n",
    "# summarize the model\n",
    "model_discriminator.summary()\n",
    "# plot the model\n",
    "#plot_model(model_discriminator, to_file='discriminator_plot.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fBZPMOqEMCP7"
   },
   "source": [
    "### Generate real samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8oNeprOLLuhv"
   },
   "outputs": [],
   "source": [
    "def generate_real_samples(n,l=-1,h=1):\n",
    "  # generate inputs in [-l, h]\n",
    "  X1 = l+(h-l)*np.random.random(size=n)\n",
    "\t# generate outputs X^2\n",
    "  X2 =func(X1)\n",
    "\t# stack arrays\n",
    "  X1 = X1.reshape(n, 1)\n",
    "  X2 = X2.reshape(n, 1)\n",
    "  X = np.hstack((X1, X2))\n",
    "  # generate class labels\n",
    "  y = np.ones((n, 1))\n",
    "  return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 168
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 896,
     "status": "ok",
     "timestamp": 1568075427056,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "dEqTejd6MaEH",
    "outputId": "28745a31-9581-494b-c5a3-afde9f674ff5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[-0.351729  , -0.05067992],\n",
       "        [-0.53748979, -0.12610645],\n",
       "        [-0.67981112, -0.16746267],\n",
       "        [-0.28882914, -0.0305136 ],\n",
       "        [ 0.98975672,  0.05637502]]), array([[1.],\n",
       "        [1.],\n",
       "        [1.],\n",
       "        [1.],\n",
       "        [1.]]))"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generate_real_samples(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "c9dQJsjvM6en"
   },
   "source": [
    "### Generate fake samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7tDgTDy8M5zO"
   },
   "outputs": [],
   "source": [
    "def generate_fake_samples(n,l=-1,h=1):\n",
    "  # generate inputs in [-l, h]\n",
    "  X1 = l+(h-l)*np.random.random(size=n)\n",
    "\t# generate outputs X^2\n",
    "  X2 = l+(h-l)*np.random.random(size=n)\n",
    "\t# stack arrays\n",
    "  X1 = X1.reshape(n, 1)\n",
    "  X2 = X2.reshape(n, 1)\n",
    "  X = np.hstack((X1, X2))\n",
    "  # generate class labels\n",
    "  y = np.zeros((n, 1))\n",
    "  return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 168
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 903,
     "status": "ok",
     "timestamp": 1568075429281,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "cs50i_XxMpw1",
    "outputId": "97a582e3-410a-4063-ca3e-09d6cf4f6a2b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[ 0.76073378, -0.7251373 ],\n",
       "        [ 0.8027109 , -0.02068532],\n",
       "        [-0.22343495, -0.5694514 ],\n",
       "        [-0.20253836,  0.44344168],\n",
       "        [-0.40488459,  0.90334664]]), array([[0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.]]))"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generate_fake_samples(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XtN7wrMxOl26"
   },
   "source": [
    "### Function to train the discriminator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "u2h3T3UsNIaN"
   },
   "outputs": [],
   "source": [
    "def train_discriminator(model, n_epochs=1000, n_batch=128):\n",
    "    half_batch = int(n_batch / 2)\n",
    "    # run epochs manually\n",
    "    for i in range(n_epochs):\n",
    "        # generate real examples\n",
    "        X_real, y_real = generate_real_samples(half_batch)\n",
    "        # update model\n",
    "        model.train_on_batch(X_real, y_real)\n",
    "        # generate fake examples\n",
    "        X_fake, y_fake = generate_fake_samples(half_batch)\n",
    "        # update model\n",
    "        model.train_on_batch(X_fake, y_fake)\n",
    "        # evaluate the model\n",
    "        _, acc_real = model.evaluate(X_real, y_real, verbose=0)\n",
    "        _, acc_fake = model.evaluate(X_fake, y_fake, verbose=0)\n",
    "        print(i, acc_real, acc_fake)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 406
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2569,
     "status": "ok",
     "timestamp": 1568075434453,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "0LpEMQIhPAeF",
    "outputId": "ce52dad1-a7a6-4382-a58e-99987dfa5006"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.015625 1.0\n",
      "1 0.0625 1.0\n",
      "2 0.015625 1.0\n",
      "3 0.015625 0.984375\n",
      "4 0.015625 1.0\n",
      "5 0.046875 1.0\n",
      "6 0.046875 1.0\n",
      "7 0.0625 0.96875\n",
      "8 0.125 1.0\n",
      "9 0.046875 1.0\n",
      "10 0.078125 1.0\n",
      "11 0.109375 1.0\n",
      "12 0.09375 0.984375\n",
      "13 0.03125 1.0\n",
      "14 0.140625 1.0\n",
      "15 0.140625 1.0\n",
      "16 0.15625 0.984375\n",
      "17 0.109375 1.0\n",
      "18 0.109375 1.0\n",
      "19 0.140625 1.0\n"
     ]
    }
   ],
   "source": [
    "train_discriminator(model_discriminator,n_epochs=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 827,
     "status": "ok",
     "timestamp": 1568075436486,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "l71--AR2QFJV",
    "outputId": "11c5467f-4aad-4201-9373-f4920fb86a94"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 199us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.43262189626693726, 1.0]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_t,y_t=generate_fake_samples(5)\n",
    "model_discriminator.evaluate(x_t,y_t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xo6yXEYqYxpR"
   },
   "source": [
    "## The Generator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0voVik87SsTF"
   },
   "source": [
    "Training the discriminator model is straightforward. The goal is to train a generator model, not a discriminator model, and that is where the complexity of GANs truly lies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CImRWUgJSu78"
   },
   "source": [
    "\n",
    "### Defining the Generator model\n",
    "\n",
    "The generator model **takes as input a point from the latent space and generates a new sample**, e.g. a vector with both the input and output elements of our function.\n",
    "\n",
    "A **latent variable** is a hidden or unobserved variable, and a latent space is a multi-dimensional vector space of these variables. We can **freely choose** the **size of the latent space** for our problem and the **shape or distribution of variables** in the latent space.\n",
    "\n",
    "This is because the latent space has no meaning until the generator model starts assigning meaning to points in the space as it learns. After training, points in the latent space will correspond to points in the output space, e.g. in the space of generated samples.\n",
    "\n",
    "We will define a small latent space of a few dimensions and use the standard approach in the GAN literature of using a **Gaussian distribution** for each variable in the latent space. We will generate new inputs by **drawing random numbers from a standard Gaussian distribution, i.e. mean of zero and a standard deviation of one**.\n",
    "\n",
    "The size of the latent dimension is parameterized in case we want to play with it later, and the output shape of the model is also parameterized, matching the function for defining the discriminator model\n",
    "\n",
    "The output layer of the generator model will have two nodes for the two elements in a generated vector and will use a **linear activation** function.\n",
    "\n",
    "Also, the generator model is **not fitted** directly, and therefore it **does not need to be compiled**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ndbia4A1QQ0m"
   },
   "outputs": [],
   "source": [
    "# define the standalone generator model\n",
    "def define_generator(latent_dim, n_outputs=2):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(50, activation='relu', kernel_initializer='he_uniform', input_dim=latent_dim))\n",
    "    model.add(Dense(n_outputs, activation='linear'))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 514
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1138,
     "status": "ok",
     "timestamp": 1568075442446,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "eA3ZdRS7Vsiv",
    "outputId": "4af4da70-9e5e-46a9-9525-af03eccaeffb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_47 (Dense)             (None, 50)                300       \n",
      "_________________________________________________________________\n",
      "dense_48 (Dense)             (None, 2)                 102       \n",
      "=================================================================\n",
      "Total params: 402\n",
      "Trainable params: 402\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# define the discriminator model\n",
    "model_gen = define_generator(5)\n",
    "# summarize the model\n",
    "model_gen.summary()\n",
    "# plot the model (does not work on my windows laptop, needs proper installation of pydot and graphviz)\n",
    "# plot_model(model_gen, to_file='generator_plot.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nfQACw2TWEu-"
   },
   "source": [
    "### Testing the Generator model\n",
    "This model cannot do much at the moment. Nevertheless, we can demonstrate how to use it to generate samples.\n",
    "\n",
    "The first step is to generate new points in the latent space. We can achieve this by calling the `randn()` NumPy function for generating arrays of random numbers drawn from a standard Gaussian.\n",
    "\n",
    "The array of random numbers can then be reshaped into samples: that is `n_rows` with five elements per row. The `generate_latent_points()` function below implements this and generates the desired number of points in the latent space that can be used as input to the generator model.\n",
    "\n",
    "Next, we can use the generated points as input the generator model to generate new samples.\n",
    "\n",
    "Note we update the `generate_fake_samples()` function in this section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WlO8lhWIWJZL"
   },
   "outputs": [],
   "source": [
    "# generate points in latent space as input for the generator\n",
    "def generate_latent_points(latent_dim, n,l=-1,h=1):\n",
    "    # generate points in the latent space\n",
    "    x_input = l+(h-l)*np.random.random(size=latent_dim*n)\n",
    "    # reshape into a batch of inputs for the network\n",
    "    x_input = x_input.reshape(n, latent_dim)\n",
    "    return x_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9MPNoALJWt8A"
   },
   "outputs": [],
   "source": [
    "def generate_fake_samples(generator, latent_dim, n):\n",
    "    # generate points in latent space\n",
    "    x_input = generate_latent_points(latent_dim, n)\n",
    "    # predict outputs\n",
    "    X = generator.predict(x_input)\n",
    "    # plot the results\n",
    "    plt.scatter(X[:, 0], X[:, 1],color='k',s=15)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1438,
     "status": "ok",
     "timestamp": 1568075447148,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "7hpFo1d6XKSy",
    "outputId": "10569d3b-73df-45cd-ea4a-5e6a6eaee70c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QY006yqtih1xEEA0tsNS93ycmXDYCQ2tSvreKz1LEeQZN+k4wGi4bgcI16SivilZNEamSJrXOUC6CAIbWpCGSVeSuiwii5OCRFUEAUauiVVNEEC3qc4TW14D80SfQB0Mgm4nvdXj0NdTPsH0CBIE+2PCbie91eFwsr37oGM4BnWzNxPc6vN6+Bkm6cuUKaaGGIQj0QSdbM/G9Dm+jr2FsbH1Xsba2xgmCDUMQ6COvTjY61cLSpKGtZdnoxN61a9e112hFNQt9AgUiB42mYFuuD/oEAjJKDppWBDaEsC3QimouWgIFGuXoiSMvbGBbwDBoCQRklKMnRrJgA9sCikQQKNAoZ4YykgUb2BZQJIJAoMjBYgPbAopEnwAANAh9AjUUwugPAHEiCASA2zWGj0CNpiIIBIDRH+EjUKOpCAIBYPRH+AjUyFNILUuCQAAY/RE+AjXyFFLLktFBQArckAZ5KvI+DcOODmoNngXAxol/QB46nc51lwKpsmVJOggAShZSCpiWAACULKSWJS2BQIU0egDA1vL4rVb5e6djOFBcPhiohzx+q3n+3rlsREMwLh34fyG3jPP4rVb5eycIBIpx6YjJoJ18SOPqN8vjt1rp793dM0+SupLOSXpd0uQ28z0g6QVJFyQdTbv8gwcPeqwuX77s09PT3m63fXp62i9fvlx1kVAzddqGpqenvdVquSRvtVo+PT193fvtdtslXZva7XZFJb3RRj2/6U1v8vHxcd+1a9fQ9Z3ndyVp3ofZjw8z8w3/LL1d0m9L+tZWQUDSDkkvSrpb0k5JZyUdSLP8mIMAMKpBO9aQDNrJ1+GzhFLGYYPASOkgdz/v7i8MmO0eSRfc/SV3vyrpMUmHR1kvgMHq1K80KB0S0rj6rdSpvnuV0Sdwu6SXe55fTF7ry8yOmNm8mc0vLS0VXjigqerUrzRoJz/KrVrLUqf67jUwCJjZU2b2gz5T2qN56/PaluNS3f2Yu0+6++SePXtSrgLAZnU4et5Qh538IHWq714Dzxh29/tHXMdFSXf2PL9D0qURlwlggJDOSo1BXeu7jHTQs5L2m9ldZrZT0oOSTpSwXgDAACMFATP7oJldlDQl6Wtmdip5/TYzOylJ7r4q6RFJpySdl/Rldz83WrEBAHkY6QJy7v6kpCf7vH5J0vt7np+UdHKUdQEA8scZwwAQMYIAAESMIAAAESMIAEDECAIAEDGCAABEjCAAABEjCABAxAgCABAxggAARIwgAAARIwgAQMQIAgAQMYIAAESMIAAAESMIAEDECAIAEDGCAABEjCCQweLiomZmZjQ+Pq6ZmRktLi5WXSQAyIQgkEG329Xc3JxWVlY0NzenbrdbdZEAIBOCQAYLCwtaXV2VJK2urmphYaHiEgFANgSBDDqdjlqtliSp1Wqp0+lUXCIAyIYgkMHs7KympqbUbrc1NTWl2dnZqosEAJk0MggU3XE7MTGh06dPa3l5WadPn9bExESuyweAsjQyCNBxCwDpNDII0HELAOk0MgjQcQsA6TQyCNBxCwDptEb5ZzPrSvpbSW+XdI+7z28x348lrUhak7Tq7pOjrHeQjY5bAMD2RgoCkn4g6UOS/jXFvO9095+OuD4AQI5GCgLufl6SzCyf0gAASlVWn4BL+rqZnTGzI9vNaGZHzGzezOaXlpZKKh4AxGlgS8DMnpL01j5vfdrdv5JyPfe5+yUzu1XSN8zsh+7eN2nv7sckHZOkyclJT7l8AEAGA4OAu98/6krc/VLy91Uze1LSPZLouQWAihWeDjKzXWbW3ngs6b1a71AGkCPuc4EsRgoCZvZBM7soaUrS18zsVPL6bWZ2MpltQtJ/mdlZSd+T9DV3/89R1gvgRlwuBVmYe7hp98nJSZ+f73vqAYBNxsfHtbKycu15u93W8vJyhSVCFczszDDnYjXyjGEgRlwuBVkQBICG4HIpyGLUM4YBBILLpSALWgIAEDGCAABEjCAAABEjCABAxAgCABAxggAARIwgAAARC/qyEWa2JOknVZdD0m5J3BVtMOopHeopPeoqnd56+g1335P2H4MOAqEws/mi74vcBNRTOtRTetRVOqPUE+kgAIgYQQAAIkYQSOdY1QWoCeopHeopPeoqncz1RJ8AAESMlgAARIwgAAARIwj0YWZdMztnZq+b2ZbDrszsx2b2nJktmFl098Ecop4eMLMXzOyCmR0ts4whMLO3mNk3zOxHyd+bt5hvLdmWFszsRNnlrMqg7cPMbjKzx5P3v2tm+8ovZRhS1NXDZrbUsx19fNAyCQL9/UDShySluUPHO929E+lY5oH1ZGY7JH1O0vskHZD0UTM7UE7xgnFU0tPuvl/S08nzfn6VbEsddz9UXvGqk3L7+JikX7j7b0r6R0n/UG4pwzDEb+nxnu3o0UHLJQj04e7n3f2FqssRupT1dI+kC+7+krtflfSYpMPFly4ohyUdTx4fl/SBCssSmjTbR2/9PSHp3WZmJZYxFIX8lggCo3FJXzezM2Z2pOrCBOp2SS/3PL+YvBaTCXd/RZKSv7duMd8bzWzezL5jZrEEijTbx7V53H1V0i8l3VJK6cKS9rf0YTP7vpk9YWZ3DlpotPcYNrOnJL21z1ufdvevpFzMfe5+ycxulfQNM/uhuzfqJq851FO/I7bGjUverp6GWMyvJ9vT3ZKeMbPn3P3FfEoYrDTbRxTbUApp6uGrkr7k7q+Z2Se03oJ613YLjTYIuPv9OSzjUvL3VTN7UuvNtUYFgRzq6aKk3qOROyRdGnGZwdmunsxs0cz2uvsrZrZX0qtbLGNje3rJzL4l6R2Smh4E0mwfG/NcNLOWpF+T9PNyiheUgXXl7j/refoFpeg/IR2UkZntMrP2xmNJ79V6Rymu96yk/WZ2l5ntlPSgpGhGviROSHooefyQpBtaUGZ2s5ndlDzeLek+Sc+XVsLqpNk+euvvI5Ke8TjPch1YV8lBxoZDks4PXKq7M22aJH1Q61H3NUmLkk4lr98m6WTy+G5JZ5PpnNbTI5WXPbR6Sp6/X9L/aP2oNsZ6ukXro4J+lPx9S/L6pKRHk8d/JOm5ZHt6TtLHqi53ifVzw/Yh6TOSDiWP3yhpVtIFSd+TdHfVZQ64rv4+2R+dlfRNSb8zaJlcNgIAIkY6CAAiRhAAgIgRBAAgYgQBAIgYQQAAIkYQAICIEQQAIGL/B29UT1iza6iPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# size of the latent space\n",
    "latent_dim = 5\n",
    "# define the discriminator model\n",
    "model_gen = define_generator(latent_dim)\n",
    "# generate and plot generated samples\n",
    "generate_fake_samples(model_gen, latent_dim, 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4ANAzPUHYUOU"
   },
   "source": [
    "### Training the Generator model\n",
    "\n",
    "***The weights in the generator model are updated based on the performance of the discriminator model***.\n",
    "\n",
    "When the discriminator is good at detecting fake samples, the generator is updated more, and when the discriminator model is relatively poor or confused when detecting fake samples, the generator model is updated less.\n",
    "\n",
    "This defines the **zero-sum or adversarial relationship** between these two models.\n",
    "\n",
    "There may be many ways to implement this using the Keras API, but perhaps the simplest approach is to create a new model that subsumes or encapsulates the generator and discriminator models.\n",
    "\n",
    "Specifically, a **new GAN model can be defined that stacks the generator and discriminator** such that the generator receives as input random points in the latent space, generates samples that are fed into the discriminator model directly, classified, and the output of this larger model can be used to update the model weights of the generator.\n",
    "\n",
    "To be clear, we are not talking about a new third model, just a **logical third model that uses the already-defined layers and weights** from the standalone generator and discriminator models.\n",
    "\n",
    "Only the discriminator is concerned with distinguishing between real and fake examples; therefore, the discriminator model can be trained in a standalone manner on examples of each.\n",
    "\n",
    "The generator model is only concerned with the discriminator’s performance on fake examples. Therefore, we will mark **all of the layers in the discriminator as not trainable when it is part of the GAN model** so that they can not be updated and overtrained on fake examples.\n",
    "\n",
    "When training the generator via this subsumed GAN model, we want the discriminator to think that the samples output by the generator are real. Therefore, **when the generator is trained as part of the GAN model, we will mark the generated samples as real (class 1)**.\n",
    "\n",
    "The discriminator will then classify the generated samples as not real (class 0) or a low probability of being real. The backpropagation process used to update the model weights will see this as a large error and will update the model weights (i.e. only the weights in the generator) to correct for this error, in turn making the generator better at generating plausible fake samples.\n",
    "\n",
    "So,\n",
    "- Inputs: Point in latent space, e.g. a five-element vector of Gaussian random numbers.\n",
    "- Outputs: Binary classification, likelihood the sample is real (or fake).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "i9jWGhA4gPTx"
   },
   "outputs": [],
   "source": [
    "# use the generator to generate n fake examples, with class labels\n",
    "def generate_fake_samples(generator, latent_dim, n):\n",
    "    # generate points in latent space\n",
    "    x_input = generate_latent_points(latent_dim, n)\n",
    "    # predict outputs\n",
    "    X = generator.predict(x_input)\n",
    "    # create class labels\n",
    "    y = np.zeros((n, 1))\n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bpvGYIJvXZ-Z"
   },
   "outputs": [],
   "source": [
    "# define the combined generator and discriminator model, for updating the generator\n",
    "def full_gan(generator, discriminator):\n",
    "    # make weights in the discriminator not trainable\n",
    "    discriminator.trainable = False\n",
    "    # connect them\n",
    "    model = Sequential()\n",
    "    # add generator\n",
    "    model.add(generator)\n",
    "    # add the discriminator\n",
    "    model.add(discriminator)\n",
    "    # compile model\n",
    "    model.compile(loss='binary_crossentropy', optimizer='adam')\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 514
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1111,
     "status": "ok",
     "timestamp": 1568075455953,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "KJ7CjnRAabL5",
    "outputId": "180bb9d0-406c-4777-ae77-a9a75bb035af"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "sequential_35 (Sequential)   (None, 2)                 402       \n",
      "_________________________________________________________________\n",
      "sequential_34 (Sequential)   (None, 1)                 401       \n",
      "=================================================================\n",
      "Total params: 803\n",
      "Trainable params: 402\n",
      "Non-trainable params: 401\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# size of the latent space\n",
    "latent_dim = 5\n",
    "# create the discriminator\n",
    "discriminator = define_discriminator()\n",
    "# create the generator\n",
    "generator = define_generator(latent_dim)\n",
    "# create the gan\n",
    "model_gan = full_gan(generator, discriminator)\n",
    "# summarize gan model\n",
    "model_gan.summary()\n",
    "# plot gan model (does not work on my windows laptop, needs proper installation of pydot and graphviz)\n",
    "#plot_model(model_gan, to_file='gan_plot.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "47sLjMBEdOQW"
   },
   "source": [
    "## Evaluating performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wNeKp7s0d9sg"
   },
   "source": [
    "### Define a function for summarizing performance by comparative plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7l1D_fDGcYWU"
   },
   "outputs": [],
   "source": [
    "def summarize_performance(epoch, generator, discriminator, latent_dim, n=500):\n",
    "    # prepare real samples\n",
    "    x_real, y_real = generate_real_samples(n)\n",
    "    # evaluate discriminator on real examples\n",
    "    _, acc_real = discriminator.evaluate(x_real, y_real, verbose=0)\n",
    "    # prepare fake examples\n",
    "    x_fake, y_fake = generate_fake_samples(generator, latent_dim, n)\n",
    "    # evaluate discriminator on fake examples\n",
    "    _, acc_fake = discriminator.evaluate(x_fake, y_fake, verbose=0)\n",
    "    # summarize discriminator performance\n",
    "    print (\"Data comparison after {} epochs\".format(epoch+1))\n",
    "    print()\n",
    "    print(\"Accuracy for the real data: {}, Accuracy for the fake data: {}\".format(acc_real, acc_fake))\n",
    "    print()\n",
    "    plt.scatter(x_real[:, 0], x_real[:, 1], color='red')\n",
    "    plt.scatter(x_fake[:, 0], x_fake[:, 1], color='blue')\n",
    "    plt.show()\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add the `summarize_performance` function to the train function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QKAGz1vtdgzn"
   },
   "outputs": [],
   "source": [
    "# train the generator and discriminator\n",
    "def train(g_model, d_model, gan_model, latent_dim, n_epochs=10000, n_batch=256, n_eval=1000):\n",
    "    # determine half the size of one batch, for updating the discriminator\n",
    "    half_batch = int(n_batch / 2)\n",
    "    # manually enumerate epochs\n",
    "    for i in range(n_epochs):\n",
    "        # prepare real samples\n",
    "        x_real, y_real = generate_real_samples(half_batch)\n",
    "        # prepare fake examples\n",
    "        x_fake, y_fake = generate_fake_samples(g_model, latent_dim, half_batch)\n",
    "        # update discriminator\n",
    "        d_model.train_on_batch(x_real, y_real)\n",
    "        d_model.train_on_batch(x_fake, y_fake)\n",
    "        # prepare points in latent space as input for the generator\n",
    "        x_gan = generate_latent_points(latent_dim, n_batch)\n",
    "        # create inverted labels for the fake samples\n",
    "        y_gan = np.ones((n_batch, 1))\n",
    "        # update the generator via the discriminator's error\n",
    "        gan_model.train_on_batch(x_gan, y_gan)\n",
    "        # evaluate the model every n_eval epochs\n",
    "        if (i+1) % n_eval == 0:\n",
    "            summarize_performance(i, g_model, d_model, latent_dim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# size of the latent space\n",
    "latent_dim = 5\n",
    "# create the discriminator\n",
    "discriminator = define_discriminator()\n",
    "# create the generator\n",
    "generator = define_generator(latent_dim)\n",
    "# create the gan\n",
    "model_gan = full_gan(generator, discriminator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Mt4PeXQUk1RV"
   },
   "source": [
    "### Showing performance of the `generator` before full training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 946,
     "status": "ok",
     "timestamp": 1568075572205,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "vKQQm7R3k0SY",
    "outputId": "cef2af53-3e94-4a69-d130-8f5bdfb7f72f"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n=500\n",
    "x,_=generate_fake_samples(generator, latent_dim, n)\n",
    "plt.scatter(x[:,0],x[:,1],color='k',s=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now actual training of the GAN model\n",
    "Function generated data in red and GAN generated data in blue.\n",
    "\n",
    "See how, at first, the randomly generated points do not look anything closer to the underlying data, and then, as the iterations go on, they 'learn' the underlying statistical distribution and starts to mimic the functional form of the original data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 92938,
     "status": "ok",
     "timestamp": 1568075668153,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "JJhGvZIPdvro",
    "outputId": "7ef80090-9a4d-4a9d-d03b-063489624173"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data comparison after 1000 epochs\n",
      "\n",
      "Accuracy for the real data: 0.48800000047683717, Accuracy for the fake data: 0.8560000004768371\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Data comparison after 2000 epochs\n",
      "\n",
      "Accuracy for the real data: 0.47200000047683716, Accuracy for the fake data: 0.812\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Data comparison after 3000 epochs\n",
      "\n",
      "Accuracy for the real data: 0.6499999990463257, Accuracy for the fake data: 0.48199999952316286\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Data comparison after 4000 epochs\n",
      "\n",
      "Accuracy for the real data: 0.48199999952316286, Accuracy for the fake data: 0.6240000004768371\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Data comparison after 5000 epochs\n",
      "\n",
      "Accuracy for the real data: 0.22600000005960463, Accuracy for the fake data: 0.8139999995231628\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Data comparison after 6000 epochs\n",
      "\n",
      "Accuracy for the real data: 0.0, Accuracy for the fake data: 1.0\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Data comparison after 7000 epochs\n",
      "\n",
      "Accuracy for the real data: 0.5280000009536743, Accuracy for the fake data: 0.6240000004768371\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Data comparison after 8000 epochs\n",
      "\n",
      "Accuracy for the real data: 0.7199999990463257, Accuracy for the fake data: 0.3520000002384186\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Data comparison after 9000 epochs\n",
      "\n",
      "Accuracy for the real data: 0.6100000009536743, Accuracy for the fake data: 0.48800000023841855\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Data comparison after 10000 epochs\n",
      "\n",
      "Accuracy for the real data: 0.49399999976158143, Accuracy for the fake data: 0.5439999995231628\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# train model\n",
    "train(generator, discriminator, model_gan, latent_dim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TCfPdyIwnRxz"
   },
   "source": [
    "## Generate synthetic data now\n",
    "Now, we can use the same old `generate_fake_samples()` function to generate as many synthetic data as needed. After training, now this function does not generate random data but a dataset closely following the algebraic function :-)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 868,
     "status": "ok",
     "timestamp": 1568076129004,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "-XpTciNNiHc5",
    "outputId": "17782ab2-9cfb-4665-e2f2-ac131b9d91bc"
   },
   "outputs": [
    {
     "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": [
    "latent_dim=5\n",
    "n=200\n",
    "x,_=generate_fake_samples(generator, latent_dim, n)\n",
    "plt.scatter(x[:,0],x[:,1],color='k',s=15)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "GAN_1D.ipynb",
   "provenance": [],
   "toc_visible": true,
   "version": "0.3.2"
  },
  "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}