GANS_MNIST.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "GANS MNIST.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "tjqYbd-FmpOx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "il_gA4bemt7-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from tensorflow.examples.tutorials.mnist import input_data\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "chMxbo4_oc-w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "outputId": "890703c6-c150-4d36-fc99-7c7184ab29a1"
      },
      "cell_type": "code",
      "source": [
        "mnist=input_data.read_data_sets('/content',one_hot=True)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-3-8d3cc5232d0e>:1: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please write your own downloading logic.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use tf.data to implement this functionality.\n",
            "Extracting /content/train-images-idx3-ubyte.gz\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use tf.data to implement this functionality.\n",
            "Extracting /content/train-labels-idx1-ubyte.gz\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use tf.one_hot on tensors.\n",
            "Extracting /content/t10k-images-idx3-ubyte.gz\n",
            "Extracting /content/t10k-labels-idx1-ubyte.gz\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "yJwEMGGLpOZ2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def generator(z,reuse=None):\n",
        "  with tf.variable_scope(\"gen\",reuse=reuse):\n",
        "    hidden1=tf.layers.dense(inputs=z,units=128,activation=tf.nn.leaky_relu)\n",
        "    hidden2=tf.layers.dense(inputs=hidden1,units=128,activation=tf.nn.leaky_relu)\n",
        "    output=tf.layers.dense(inputs=hidden2,units=784,activation=tf.nn.tanh)\n",
        "    return output\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "C9v-55QukZ8X",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def discriminator(x,reuse=None):\n",
        "  with tf.variable_scope(\"dis\",reuse=reuse):\n",
        "    hidden1=tf.layers.dense(inputs=x,units=128,activation=tf.nn.leaky_relu)\n",
        "    hidden2=tf.layers.dense(inputs=hidden1,units=128,activation=tf.nn.leaky_relu)\n",
        "    logits=tf.layers.dense(inputs=hidden2,units=1)\n",
        "    output=tf.sigmoid(logits)\n",
        "    return output,logits"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "o3aY3oYXlfmB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "z=tf.placeholder(tf.float32,shape=[None,100])\n",
        "real_images=tf.placeholder(tf.float32,shape=[None,784])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ZBToRabpmCH6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "G=generator(z)\n",
        "\n",
        "real_output,real_logits=discriminator(real_images)\n",
        "fake_output,fake_logits=discriminator(G,reuse=True)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "eMZyQS8ZmrQe",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def loss_func(logits_in,labels_in):\n",
        "  return tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits_in,labels=labels_in))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YKksc2Bh6XSl",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "D_loss_real=loss_func(logits_in=real_logits,labels_in=tf.ones_like(real_logits)*0.9)\n",
        "D_loss_fake=loss_func(logits_in=fake_logits,labels_in=tf.zeros_like(fake_logits))\n",
        "D_loss=D_loss_real+D_loss_fake"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "tyXES2h_6rUN",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "G_loss=loss_func(logits_in=fake_logits,labels_in=tf.ones_like(fake_logits))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qwjpnmsL8B2T",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learning_rate=0.001\n",
        "train_vars=tf.trainable_variables()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2gLE0ZJt8PFP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "d_vars=[x for x in train_vars if \"dis\" in x.name]\n",
        "g_vars=[y for y in train_vars if \"gen\" in y.name]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "OB6MCTrY8xe7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "D_trainer=tf.train.AdamOptimizer(learning_rate).minimize(D_loss,var_list=d_vars)\n",
        "G_trainer=tf.train.AdamOptimizer(learning_rate).minimize(G_loss,var_list=g_vars)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bQhY2Q1a9ODZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "init=tf.global_variables_initializer()\n",
        "num_epochs=500\n",
        "batch_size=100\n",
        "num_batches=mnist.train.num_examples//batch_size\n",
        "samples=[]\n",
        "saver=tf.train.Saver(var_list=g_vars)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2CN8xA9P9jwz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 8417
        },
        "outputId": "6d2575d1-ab94-42b9-c82d-248669309759"
      },
      "cell_type": "code",
      "source": [
        "with tf.Session() as sess:\n",
        "  sess.run(init)\n",
        "  for i in range(num_epochs):\n",
        "    for j in range(num_batches):\n",
        "      batch=mnist.train.next_batch(batch_size)\n",
        "      batch_images=batch[0].reshape([batch_size,784])\n",
        "      batch_images=batch_images*2-1\n",
        "      batch_z=np.random.uniform(-1,1,size=[batch_size,100])\n",
        "      \n",
        "      _=sess.run(D_trainer,feed_dict={z:batch_z,real_images:batch_images})\n",
        "      _=sess.run(G_trainer,feed_dict={z:batch_z})\n",
        "      loss=sess.run(G_loss,feed_dict={z:batch_z})\n",
        "    \n",
        "    \n",
        "    print(\"Currently on step {} , loss={}\".format(i,loss))\n",
        "    sample_z=np.random.uniform(-1,1,size=[1,100])\n",
        "    sample_gen_image=sess.run(generator(z,reuse=True),feed_dict={z:sample_z})\n",
        "    samples.append(sample_gen_image)\n",
        "  saver.save(sess,'/content/500_epochs_model.ckpt')\n",
        "                              \n",
        "    \n",
        "  \n",
        "  "
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Currently on step 0 , loss=3.505650043487549\n",
            "Currently on step 1 , loss=5.201396465301514\n",
            "Currently on step 2 , loss=1.502576470375061\n",
            "Currently on step 3 , loss=0.7748628854751587\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "jxWCkOLI9qCu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#plt.imshow(samples[99].reshape(28,28),cmap=\"Greys\")\n",
        "new_samples=[]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kamsRZqeM4hT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3b70fbe5-adb9-4e06-8a80-18358e9fa0d7"
      },
      "cell_type": "code",
      "source": [
        "with tf.Session() as sess:\n",
        "  saver.restore(sess,'/content/500_epochs_model.ckpt')\n",
        "  for x in range(50):\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",
        "    new_samples.append(gen_sample)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Restoring parameters from /content/500_epochs_model.ckpt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "KC9lFGGWQFFv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "8d578e5e-6a50-442c-9ebc-50f215d3a2c1"
      },
      "cell_type": "code",
      "source": [
        "plt.imshow(new_samples[34].reshape(28,28),cmap=\"Greys\")"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f569c720d68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dzfxTRDwJHAgsSyk916tCUkrbgG0RUb96sO6Ucx1waNcLo2ltANdGxH8wvqm0O1XbMDBU\nW7wKeAZY3OvfrUldw3TpN+v1zbh+egb+T8Ay4CLgCuB/ImJqb0vK6qffDkaugZemlP4ZeIeR+fp6\npm6a79HTeff0dxtVV9d+s24f0dcycgTf4TBGbo70XEppDfBwbfHPEfEXYA7wUe+q2sXGiNg3pbSJ\nkdr65tQ5pdQ3U2mPnuY7Ivrid+vl9OPdPqL/AVgCEBHfBdamlDZ0uYYxRcSlEfHT2ufZwCHAmt5W\ntYvngYtrny8GVvSwlgb9MpX2WNN80we/W6+nH+/WbKo7RcQvgLOAb4Efp5Te7WoBTUTEAcBvgVnA\nVEau0Z/pYT0nA7cDc4GtjPxH51LgAWA68DEj01Vv7ZPa7gKWMv6ptDtV21jTfF8B/Joe/m5tmn68\nZV0PuqTu6/XNOEldYNClAhh0qQAGXSqAQZcKYNClAhh0qQD/DwMVRT7JeC55AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f569c8bdc88>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "aemtWQUtQFzI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}