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A-Simple-GAN

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# G.A.N\n",
    "> Generative Adversial Network:\n",
    "> -  The main focus for GAN is to generate data from scratch.\n",
    "> -  It brings us closer to understanding intelligence.\n",
    "- It trains two deep networks, called Generator and Discriminator, that compete and cooperate with each other. In the course of training, both networks eventually learn how to perform their tasks.\n",
    "> > - The generator never actually sees examples from the domain and is adapted based on how well the discriminator performs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Original Article: How to Develop a 1D GAN](https://machinelearningmastery.com/how-to-develop-a-generative-adversarial-network-for-a-1-dimensional-function-from-scratch-in-keras/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# The Import Statements:\n",
    "from matplotlib import pyplot\n",
    "import numpy as np\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": {},
   "source": [
    "### Defining a 1 D function:\n",
    "> y=f(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def function_1D(x):\n",
    "    return x*x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs=np.arange(-0.5,0.6,0.1)\n",
    "outputs=[function_1D(x) for x in inputs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "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": [
    "# plot the result\n",
    "pyplot.plot(inputs, outputs)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining random values\n",
    "def generate_samples(n=100):\n",
    "    x1=np.random.rand(n)-0.5\n",
    "    x2=x1*x1\n",
    "    x1=x1.reshape(n,1)\n",
    "    x2=x2.reshape(n,1)\n",
    "    return np.hstack((x1,x2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WTY/wwmdmvctB3wO88JlZb/PQTQ+otfDZtp0H2tQiM2sl9+h7QK2Fz2odN2smDxu2n3v0PaDWwmf1FkQza4bSsGFhYpLg7LDh8Fih3U3rKQ76HrB+1VUM9Pedc2ygv6/hgmhms+Vhw87goZseUHqb7LfP1krDYwUKHjbsCA76HjE0mKsa7B4/tWYbHiuweWScicmTNa/xsGFrOeh7mMsurdkq/01V42HD1vMYfQ/z+Kk12+aR8bohD/DIrSvckWgxB30PqzVOWpiY5Lqtr7gywqZleKxQd7gGIDd/wCHfBg76HlZvnLQwMcl92/d5z1lLZXiswFeee73uNR6yaR8HfQ+rVnZZLoCndx12z97q2ji8n/u276u7beWCi/s9ZNNGnoztYeVll7XK4AL4ox1v+AfUqhoeK/DUrsN1r1lwcT9jf3xji1pk1aTq0UtaLemApIOSNlQ5f5Gk7cn53ZKWJMeXSJqUtC/5+G/Nbb7N1tBgjtc2XE+uzjDOiZOnufPPf9jCVlm32DwyXvf8QH8fm26uvaextUbDoJfUBzwG3AQsA9ZKWlZx2d3AOxHxEeBR4Gtl596OiKuTjy81qd3WZOtXXUW9bcRfe/s4H9rwoodx7Bz1Jl/7JA/XdIg0PfprgIMRcSgi3gOeBW6puOYW4FvJ588Dn5ZULzeswwwN5rhz5eK615wGvrx9n8PeGB4rcN3WV+pe843Pf8wh3yHSBH0OOFL29dHkWNVrIuIU8Evg0uTclZLGJP0vSb9T7Q+QdI+kUUmjx44dm9YNWPNsGVpBml/P9z3nsO9l5QuV1fKrF/Y55DvIXFfd/AxYHBGDwP3AtyW9v/KiiHgiIvIRkV+4cOEcN8nqufPa+r16gAj4ynded9j3qGoP2pXr7xP/+d+taGGLrJE0QV8AFpV9fXlyrOo1ki4APgD8IiLejYhfAETEXuBt4J/PttE2d7YMreC6D1/S8Lqp08FDL9SfiLNsqrcgWW7+ANtu85BNp0kT9HuApZKulHQhcAcwUnHNCHBX8vltwCsREZIWJpO5SPoQsBQ41Jym21x5+vc+wdJ/9qsNr3vnRP2nIC2baj1ol5s/wGsbrnfId6CGQZ+Mud8L7ATeAp6LiHFJD0tak1z2TeBSSQcpDtGUSjA/CbwhaR/FSdovRcTxZt+ENd9L9//rVD176z3e36D7KOo8zdYO+Xw+RkdH290MSwyPFfjy9n1Vz80f6GffJj8I04u8vHXnkbQ3IvLVzvnJWKtraDDH6E+On/f0Y/88sXmNH4TpVbX2N7DO5KC3hrYMrSB/xSXuwfUQ99izxUFvqbgH1zu8IU32OOjNDDjbi6/2IFRpQxoHfXdy0FtT+S1/d0qzBaA39O5eDnprGr/l716NnnYFb+jdzbzxiDWN96DtXo16666T724OemuaWmHht/ydr15vPTd/wMsNdzkP3VjTfHD+QNWJPL/l7zyVcyn/5l8s5K/3Fs55RzbQ3+eAzwj36K1p/Gh8dyjt8VqYmCQozqX89d4Cn/14jtz8AYR78VnjHr01TfketLWqblyV0z7DYwUeemG86mJ0kyenePVvj/Hahuvb0DKbaw56a6p6D1a5Kqd9Ng7v5+ldh6m3spXnUrLLQzfWMq7KaY/hsULDkAfPpWSZe/TWMrV6jIWJSa7c8KKHcpps4/B+ntl9hKkUK9QKPJeSYe7RW8vU6zGWJgUf2LHfWxQ2wcbh/Ty163DqkL9z5WL/gs0wB721TLWqnEoeymmOZ3YfSXXd/IF+Hr39arYMeY/XLPPQjbVMZVVOrb6mJwVnr1FPvtSLd8D3Bge9tVR5Vc51W1/xA1azVKtctU+qGfY5z4X0HAe9tc36VVedt2Ji5QNWrruvrbJksrxcde21i87bFQxgnXvxPclBb23T6AEr192fr96a8XB2jqP04FOp6qZPYu21ixzyPcqbg1vHqjW0A705/JBmzXgojr///dbfbU2jrGN4c3DrSvUmZQsTk9y3fR9f3r4v06Ffb9mCWjzHYZVcXmkdq1FgVY5NZ63+fniswPrnX59WyPvBJ6vGPXrrWNUma2uZPDnFg9/dz1eeez0zY9Lbdh7g5FT6oVU/+GS1OOitY5VP1tYaqy/3j++d/YUwFXGm6qTTw758qYLyX1BpnicQxXc2WR6+stlz0FtHK9Xdp52IrPTM7iPkr7ikI0s0h8cK/NGONzhx8vSZY+W/oGpt5FLicLe0XHVjXaO8tLDUk01joL/vvF8Q8wRfuLY1NeXlPXYJBi6Yx4mTp+veQ5/ENz7/MdY///p5wzf988S2z33MAW/ncNWNZUL5U7WVD1LVW1Kh2ruA08GZnnOpx1+YmDzzROlse8u16t0jONODr/eLairizJ9dXnUzf6CfzWuWO+RtWlL16CWtBv4L0Af8RURsrTh/EfCXwMeBXwC3R8SPk3MPAHcDU8B/jIid9f4s9+htJkqrNU7XPMFFF5zf4wfo7xNEUBpZkeDOFO8CZjrMVK5P4u1H/u2MX2+9p16PvmF5paQ+4DHgJmAZsFbSsorL7gbeiYiPAI8CX0teuwy4A1gOrAb+a/L9zJpqy9AK1q1cTJ8EFINy3crF5BqUaJ6O6j1+gJNTZ0Meir3xp3YdZuPw/rrfs9oGK9O19tpFs3q9Wbk0QzfXAAcj4hCApGeBW4A3y665BdicfP488GeSlBx/NiLeBf5e0sHk+/2wOc03O2vL0IrzetvN6F1Xemb3kbq9+tmsvpn2XYPZdKQJ+hxQvrj1UeDaWtdExClJvwQuTY7vqnjteYOLku4B7gFYvHhx2rabNVQay35gxxtMlnfPEwP986oer6fREsCNqmXKuTzSWqEjnoyNiCciIh8R+YULF7a7OZYxQ4M53vrqTVWHdh659aMNN0OpVPoetTTaYKV/XjHgc/MHePT2q/nx1t/ltQ3XO+RtzqTp0ReA8gHDy5Nj1a45KukC4AMUJ2XTvNasJaoN7ZRUVt0suLi/5tIDjcbPKx/0alYlj9lMNay6SYL774BPUwzpPcAXImK87Jo/AFZExJck3QHcGhGfl7Qc+DbFcfkPAi8DSyOi5oCpq26sU1Q+0OTxc+tks6qjT8bc7wV2UiyvfDIixiU9DIxGxAjwTeCvksnW4xQrbUiue47ixO0p4A/qhbxZJymv2zfrZn4y1swsA2ZVR29mZt3NQW9mlnEOejOzjHPQm5llnIPezCzjHPRmZhnnoDczyzgHvZlZxjnozcwyzkFvZpZxDnozs4xz0JuZZZyD3sws4xz0ZmYZ13HLFEs6Bvyk3e2YgcuAf2h3I9qgF+/b99w7uum+r4iIqnuxdlzQdytJo7XWgs6yXrxv33PvyMp9e+jGzCzjHPRmZhnnoG+eJ9rdgDbpxfv2PfeOTNy3x+jNzDLOPXozs4xz0JuZZZyDfoYkXSLpJUk/Sv67oM6175d0VNKftbKNcyHNfUu6WtIPJY1LekPS7e1o62xJWi3pgKSDkjZUOX+RpO3J+d2SlrS+lc2V4p7vl/Rm8vf6sqQr2tHOZmt032XXfVZSSOqqkksH/cxtAF6OiKXAy8nXtXwV+N8tadXcS3PfJ4B/HxHLgdXAn0qa38I2zpqkPuAx4CZgGbBW0rKKy+4G3omIjwCPAl9rbSubK+U9jwH5iPgo8Dzw9da2svlS3jeSfg34T8Du1rZw9hz0M3cL8K3k828BQ9UukvRx4NeB/9Gids21hvcdEX8XET9KPv8p8HOg6hN7Hewa4GBEHIqI94BnKd57ufL/F88Dn5akFrax2Rrec0S8GhEnki93AZe3uI1zIc3fNRQ7bF8D/qmVjWsGB/3M/XpE/Cz5/P9QDPNzSJoHfAP4w1Y2bI41vO9ykq4BLgTenuuGNVkOOFL29dHkWNVrIuIU8Evg0pa0bm6kuedydwPfn9MWtUbD+5b0r4BFEfFiKxvWLBe0uwGdTNL/BH6jyqkHy7+IiJBUrU7194HvRcTRburoNeG+S9/nN4G/Au6KiNPNbaW1k6R1QB74VLvbMteSDtufAF9sc1NmzEFfR0TcUOucpP8r6Tcj4mdJoP28ymWfAH5H0u8D7wMulPT/IqLeeH7bNeG+kfR+4EXgwYjYNUdNnUsFYFHZ15cnx6pdc1TSBcAHgF+0pnlzIs09I+kGir/0PxUR77aobXOp0X3/GvAvgR8kHbbfAEYkrYmI0Za1chY8dDNzI8Bdyed3Af+98oKIuDMiFkfEEorDN3/Z6SGfQsP7lnQh8F2K9/t8C9vWTHuApZKuTO7nDor3Xq78/8VtwCvR3U8gNrxnSYPA48CaiKj6S74L1b3viPhlRFwWEUuSn+VdFO+/K0IeHPSzsRX4jKQfATckXyMpL+kv2tqyuZXmvj8PfBL4oqR9ycfV7WnuzCRj7vcCO4G3gOciYlzSw5LWJJd9E7hU0kHgfupXXnW8lPe8jeK70+8kf6+Vv/y6Tsr77mpeAsHMLOPcozczyzgHvZlZxjnozcwyzkFvZpZxDnozs4xz0JuZZZyD3sws4/4/YCNZFSD5BhwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate samples and plotting them\n",
    "data = generate_samples()\n",
    "\n",
    "pyplot.scatter(data[:, 0], data[:, 1])\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Note:\n",
    "> *a sample is comprised of a vector with two elements, one for the input and one for the output of our one-dimensional function.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Discriminator\n",
    "- The difference from a typical CNN is the absence of max-pooling in between layers.\n",
    "- will have 1 hidden layer with 25 nodes.\n",
    "- will use the ReLU activation function\n",
    "- The output layer will have 1 node for the binary classification using the sigmoid activation function.\n",
    "- Loss Function: Binary Cross Entropy\n",
    "- Optimizer : Adam version of stochastic Gradient Descent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Code for the Discriminator Unit:\n",
    "def define_discriminator(n_inputs=2):\n",
    "\tmodel = Sequential()\n",
    "\tmodel.add(Dense(25, activation='relu', kernel_initializer='he_uniform', input_dim=n_inputs))\n",
    "\tmodel.add(Dense(1, activation='sigmoid'))\n",
    "\t# compile model\n",
    "\tmodel.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\treturn model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 25)                75        \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 26        \n",
      "=================================================================\n",
      "Total params: 101\n",
      "Trainable params: 101\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# define the discriminator model\n",
    "model = define_discriminator()\n",
    "# summarize the model\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_real_samples(n):\n",
    "    x1=np.random.rand(n)-0.5\n",
    "    x2=x1*x1\n",
    "    x1=x1.reshape(n,1)\n",
    "    x2=x2.reshape(n,1)\n",
    "    X= np.hstack((x1,x2))\n",
    "    y=np.ones((n,1))\n",
    "    return X,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_fake_samples(n):\n",
    "\t# generate inputs in [-1, 1]\n",
    "\tX1 = -1 + np.random.rand(n) * 2\n",
    "\t# generate outputs in [-1, 1]\n",
    "\tX2 = -1 + np.random.rand(n) * 2\n",
    "\t# stack arrays\n",
    "\tX1 = X1.reshape(n, 1)\n",
    "\tX2 = X2.reshape(n, 1)\n",
    "\tX = np.hstack((X1, X2))\n",
    "\t# generate class labels\n",
    "\ty = np.zeros((n, 1))\n",
    "\treturn X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#training the discriminator model\n",
    "def train_discriminator(model, n_epochs=1000, n_batch=128):\n",
    "\thalf_batch = int(n_batch / 2)\n",
    "\t# run epochs manually\n",
    "\tfor i in range(n_epochs):\n",
    "\t\t# generate real examples\n",
    "\t\tX_real, y_real = generate_real_samples(half_batch)\n",
    "\t\t# update model\n",
    "\t\tmodel.train_on_batch(X_real, y_real)\n",
    "\t\t# generate fake examples\n",
    "\t\tX_fake, y_fake = generate_fake_samples(half_batch)\n",
    "\t\t# update model\n",
    "\t\tmodel.train_on_batch(X_fake, y_fake)\n",
    "\t\t# evaluate the model\n",
    "\t\t_, acc_real = model.evaluate(X_real, y_real, verbose=0)\n",
    "\t\t_, acc_fake = model.evaluate(X_fake, y_fake, verbose=0)\n",
    "\t\tprint(i, acc_real, acc_fake)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    {
     "name": "stdout",
     "output_type": "stream",
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      "966 1.0 0.921875\n",
      "967 1.0 0.796875\n",
      "968 1.0 0.875\n",
      "969 1.0 0.921875\n",
      "970 1.0 0.828125\n",
      "971 1.0 0.9375\n",
      "972 1.0 0.890625\n",
      "973 1.0 0.921875\n",
      "974 1.0 0.875\n",
      "975 1.0 0.9375\n",
      "976 1.0 0.90625\n",
      "977 1.0 0.953125\n",
      "978 1.0 0.859375\n",
      "979 1.0 0.828125\n",
      "980 1.0 0.9375\n",
      "981 1.0 0.953125\n",
      "982 1.0 0.90625\n",
      "983 1.0 0.828125\n",
      "984 1.0 0.78125\n",
      "985 1.0 0.90625\n",
      "986 1.0 0.921875\n",
      "987 1.0 0.90625\n",
      "988 1.0 0.921875\n",
      "989 1.0 0.890625\n",
      "990 1.0 0.90625\n",
      "991 1.0 0.890625\n",
      "992 1.0 0.78125\n",
      "993 1.0 0.859375\n",
      "994 1.0 0.765625\n",
      "995 1.0 0.8125\n",
      "996 1.0 0.890625\n",
      "997 1.0 0.890625\n",
      "998 1.0 0.875\n",
      "999 1.0 0.84375\n"
     ]
    }
   ],
   "source": [
    "# define the discriminator model\n",
    "model = define_discriminator()\n",
    "# fit the model\n",
    "train_discriminator(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"*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": {},
   "source": [
    "# The Generator Model\n",
    "*We will define a small latent space of five 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",
    "* Specs:\n",
    "> * Single Hidden Layer with 5 nodes\n",
    "> * ReLU activation Function\n",
    "> * He weight initialization\n",
    "> * Output layer will have 2 nodes+ will use linear activation function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the generator model unit\n",
    "def define_generator(latent_dim, n_outputs=2):\n",
    "\tmodel = Sequential()\n",
    "\tmodel.add(Dense(15, activation='relu', kernel_initializer='he_uniform', input_dim=latent_dim))\n",
    "\tmodel.add(Dense(n_outputs, activation='linear'))\n",
    "\treturn model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_5 (Dense)              (None, 15)                90        \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 2)                 32        \n",
      "=================================================================\n",
      "Total params: 122\n",
      "Trainable params: 122\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# define the discriminator model\n",
    "model = define_generator(5)\n",
    "# summarize the model\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate points in latent space as input for the generator\n",
    "def generate_latent_points(latent_dim, n):\n",
    "\t# generate points in the latent space\n",
    "\tx_input = np.random.randn(latent_dim * n)\n",
    "\t# reshape into a batch of inputs for the network\n",
    "\tx_input = x_input.reshape(n, latent_dim)\n",
    "\treturn x_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use the generator to generate n fake examples and plot the results\n",
    "def generate_fake_samples(generator, latent_dim, n):\n",
    "\t# generate points in latent space\n",
    "\tx_input = generate_latent_points(latent_dim, n)\n",
    "\t# predict outputs\n",
    "\tX = generator.predict(x_input)\n",
    "    # create class labels\n",
    "\ty = np.zeros((n, 1))\n",
    "\treturn X, y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*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.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the combined generator and discriminator model, for updating the generator\n",
    "def define_gan(generator, discriminator):\n",
    "\t# make weights in the discriminator not trainable\n",
    "\tdiscriminator.trainable = False\n",
    "\t# connect them\n",
    "\tmodel = Sequential()\n",
    "\t# add generator\n",
    "\tmodel.add(generator)\n",
    "\t# add the discriminator\n",
    "\tmodel.add(discriminator)\n",
    "\t# compile model\n",
    "\tmodel.compile(loss='binary_crossentropy', optimizer='adam')\n",
    "\treturn model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train the generator and discriminator\n",
    "def train(g_model, d_model, gan_model, latent_dim, n_epochs=10000, n_batch=128, n_eval=2000):\n",
    "\t# determine half the size of one batch, for updating the discriminator\n",
    "\thalf_batch = int(n_batch / 2)\n",
    "\t# manually enumerate epochs\n",
    "\tfor i in range(n_epochs):\n",
    "\t\t# prepare real samples\n",
    "\t\tx_real, y_real = generate_real_samples(half_batch)\n",
    "\t\t# prepare fake examples\n",
    "\t\tx_fake, y_fake = generate_fake_samples(g_model, latent_dim, half_batch)\n",
    "\t\t# update discriminator\n",
    "\t\td_model.train_on_batch(x_real, y_real)\n",
    "\t\td_model.train_on_batch(x_fake, y_fake)\n",
    "\t\t# prepare points in latent space as input for the generator\n",
    "\t\tx_gan = generate_latent_points(latent_dim, n_batch)\n",
    "\t\t# create inverted labels for the fake samples\n",
    "\t\ty_gan = np.ones((n_batch, 1))\n",
    "\t\t# update the generator via the discriminator's error\n",
    "\t\tgan_model.train_on_batch(x_gan, y_gan)\n",
    "\t\t# evaluate the model every n_eval epochs\n",
    "\t\tif (i+1) % n_eval == 0:\n",
    "\t\t\tsummarize_performance(i, g_model, d_model, latent_dim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# evaluate the discriminator and plot real and fake points\n",
    "def summarize_performance(epoch, generator, discriminator, latent_dim, n=100):\n",
    "\t# prepare real samples\n",
    "\tx_real, y_real = generate_real_samples(n)\n",
    "\t# evaluate discriminator on real examples\n",
    "\t_, acc_real = discriminator.evaluate(x_real, y_real, verbose=0)\n",
    "\t# prepare fake examples\n",
    "\tx_fake, y_fake = generate_fake_samples(generator, latent_dim, n)\n",
    "\t# evaluate discriminator on fake examples\n",
    "\t_, acc_fake = discriminator.evaluate(x_fake, y_fake, verbose=0)\n",
    "\t# summarize discriminator performance\n",
    "\tprint(epoch, acc_real, acc_fake)\n",
    "\t# scatter plot real and fake data points\n",
    "\tpyplot.scatter(x_real[:, 0], x_real[:, 1], color='red')\n",
    "\tpyplot.scatter(x_fake[:, 0], x_fake[:, 1], color='blue')\n",
    "\tpyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1999 0.7400000095367432 0.4399999976158142\n"
     ]
    },
    {
     "data": {
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\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",
    "# create the discriminator\n",
    "discriminator = define_discriminator()\n",
    "# create the generator\n",
    "generator = define_generator(latent_dim)\n",
    "# create the gan\n",
    "gan_model = define_gan(generator, discriminator)\n",
    "# train model\n",
    "train(generator, discriminator, gan_model, latent_dim)"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}