GAN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7zUFIVFgBqiE"
   },
   "source": [
    "# GAN - Generative Adversarial Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "u8gChRQzBzHR"
   },
   "source": [
    "## Importing the libraries and dependencies and drive\n",
    "The following cell block imports all the libraries, functions, methods that are being used inn this jupyter notebook.<br>\n",
    "The Deep Learning/Machine Learning Libarary being used here is the high level keras API of tensorflow 2.X.<Br>\n",
    "And the plotting is done with matplotlib or seaborn plotting packages.\n",
    "\n",
    "Along with this all the constants that will be common throughout the file are defined and declared here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "V8ycuGZfBwjo",
    "outputId": "9a121beb-059a-4f43-f51c-a6d8d4786f5c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "9E_QU8qzB3a2"
   },
   "outputs": [],
   "source": [
    "import random, math, os, pickle, random, librosa\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow_datasets as tfds\n",
    "from itertools import combinations\n",
    "%matplotlib inline\n",
    "\n",
    "EPOCHS = 200\n",
    "BATCH_SIZE = 128\n",
    "IMG_SIZE = (28,28)\n",
    "SEED = 100\n",
    "CLASSES = 10\n",
    "LEARNING_RATE = 0.001\n",
    "ACCURACY_THRESHOLD = 0.98"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SGS71F50B6iA"
   },
   "source": [
    "## Loading Data\n",
    "The following block of code loads MNIST data in two formats.\n",
    "- First is through the dataset module from keras which loads data in numpy arrays.\n",
    "- Second is the TFDS - Tensorflow dataset module which loads data in the format of tenorflow_dataset whic makes it fast o process and load data onto and away from the memory(GPU/CPU)\n",
    "\n",
    "I experimented with both the above techniques but the second one was very fast and thats what i am sticking with.\n",
    "As it loads the next batch of data onto GPU memory while the current batch is being operated on - this is done with the prefetching functionality of the tensirflow_dataset module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 208,
     "referenced_widgets": [
      "593ae7356e944c9c94b42f2d414361ed",
      "a6f6f1182f0440fead7e7e0df6cdb975",
      "045e6f349f944b91b39071ed03d6d37a",
      "25e251dd28b04be290d2fea8182a5541",
      "2ab17a06972d481ca7d711524415c2c2",
      "c797e0f2040845ea8f5db53eefed5269",
      "306c974cd36a428592bcc65461601bdd",
      "903c128ddab64ff29b1a90c237ec1bf3",
      "e8a231f12c6748b586e57d3f8bba69de",
      "22212a9e0cf3444d9a9252eece1b7edb",
      "a5224158997c4b9da9c39c677b2a80ca"
     ]
    },
    "id": "h3e2sK3PB9AG",
    "outputId": "298efd2f-8ff0-42e6-f565-5a64aa86bdaf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mDownloading and preparing dataset mnist/3.0.1 (download: 11.06 MiB, generated: 21.00 MiB, total: 32.06 MiB) to /root/tensorflow_datasets/mnist/3.0.1...\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Dataset mnist is hosted on GCS. It will automatically be downloaded to your\n",
      "local data directory. If you'd instead prefer to read directly from our public\n",
      "GCS bucket (recommended if you're running on GCP), you can instead pass\n",
      "`try_gcs=True` to `tfds.load` or set `data_dir=gs://tfds-data/datasets`.\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "593ae7356e944c9c94b42f2d414361ed",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dl Completed...:   0%|          | 0/4 [00:00<?, ? file/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[1mDataset mnist downloaded and prepared to /root/tensorflow_datasets/mnist/3.0.1. Subsequent calls will reuse this data.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# df_train, df_test = tf.keras.datasets.mnist.load_data()\n",
    "\n",
    "(df_train, df_test), df_info = tfds.load(\n",
    "    'mnist',\n",
    "    split=['train', 'test'],\n",
    "    shuffle_files = False,\n",
    "    with_info = True,\n",
    "    as_supervised = True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TyRGR8byB-jT",
    "outputId": "8333dfd4-e202-48b7-ace0-89eb5f2299c7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tfds.core.DatasetInfo(\n",
       "    name='mnist',\n",
       "    version=3.0.1,\n",
       "    description='The MNIST database of handwritten digits.',\n",
       "    homepage='http://yann.lecun.com/exdb/mnist/',\n",
       "    features=FeaturesDict({\n",
       "        'image': Image(shape=(28, 28, 1), dtype=tf.uint8),\n",
       "        'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=10),\n",
       "    }),\n",
       "    total_num_examples=70000,\n",
       "    splits={\n",
       "        'test': 10000,\n",
       "        'train': 60000,\n",
       "    },\n",
       "    supervised_keys=('image', 'label'),\n",
       "    citation=\"\"\"@article{lecun2010mnist,\n",
       "      title={MNIST handwritten digit database},\n",
       "      author={LeCun, Yann and Cortes, Corinna and Burges, CJ},\n",
       "      journal={ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist},\n",
       "      volume={2},\n",
       "      year={2010}\n",
       "    }\"\"\",\n",
       "    redistribution_info=,\n",
       ")"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PaKE_UtucllE"
   },
   "source": [
    "## Labels for the data & Look at the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "24NDCXWDcxCZ"
   },
   "outputs": [],
   "source": [
    "(train_imgs, train_labels), (test_imgs, test_labels) = tf.keras.datasets.mnist.load_data()\n",
    "train_imgs = train_imgs.reshape((-1,train_imgs.shape[1]*train_imgs.shape[2]))\n",
    "test_imgs = test_imgs.reshape((-1,test_imgs.shape[1]*test_imgs.shape[2]))\n",
    "train_imgs = 2*((train_imgs/255.)-0.5)\n",
    "test_imgs = 2*((test_imgs/255.)-0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BvGM3-aAcg5U",
    "outputId": "90dcbda9-ab86-4c57-c57f-e2ba59cf672e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: '0',\n",
       " 1: '1',\n",
       " 2: '2',\n",
       " 3: '3',\n",
       " 4: '4',\n",
       " 5: '5',\n",
       " 6: '6',\n",
       " 7: '7',\n",
       " 8: '8',\n",
       " 9: '9'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{df_info.features['label'].str2int(x):x for x in df_info.features['label'].names}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 534
    },
    "id": "mr7lGXtHcphq",
    "outputId": "723d9f3e-8a1d-41cb-b245-1345a7961dc9"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 648x648 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = tfds.show_examples(df_train, df_info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UwhlP7EbumYx"
   },
   "source": [
    "## Model Architecture, Custom fnction & Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vTYxyI_vlNtJ",
    "outputId": "bb63f056-4c94-4c4c-f1f3-97132fd858df"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch : 0 Discriminator Loss : 0.7913 Real Img Acc : 0.627 Fake Img Acc : 0.2673 Generator Loss : 0.6399\n",
      "Epoch : 1 Discriminator Loss : 0.7269 Real Img Acc : 0.592 Fake Img Acc : 0.3293 Generator Loss : 0.6378\n",
      "Epoch : 2 Discriminator Loss : 0.7055 Real Img Acc : 0.646 Fake Img Acc : 0.3302 Generator Loss : 0.6337\n",
      "Epoch : 3 Discriminator Loss : 0.6979 Real Img Acc : 0.6554 Fake Img Acc : 0.3449 Generator Loss : 0.6203\n",
      "Epoch : 4 Discriminator Loss : 0.6884 Real Img Acc : 0.6717 Fake Img Acc : 0.3647 Generator Loss : 0.6018\n",
      "Epoch : 5 Discriminator Loss : 0.6838 Real Img Acc : 0.657 Fake Img Acc : 0.3946 Generator Loss : 0.5988\n",
      "Epoch : 6 Discriminator Loss : 0.6801 Real Img Acc : 0.6478 Fake Img Acc : 0.4172 Generator Loss : 0.5891\n",
      "Epoch : 7 Discriminator Loss : 0.6705 Real Img Acc : 0.6481 Fake Img Acc : 0.4536 Generator Loss : 0.5813\n",
      "Epoch : 8 Discriminator Loss : 0.6634 Real Img Acc : 0.6143 Fake Img Acc : 0.52 Generator Loss : 0.5621\n",
      "Epoch : 9 Discriminator Loss : 0.6643 Real Img Acc : 0.6368 Fake Img Acc : 0.4868 Generator Loss : 0.5678\n",
      "Epoch : 10 Discriminator Loss : 0.6532 Real Img Acc : 0.6457 Fake Img Acc : 0.5259 Generator Loss : 0.5643\n",
      "Epoch : 11 Discriminator Loss : 0.6468 Real Img Acc : 0.6081 Fake Img Acc : 0.5849 Generator Loss : 0.5506\n",
      "Epoch : 12 Discriminator Loss : 0.643 Real Img Acc : 0.6201 Fake Img Acc : 0.5869 Generator Loss : 0.5516\n",
      "Epoch : 13 Discriminator Loss : 0.6138 Real Img Acc : 0.6495 Fake Img Acc : 0.6392 Generator Loss : 0.5519\n",
      "Epoch : 14 Discriminator Loss : 0.6063 Real Img Acc : 0.6386 Fake Img Acc : 0.6754 Generator Loss : 0.5495\n",
      "Epoch : 15 Discriminator Loss : 0.5663 Real Img Acc : 0.6795 Fake Img Acc : 0.7269 Generator Loss : 0.5666\n",
      "Epoch : 16 Discriminator Loss : 0.5598 Real Img Acc : 0.6622 Fake Img Acc : 0.7582 Generator Loss : 0.5795\n",
      "Epoch : 17 Discriminator Loss : 0.5416 Real Img Acc : 0.6815 Fake Img Acc : 0.7758 Generator Loss : 0.6071\n",
      "Epoch : 18 Discriminator Loss : 0.5978 Real Img Acc : 0.6208 Fake Img Acc : 0.7313 Generator Loss : 0.5611\n",
      "Epoch : 19 Discriminator Loss : 0.5916 Real Img Acc : 0.6025 Fake Img Acc : 0.751 Generator Loss : 0.5699\n",
      "Epoch : 20 Discriminator Loss : 0.622 Real Img Acc : 0.5609 Fake Img Acc : 0.724 Generator Loss : 0.557\n",
      "Epoch : 21 Discriminator Loss : 0.6511 Real Img Acc : 0.5117 Fake Img Acc : 0.704 Generator Loss : 0.5437\n",
      "Epoch : 22 Discriminator Loss : 0.6535 Real Img Acc : 0.5206 Fake Img Acc : 0.6834 Generator Loss : 0.5382\n",
      "Epoch : 23 Discriminator Loss : 0.6584 Real Img Acc : 0.5226 Fake Img Acc : 0.6646 Generator Loss : 0.5395\n",
      "Epoch : 24 Discriminator Loss : 0.6704 Real Img Acc : 0.4887 Fake Img Acc : 0.6643 Generator Loss : 0.5378\n",
      "Epoch : 25 Discriminator Loss : 0.6757 Real Img Acc : 0.4526 Fake Img Acc : 0.6701 Generator Loss : 0.5347\n",
      "Epoch : 26 Discriminator Loss : 0.6775 Real Img Acc : 0.4726 Fake Img Acc : 0.6437 Generator Loss : 0.5373\n",
      "Epoch : 27 Discriminator Loss : 0.6763 Real Img Acc : 0.4845 Fake Img Acc : 0.6379 Generator Loss : 0.5373\n",
      "Epoch : 28 Discriminator Loss : 0.6759 Real Img Acc : 0.4777 Fake Img Acc : 0.6453 Generator Loss : 0.5399\n",
      "Epoch : 29 Discriminator Loss : 0.6796 Real Img Acc : 0.4579 Fake Img Acc : 0.6482 Generator Loss : 0.5338\n",
      "Epoch : 30 Discriminator Loss : 0.6783 Real Img Acc : 0.4727 Fake Img Acc : 0.6407 Generator Loss : 0.5346\n",
      "Epoch : 31 Discriminator Loss : 0.6851 Real Img Acc : 0.4478 Fake Img Acc : 0.6424 Generator Loss : 0.5367\n",
      "Epoch : 32 Discriminator Loss : 0.6829 Real Img Acc : 0.4635 Fake Img Acc : 0.6294 Generator Loss : 0.5372\n",
      "Epoch : 33 Discriminator Loss : 0.6773 Real Img Acc : 0.4687 Fake Img Acc : 0.6443 Generator Loss : 0.5329\n",
      "Epoch : 34 Discriminator Loss : 0.6775 Real Img Acc : 0.4709 Fake Img Acc : 0.6369 Generator Loss : 0.5324\n",
      "Epoch : 35 Discriminator Loss : 0.6782 Real Img Acc : 0.4747 Fake Img Acc : 0.6394 Generator Loss : 0.5342\n",
      "Epoch : 36 Discriminator Loss : 0.6736 Real Img Acc : 0.4883 Fake Img Acc : 0.642 Generator Loss : 0.533\n",
      "Epoch : 37 Discriminator Loss : 0.68 Real Img Acc : 0.4609 Fake Img Acc : 0.6362 Generator Loss : 0.5369\n",
      "Epoch : 38 Discriminator Loss : 0.6722 Real Img Acc : 0.4846 Fake Img Acc : 0.6509 Generator Loss : 0.5295\n",
      "Epoch : 39 Discriminator Loss : 0.6708 Real Img Acc : 0.4673 Fake Img Acc : 0.6716 Generator Loss : 0.5248\n",
      "Epoch : 40 Discriminator Loss : 0.6706 Real Img Acc : 0.4853 Fake Img Acc : 0.6429 Generator Loss : 0.5375\n",
      "Epoch : 41 Discriminator Loss : 0.6704 Real Img Acc : 0.4881 Fake Img Acc : 0.6496 Generator Loss : 0.5332\n",
      "Epoch : 42 Discriminator Loss : 0.6744 Real Img Acc : 0.4736 Fake Img Acc : 0.6522 Generator Loss : 0.5308\n",
      "Epoch : 43 Discriminator Loss : 0.6665 Real Img Acc : 0.4935 Fake Img Acc : 0.6572 Generator Loss : 0.5314\n",
      "Epoch : 44 Discriminator Loss : 0.6602 Real Img Acc : 0.4978 Fake Img Acc : 0.6695 Generator Loss : 0.5299\n",
      "Epoch : 45 Discriminator Loss : 0.6599 Real Img Acc : 0.48 Fake Img Acc : 0.691 Generator Loss : 0.5212\n",
      "Epoch : 46 Discriminator Loss : 0.6576 Real Img Acc : 0.4829 Fake Img Acc : 0.6897 Generator Loss : 0.5211\n",
      "Epoch : 47 Discriminator Loss : 0.6568 Real Img Acc : 0.4799 Fake Img Acc : 0.7014 Generator Loss : 0.5209\n",
      "Epoch : 48 Discriminator Loss : 0.6581 Real Img Acc : 0.5016 Fake Img Acc : 0.6737 Generator Loss : 0.5289\n",
      "Epoch : 49 Discriminator Loss : 0.6512 Real Img Acc : 0.4951 Fake Img Acc : 0.7037 Generator Loss : 0.5242\n",
      "Epoch : 50 Discriminator Loss : 0.6514 Real Img Acc : 0.5126 Fake Img Acc : 0.6829 Generator Loss : 0.5285\n",
      "Epoch : 51 Discriminator Loss : 0.6558 Real Img Acc : 0.4774 Fake Img Acc : 0.7066 Generator Loss : 0.5223\n",
      "Epoch : 52 Discriminator Loss : 0.6576 Real Img Acc : 0.4958 Fake Img Acc : 0.6879 Generator Loss : 0.5291\n",
      "Epoch : 53 Discriminator Loss : 0.6503 Real Img Acc : 0.4948 Fake Img Acc : 0.7143 Generator Loss : 0.5232\n",
      "Epoch : 54 Discriminator Loss : 0.6466 Real Img Acc : 0.4999 Fake Img Acc : 0.7066 Generator Loss : 0.5271\n",
      "Epoch : 55 Discriminator Loss : 0.6558 Real Img Acc : 0.4967 Fake Img Acc : 0.6914 Generator Loss : 0.5281\n",
      "Epoch : 56 Discriminator Loss : 0.6626 Real Img Acc : 0.4787 Fake Img Acc : 0.6867 Generator Loss : 0.5299\n",
      "Epoch : 57 Discriminator Loss : 0.6423 Real Img Acc : 0.5076 Fake Img Acc : 0.7109 Generator Loss : 0.5329\n",
      "Epoch : 58 Discriminator Loss : 0.64 Real Img Acc : 0.5176 Fake Img Acc : 0.7142 Generator Loss : 0.5309\n",
      "Epoch : 59 Discriminator Loss : 0.6557 Real Img Acc : 0.48 Fake Img Acc : 0.6993 Generator Loss : 0.5295\n",
      "Epoch : 60 Discriminator Loss : 0.6332 Real Img Acc : 0.5355 Fake Img Acc : 0.7188 Generator Loss : 0.5274\n",
      "Epoch : 61 Discriminator Loss : 0.6428 Real Img Acc : 0.4893 Fake Img Acc : 0.73 Generator Loss : 0.5302\n",
      "Epoch : 62 Discriminator Loss : 0.6279 Real Img Acc : 0.5301 Fake Img Acc : 0.7233 Generator Loss : 0.5396\n",
      "Epoch : 63 Discriminator Loss : 0.6351 Real Img Acc : 0.5184 Fake Img Acc : 0.7247 Generator Loss : 0.531\n",
      "Epoch : 64 Discriminator Loss : 0.6327 Real Img Acc : 0.5258 Fake Img Acc : 0.7285 Generator Loss : 0.5366\n",
      "Epoch : 65 Discriminator Loss : 0.628 Real Img Acc : 0.5261 Fake Img Acc : 0.7342 Generator Loss : 0.5388\n",
      "Epoch : 66 Discriminator Loss : 0.6132 Real Img Acc : 0.548 Fake Img Acc : 0.7427 Generator Loss : 0.5418\n",
      "Epoch : 67 Discriminator Loss : 0.6073 Real Img Acc : 0.5566 Fake Img Acc : 0.757 Generator Loss : 0.5474\n",
      "Epoch : 68 Discriminator Loss : 0.5927 Real Img Acc : 0.5946 Fake Img Acc : 0.7551 Generator Loss : 0.5635\n",
      "Epoch : 69 Discriminator Loss : 0.599 Real Img Acc : 0.5632 Fake Img Acc : 0.766 Generator Loss : 0.5562\n",
      "Epoch : 70 Discriminator Loss : 0.5776 Real Img Acc : 0.6016 Fake Img Acc : 0.781 Generator Loss : 0.577\n",
      "Epoch : 71 Discriminator Loss : 0.5771 Real Img Acc : 0.5915 Fake Img Acc : 0.7845 Generator Loss : 0.5707\n",
      "Epoch : 72 Discriminator Loss : 0.5616 Real Img Acc : 0.5975 Fake Img Acc : 0.8009 Generator Loss : 0.5962\n",
      "Epoch : 73 Discriminator Loss : 0.56 Real Img Acc : 0.5976 Fake Img Acc : 0.7998 Generator Loss : 0.5993\n",
      "Epoch : 74 Discriminator Loss : 0.5552 Real Img Acc : 0.6012 Fake Img Acc : 0.8056 Generator Loss : 0.6019\n",
      "Epoch : 75 Discriminator Loss : 0.5544 Real Img Acc : 0.606 Fake Img Acc : 0.8061 Generator Loss : 0.5974\n",
      "Epoch : 76 Discriminator Loss : 0.5418 Real Img Acc : 0.6156 Fake Img Acc : 0.8195 Generator Loss : 0.6135\n",
      "Epoch : 77 Discriminator Loss : 0.5546 Real Img Acc : 0.6039 Fake Img Acc : 0.8074 Generator Loss : 0.6182\n",
      "Epoch : 78 Discriminator Loss : 0.5436 Real Img Acc : 0.6193 Fake Img Acc : 0.8099 Generator Loss : 0.6151\n",
      "Epoch : 79 Discriminator Loss : 0.5185 Real Img Acc : 0.6391 Fake Img Acc : 0.8332 Generator Loss : 0.6549\n",
      "Epoch : 80 Discriminator Loss : 0.5124 Real Img Acc : 0.6437 Fake Img Acc : 0.8348 Generator Loss : 0.6514\n",
      "Epoch : 81 Discriminator Loss : 0.498 Real Img Acc : 0.6571 Fake Img Acc : 0.849 Generator Loss : 0.6634\n",
      "Epoch : 82 Discriminator Loss : 0.4998 Real Img Acc : 0.6524 Fake Img Acc : 0.8464 Generator Loss : 0.6796\n",
      "Epoch : 83 Discriminator Loss : 0.4691 Real Img Acc : 0.6885 Fake Img Acc : 0.8608 Generator Loss : 0.7148\n",
      "Epoch : 84 Discriminator Loss : 0.4451 Real Img Acc : 0.7059 Fake Img Acc : 0.8693 Generator Loss : 0.7635\n",
      "Epoch : 85 Discriminator Loss : 0.4326 Real Img Acc : 0.7113 Fake Img Acc : 0.8803 Generator Loss : 0.7683\n",
      "Epoch : 86 Discriminator Loss : 0.448 Real Img Acc : 0.7007 Fake Img Acc : 0.8742 Generator Loss : 0.7703\n",
      "Epoch : 87 Discriminator Loss : 0.4682 Real Img Acc : 0.6861 Fake Img Acc : 0.8588 Generator Loss : 0.7351\n",
      "Epoch : 88 Discriminator Loss : 0.4298 Real Img Acc : 0.722 Fake Img Acc : 0.8754 Generator Loss : 0.8006\n",
      "Epoch : 89 Discriminator Loss : 0.4048 Real Img Acc : 0.7407 Fake Img Acc : 0.8853 Generator Loss : 0.8462\n",
      "Epoch : 90 Discriminator Loss : 0.3777 Real Img Acc : 0.7615 Fake Img Acc : 0.8993 Generator Loss : 0.8963\n",
      "Epoch : 91 Discriminator Loss : 0.3819 Real Img Acc : 0.7568 Fake Img Acc : 0.9003 Generator Loss : 0.8814\n",
      "Epoch : 92 Discriminator Loss : 0.3391 Real Img Acc : 0.791 Fake Img Acc : 0.9154 Generator Loss : 0.9812\n",
      "Epoch : 93 Discriminator Loss : 0.3349 Real Img Acc : 0.7917 Fake Img Acc : 0.9138 Generator Loss : 1.0005\n",
      "Epoch : 94 Discriminator Loss : 0.3274 Real Img Acc : 0.8006 Fake Img Acc : 0.9204 Generator Loss : 1.018\n",
      "Epoch : 95 Discriminator Loss : 0.3233 Real Img Acc : 0.7995 Fake Img Acc : 0.9261 Generator Loss : 1.0358\n",
      "Epoch : 96 Discriminator Loss : 0.3543 Real Img Acc : 0.7792 Fake Img Acc : 0.9104 Generator Loss : 0.9828\n",
      "Epoch : 97 Discriminator Loss : 0.3058 Real Img Acc : 0.8145 Fake Img Acc : 0.9301 Generator Loss : 1.0679\n",
      "Epoch : 98 Discriminator Loss : 0.3457 Real Img Acc : 0.7841 Fake Img Acc : 0.919 Generator Loss : 0.9704\n",
      "Epoch : 99 Discriminator Loss : 0.3168 Real Img Acc : 0.8053 Fake Img Acc : 0.9252 Generator Loss : 1.0156\n"
     ]
    }
   ],
   "source": [
    "def build_discriminator(random_vec_size = 100, img_size = 784, num_labels = 10):\n",
    "    decoder_inp = tf.keras.layers.Input(shape = (img_size + num_labels))\n",
    "\n",
    "    # Discriminator - Decoder\n",
    "    de_layer = tf.keras.layers.Dense(794, activation = 'relu', kernel_initializer='HeNormal')(decoder_inp)\n",
    "    de_layer = tf.keras.layers.Dropout(0.2)(de_layer)\n",
    "    de_layer = tf.keras.layers.Dense(400, activation = 'relu', kernel_initializer='HeNormal')(de_layer)\n",
    "    de_layer = tf.keras.layers.Dropout(0.2)(de_layer)\n",
    "    de_layer = tf.keras.layers.Dense(200, activation = 'relu', kernel_initializer='HeNormal')(de_layer)\n",
    "    de_layer = tf.keras.layers.Dropout(0.2)(de_layer)\n",
    "    de_layer = tf.keras.layers.Dense(100, activation = 'relu', kernel_initializer='HeNormal')(de_layer)\n",
    "    de_layer_out = tf.keras.layers.Dense(1, activation='sigmoid')(de_layer)\n",
    "\n",
    "    decoder = tf.keras.models.Model(inputs = decoder_inp, outputs = de_layer_out)\n",
    "    decoder.compile(loss = 'binary_crossentropy',\n",
    "                    optimizer = tf.keras.optimizers.Adam(0.00005, 0.5), \n",
    "                    metrics=['accuracy'])\n",
    "    return decoder\n",
    "\n",
    "def build_generator(random_vec_size = 100, img_size = 784, num_labels = 10):\n",
    "    encoder_inp = tf.keras.layers.Input(shape = (random_vec_size + num_labels))\n",
    "\n",
    "    # Generator - Encoder\n",
    "    en_layer = tf.keras.layers.Dense(110, activation = 'relu',kernel_initializer='HeNormal')(encoder_inp)\n",
    "    en_layer = tf.keras.layers.Dense(200, activation = 'relu',kernel_initializer='HeNormal')(en_layer)\n",
    "    en_layer = tf.keras.layers.Dense(400, activation = 'relu',kernel_initializer='HeNormal')(en_layer)\n",
    "    en_layer_out = tf.keras.layers.Dense(784, activation = 'tanh')(en_layer)\n",
    "\n",
    "    encoder = tf.keras.models.Model(inputs = encoder_inp, outputs = en_layer_out)\n",
    "    # encoder.compile(loss = 'binary_crossentropy',\n",
    "    #                 optimizer = tf.keras.optimizers.Adam(0.0002, 0.5), \n",
    "    #                 metrics=['accuracy'])\n",
    "\n",
    "    return encoder\n",
    "\n",
    "def build_gan(generator, discriminator, latent_dim = 100):\n",
    "    rand_data = tf.keras.layers.Input(shape=(latent_dim,))\n",
    "    one_hot_labels = tf.keras.layers.Input(shape=(10,))\n",
    "\n",
    "    gen_inp = tf.keras.layers.concatenate([rand_data, one_hot_labels])\n",
    "    features = generator(gen_inp)\n",
    "\n",
    "    dis_inp = tf.keras.layers.concatenate([features, one_hot_labels])\n",
    "    output = discriminator(dis_inp)\n",
    "    \n",
    "    discriminator.trainable = False\n",
    "    \n",
    "    model = tf.keras.models.Model([rand_data, one_hot_labels], outputs = output)\n",
    "    model.compile(loss='binary_crossentropy', \n",
    "                  optimizer=tf.keras.optimizers.Adam(0.0005, 0.5), \n",
    "                  metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "def get_random_batch(imgs, lbls, idx, batch_size = 100):\n",
    "\n",
    "    imgs_batch = imgs[idx:idx+batch_size]\n",
    "    lbls_batch = np.eye(10)[lbls[idx:idx+batch_size]]\n",
    "    \n",
    "    return imgs_batch, lbls_batch \n",
    "\n",
    "discriminator = build_discriminator()\n",
    "generator = build_generator()\n",
    "gan = build_gan(generator, discriminator)\n",
    "\n",
    "def train(epochs=100, batch_size=100, latent_dim = 100):\n",
    "    real_accs, fake_accs = [], []\n",
    "    gen_losses, d_losses = [], []\n",
    "\n",
    "    for epoch in range(epochs):\n",
    "        epoch_real_acc, epoch_fake_acc = [], []\n",
    "        epoch_gen_losses, epoch_d_losses = [], []\n",
    "\n",
    "        for i in range(0,len(train_imgs),\n",
    "                       batch_size):\n",
    "            discriminator.trainable = True\n",
    "            \n",
    "            img, labels = get_random_batch(train_imgs, train_labels, i)\n",
    "            real_dis_loss, real_dis_acc = discriminator.train_on_batch(np.concatenate([img, labels], axis=1), \n",
    "                                                                              np.ones(shape= (batch_size, 1)))\n",
    "            epoch_real_acc.append(real_dis_acc)\n",
    "\n",
    "            z = np.random.normal(size = (batch_size, latent_dim))\n",
    "            fake_labels = np.eye(10)[np.random.choice(10, batch_size,replace=True)]\n",
    "            gen_img = generator.predict(np.concatenate([z,fake_labels], axis=1))\n",
    "            fake_dis_loss, fake_dis_acc = discriminator.train_on_batch(np.concatenate([gen_img, fake_labels], axis=1), \n",
    "                                                                              np.zeros(shape = (batch_size,1)))\n",
    "            epoch_fake_acc.append(fake_dis_acc)\n",
    "\n",
    "            d_loss=(real_dis_loss + fake_dis_loss)/2\n",
    "            epoch_d_losses.append(d_loss)\n",
    "\n",
    "            discriminator.trainable = False\n",
    "            z = np.random.normal(0,1,size = (batch_size, latent_dim))\n",
    "            labels = np.eye(10)[np.random.choice(10, batch_size,replace=True)]\n",
    "            g_loss = gan.train_on_batch([z,labels], \n",
    "                                        np.ones(shape= (batch_size, 1)))\n",
    "            epoch_gen_losses.append(g_loss)\n",
    "            \n",
    "        \n",
    "        epoch_real_accuracy = np.mean(epoch_real_acc)\n",
    "        epoch_fake_accuracy = np.mean(epoch_fake_acc)\n",
    "        epoch_gen_loss = np.mean(epoch_gen_losses)\n",
    "        epoch_d_loss = np.mean(epoch_d_losses)\n",
    "\n",
    "        real_accs.append(epoch_real_accuracy)\n",
    "        fake_accs.append(epoch_fake_accuracy)\n",
    "        gen_losses.append(epoch_gen_loss)\n",
    "        d_losses.append(epoch_d_loss)\n",
    "        print(f\"Epoch : {epoch} Discriminator Loss : {round(epoch_d_loss, 4)}\",\n",
    "              f\"Real Img Acc : {round(epoch_real_accuracy, 4)} Fake Img Acc : {round(epoch_fake_accuracy, 4)}\",\n",
    "              f\"Generator Loss : {round(epoch_gen_loss, 4)}\")\n",
    "\n",
    "    return (real_accs,fake_accs,gen_losses,d_losses)\n",
    "\n",
    "history=train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "u4MFxXbt0HEj"
   },
   "source": [
    "Training was only done for 100 epochs because it took around 3 hours to train the above question 3 code (even on colab pro)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x5KnV4Hmuvj9"
   },
   "source": [
    "## Plotting the accuracies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "ovKkAbrtyhHD",
    "outputId": "0d242a1f-c18f-4b10-bc18-c6b3ce7a3c16"
   },
   "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": [
    "plt.plot(history[0], label='real_accs')\n",
    "plt.plot(history[1], label = 'fake_accs')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xbLCAzU_uy26"
   },
   "source": [
    "## Plotting the OUtput"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 440
    },
    "id": "AKpsTJ-Cg_lN",
    "outputId": "800669af-a60a-4a92-afc4-ddabddf62da2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gen_image = 0.5 * generator.predict(np.concatenate([np.random.normal(0,1,(10, 100)),\n",
    "                                              np.eye(10)], axis=1)) + 0.5\n",
    "    \n",
    "fig, axes = plt.subplots(2, 5, figsize = (10,10))\n",
    "count = 0\n",
    "for i in range(2):\n",
    "    for j in range(5):\n",
    "        axes[i,j].imshow(gen_image[count,:].reshape((28,28)),cmap = 'gray')\n",
    "        axes[i,j].axis('off')\n",
    "        axes[i,j].set_title(\"Digit: %d\" % np.argmax(labels[count,:]))\n",
    "        count+=1\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 574
    },
    "id": "-GhnlNntr-_6",
    "outputId": "d2e342dc-151e-44c5-a699-0c0ab8bdbd00"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 100 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_images(mod):\n",
    "    plt.figure(figsize=(10, 10))\n",
    "    for i in range(10):\n",
    "        for j in range(10):\n",
    "            ax=plt.subplot2grid((10, 10), (i, j))\n",
    "            z = np.random.normal(0,1,(1, 100))\n",
    "            label = np.zeros(shape = (1, 10))\n",
    "            label[0,i] = 1\n",
    "            gen_image = mod.predict(np.concatenate([z, label], axis=1))\n",
    "            gen_image = 0.5*gen_image + 0.5\n",
    "            try:\n",
    "                ax.imshow(gen_image.reshape((28,28)), cmap='gray')\n",
    "            except:\n",
    "                pass\n",
    "            plt.axis('off')\n",
    "    plt.show()\n",
    "\n",
    "plot_images(generator)"
   ]
  }
 ],
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  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [
    "AbjLDB4G9oJb",
    "jGIhh0HKPLIY",
    "bnntYt62Ygjd"
   ],
   "name": "Assignment_5_DLS.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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    "version": 3
   },
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   "name": "python",
   "nbconvert_exporter": "python",
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