{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled0.ipynb",
      "version": "0.3.2",
      "views": {},
      "default_view": {},
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "quosgQlOUFhN",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "58d3eb72-83d0-4aac-90a4-86064819d3af",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1523223980564,
          "user_tz": 420,
          "elapsed": 2379,
          "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": "S6K17-TCUFmf",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "0e5b7de1-0cc8-432f-d8c1-5b23f461c0f3",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1523223981173,
          "user_tz": 420,
          "elapsed": 456,
          "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 0x7f2c0a4dc5f8>"
            ]
          },
          "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 0x7f2c160fc940>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "_JlE_1qKUFrD",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "inputs_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='inputs')\n",
        "targets_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='targets')\n",
        "\n",
        "### Encoder\n",
        "conv1 = tf.layers.conv2d(inputs_, 16, (3,3), padding='same', activation=tf.nn.relu)\n",
        "# Now 28x28x16\n",
        "maxpool1 = tf.layers.max_pooling2d(conv1, (2,2), (2,2), padding='same')\n",
        "# Now 14x14x16\n",
        "conv2 = tf.layers.conv2d(maxpool1, 8, (3,3), padding='same', activation=tf.nn.relu)\n",
        "# Now 14x14x8\n",
        "maxpool2 = tf.layers.max_pooling2d(conv2, (2,2), (2,2), padding='same')\n",
        "# Now 7x7x8\n",
        "conv3 = tf.layers.conv2d(maxpool2, 8, (3,3), padding='same', activation=tf.nn.relu)\n",
        "# Now 7x7x8\n",
        "encoded = tf.layers.max_pooling2d(conv3, (2,2), (2,2), padding='same')\n",
        "# Now 4x4x8\n",
        "\n",
        "### Decoder\n",
        "upsample1 = tf.image.resize_nearest_neighbor(encoded, (7,7))\n",
        "# Now 7x7x8\n",
        "conv4 = tf.layers.conv2d(upsample1, 8, (3,3), padding='same', activation=tf.nn.relu)\n",
        "# Now 7x7x8\n",
        "upsample2 = tf.image.resize_nearest_neighbor(conv4, (14,14))\n",
        "# Now 14x14x8\n",
        "conv5 = tf.layers.conv2d(upsample2, 8, (3,3), padding='same', activation=tf.nn.relu)\n",
        "# Now 14x14x8\n",
        "upsample3 = tf.image.resize_nearest_neighbor(conv5, (28,28))\n",
        "# Now 28x28x8\n",
        "conv6 = tf.layers.conv2d(upsample3, 16, (3,3), padding='same', activation=tf.nn.relu)\n",
        "# Now 28x28x16\n",
        "\n",
        "logits = tf.layers.conv2d(conv6, 1, (3,3), padding='same', activation=None)\n",
        "#Now 28x28x1\n",
        "\n",
        "decoded = tf.nn.sigmoid(logits, name='decoded')\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": "NR7lvV2AUFtr",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "sess = tf.Session()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kkGi9JpUUFwx",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "164837ee-46c0-44bc-ff57-d9f59c49a87f",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1523225723939,
          "user_tz": 420,
          "elapsed": 1739379,
          "user": {
            "displayName": "Arun Rajendran",
            "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
            "userId": "100710189298922321078"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "epochs = 20\n",
        "batch_size = 200\n",
        "sess.run(tf.global_variables_initializer())\n",
        "for e in range(epochs):\n",
        "    loss=[]\n",
        "    for ii in range(mnist.train.num_examples//batch_size):\n",
        "        batch = mnist.train.next_batch(batch_size)\n",
        "        imgs = batch[0].reshape((-1, 28, 28, 1))\n",
        "        batch_cost, _ = sess.run([cost, opt], feed_dict={inputs_: imgs,\n",
        "                                                         targets_: imgs})\n",
        "        loss.append(batch_cost)\n",
        "    training_loss=np.mean(batch_cost)\n",
        "    print(\"Epoch: {}/{}...\".format(e+1, epochs),\n",
        "          \"Training loss: {:.4f}\".format(training_loss))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 1/20... Training loss: 0.1633\n",
            "Epoch: 2/20... Training loss: 0.1351\n",
            "Epoch: 3/20... Training loss: 0.1288\n",
            "Epoch: 4/20... Training loss: 0.1199\n",
            "Epoch: 5/20... Training loss: 0.1118\n",
            "Epoch: 6/20... Training loss: 0.1169\n",
            "Epoch: 7/20... Training loss: 0.1090\n",
            "Epoch: 8/20... Training loss: 0.1083\n",
            "Epoch: 9/20... Training loss: 0.1090\n",
            "Epoch: 10/20... Training loss: 0.1067\n",
            "Epoch: 11/20... Training loss: 0.1082\n",
            "Epoch: 12/20... Training loss: 0.1055\n",
            "Epoch: 13/20... Training loss: 0.1006\n",
            "Epoch: 14/20... Training loss: 0.1043\n",
            "Epoch: 15/20... Training loss: 0.1003\n",
            "Epoch: 16/20... Training loss: 0.1007\n",
            "Epoch: 17/20... Training loss: 0.1002\n",
            "Epoch: 18/20... Training loss: 0.1008\n",
            "Epoch: 19/20... Training loss: 0.0995\n",
            "Epoch: 20/20... Training loss: 0.1032\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "vXBhRTs_UFzy",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "outputId": "7381cbc9-0da6-4a6f-98b0-ba8a68971db6",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1523225725482,
          "user_tz": 420,
          "elapsed": 1491,
          "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 = sess.run(decoded, feed_dict={inputs_: in_imgs.reshape((10, 28, 28, 1))})\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",
        "\n",
        "fig.tight_layout(pad=0.1)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YAwAAAABQChasAQAAAAAohTZ1D+vhw4eH7LDDDmuBkQBtzaJFi0LWv3//FhgJ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\nQClYsAYAAAAAoBT+Lya7zoOi3JioAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f2c0c5890f0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "3YxH2GhCUGMM",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "sess.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RzsG1JuaUGVI",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "# Denoising"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "P_dDk22NUGeo",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "inputs_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='inputs')\n",
        "targets_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='targets')\n",
        "\n",
        "### Encoder\n",
        "conv1 = tf.layers.conv2d(inputs_, 32, (3,3), padding='same', activation=tf.nn.relu)\n",
        "# Now 28x28x32\n",
        "maxpool1 = tf.layers.max_pooling2d(conv1, (2,2), (2,2), padding='same')\n",
        "# Now 14x14x32\n",
        "conv2 = tf.layers.conv2d(maxpool1, 32, (3,3), padding='same', activation=tf.nn.relu)\n",
        "# Now 14x14x32\n",
        "maxpool2 = tf.layers.max_pooling2d(conv2, (2,2), (2,2), padding='same')\n",
        "# Now 7x7x32\n",
        "conv3 = tf.layers.conv2d(maxpool2, 16, (3,3), padding='same', activation=tf.nn.relu)\n",
        "# Now 7x7x16\n",
        "encoded = tf.layers.max_pooling2d(conv3, (2,2), (2,2), padding='same')\n",
        "# Now 4x4x16\n",
        "\n",
        "### Decoder\n",
        "upsample1 = tf.image.resize_nearest_neighbor(encoded, (7,7))\n",
        "# Now 7x7x16\n",
        "conv4 = tf.layers.conv2d(upsample1, 16, (3,3), padding='same', activation=tf.nn.relu)\n",
        "# Now 7x7x16\n",
        "upsample2 = tf.image.resize_nearest_neighbor(conv4, (14,14))\n",
        "# Now 14x14x16\n",
        "conv5 = tf.layers.conv2d(upsample2, 32, (3,3), padding='same', activation=tf.nn.relu)\n",
        "# Now 14x14x32\n",
        "upsample3 = tf.image.resize_nearest_neighbor(conv5, (28,28))\n",
        "# Now 28x28x32\n",
        "conv6 = tf.layers.conv2d(upsample3, 32, (3,3), padding='same', activation=tf.nn.relu)\n",
        "# Now 28x28x32\n",
        "\n",
        "logits = tf.layers.conv2d(conv6, 1, (3,3), padding='same', activation=None)\n",
        "#Now 28x28x1\n",
        "\n",
        "decoded = tf.nn.sigmoid(logits, name='decoded')\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": "rybBLFl9Ubnx",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "48810182-61c9-4bf9-aecb-b439f4d79642",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1523229982770,
          "user_tz": 420,
          "elapsed": 4253555,
          "user": {
            "displayName": "Arun Rajendran",
            "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
            "userId": "100710189298922321078"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "sess = tf.Session()\n",
        "epochs = 20\n",
        "batch_size = 200\n",
        "# Set's how much noise we're adding to the MNIST images\n",
        "noise_factor = 0.5\n",
        "sess.run(tf.global_variables_initializer())\n",
        "for e in range(epochs):\n",
        "    loss=[]\n",
        "    for ii in range(mnist.train.num_examples//batch_size):\n",
        "        batch = mnist.train.next_batch(batch_size)\n",
        "        # Get images from the batch\n",
        "        imgs = batch[0].reshape((-1, 28, 28, 1))\n",
        "        \n",
        "        # Add random noise to the input images\n",
        "        noisy_imgs = imgs + noise_factor * np.random.randn(*imgs.shape)\n",
        "        # Clip the images to be between 0 and 1\n",
        "        noisy_imgs = np.clip(noisy_imgs, 0., 1.)\n",
        "        \n",
        "        # Noisy images as inputs, original images as targets\n",
        "        batch_cost, _ = sess.run([cost, opt], feed_dict={inputs_: noisy_imgs,\n",
        "                                                         targets_: imgs})\n",
        "        loss.append(batch_cost)\n",
        "    training_loss=np.mean(batch_cost)\n",
        "    print(\"Epoch: {}/{}...\".format(e+1, epochs),\n",
        "          \"Training loss: {:.4f}\".format(training_loss))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 1/20... Training loss: 0.1694\n",
            "Epoch: 2/20... Training loss: 0.1427\n",
            "Epoch: 3/20... Training loss: 0.1280\n",
            "Epoch: 4/20... Training loss: 0.1264\n",
            "Epoch: 5/20... Training loss: 0.1245\n",
            "Epoch: 6/20... Training loss: 0.1204\n",
            "Epoch: 7/20... Training loss: 0.1202\n",
            "Epoch: 8/20... Training loss: 0.1119\n",
            "Epoch: 9/20... Training loss: 0.1143\n",
            "Epoch: 10/20... Training loss: 0.1159\n",
            "Epoch: 11/20... Training loss: 0.1138\n",
            "Epoch: 12/20... Training loss: 0.1147\n",
            "Epoch: 13/20... Training loss: 0.1107\n",
            "Epoch: 14/20... Training loss: 0.1082\n",
            "Epoch: 15/20... Training loss: 0.1093\n",
            "Epoch: 16/20... Training loss: 0.1085\n",
            "Epoch: 17/20... Training loss: 0.1133\n",
            "Epoch: 18/20... Training loss: 0.1092\n",
            "Epoch: 19/20... Training loss: 0.1051\n",
            "Epoch: 20/20... Training loss: 0.1059\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "5PL7rsirUbt9",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "outputId": "0d8087e2-114d-41f8-acea-cc88cf2e7936",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1523229984306,
          "user_tz": 420,
          "elapsed": 1504,
          "user": {
            "displayName": "Arun Rajendran",
            "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
            "userId": "100710189298922321078"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "# Check out performance\n",
        "fig, axes = plt.subplots(nrows=2, ncols=10, sharex=True, sharey=True, figsize=(20,4))\n",
        "in_imgs = mnist.test.images[:10]\n",
        "noisy_imgs = in_imgs + noise_factor * np.random.randn(*in_imgs.shape)\n",
        "noisy_imgs = np.clip(noisy_imgs, 0., 1.)\n",
        "\n",
        "reconstructed = sess.run(decoded, feed_dict={inputs_: noisy_imgs.reshape((10, 28, 28, 1))})\n",
        "\n",
        "for images, row in zip([noisy_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": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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R30+65zSOT23zs0k71+O/sDbGGGOMMcYYY4wxxhiTE3jB2hhjjDHGGGOMMcYY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\npk6dKtlvv/0mWaoEk4j7ZXpmcR8XAq+dUH/bqlU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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f2c169364e0>"
            ]
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          "metadata": {
            "tags": []
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      ]
    }
  ]
}