1_Generative_Adversarial_Networks.ipynb

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  "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": "3.7.3"
    },
    "colab": {
      "name": "Generative_Adversarial_Networks.ipynb",
      "provenance": []
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "m6ojb5MpYqjL"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Li-o9zjFYqjP"
      },
      "source": [
        "from tensorflow.keras.datasets import mnist"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q8tFk_pGYqjR",
        "outputId": "652039be-e93a-463c-f6fa-5037165c7cbb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9H5Ge2qWYqjU",
        "outputId": "fbe707bc-7902-4ac7-f812-37ef094b9499",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "plt.imshow(X_train[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f2cef485ef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LJX1U0jpJmyT9l6SeLurtPyQ9JelJDQVrdod6W6ihQ/QnJW2o/Z3b6feu0Fdb3jculwWS4AQdkARhB5Ig7EAShB1IgrADSRB2IAnCDiTx/65XcTNOWsh5AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mhmTxZkKYqjX",
        "outputId": "c4035bed-c41a-40c8-ee13-65f853a94726",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "y_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 0, 4, ..., 5, 6, 8], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YgqNtblSYqja"
      },
      "source": [
        "## Filtering out the Data for Faster Training on Smaller Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "76lY189_Yqja"
      },
      "source": [
        "only_zeros = X_train[y_train==0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G2jPkQdEYqjd",
        "outputId": "c68c2ae8-1606-4979-e865-26a301fa8916",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "only_zeros.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5923, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UAAzM6ipYqjf",
        "outputId": "7eebbd90-fc19-42b0-a870-9a63998bf4df"
      },
      "source": [
        "plt.imshow(only_zeros[950])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x1d8d9482508>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qkK2k_yrYqji"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import Dense,Reshape,Flatten\n",
        "from tensorflow.keras.models import Sequential"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rsi0TUbcYqjk"
      },
      "source": [
        "np.random.seed(42)\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "codings_size = 100"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dKklGWYlYqjm"
      },
      "source": [
        "generator = Sequential()\n",
        "generator.add(Dense(100, activation=\"relu\", input_shape=[codings_size]))\n",
        "generator.add(Dense(150,activation='relu'))\n",
        "generator.add(Dense(784, activation=\"sigmoid\")) # 28*28 = 784\n",
        "generator.add(Reshape([28,28]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4MaGsGseYqjp"
      },
      "source": [
        "discriminator = Sequential()\n",
        "discriminator.add(Flatten(input_shape=[28,28]))\n",
        "discriminator.add(Dense(150,activation='relu'))\n",
        "discriminator.add(Dense(100,activation='relu'))\n",
        "discriminator.add(Dense(1,activation=\"sigmoid\"))\n",
        "\n",
        "discriminator.compile(loss=\"binary_crossentropy\", optimizer=\"adam\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BPbaDPFHYqjr"
      },
      "source": [
        "GAN = Sequential([generator, discriminator])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F6OJKLv3Yqjt"
      },
      "source": [
        "discriminator.trainable = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SKv6fPp2Yqjw"
      },
      "source": [
        "GAN.compile(loss=\"binary_crossentropy\", optimizer=\"adam\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lk487QkJYqjz",
        "outputId": "e0391aea-3d1b-4d85-d720-7d2b1ac3e549"
      },
      "source": [
        "GAN.layers"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<tensorflow.python.keras.engine.sequential.Sequential at 0x1d8d99390c8>,\n",
              " <tensorflow.python.keras.engine.sequential.Sequential at 0x1d8d9a48908>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a3PNm8WeYqj1",
        "outputId": "eac10501-0099-4099-df74-ebaae0915b30"
      },
      "source": [
        "GAN.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "sequential (Sequential)      (None, 28, 28)            143634    \n",
            "_________________________________________________________________\n",
            "sequential_1 (Sequential)    (None, 1)                 132951    \n",
            "=================================================================\n",
            "Total params: 276,585\n",
            "Trainable params: 143,634\n",
            "Non-trainable params: 132,951\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xmKAwsTAYqj4",
        "outputId": "12bf651b-87a8-4cb7-c278-1605faf6c100"
      },
      "source": [
        "GAN.layers[0].summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 100)               10100     \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 150)               15150     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 784)               118384    \n",
            "_________________________________________________________________\n",
            "reshape (Reshape)            (None, 28, 28)            0         \n",
            "=================================================================\n",
            "Total params: 143,634\n",
            "Trainable params: 143,634\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aoXTtXrRYqj6",
        "outputId": "24fd2e45-1e18-47e8-e7b1-45d326de1c69"
      },
      "source": [
        "GAN.layers[1].summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten (Flatten)            (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 150)               117750    \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 100)               15100     \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 1)                 101       \n",
            "=================================================================\n",
            "WARNING:tensorflow:Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n",
            "Total params: 265,902\n",
            "Trainable params: 132,951\n",
            "Non-trainable params: 132,951\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JYkQlF5lYqj9"
      },
      "source": [
        "### Setting up Training Batches"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vTIV-BSJYqj9"
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vjjt_OFYYqkA"
      },
      "source": [
        "batch_size = 32"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j2FpqAdQYqkC"
      },
      "source": [
        "https://stackoverflow.com/questions/46444018/meaning-of-buffer-size-in-dataset-map-dataset-prefetch-and-dataset-shuffle\n",
        "\n",
        "The buffer_size in Dataset.shuffle() can affect the randomness of your dataset, and hence the order in which elements are produced. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BpMxSj7dYqkC"
      },
      "source": [
        "# my_data = X_train\n",
        "my_data = only_zeros"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q51t2qmsYqkE"
      },
      "source": [
        "dataset = tf.data.Dataset.from_tensor_slices(my_data).shuffle(buffer_size=1000)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SR-S4fieYqkG",
        "outputId": "98288611-d9cc-4fc6-c03a-78cce1ae6a76"
      },
      "source": [
        "type(dataset)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensorflow.python.data.ops.dataset_ops.ShuffleDataset"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pfMPCWOcYqkJ"
      },
      "source": [
        "dataset = dataset.batch(batch_size, drop_remainder=True).prefetch(1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bVRp_K1ZYqkL"
      },
      "source": [
        "epochs = 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JyV2beK_YqkO"
      },
      "source": [
        "**NOTE: The generator never actually sees any real images. It learns by viewing the gradients going back through the discriminator. The better the discrimnator gets through training, the more information the discriminator contains in its gradients, which means the generator can being to make progress in learning how to generate fake images, in our case, fake zeros.**\n",
        "\n",
        "## Training Loop"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N9Y0-6YLYqkO",
        "outputId": "fa8a9cfe-f865-4d05-f3de-526645677732"
      },
      "source": [
        "# Grab the seprate components\n",
        "generator, discriminator = GAN.layers\n",
        "\n",
        "# For every epcoh\n",
        "for epoch in range(epochs):\n",
        "    print(f\"Currently on Epoch {epoch+1}\")\n",
        "    i = 0\n",
        "    # For every batch in the dataset\n",
        "    for X_batch in dataset:\n",
        "        i=i+1\n",
        "        if i%100 == 0:\n",
        "            print(f\"\\tCurrently on batch number {i} of {len(my_data)//batch_size}\")\n",
        "        #####################################\n",
        "        ## TRAINING THE DISCRIMINATOR ######\n",
        "        ###################################\n",
        "        \n",
        "        # Create Noise\n",
        "        noise = tf.random.normal(shape=[batch_size, codings_size])\n",
        "        \n",
        "        # Generate numbers based just on noise input\n",
        "        gen_images = generator(noise)\n",
        "        \n",
        "        # Concatenate Generated Images against the Real Ones\n",
        "        # TO use tf.concat, the data types must match!\n",
        "        X_fake_vs_real = tf.concat([gen_images, tf.dtypes.cast(X_batch,tf.float32)], axis=0)\n",
        "        \n",
        "        # Targets set to zero for fake images and 1 for real images\n",
        "        y1 = tf.constant([[0.]] * batch_size + [[1.]] * batch_size)\n",
        "        \n",
        "        # This gets rid of a Keras warning\n",
        "        discriminator.trainable = True\n",
        "        \n",
        "        # Train the discriminator on this batch\n",
        "        discriminator.train_on_batch(X_fake_vs_real, y1)\n",
        "        \n",
        "        \n",
        "        #####################################\n",
        "        ## TRAINING THE GENERATOR     ######\n",
        "        ###################################\n",
        "        \n",
        "        # Create some noise\n",
        "        noise = tf.random.normal(shape=[batch_size, codings_size])\n",
        "        \n",
        "        # We want discriminator to belive that fake images are real\n",
        "        y2 = tf.constant([[1.]] * batch_size)\n",
        "        \n",
        "        # Avois a warning\n",
        "        discriminator.trainable = False\n",
        "        \n",
        "        GAN.train_on_batch(noise, y2)\n",
        "        \n",
        "print(\"TRAINING COMPLETE\")            "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Currently on Epoch 1\n",
            "\tCurrently on batch number 100 of 185\n",
            "TRAINING COMPLETE\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gs7i8_uYYqkQ"
      },
      "source": [
        "# Most likely your generator will only learn to create one type of noisey zero\n",
        "# Regardless of what noise is passed in."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ekPh22T-YqkS"
      },
      "source": [
        "https://www.quora.com/What-does-it-mean-if-all-produced-images-of-a-GAN-look-the-same"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HxcQHUa0YqkS"
      },
      "source": [
        "noise = tf.random.normal(shape=[10, codings_size])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HigqRanyYqkU",
        "outputId": "e50d05b4-4e2b-4f08-f809-e542d8bc0d5c"
      },
      "source": [
        "noise.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([10, 100])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5dj2B9f3YqkX",
        "outputId": "236334a9-6f79-4718-97a2-dbf9dd885ebe"
      },
      "source": [
        "plt.imshow(noise)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x1d8dada1208>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "654THN-hYqkZ"
      },
      "source": [
        "image = generator(noise)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8-x1WkpOYqkb",
        "outputId": "ea6aefeb-9826-430b-c118-1e92096df342"
      },
      "source": [
        "plt.imshow(image[5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x1d8dadeeac8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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S1H8eSee0XM+JKqfx7qcTphkfmH1XZ/rzptoI+0xTSQ1S/++qiPiEpBskfa14uYrOdDSNd7/MMM34QKg7/XlTbYR9t6Ql075fLGlvC3XMKCL2FrcHJD2uwZuKev/xGXSL2wMt1/P/Bmka75mmGdcA7Ls2pz9vI+zPS7rE9odtz5F0s6R1LdTxPrbnF2+cyPZ8SZ/R4E1FvU7SrcX9WyU90WIt7zEo03iXTTOulvdd69OfR0TfvySt0NQ78r+U9Ndt1FBS18WSXiy+Xmm7NkmPaOpl3bimXhF9WdKZkjZKerW4XThAtf2zpJclvaSpYC1qqbarNfWn4UuSthZfK9red4m6+rLf+LgskAk+QQdkgrADmSDsQCYIO5AJwg5kgrADmSDsQCb+D40n4imxL+a8AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yibmEcs5crCI"
      },
      "source": [
        "**Note: This Model gets stuck again and again fooling the discriminator generating same output**"
      ]
    }
  ]
}