{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled0.ipynb",
      "version": "0.3.2",
      "views": {},
      "default_view": {},
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "ki6xJ70JGL66",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "ecce5145-fd89-4e18-85e4-886c76c92740",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1523207055580,
          "user_tz": 420,
          "elapsed": 2348,
          "user": {
            "displayName": "Arun Rajendran",
            "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
            "userId": "100710189298922321078"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.examples.tutorials.mnist import input_data\n",
        "mnist = input_data.read_data_sets('MNIST_data', validation_size=0)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
            "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
            "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
            "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "_L_NeNoPGT-G",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "a52f9894-1b4d-478d-edbf-2e8d9c1b651a",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1523207056072,
          "user_tz": 420,
          "elapsed": 414,
          "user": {
            "displayName": "Arun Rajendran",
            "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
            "userId": "100710189298922321078"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "img = mnist.train.images[2]\n",
        "plt.imshow(img.reshape((28, 28)), cmap='Greys_r')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f0aca9571d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ARMTXgX0ppY+7XMOEIuKmiPhB8/UC4Bxgb2+rOs6LwIrm6xXAth7W0qJfptKeaJpv+uC4\n9Xr68a5/TTUi/gX4B+Bz4Hsppde6WkAbEfFXwFPA6cDJjF6j/6qH9SwE1gEXAIcY/UfnJuBJ4FTg\nHeDmlNKhPqntEeBu4NhU2iml/T2o7TZGh8BvjGleA/yMHh63NnU9wegQvuPHzO+jSxno9c04SV1g\n0KUMGHQpAwZdyoBBlzJg0KUMGHQpA/8PepcDf6ebt3oAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f0ae89ce828>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "97HWqo28GUUQ",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "# Size of the encoding layer (the hidden layer)\n",
        "encoding_dim = 32\n",
        "\n",
        "image_size = mnist.train.images.shape[1]\n",
        "\n",
        "inputs_ = tf.placeholder(tf.float32, (None, image_size), name='inputs')\n",
        "targets_ = tf.placeholder(tf.float32, (None, image_size), name='targets')\n",
        "\n",
        "# Output of hidden layer\n",
        "encoded = tf.layers.dense(inputs_, encoding_dim, activation=tf.nn.relu)\n",
        "\n",
        "# Output layer logits\n",
        "logits = tf.layers.dense(encoded, image_size, activation=None)\n",
        "# Sigmoid output from\n",
        "decoded = tf.nn.sigmoid(logits, name='output')\n",
        "\n",
        "loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=targets_, logits=logits)\n",
        "cost = tf.reduce_mean(loss)\n",
        "opt = tf.train.AdamOptimizer(0.001).minimize(cost)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Kh5HnUmGGUQ9",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "# Create the session\n",
        "sess = tf.Session()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jX9LVgNLGUOW",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "fcbf4c12-84f2-4015-c25c-c3a31c7e974b",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1523207666345,
          "user_tz": 420,
          "elapsed": 608040,
          "user": {
            "displayName": "Arun Rajendran",
            "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
            "userId": "100710189298922321078"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "epochs = 200\n",
        "batch_size = 200\n",
        "sess.run(tf.global_variables_initializer())\n",
        "for e in range(epochs):\n",
        "    for ii in range(mnist.train.num_examples//batch_size):\n",
        "        batch = mnist.train.next_batch(batch_size)\n",
        "        feed = {inputs_: batch[0], targets_: batch[0]}\n",
        "        batch_cost, _ = sess.run([cost, opt], feed_dict=feed)\n",
        "    \n",
        "    if( (e+1) % 10 == 0):\n",
        "        print(\"Epoch: {}/{}...\".format(e+1, epochs),\n",
        "              \"Training loss: {:.4f}\".format(batch_cost))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 10/200... Training loss: 0.0953\n",
            "Epoch: 20/200... Training loss: 0.0939\n",
            "Epoch: 30/200... Training loss: 0.0936\n",
            "Epoch: 40/200... Training loss: 0.0921\n",
            "Epoch: 50/200... Training loss: 0.0926\n",
            "Epoch: 60/200... Training loss: 0.0936\n",
            "Epoch: 70/200... Training loss: 0.0938\n",
            "Epoch: 80/200... Training loss: 0.0937\n",
            "Epoch: 90/200... Training loss: 0.0912\n",
            "Epoch: 100/200... Training loss: 0.0936\n",
            "Epoch: 110/200... Training loss: 0.0882\n",
            "Epoch: 120/200... Training loss: 0.0924\n",
            "Epoch: 130/200... Training loss: 0.0890\n",
            "Epoch: 140/200... Training loss: 0.0912\n",
            "Epoch: 150/200... Training loss: 0.0904\n",
            "Epoch: 160/200... Training loss: 0.0918\n",
            "Epoch: 170/200... Training loss: 0.0905\n",
            "Epoch: 180/200... Training loss: 0.0916\n",
            "Epoch: 190/200... Training loss: 0.0898\n",
            "Epoch: 200/200... Training loss: 0.0900\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Tm0WG51OGUKh",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "outputId": "c95adccf-2d75-4123-9e51-160294cfb364",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1523207667858,
          "user_tz": 420,
          "elapsed": 1451,
          "user": {
            "displayName": "Arun Rajendran",
            "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
            "userId": "100710189298922321078"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(nrows=2, ncols=10, sharex=True, sharey=True, figsize=(20,4))\n",
        "in_imgs = mnist.test.images[:10]\n",
        "reconstructed, compressed = sess.run([decoded, encoded], feed_dict={inputs_: in_imgs})\n",
        "\n",
        "for images, row in zip([in_imgs, reconstructed], axes):\n",
        "    for img, ax in zip(images, row):\n",
        "        ax.imshow(img.reshape((28, 28)), cmap='Greys_r')\n",
        "        ax.get_xaxis().set_visible(False)\n",
        "        ax.get_yaxis().set_visible(False)\n",
        "\n",
        "fig.tight_layout(pad=0.1)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WrAEAAAAAKAUL1gAAAAAAlEKbuof1brvtFrL99tuvBUYCtDWff/55yPr169cC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\nrlX0c3zvvfca/dh//vOfIWt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hh2Gf8ePHF3rNTNECzbJ9bmWW/ftYfvnlQ5bNgeyx2TGp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\nAKVgwRoAAAAAgFKwYA0AAAAAQCn8X+YpP2Q1L/ASAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f0ad6db2630>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "DmUeRJMyGUHy",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "sess.close()"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}