GAN_mnist.ipynb

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  "metadata": {
    "colab": {
      "name": "GAN_mnist.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "_iOAPc47SSVK",
        "colab_type": "code",
        "outputId": "3dbf9b31-d4bc-4ef8-8702-b0c0c7159249",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 63
        }
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          "metadata": {
            "tags": []
          }
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ukkck3E3TDcy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Vh4wmS2TV_5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# from tensorflow.examples.tutorials.mnist import input_data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pudxF3Q3bklc",
        "colab_type": "code",
        "outputId": "1b773809-194b-4c28-9cb2-bfd0dfeda89b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qvRlPBgjjOMk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "IMG_PATH = \"/content/drive/My Drive/Colab Notebooks/idcard/*.jpg\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WjgE-3cHjW7o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import glob, cv2\n",
        "images = []\n",
        "for img in glob.glob(IMG_PATH):\n",
        "    images.append(cv2.resize(cv2.imread(img,cv2.IMREAD_GRAYSCALE),(28,28)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zsvhGrsMlKBs",
        "colab_type": "code",
        "outputId": "18feb022-65bd-44a8-891e-54d229e8c6e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(images)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "45"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bgR9u0t1ptXX",
        "colab_type": "code",
        "outputId": "bf7cf534-98fc-40be-e5c6-87bb51bafd30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "images[0].shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mJ9woxKRThEH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# mnist = input_data.read_data_sets(\"../03-Convolutional-Neural-Networks/MNIST_data/\", one_hot=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T-PPX3KYT9g_",
        "colab_type": "code",
        "outputId": "2c79666c-2ee8-459a-c1f7-1e9b492e3477",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "plt.imshow(images[20])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f2127dfe5c0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BwLcBrJ9Zt4QQM82UxG5mx8c0PgRgS+i9Qoi5gTnpnw0AZnYHgPcAmA+gG8AXin+vBeAA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\nCf8P308D7kqrkvIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MrpWNTgIUafz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def generator(z, reuse=None):\n",
        "  with tf.variable_scope('gen',reuse=reuse):\n",
        "    hidden1 = tf.layers.dense(inputs=z,units=128)\n",
        "    \n",
        "    alpha = 0.01\n",
        "    hidden1 = tf.maximum(alpha*hidden1,hidden1)\n",
        "    \n",
        "    hidden2 = tf.layers.dense(inputs=hidden1,units=128)\n",
        "    \n",
        "    hidden2 = tf.maximum(alpha*hidden2,hidden2)\n",
        "    \n",
        "    output = tf.layers.dense(hidden2,units=784,activation=tf.nn.tanh)\n",
        "    \n",
        "    return output\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PCCWikyjZIyM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def discriminator(x, reuse=None):\n",
        "  with tf.variable_scope('dis',reuse=reuse):\n",
        "    hidden1 = tf.layers.dense(inputs=x,units=128)\n",
        "    \n",
        "    alpha = 0.01\n",
        "    hidden1 = tf.maximum(alpha*hidden1,hidden1)\n",
        "    \n",
        "    hidden2 = tf.layers.dense(inputs=hidden1,units=128)\n",
        "    \n",
        "    hidden2 = tf.maximum(alpha*hidden2,hidden2)\n",
        "    \n",
        "    logits = tf.layers.dense(hidden2,units=1)\n",
        "    output = tf.sigmoid(logits)\n",
        "    \n",
        "    return output,logits\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1D9F0bDiZfx8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "real_images = tf.placeholder(tf.float32,shape=[None,784])\n",
        "z = tf.placeholder(tf.float32,shape=[None,100])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w8YDmzYva4mK",
        "colab_type": "code",
        "outputId": "056fe807-51e0-43bc-f890-3ae15f7731eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "source": [
        "G = generator(z)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-11-ffc3cc4b742a>:3: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.Dense instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/layers/core.py:187: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `layer.__call__` method instead.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KcM_lWj8a8y7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "D_output_real, D_logits_real = discriminator(real_images)\n",
        "D_output_fake, D_logits_fake = discriminator(G,reuse=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kndxGu04cFcW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# LOSSES\n",
        "\n",
        "def loss_func(logits_in,labels_in):\n",
        "  return tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(\n",
        "  logits=logits_in,labels=labels_in))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HkJZPDIrdBEJ",
        "colab_type": "code",
        "outputId": "11e06db6-3dc5-4d1c-b736-a41803bcdeb0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "D_real_loss = loss_func(D_logits_real,tf.ones_like(D_logits_real)*0.9)\n",
        "D_fake_loss = loss_func(D_logits_fake,tf.zeros_like(D_logits_real))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OI3P2k23eN3f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "D_loss = D_real_loss + D_fake_loss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PE-A7GJ8eR6i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "G_loss = loss_func(D_logits_fake,tf.ones_like(D_logits_fake))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6L3-lAMXecSF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learning_rate = 0.001"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0UwRmSGJegnR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tvars = tf.trainable_variables()\n",
        "\n",
        "d_vars = [var for var in tvars if 'dis' in var.name]\n",
        "g_vars = [var for var in tvars if 'gen' in var.name]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ex22xt7We8Zc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "D_trainer = tf.train.AdamOptimizer(learning_rate).minimize(D_loss,var_list=d_vars)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TDDGAZxYfTPM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "G_trainer = tf.train.AdamOptimizer(learning_rate).minimize(G_loss,var_list=g_vars)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "208OJq1KfYAR",
        "colab_type": "code",
        "outputId": "be08fdc0-b920-4960-db65-8562454c9f2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "d_vars"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<tf.Variable 'dis/dense/kernel:0' shape=(784, 128) dtype=float32_ref>,\n",
              " <tf.Variable 'dis/dense/bias:0' shape=(128,) dtype=float32_ref>,\n",
              " <tf.Variable 'dis/dense_1/kernel:0' shape=(128, 128) dtype=float32_ref>,\n",
              " <tf.Variable 'dis/dense_1/bias:0' shape=(128,) dtype=float32_ref>,\n",
              " <tf.Variable 'dis/dense_2/kernel:0' shape=(128, 1) dtype=float32_ref>,\n",
              " <tf.Variable 'dis/dense_2/bias:0' shape=(1,) dtype=float32_ref>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6J5a8UdgfZZB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size = 15\n",
        "epochs = 200"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kp6b873Zf4hI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "init = tf.global_variables_initializer()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sUJu5lchgBwT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "samples = []\n",
        "saver = tf.train.Saver(var_list=g_vars)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zhoMfGDWrSgI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def next_batch(total_images, batch_number, batch_size):\n",
        "\n",
        "  return total_images[batch_number * batch_size : batch_number*batch_size + batch_size]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rJDgeNzhgPm-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with tf.Session() as sess:\n",
        "    \n",
        "    sess.run(init)\n",
        "    \n",
        "    for e in range(epochs):\n",
        "        num_batches = 3\n",
        "        \n",
        "        for i in range(num_batches):\n",
        "            \n",
        "            batch_images = next_batch(images, i, batch_size)\n",
        "            # batch = np.array(batch).flatten()\n",
        "            \n",
        "            batch_images = batch.reshape((batch_size, 784))\n",
        "            # batch_images = batch_images*2 - 1\n",
        "\n",
        "            batch_z = np.random.uniform(-1, 1, size=(batch_size, 100))\n",
        "            \n",
        "            _ = sess.run(D_trainer, feed_dict={real_images: batch_images, z: batch_z})\n",
        "            _ = sess.run(G_trainer, feed_dict={z: batch_z})\n",
        "        \n",
        "            \n",
        "        if epochs%5==0:\n",
        "          print(\"Currently on Epoch {} of {} total...\".format(e+1, epochs))\n",
        "        \n",
        "        sample_z = np.random.uniform(-1, 1, size=(1, 100))\n",
        "        gen_sample = sess.run(generator(z ,reuse=True),feed_dict={z: sample_z})\n",
        "        \n",
        "        samples.append(gen_sample)\n",
        "        \n",
        "        saver.save(sess, './models/30_epoch_model.ckpt')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L9D1TRnUimQA",
        "colab_type": "code",
        "outputId": "b994deba-c845-408d-da72-221d1b44966a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "saver = tf.train.Saver(var_list=g_vars)\n",
        "\n",
        "new_samples = []\n",
        "with tf.Session() as sess:\n",
        "    \n",
        "    saver.restore(sess,'./models/30_epoch_model.ckpt')\n",
        "    \n",
        "    for x in range(5):\n",
        "        sample_z = np.random.uniform(-1,1,size=(1,100))\n",
        "        gen_sample = sess.run(generator(z,reuse=True),feed_dict={z:sample_z})\n",
        "        \n",
        "        new_samples.append(gen_sample)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Restoring parameters from ./models/30_epoch_model.ckpt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u_hrV7e_ENft",
        "colab_type": "code",
        "outputId": "ea4692e3-0f06-4b3f-86af-b81b84c98b55",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "plt.imshow(new_samples[4].reshape(28,28))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f2128071198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uNbXCqHHQGfbfdUG/7Vi0pKTC9vGR7+wiMl5EtovI8iOuGyMiW0RkcfKrf9TtEELSS2U+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\nSCDQ7IQEAs1OSCDQ7IQEwv8Dd59Xn5v4+t8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}