GAN using MNIST Dataset.ipynb

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     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Importing Libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from keras.datasets import mnist\n",
    "from keras.layers import Dense, Dropout, Input\n",
    "from keras.models import Model, Sequential\n",
    "from keras.layers.advanced_activations import LeakyReLU\n",
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "source": [
    "def load_data():\n",
    "    (x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "    x_train = (x_train.astype(np.float32) - 127.5)/127.5\n",
    "    x_train = x_train.reshape(60000, 784)\n",
    "    return (x_train, y_train, x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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     "timestamp": 1592850676057,
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     "name": "stdout",
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     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 256)               25856     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)    (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 512)               131584    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)    (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1024)              525312    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_3 (LeakyReLU)    (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 784)               803600    \n",
      "=================================================================\n",
      "Total params: 1,486,352\n",
      "Trainable params: 1,486,352\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def create_generator():\n",
    "    generator = Sequential()\n",
    "    generator.add(Dense(units=256,input_dim=100))\n",
    "    generator.add(LeakyReLU(0.2))\n",
    "    \n",
    "    generator.add(Dense(units=512))\n",
    "    generator.add(LeakyReLU(0.2))\n",
    "    \n",
    "    generator.add(Dense(units=1024))\n",
    "    generator.add(LeakyReLU(0.2))\n",
    "    \n",
    "    generator.add(Dense(units=784, activation='tanh'))\n",
    "    \n",
    "    generator.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0002, beta_1=0.5))\n",
    "    return generator\n",
    "\n",
    "gen = create_generator()\n",
    "gen.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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     "height": 440
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     "timestamp": 1592850676059,
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_5 (Dense)              (None, 1024)              803840    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_4 (LeakyReLU)    (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 512)               524800    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_5 (LeakyReLU)    (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 256)               131328    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_6 (LeakyReLU)    (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 1,460,225\n",
      "Trainable params: 1,460,225\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def create_discriminator():\n",
    "    discriminator = Sequential()\n",
    "    discriminator.add(Dense(units=1024,input_dim=784))\n",
    "    discriminator.add(LeakyReLU(0.2))\n",
    "    discriminator.add(Dropout(0.3))\n",
    "           \n",
    "    discriminator.add(Dense(units=512))\n",
    "    discriminator.add(LeakyReLU(0.2))\n",
    "    discriminator.add(Dropout(0.3))\n",
    "       \n",
    "    discriminator.add(Dense(units=256))\n",
    "    discriminator.add(LeakyReLU(0.2))\n",
    "    \n",
    "    discriminator.add(Dense(units=1, activation='sigmoid'))\n",
    "    \n",
    "    discriminator.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0002, beta_1=0.5))\n",
    "    return discriminator\n",
    "\n",
    "disc = create_discriminator()\n",
    "disc.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
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     "height": 245
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 14892,
     "status": "ok",
     "timestamp": 1592850676060,
     "user": {
      "displayName": "Siddhartha Dutta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiT79p-YwUAMLJLPMEjadbhQl383sQDEQ2dCLM=s64",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "sequential_1 (Sequential)    (None, 784)               1486352   \n",
      "_________________________________________________________________\n",
      "sequential_2 (Sequential)    (None, 1)                 1460225   \n",
      "=================================================================\n",
      "Total params: 2,946,577\n",
      "Trainable params: 1,486,352\n",
      "Non-trainable params: 1,460,225\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def create_gan(discriminator, generator):\n",
    "    discriminator.trainable = False\n",
    "    gan_input = Input(shape=(100,))\n",
    "    x = generator(gan_input)\n",
    "    gan_output = discriminator(x)\n",
    "    gan = Model(inputs=gan_input, outputs=gan_output)\n",
    "    gan.compile(loss='binary_crossentropy', optimizer='adam')\n",
    "    return gan\n",
    "\n",
    "gan = create_gan(disc,gen)\n",
    "gan.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
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     "status": "ok",
     "timestamp": 1592850723486,
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     },
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    },
    "id": "nr4bpcANjgUv"
   },
   "outputs": [],
   "source": [
    "def plot_generated_images(epoch, generator, examples=10, dim=(10,10), figsize=(10,10)):\n",
    "    noise = np.random.normal(loc=0, scale=1, size=[examples, 100])\n",
    "    generated_images = generator.predict(noise)\n",
    "    generated_images = generated_images.reshape(10,28,28)\n",
    "    plt.figure(figsize=figsize)\n",
    "    for i in range(generated_images.shape[0]):\n",
    "        plt.subplot(dim[0], dim[1], i+1)\n",
    "        plt.imshow(generated_images[i], interpolation='nearest')\n",
    "        plt.axis('off')\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('gan_generated_image %d.png' %epoch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
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     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
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     "timestamp": 1592851767110,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1\n",
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      "Epoch 365\n",
      "Epoch 366\n",
      "Epoch 367\n",
      "Epoch 368\n",
      "Epoch 369\n",
      "Epoch 370\n",
      "Epoch 371\n",
      "Epoch 372\n",
      "Epoch 373\n",
      "Epoch 374\n",
      "Epoch 375\n",
      "Epoch 376\n",
      "Epoch 377\n",
      "Epoch 378\n",
      "Epoch 379\n",
      "Epoch 380\n",
      "Epoch 381\n",
      "Epoch 382\n",
      "Epoch 383\n",
      "Epoch 384\n",
      "Epoch 385\n",
      "Epoch 386\n",
      "Epoch 387\n",
      "Epoch 388\n",
      "Epoch 389\n",
      "Epoch 390\n",
      "Epoch 391\n",
      "Epoch 392\n",
      "Epoch 393\n",
      "Epoch 394\n",
      "Epoch 395\n",
      "Epoch 396\n",
      "Epoch 397\n",
      "Epoch 398\n",
      "Epoch 399\n",
      "Epoch 400\n",
      "Epoch 401\n",
      "Epoch 402\n",
      "Epoch 403\n",
      "Epoch 404\n",
      "Epoch 405\n",
      "Epoch 406\n",
      "Epoch 407\n",
      "Epoch 408\n",
      "Epoch 409\n",
      "Epoch 410\n",
      "Epoch 411\n",
      "Epoch 412\n",
      "Epoch 413\n",
      "Epoch 414\n",
      "Epoch 415\n",
      "Epoch 416\n",
      "Epoch 417\n",
      "Epoch 418\n",
      "Epoch 419\n",
      "Epoch 420\n",
      "Epoch 421\n",
      "Epoch 422\n",
      "Epoch 423\n",
      "Epoch 424\n",
      "Epoch 425\n",
      "Epoch 426\n",
      "Epoch 427\n",
      "Epoch 428\n",
      "Epoch 429\n",
      "Epoch 430\n",
      "Epoch 431\n",
      "Epoch 432\n",
      "Epoch 433\n",
      "Epoch 434\n",
      "Epoch 435\n",
      "Epoch 436\n",
      "Epoch 437\n",
      "Epoch 438\n",
      "Epoch 439\n",
      "Epoch 440\n",
      "Epoch 441\n",
      "Epoch 442\n",
      "Epoch 443\n",
      "Epoch 444\n",
      "Epoch 445\n",
      "Epoch 446\n",
      "Epoch 447\n",
      "Epoch 448\n",
      "Epoch 449\n",
      "Epoch 450\n",
      "Epoch 451\n",
      "Epoch 452\n",
      "Epoch 453\n",
      "Epoch 454\n",
      "Epoch 455\n",
      "Epoch 456\n",
      "Epoch 457\n",
      "Epoch 458\n",
      "Epoch 459\n",
      "Epoch 460\n",
      "Epoch 461\n",
      "Epoch 462\n",
      "Epoch 463\n",
      "Epoch 464\n",
      "Epoch 465\n",
      "Epoch 466\n",
      "Epoch 467\n",
      "Epoch 468\n",
      "Epoch 469\n",
      "Epoch 470\n",
      "Epoch 471\n",
      "Epoch 472\n",
      "Epoch 473\n",
      "Epoch 474\n",
      "Epoch 475\n",
      "Epoch 476\n",
      "Epoch 477\n",
      "Epoch 478\n",
      "Epoch 479\n",
      "Epoch 480\n",
      "Epoch 481\n",
      "Epoch 482\n",
      "Epoch 483\n",
      "Epoch 484\n",
      "Epoch 485\n",
      "Epoch 486\n",
      "Epoch 487\n",
      "Epoch 488\n",
      "Epoch 489\n",
      "Epoch 490\n",
      "Epoch 491\n",
      "Epoch 492\n",
      "Epoch 493\n",
      "Epoch 494\n",
      "Epoch 495\n",
      "Epoch 496\n",
      "Epoch 497\n",
      "Epoch 498\n",
      "Epoch 499\n",
      "Epoch 500\n",
      "Epoch 501\n",
      "Epoch 502\n",
      "Epoch 503\n",
      "Epoch 504\n",
      "Epoch 505\n",
      "Epoch 506\n",
      "Epoch 507\n",
      "Epoch 508\n",
      "Epoch 509\n",
      "Epoch 510\n",
      "Epoch 511\n",
      "Epoch 512\n",
      "Epoch 513\n",
      "Epoch 514\n",
      "Epoch 515\n",
      "Epoch 516\n",
      "Epoch 517\n",
      "Epoch 518\n",
      "Epoch 519\n",
      "Epoch 520\n",
      "Epoch 521\n",
      "Epoch 522\n",
      "Epoch 523\n",
      "Epoch 524\n",
      "Epoch 525\n",
      "Epoch 526\n",
      "Epoch 527\n",
      "Epoch 528\n",
      "Epoch 529\n",
      "Epoch 530\n",
      "Epoch 531\n",
      "Epoch 532\n",
      "Epoch 533\n",
      "Epoch 534\n",
      "Epoch 535\n",
      "Epoch 536\n",
      "Epoch 537\n",
      "Epoch 538\n",
      "Epoch 539\n",
      "Epoch 540\n",
      "Epoch 541\n",
      "Epoch 542\n",
      "Epoch 543\n",
      "Epoch 544\n",
      "Epoch 545\n",
      "Epoch 546\n",
      "Epoch 547\n",
      "Epoch 548\n",
      "Epoch 549\n",
      "Epoch 550\n",
      "Epoch 551\n",
      "Epoch 552\n",
      "Epoch 553\n",
      "Epoch 554\n",
      "Epoch 555\n",
      "Epoch 556\n",
      "Epoch 557\n",
      "Epoch 558\n",
      "Epoch 559\n",
      "Epoch 560\n",
      "Epoch 561\n",
      "Epoch 562\n",
      "Epoch 563\n",
      "Epoch 564\n",
      "Epoch 565\n",
      "Epoch 566\n",
      "Epoch 567\n",
      "Epoch 568\n",
      "Epoch 569\n",
      "Epoch 570\n",
      "Epoch 571\n",
      "Epoch 572\n",
      "Epoch 573\n",
      "Epoch 574\n",
      "Epoch 575\n",
      "Epoch 576\n",
      "Epoch 577\n",
      "Epoch 578\n",
      "Epoch 579\n",
      "Epoch 580\n",
      "Epoch 581\n",
      "Epoch 582\n",
      "Epoch 583\n",
      "Epoch 584\n",
      "Epoch 585\n",
      "Epoch 586\n",
      "Epoch 587\n",
      "Epoch 588\n",
      "Epoch 589\n",
      "Epoch 590\n",
      "Epoch 591\n",
      "Epoch 592\n",
      "Epoch 593\n",
      "Epoch 594\n",
      "Epoch 595\n",
      "Epoch 596\n",
      "Epoch 597\n",
      "Epoch 598\n",
      "Epoch 599\n",
      "Epoch 600\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAABKCAYAAACmTKKMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAGmElEQVR4nO3d3XHbOBQG0DizVWwV20RmK0iVqSCTJlJFyojz4NEYkXlBAKRE/JzzFpvWCt9CIHEFgi+vr6+fAACAN5+vfgMAANATF8gAAJBwgQwAAAkXyAAAkHCBDAAACRfIAACQ+Cf3yy+fvy63B9yP399eSo6TTUw2MdnEZBOTTUw2MdnEZLNNLu9UkCf0/dfPT99//bz6bQAADMkFMgAAJLJLLBjT///+d/VbAAAYlgoyAAAkXCADAEDCBXKnam60e9SxAAArcoEMAAAJN+ldaKuSe3+D3e2Y289zf3N/7BY38AHA9UrO2VxHBRkAABLDVZBnmnFttSFaH1xTHS6pTK9mpn5zNtnEZBOTDS30m3c13xivnNdVfUYFGQAAEsNVkGvW2/Zua3YYVYFb2pn+TTQzHTm/Grl2r5bFPdm8u2/vqp+XLbKJRRnI5qOZxptchbelLTNlc6arxhoVZAAASHRfQa7Zs3e0GVbLbhMlu1jkjh0to1ZRv5m93SVks6/kG5xVPkv3SsZk2bzRb+Yeb846h0d/u+ozC2r6zCM/TyrIAACQ6LKCvLWuZ+Q9fkve+1UzxTPWOvci129yRm5zqdk+U2eSTaz1LnrZxFbNZvaxeG/MqHnuwZa9bEb/ZqLHPqOCDAAACRfIAACQ6HKJxVbJfOQthc644W7rtW7HtHwNMePSipJt7VZly7/YbOPNmVZtdwnZxFYci1u2edvbHvDIf380JePws6kgAwBAossKciranH5ELTPKmtl37piZKsY3JdmUzNBnyOKI1dufmmm8OZtvHmKy+duRm816lvv/Gp1jW85TK27RmvtG/ao2qyADAECi+wryzQyzp/v3vrVO68jrzrq2q1TNureR+9FZVL1isojpNzHZvMndKzNyNo/a9tE5vKzPPJsKMgAAJIapII80y9yzNSu6n0Ee2cUiNxudKcea2XzJXcIzZZMTtXOr36ySyc1q7W2x99lamWw+WnW8iaqfNeetVSvKvfQDFWQAAEh0WUGefdZUcgdspOS43DFXr+l5pJqqcI/rnR6pNZsVrPxtwp7Zx+IjZBNbebxpeeT0va3dG2Y/T/U4DqsgAwBAoosK8qoz8a21V1EWNU/dW+WO2Jo9oVfbB/lINrOTTUw2MdnEVh2LS957rk/UnPdHy2bPCH1GBRkAABIukAEAIHHJEou9x7mWLD0YeYuYra8NSrZ1i45rWVIx0leB0f/7I9mM0O4aNZ+pG9n8/ftUyWNiZ8hpbysq2ZyTzWyMN29y5/CaPhBt85p7+NVoRuwzKsgAAJC4pIK8d/U/6yODayq8JbOrlhv6RtTy3muqPyNnc1PThpYN7Ed2ZLypfa3R7PUB2ZT9e/TqXi3jzUdHzlPRa810nhqxz6ggAwBAoos1yCXbfUT/HlFJG87eum3kyvtN1G9Kts8p6UcjV4FaPlMjt7fG6uNNjmxisomtOt7k2rB3zq7Z5q3kPqXRchyxz6ggAwBAoqs1yLmZxdUziTO1VIFrZq43Z6z56cmR91Yy++657XtaPlOrWH28yZFNTDax1cebkvadcV4+4+960dJnrm6zCjIAACS6eNT0auu3ztJyB2wvd4c+wsp3le+RzbuSvr9qNvfkEMutD13drONN7jy691jkGfc2PlOPuaggAwBAwgUyAAAknrrEoqVs3kup/RFqbrirUbKtTMmxvdi7wW6ENjyKbGJ7bV/5K8/SB+nI5l3Ntl2zMt7sO/JAsPvXmCHHkfuMCjIAACSeWkE+MlvqeZbxTDWba+f+/nbsCHnuvceWB4XMQjaxlvFmpmxaKp2ykU2O8ebNkX5Tc/P8DEbuMyrIAACQuHSbt6jSuTXDuv/diDOt+1lRy1ql3OyzJJsRc7tX0296mYk+i2xiq2XTcq9H7ueyiX8+UzY1Vs+m5Fql5fVmyCYyUp9RQQYAgMTL6+tr+Msvn7/Gv5zUj9/fXkqOOzubvdlmSQX5/udnuyqbEcgmJpuYbGKyickmdnU2zzoftyjJRp95p4IMAACJS9Ygr7DOptWROz5np9/EZBOTDZzLZ+ojmeSNmI8KMgAAJC6pII80g3iW6G7NEWddjyKDmGxisilnvInJ5p0MPlrhqXhHjNh+FWQAAEi4QAYAgMSlDwrho/uvIUb8WgIYk/EmJhta6DfjUkEGAICEC+TOff/1s+mRlQAAtHGBDAAAieyjpgEAYDUqyAAAkHCBDAAACRfIAACQcIEMAAAJF8gAAJBwgQwAAIk/EDZu/FaMfgcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def training(epochs=1, batch_size=128):\n",
    "    (X_train, y_train, X_test, y_test) = load_data()\n",
    "    batch_count = X_train.shape[0] / batch_size\n",
    "\n",
    "    generator = create_generator()\n",
    "    discriminator = create_discriminator()\n",
    "    gan = create_gan(discriminator, generator)\n",
    "    \n",
    "    for e in range(1,epochs+1):\n",
    "        print(\"Epoch %d\" %e)\n",
    "        for _ in range(batch_size):\n",
    "            noise = np.random.normal(0,1, [batch_size, 100])\n",
    "            generated_images = generator.predict(noise)\n",
    "            image_batch = X_train[np.random.randint(low=0, high=X_train.shape[0], size=batch_size)]\n",
    "            X= np.concatenate([image_batch, generated_images])\n",
    "            \n",
    "            y_dis=np.zeros(2*batch_size)\n",
    "            y_dis[:batch_size]=0.9\n",
    "            \n",
    "            discriminator.trainable = True\n",
    "            discriminator.train_on_batch(X, y_dis)\n",
    "            \n",
    "            noise = np.random.normal(0,1, [batch_size, 100])\n",
    "            y_gen = np.ones(batch_size)\n",
    "            \n",
    "            discriminator.trainable=False\n",
    "            gan.train_on_batch(noise, y_gen)\n",
    "            \n",
    "        if e == 1 or e % 100 == 0:           \n",
    "            plot_generated_images(e, generator)\n",
    "\n",
    "training(600,128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 176073,
     "status": "ok",
     "timestamp": 1592851943216,
     "user": {
      "displayName": "Siddhartha Dutta",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiT79p-YwUAMLJLPMEjadbhQl383sQDEQ2dCLM=s64",
      "userId": "06895572418557294109"
     },
     "user_tz": -330
    },
    "id": "Fn7aZVSgrYv6",
    "outputId": "bad7eac6-6fd2-4c40-a1b5-7e855c3751b4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1\n",
      "Epoch 2\n",
      "Epoch 3\n",
      "Epoch 4\n",
      "Epoch 5\n",
      "Epoch 6\n",
      "Epoch 7\n",
      "Epoch 8\n",
      "Epoch 9\n",
      "Epoch 10\n",
      "Epoch 11\n",
      "Epoch 12\n",
      "Epoch 13\n",
      "Epoch 14\n",
      "Epoch 15\n",
      "Epoch 16\n",
      "Epoch 17\n",
      "Epoch 18\n",
      "Epoch 19\n",
      "Epoch 20\n",
      "Epoch 21\n",
      "Epoch 22\n",
      "Epoch 23\n",
      "Epoch 24\n",
      "Epoch 25\n",
      "Epoch 26\n",
      "Epoch 27\n",
      "Epoch 28\n",
      "Epoch 29\n",
      "Epoch 30\n",
      "Epoch 31\n",
      "Epoch 32\n",
      "Epoch 33\n",
      "Epoch 34\n",
      "Epoch 35\n",
      "Epoch 36\n",
      "Epoch 37\n",
      "Epoch 38\n",
      "Epoch 39\n",
      "Epoch 40\n",
      "Epoch 41\n",
      "Epoch 42\n",
      "Epoch 43\n",
      "Epoch 44\n",
      "Epoch 45\n",
      "Epoch 46\n",
      "Epoch 47\n",
      "Epoch 48\n",
      "Epoch 49\n",
      "Epoch 50\n",
      "Epoch 51\n",
      "Epoch 52\n",
      "Epoch 53\n",
      "Epoch 54\n",
      "Epoch 55\n",
      "Epoch 56\n",
      "Epoch 57\n",
      "Epoch 58\n",
      "Epoch 59\n",
      "Epoch 60\n",
      "Epoch 61\n",
      "Epoch 62\n",
      "Epoch 63\n",
      "Epoch 64\n",
      "Epoch 65\n",
      "Epoch 66\n",
      "Epoch 67\n",
      "Epoch 68\n",
      "Epoch 69\n",
      "Epoch 70\n",
      "Epoch 71\n",
      "Epoch 72\n",
      "Epoch 73\n",
      "Epoch 74\n",
      "Epoch 75\n",
      "Epoch 76\n",
      "Epoch 77\n",
      "Epoch 78\n",
      "Epoch 79\n",
      "Epoch 80\n",
      "Epoch 81\n",
      "Epoch 82\n",
      "Epoch 83\n",
      "Epoch 84\n",
      "Epoch 85\n",
      "Epoch 86\n",
      "Epoch 87\n",
      "Epoch 88\n",
      "Epoch 89\n",
      "Epoch 90\n",
      "Epoch 91\n",
      "Epoch 92\n",
      "Epoch 93\n",
      "Epoch 94\n",
      "Epoch 95\n",
      "Epoch 96\n",
      "Epoch 97\n",
      "Epoch 98\n",
      "Epoch 99\n",
      "Epoch 100\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "training(100,128)"
   ]
  },
  {
   "cell_type": "code",
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