6_Unsupervised_final.ipynb

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      "name": "6.Unsupervised_final.ipynb",
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    {
      "metadata": {
        "id": "JsZlBCxbro4e",
        "colab_type": "text"
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      "cell_type": "markdown",
      "source": [
        "<small><i>June 2016 - This notebook was created by [Oriol Pujol Vila](http://www.maia.ub.es/~oriol). Source and [license](./LICENSE.txt) info are in the folder.</i></small>"
      ]
    },
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      "metadata": {
        "id": "cwth3APAro4g",
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      "cell_type": "markdown",
      "source": [
        "# Unsupervised learning"
      ]
    },
    {
      "metadata": {
        "id": "-01LWpsNro4h",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "+ Autoencoders\n",
        "+ Pretraining\n",
        "+ Manifold learning\n",
        "+ Sparse coding"
      ]
    },
    {
      "metadata": {
        "id": "Iew9RTaLro4i",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Autoencoders"
      ]
    },
    {
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          "height": 122
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        "executionInfo": {
          "status": "ok",
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          "user_tz": -120,
          "elapsed": 2771,
          "user": {
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      "cell_type": "code",
      "source": [
        "!pip install tqdm"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tqdm\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/93/24/6ab1df969db228aed36a648a8959d1027099ce45fad67532b9673d533318/tqdm-4.23.4-py2.py3-none-any.whl (42kB)\n",
            "\u001b[K    100% |████████████████████████████████| 51kB 3.6MB/s \n",
            "\u001b[?25hInstalling collected packages: tqdm\n",
            "Successfully installed tqdm-4.23.4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
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          "height": 34
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        "outputId": "fb1b34c8-576a-47e6-9ecc-653b7a40b89f",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528902374398,
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      "cell_type": "code",
      "source": [
        "%reset\n",
        "%matplotlib inline\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "tf.reset_default_graph() "
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Once deleted, variables cannot be recovered. Proceed (y/[n])? y\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "sDV_N0zaro4p",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "39ca0caa-f504-442a-efdf-9d08c963b1eb",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528902383023,
          "user_tz": -120,
          "elapsed": 623,
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      "cell_type": "code",
      "source": [
        "# Network Parameters\n",
        "n_input = 64\n",
        "n_hidden_1 = 64  # 1st layer num features\n",
        "n_hidden_2 = 32  # 2nd layer num features\n",
        "n_hidden_3 = 10  # 3rd layer num features\n",
        "n_hidden_4 = 10  # 4th layer num features\n",
        "n_hidden_5 = 32  # 5th layer num features\n",
        "n_hidden_6 = 64  # 6th layer num features\n",
        "\n",
        "# Parameters\n",
        "learning_rate = 1e-3"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
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        },
        "outputId": "c41812c8-1202-41e5-b6b1-20e1a87cbe22",
        "executionInfo": {
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      "cell_type": "code",
      "source": [
        "# Graph inputs\n",
        "x = tf.placeholder(\"float\", [None, n_input], name='x')\n",
        "y = tf.placeholder(\"float\", [None, n_input], name='y')\n",
        "\n",
        "keep_prob = tf.placeholder(tf.float32) #Input parameter: dropout probability\n",
        "\n",
        "# Store layers weight & bias\n",
        "c = 0.1 \n",
        "weights = {\n",
        "    'h1': tf.Variable(c*tf.random_normal([n_input, n_hidden_1]), name='W1'),\n",
        "    'h2': tf.Variable(c*tf.random_normal([n_hidden_1, n_hidden_2]), name='W2'),\n",
        "    'h3': tf.Variable(c*tf.random_normal([n_hidden_2, n_hidden_3]), name='W3'),\n",
        "    'h4': tf.Variable(c*tf.random_normal([n_hidden_3, n_hidden_4]), name='W4'),\n",
        "    'h5': tf.Variable(c*tf.random_normal([n_hidden_4, n_hidden_5]), name='W5'),\n",
        "    'h6': tf.Variable(c*tf.random_normal([n_hidden_5, n_hidden_6]), name='W6'),\n",
        "    'out': tf.Variable(c*tf.random_normal([n_hidden_6, n_input]))\n",
        "}\n",
        "biases = {\n",
        "    'b1': tf.Variable(c*tf.random_normal([n_hidden_1]), name='b1'),\n",
        "    'b2': tf.Variable(c*tf.random_normal([n_hidden_2]), name='b2'),\n",
        "    'b3': tf.Variable(c*tf.random_normal([n_hidden_3]), name='b3'),\n",
        "    'b4': tf.Variable(c*tf.random_normal([n_hidden_4]), name='b4'),\n",
        "    'b5': tf.Variable(c*tf.random_normal([n_hidden_5]), name='b5'),\n",
        "    'b6': tf.Variable(c*tf.random_normal([n_hidden_6]), name='b6'),\n",
        "    'out': tf.Variable(c*tf.random_normal([n_input]))\n",
        "}\n",
        "\n",
        "\n",
        "pre_layer_drop = tf.nn.dropout(x, keep_prob)\n",
        "layer_1 = tf.nn.relu(tf.add(tf.matmul(pre_layer_drop, weights['h1']), biases['b1']))\n",
        "layer_2 = tf.nn.relu(tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']))\n",
        "layer_3 = tf.nn.relu(tf.add(tf.matmul(layer_2, weights['h3']), biases['b3']))\n",
        "layer_4 = tf.nn.relu(tf.add(tf.matmul(layer_3, weights['h4']), biases['b4']))\n",
        "layer_5 = tf.nn.relu(tf.add(tf.matmul(layer_4, weights['h5']), biases['b5']))\n",
        "layer_6 = tf.nn.relu(tf.add(tf.matmul(layer_5, weights['h6']), biases['b6']))\n",
        "output = tf.add(tf.matmul(layer_6, weights['out']), biases['out'])"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
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        "id": "XPxUxuWcro4t",
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            "startup": false,
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        "outputId": "920440f8-3923-4e2d-c6bd-bb22de963998",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528902385319,
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          "elapsed": 632,
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      },
      "cell_type": "code",
      "source": [
        "#Reconstruction loss\n",
        "loss = tf.reduce_mean(tf.pow(output - y, 2))"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "metadata": {
        "id": "U2dFlafNro4w",
        "colab_type": "code",
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            "startup": false,
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        "outputId": "f76bde76-83c6-4140-8157-7a143c1dfaf0",
        "executionInfo": {
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          "timestamp": 1528902386874,
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      },
      "cell_type": "code",
      "source": [
        "#Optimizer\n",
        "opt = tf.train.AdamOptimizer(learning_rate).minimize(loss)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {
        "id": "swivB1wmro4y",
        "colab_type": "code",
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            "startup": false,
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          "height": 17
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        "outputId": "0e0f3bd4-79ce-446b-f87d-0377e88779f6",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528902401729,
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      "cell_type": "code",
      "source": [
        "init = tf.global_variables_initializer()"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hOh_78g4ro40",
        "colab_type": "code",
        "colab": {
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            "startup": false,
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        },
        "outputId": "ab2bfedd-80cb-432b-82ac-55bc6088d352",
        "executionInfo": {
          "status": "ok",
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      },
      "cell_type": "code",
      "source": [
        "# Load data\n",
        "from sklearn.datasets import load_digits\n",
        "data = load_digits()\n",
        "\n",
        "idx = np.random.permutation(data.data.shape[0])\n",
        "idx_train = idx[:-100]\n",
        "idx_test = idx[-100:]\n",
        "\n",
        "train = data.data[idx_train,:]\n",
        "test = data.data[idx_test,:]"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "metadata": {
        "id": "HgySTTXZro42",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
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          "height": 51
        },
        "outputId": "ae70c50a-85df-4698-e27d-cc0ea28bebf4",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528902525432,
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          "elapsed": 96655,
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      },
      "cell_type": "code",
      "source": [
        "sess = tf.Session()\n",
        "sess.run(init)\n",
        "\n",
        "import tqdm\n",
        "training_epochs = 100000\n",
        "display_step = 50\n",
        "\n",
        "# Training cycle\n",
        "cost = []\n",
        "batch_size=16\n",
        "for epoch in tqdm.tqdm(xrange(training_epochs)):\n",
        "    xs = train[np.random.randint(0,train.shape[0],batch_size),:]/16.\n",
        "    sess.run(opt, feed_dict={x: xs, y: xs, keep_prob: 0.8})\n",
        "    if epoch % display_step == 1:\n",
        "        cost.append(sess.run(loss, feed_dict={x: xs, y: xs, keep_prob: 1.}))\n",
        "\n",
        "print \"Optimization Finished!\""
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 100000/100000 [01:35<00:00, 1043.48it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Optimization Finished!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
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        "id": "Uqrtk2JCro46",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
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          "height": 840
        },
        "outputId": "c328eb0f-166a-4e9f-e069-ac64d6381d90",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528902530345,
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      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(cost[10:])\n",
        "plt.show()\n",
        "\n",
        "N = 8\n",
        "xs = test[np.random.randint(0,test.shape[0],N),:]/16.\n",
        "rec = sess.run(output, feed_dict={x: xs, keep_prob: 1.})\n",
        "\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "f, axarr = plt.subplots(N, 2)\n",
        "for i in xrange(N):\n",
        "        axarr[i,0].imshow(xs[i].reshape((8,8)),cmap='gray', interpolation='nearest')\n",
        "        axarr[i,1].imshow(rec[i].reshape((8,8)),cmap='gray', interpolation='nearest')\n",
        "\n",
        "f.set_size_inches(10,10) "
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SpSPZ3TuycmNhyEbELbvKyUj3bWNpbmJ3ap5llGN2doa36sR6MQO8id3j/jeW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\nIUQc+n8g9l1IKYv06wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f7df4a80e50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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upk2bvLHjx497Y93d3ZHaACDkrbfe8sY2btwYPPcf/uEfvLGqqipv7D//8z+z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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f7dfe2d8d10>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "NTOG7O-oEmMh",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "8d44c41b-0546-4bff-d6ce-9ad92f004a65",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528927286536,
          "user_tz": -120,
          "elapsed": 1622,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        "!ls -l "
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 1756\r\n",
            "-rw-r--r-- 1 root root 162531 Jun 13 21:23 autoencoder_digits_part1_autoencoder (1).ckpt\r\n",
            "-rw-r--r-- 1 root root 162531 Jun 13 21:32 autoencoder_digits_part1_autoencoder (2).ckpt\r\n",
            "-rw-r--r-- 1 root root 162531 Jun 13 21:44 autoencoder_digits_part1_autoencoder (3).ckpt\r\n",
            "-rw-r--r-- 1 root root 162531 Jun 13 21:45 autoencoder_digits_part1_autoencoder (4).ckpt\r\n",
            "-rw-r--r-- 1 root root 162531 Jun 13 21:51 autoencoder_digits_part1_autoencoder (5).ckpt\r\n",
            "-rw-r--r-- 1 root root 162531 Jun 13 21:22 autoencoder_digits_part1_autoencoder.ckpt\r\n",
            "drwxr-xr-x 1 root root   4096 May 21 17:02 datalab\r\n",
            "-rw-r--r-- 1 root root  55743 Jun 13 21:51 digits_idx_part1_autoencoder (10).pkl\r\n",
            "-rw-r--r-- 1 root root  55743 Jun 13 21:57 digits_idx_part1_autoencoder (11).pkl\r\n",
            "-rw-r--r-- 1 root root  55743 Jun 13 21:18 digits_idx_part1_autoencoder (1).pkl\r\n",
            "-rw-r--r-- 1 root root  55743 Jun 13 21:19 digits_idx_part1_autoencoder (2).pkl\r\n",
            "-rw-r--r-- 1 root root  55743 Jun 13 21:19 digits_idx_part1_autoencoder (3).pkl\r\n",
            "-rw-r--r-- 1 root root  55743 Jun 13 21:20 digits_idx_part1_autoencoder (4).pkl\r\n",
            "-rw-r--r-- 1 root root  55743 Jun 13 21:21 digits_idx_part1_autoencoder (5).pkl\r\n",
            "-rw-r--r-- 1 root root  55743 Jun 13 21:22 digits_idx_part1_autoencoder (6).pkl\r\n",
            "-rw-r--r-- 1 root root  55743 Jun 13 21:23 digits_idx_part1_autoencoder (7).pkl\r\n",
            "-rw-r--r-- 1 root root  55743 Jun 13 21:32 digits_idx_part1_autoencoder (8).pkl\r\n",
            "-rw-r--r-- 1 root root  55743 Jun 13 21:45 digits_idx_part1_autoencoder (9).pkl\r\n",
            "-rw-r--r-- 1 root root 118927 Jun 13 21:15 digits_idx_part1_autoencoder.pkl\r\n",
            "-rw-r--r-- 1 root root  55743 Jun 13 21:16 digits_idx_part1_autoencoder.pkl.txt\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "hCF8jSK2ro49",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Check reconstruction"
      ]
    },
    {
      "metadata": {
        "id": "PABO5hNRro49",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 249
        },
        "outputId": "15c1c3d4-3727-4ea3-fe5b-829a29384404",
        "executionInfo": {
          "status": "error",
          "timestamp": 1528927710000,
          "user_tz": -120,
          "elapsed": 1496,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "\n",
        "tf.reset_default_graph() \n",
        "# Network Parameters\n",
        "n_input = 64\n",
        "n_hidden_1 = 64  # 1st layer num features\n",
        "n_hidden_2 = 32  # 2nd layer num features\n",
        "n_hidden_3 = 10  # 3nd layer num features\n",
        "n_hidden_4 = 10  # 4nd layer num features\n",
        "n_hidden_5 = 32  # 4nd layer num features\n",
        "n_hidden_6 = 64  # 1st layer num features\n",
        "\n",
        "\n",
        "\n",
        "# Parameters\n",
        "learning_rate = 1e-3\n",
        "# tf Graph input\n",
        "x = tf.placeholder(\"float\", [None, n_input], name='x')\n",
        "y = tf.placeholder(\"float\", [None, n_input], name='y')\n",
        "# Store layers weight & bias\n",
        "c = 0.1\n",
        "weights = {\n",
        "    'h1': tf.Variable(c*tf.random_normal([n_input, n_hidden_1]), name='W1'),\n",
        "    'h2': tf.Variable(c*tf.random_normal([n_hidden_1, n_hidden_2]), name='W2'),\n",
        "    'h3': tf.Variable(c*tf.random_normal([n_hidden_2, n_hidden_3]), name='W3'),\n",
        "    'h4': tf.Variable(c*tf.random_normal([n_hidden_3, n_hidden_4]), name='W4'),\n",
        "    'h5': tf.Variable(c*tf.random_normal([n_hidden_4, n_hidden_5]), name='W5'),\n",
        "    'h6': tf.Variable(c*tf.random_normal([n_hidden_5, n_hidden_6]), name='W6'),\n",
        "    'out': tf.Variable(c*tf.random_normal([n_hidden_6, n_input]))\n",
        "}\n",
        "biases = {\n",
        "    'b1': tf.Variable(c*tf.random_normal([n_hidden_1]), name='b1'),\n",
        "    'b2': tf.Variable(c*tf.random_normal([n_hidden_2]), name='b2'),\n",
        "    'b3': tf.Variable(c*tf.random_normal([n_hidden_3]), name='b3'),\n",
        "    'b4': tf.Variable(c*tf.random_normal([n_hidden_4]), name='b4'),\n",
        "    'b5': tf.Variable(c*tf.random_normal([n_hidden_5]), name='b5'),\n",
        "    'b6': tf.Variable(c*tf.random_normal([n_hidden_6]), name='b6'),\n",
        "    'out': tf.Variable(c*tf.random_normal([n_input]))\n",
        "}\n",
        "\n",
        "keep_prob = tf.placeholder(tf.float32)\n",
        "pre_layer_drop = tf.nn.dropout(x, keep_prob)\n",
        "layer_1 = tf.nn.relu(tf.add(tf.matmul(pre_layer_drop, weights['h1']), biases['b1']))\n",
        "layer_2 = tf.nn.relu(tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']))\n",
        "layer_3 = tf.nn.relu(tf.add(tf.matmul(layer_2, weights['h3']), biases['b3']))\n",
        "layer_4 = tf.nn.relu(tf.add(tf.matmul(layer_3, weights['h4']), biases['b4']))\n",
        "layer_5 = tf.nn.relu(tf.add(tf.matmul(layer_4, weights['h5']), biases['b5']))\n",
        "layer_6 = tf.nn.relu(tf.add(tf.matmul(layer_5, weights['h6']), biases['b6']))\n",
        "output = tf.add(tf.matmul(layer_6, weights['out']), biases['out'])\n",
        "\n",
        "loss = tf.reduce_mean(tf.pow(output - y, 2))\n",
        "opt = tf.train.AdamOptimizer(learning_rate).minimize(loss)\n",
        "\n",
        "init = tf.global_variables_initializer()\n",
        "\n",
        "# Add ops to save and restore all the variables.\n",
        "saver = tf.train.Saver()\n",
        "\n",
        "import tqdm\n",
        "training_epochs = 100000\n",
        "display_step = 50\n",
        "\n",
        "from sklearn.datasets import load_digits\n",
        "data = load_digits()\n",
        "\n",
        "import pickle\n",
        "\n",
        "\n",
        "from google.colab import files\n",
        "print \"Upload file ... digits_idx_part1_autoencoder.pkl\"\n",
        "uploaded = files.upload()\n",
        "import io\n",
        "idx_train,idx_test = pickle.load(io.StringIO(uploaded['digits_idx_part1_autoencoder.pkl'].decode('utf-8')))\n",
        "\n",
        "train = data.data[idx_train,:]\n",
        "test = data.data[idx_test,:]\n",
        "\n",
        "\n",
        "sess = tf.Session()\n",
        "sess.run(init)\n",
        "\n",
        "print \"Upload file ... autoencoder_digits_part1_autoencoder.ckpt\"\n",
        "uploaded = files.upload()\n",
        "saver.restore(sess, \"autoencoder_digits_part1_autoencoder.ckpt\")\n",
        "print(\"Model restored.\")\n",
        "\n",
        "N = 8\n",
        "xs = test[np.random.randint(0,test.shape[0],N),:]/16.\n",
        "rec = sess.run(output, feed_dict={x: xs, keep_prob: 1.})\n",
        "\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "f, axarr = plt.subplots(N, 2)\n",
        "for i in xrange(N):\n",
        "        axarr[i,0].imshow(xs[i].reshape((8,8)),cmap='gray', interpolation='nearest')\n",
        "        axarr[i,1].imshow(rec[i].reshape((8,8)),cmap='gray', interpolation='nearest')\n",
        "\n",
        "f.set_size_inches(10,10) \n"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Upload file ... digits_idx_part1_autoencoder.pkl\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0mTraceback (most recent call last)",
            "\u001b[0;32m<ipython-input-60-12fb1ebf2c66>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     75\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m \u001b[0;31m#idx_train,idx_test = pickle.load(io.StringIO(uploaded['digits_idx_part1_autoencoder.pkl'].decode('utf-8')))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 77\u001b[0;31m \u001b[0midx_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0midx_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mStringIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'digits_idx_part1_autoencoder.pkl'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     78\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: initial_value must be unicode or None, not str"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "RD7dB16fro5A",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Learning representations and pretraining"
      ]
    },
    {
      "metadata": {
        "id": "mXe8ZVtSro5B",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "ee2f238a-02d1-47e0-f7d8-debcb2a2e293",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528928073893,
          "user_tz": -120,
          "elapsed": 11419,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "\n",
        "tf.reset_default_graph() \n",
        "# Network Parameters\n",
        "n_input = 64\n",
        "n_hidden_1 = 64  # 1st layer num features\n",
        "n_hidden_2 = 32  # 2nd layer num features\n",
        "n_hidden_3 = 10  # 3rd layer num features\n",
        "n_hidden_4 = 10  # 4th layer num features\n",
        "n_hidden_5 = 32  # 5th layer num features\n",
        "n_hidden_6 = 64  # 6th layer num features\n",
        "\n",
        "\n",
        "\n",
        "# Parameters\n",
        "learning_rate = 1e-3\n",
        "# tf Graph input\n",
        "x = tf.placeholder(\"float\", [None, n_input], name='x')\n",
        "y = tf.placeholder(\"float\", [None, n_input], name='y')\n",
        "# Store layers weight & bias\n",
        "c = 0.1\n",
        "weights = {\n",
        "    'h1': tf.Variable(c*tf.random_normal([n_input, n_hidden_1]), name='W1'),\n",
        "    'h2': tf.Variable(c*tf.random_normal([n_hidden_1, n_hidden_2]), name='W2'),\n",
        "    'h3': tf.Variable(c*tf.random_normal([n_hidden_2, n_hidden_3]), name='W3'),\n",
        "    'h4': tf.Variable(c*tf.random_normal([n_hidden_3, n_hidden_4]), name='W4'),\n",
        "    'h5': tf.Variable(c*tf.random_normal([n_hidden_4, n_hidden_5]), name='W5'),\n",
        "    'h6': tf.Variable(c*tf.random_normal([n_hidden_5, n_hidden_6]), name='W6'),\n",
        "    'out': tf.Variable(c*tf.random_normal([n_hidden_6, n_input]))\n",
        "}\n",
        "biases = {\n",
        "    'b1': tf.Variable(c*tf.random_normal([n_hidden_1]), name='b1'),\n",
        "    'b2': tf.Variable(c*tf.random_normal([n_hidden_2]), name='b2'),\n",
        "    'b3': tf.Variable(c*tf.random_normal([n_hidden_3]), name='b3'),\n",
        "    'b4': tf.Variable(c*tf.random_normal([n_hidden_4]), name='b4'),\n",
        "    'b5': tf.Variable(c*tf.random_normal([n_hidden_5]), name='b5'),\n",
        "    'b6': tf.Variable(c*tf.random_normal([n_hidden_6]), name='b6'),\n",
        "    'out': tf.Variable(c*tf.random_normal([n_input]))\n",
        "}\n",
        "\n",
        "keep_prob = tf.placeholder(tf.float32)\n",
        "pre_layer_drop = tf.nn.dropout(x, keep_prob)\n",
        "layer_1 = tf.nn.relu(tf.add(tf.matmul(pre_layer_drop, weights['h1']), biases['b1']))\n",
        "layer_2 = tf.nn.relu(tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']))\n",
        "layer_3 = tf.nn.relu(tf.add(tf.matmul(layer_2, weights['h3']), biases['b3']))\n",
        "layer_4 = tf.nn.relu(tf.add(tf.matmul(layer_3, weights['h4']), biases['b4']))\n",
        "layer_5 = tf.nn.relu(tf.add(tf.matmul(layer_4, weights['h5']), biases['b5']))\n",
        "layer_6 = tf.nn.relu(tf.add(tf.matmul(layer_5, weights['h6']), biases['b6']))\n",
        "output = tf.add(tf.matmul(layer_6, weights['out']), biases['out'])\n",
        "\n",
        "loss = tf.reduce_mean(tf.pow(output - y, 2))\n",
        "opt = tf.train.AdamOptimizer(learning_rate).minimize(loss)\n",
        "\n",
        "init = tf.initialize_all_variables()\n",
        "\n",
        "# Add ops to save and restore all the variables.\n",
        "saver = tf.train.Saver()\n",
        "\n",
        "import tqdm\n",
        "training_epochs = 100000\n",
        "display_step = 50\n",
        "\n",
        "from sklearn.datasets import load_digits\n",
        "data = load_digits()\n",
        "\n",
        "from google.colab import files\n",
        "print \"Upload file ... digits_idx_part1_autoencoder.pkl\"\n",
        "uploaded = files.upload()\n",
        "import io\n",
        "idx_train,idx_test = pickle.load(io.StringIO(uploaded['digits_idx_part1_autoencoder.pkl'].decode('utf-8')))\n",
        "\n",
        "\n",
        "train = data.data[idx_train,:]/16.\n",
        "test = data.data[idx_test,:]/16.\n",
        "train_y = data.target[idx_train]\n",
        "test_y = data.target[idx_test]\n",
        "\n"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Upload file ... digits_idx_part1_autoencoder.pkl\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-58c24c31-9f1f-4ff1-88fd-8724514e65e1\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-58c24c31-9f1f-4ff1-88fd-8724514e65e1\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving digits_idx_part1_autoencoder.pkl to digits_idx_part1_autoencoder (12).pkl\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "I_wkby5Dro5E",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "2cddf9ed-6d45-4fc9-fabd-560102cff50e",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528928084137,
          "user_tz": -120,
          "elapsed": 559,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "sess = tf.Session()\n",
        "sess.run(init)\n",
        "\n",
        "saver.restore(sess, \"autoencoder_digits_part1_autoencoder.ckpt\")\n",
        "print(\"Model restored.\")\n",
        "\n",
        "code = sess.run(layer_3, feed_dict={x: train, keep_prob: 1.})"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Restoring parameters from autoencoder_digits_part1_autoencoder.ckpt\n",
            "Model restored.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "e3BjwcJOro5G",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4b19418b-ec9f-4fa8-d11d-0575d3489d85",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528928089565,
          "user_tz": -120,
          "elapsed": 2449,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        "code.shape"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1697, 10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "metadata": {
        "id": "l7U1JYpAro5J",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "b54a9d97-ab58-4596-b5dd-9922959b363b",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528928092743,
          "user_tz": -120,
          "elapsed": 686,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "clf = RandomForestClassifier(3,random_state=0)\n",
        "clf.fit(code,train_y)\n",
        "\n",
        "clf2 = RandomForestClassifier(3,random_state=0)\n",
        "clf2.fit(train,train_y)"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
              "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
              "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "            min_samples_leaf=1, min_samples_split=2,\n",
              "            min_weight_fraction_leaf=0.0, n_estimators=3, n_jobs=1,\n",
              "            oob_score=False, random_state=0, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "metadata": {
        "id": "B3-1SVx9ro5M",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "b8525b74-0030-46b9-99f3-d93f4cabfbbf",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528928095813,
          "user_tz": -120,
          "elapsed": 867,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        "code = sess.run(layer_3, feed_dict={x: test, keep_prob: 1.})\n",
        "print clf.score(code,test_y)\n",
        "print clf2.score(test,test_y)"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.88\n",
            "0.85\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "LkuZNl9Oro5P",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Sparse coding"
      ]
    },
    {
      "metadata": {
        "id": "X_bEpwiuro5R",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 189
        },
        "outputId": "30c1a3e5-efc3-480e-9fb5-402356cd7acc",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528928266418,
          "user_tz": -120,
          "elapsed": 33234,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
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      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "\n",
        "tf.reset_default_graph()\n",
        "# Network Parameters\n",
        "n_input = 64\n",
        "n_hidden_1 = 64  # 1st layer num features\n",
        "n_hidden_2 = 128  # 2nd layer num features\n",
        "n_hidden_3 = 256  # 3nd layer num features\n",
        "n_hidden_4 = 256  # 4nd layer num features\n",
        "n_hidden_5 = 128  # 4nd layer num features\n",
        "n_hidden_6 = 64  # 1st layer num features\n",
        "\n",
        "\n",
        "\n",
        "# Parameters\n",
        "learning_rate = 1e-3\n",
        "# tf Graph input\n",
        "x = tf.placeholder(\"float\", [None, n_input], name='x')\n",
        "y = tf.placeholder(\"float\", [None, n_input], name='y')\n",
        "lmd = tf.placeholder(\"float\",[], name = 'lambda')\n",
        "# Store layers weight & bias\n",
        "c = 0.1\n",
        "weights = {\n",
        "    'h1': tf.Variable(c*tf.random_normal([n_input, n_hidden_1]), name='W1'),\n",
        "    'h2': tf.Variable(c*tf.random_normal([n_hidden_1, n_hidden_2]), name='W2'),\n",
        "    'h3': tf.Variable(c*tf.random_normal([n_hidden_2, n_hidden_3]), name='W3'),\n",
        "    'h4': tf.Variable(c*tf.random_normal([n_hidden_3, n_hidden_4]), name='W4'),\n",
        "    'h5': tf.Variable(c*tf.random_normal([n_hidden_4, n_hidden_5]), name='W5'),\n",
        "    'h6': tf.Variable(c*tf.random_normal([n_hidden_5, n_hidden_6]), name='W6'),\n",
        "    'out': tf.Variable(c*tf.random_normal([n_hidden_6, n_input]))\n",
        "}\n",
        "biases = {\n",
        "    'b1': tf.Variable(c*tf.random_normal([n_hidden_1]), name='b1'),\n",
        "    'b2': tf.Variable(c*tf.random_normal([n_hidden_2]), name='b2'),\n",
        "    'b3': tf.Variable(c*tf.random_normal([n_hidden_3]), name='b3'),\n",
        "    'b4': tf.Variable(c*tf.random_normal([n_hidden_4]), name='b4'),\n",
        "    'b5': tf.Variable(c*tf.random_normal([n_hidden_5]), name='b5'),\n",
        "    'b6': tf.Variable(c*tf.random_normal([n_hidden_6]), name='b6'),\n",
        "    'out': tf.Variable(c*tf.random_normal([n_input]))\n",
        "}\n",
        "\n",
        "keep_prob = tf.placeholder(tf.float32)\n",
        "pre_layer_drop = tf.nn.dropout(x, keep_prob)\n",
        "layer_1 = tf.nn.relu(tf.add(tf.matmul(pre_layer_drop, weights['h1']), biases['b1']))\n",
        "layer_2 = tf.nn.relu(tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']))\n",
        "layer_3 = tf.nn.relu(tf.add(tf.matmul(layer_2, weights['h3']), biases['b3']))\n",
        "layer_4 = tf.nn.relu(tf.add(tf.matmul(layer_3, weights['h4']), biases['b4']))\n",
        "layer_5 = tf.nn.relu(tf.add(tf.matmul(layer_4, weights['h5']), biases['b5']))\n",
        "layer_6 = tf.nn.relu(tf.add(tf.matmul(layer_5, weights['h6']), biases['b6']))\n",
        "output = tf.add(tf.matmul(layer_6, weights['out']), biases['out'])\n",
        "\n",
        "loss = tf.reduce_mean(tf.pow(output - y, 2))\n",
        "loss2 = tf.reduce_mean(tf.abs(layer_3))\n",
        "floss = loss + lmd * loss2\n",
        "\n",
        "opt = tf.train.AdamOptimizer(learning_rate).minimize(floss)\n",
        "\n",
        "init = tf.initialize_all_variables()\n",
        "\n",
        "# Add ops to save and restore all the variables.\n",
        "saver = tf.train.Saver()\n",
        "\n",
        "import tqdm\n",
        "training_epochs = 100000\n",
        "display_step = 50\n",
        "\n",
        "from sklearn.datasets import load_digits\n",
        "data = load_digits()\n",
        "\n",
        "idx = np.random.permutation(data.data.shape[0])\n",
        "idx_train = idx[:-100]\n",
        "idx_test = idx[-100:]\n",
        "\n",
        "from google.colab import files\n",
        "print \"Upload file ... digits_idx_part2_sparse.pkl\"\n",
        "uploaded = files.upload()\n",
        "import io\n",
        "idx_train,idx_test = pickle.load(io.StringIO(uploaded['digits_idx_part2_sparse.pkl'].decode('utf-8')))\n",
        "\n",
        "\n",
        "train = data.data[idx_train,:]\n",
        "test = data.data[idx_test,:]\n",
        "\n",
        "\n",
        "sess = tf.Session()\n",
        "sess.run(init)\n",
        "print \"Upload file ... autoencoder_digits_part2_sparse.ckpt\"\n",
        "uploaded = files.upload()\n",
        "saver.restore(sess, \"autoencoder_digits_part2_sparse.ckpt\")\n",
        "print(\"Model restored.\")\n"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Upload file ... digits_idx_part2_sparse.pkl\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-8c41c511-4d3b-4aa0-8e4d-f67455634bcd\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-8c41c511-4d3b-4aa0-8e4d-f67455634bcd\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving digits_idx_part2_sparse.pkl to digits_idx_part2_sparse.pkl\n",
            "Upload file ... autoencoder_digits_part2_sparse.ckpt\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-24e3676e-a9b9-4c22-94df-771cacd758f2\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-24e3676e-a9b9-4c22-94df-771cacd758f2\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving autoencoder_digits_part2_sparse.ckpt to autoencoder_digits_part2_sparse.ckpt\n",
            "INFO:tensorflow:Restoring parameters from autoencoder_digits_part2_sparse.ckpt\n",
            "Model restored.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "qVLvekN6ro5T",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "f87ca8c8-50ec-4e67-f4f2-11c5e16dc06d",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528928271533,
          "user_tz": -120,
          "elapsed": 840,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        "code = sess.run(layer_3, feed_dict={x: train, keep_prob: 1.})"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "metadata": {
        "id": "aUCb0_h8ro5V",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 901
        },
        "outputId": "472eac25-fd39-473d-f8ef-7af34eb21a29",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528928273386,
          "user_tz": -120,
          "elapsed": 832,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        "code[1]"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 2.3674152 ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.46560624, 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.09887764,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 1.1630348 ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.06677531,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.8062868 ,\n",
              "       0.        , 0.        , 0.46508172, 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        ], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "metadata": {
        "id": "kOKexY95ro5Y",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "af18f872-e2d4-42be-e8ff-bd537418301b",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528928275665,
          "user_tz": -120,
          "elapsed": 585,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        "non_zero=[]\n",
        "for i in xrange(code.shape[0]):\n",
        "    non_zero.append(len(np.where(code[i]>1e-6)[0]))"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "metadata": {
        "id": "d4M3h-_Uro5Z",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "outputId": "5ed0ac37-6b92-4cd5-b11e-b6662fc3d099",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528928278703,
          "user_tz": -120,
          "elapsed": 763,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        "# Histogram of coded values\n",
        "plt.hist(non_zero)"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 21., 256., 605., 464., 131., 141.,  55.,  19.,   2.,   3.]),\n",
              " array([ 3. ,  4.8,  6.6,  8.4, 10.2, 12. , 13.8, 15.6, 17.4, 19.2, 21. ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VZj7S7YxVlnHgduD71aLvZua7GsZXAB+gnvXOzLy26yHrOS4GVjcsenlmLmkYfxK4p2H8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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fec7c9e7fd0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "lwNY3oXFro5c",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "2e181835-46a7-4c9c-9fcc-762bc223d393",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528928282444,
          "user_tz": -120,
          "elapsed": 564,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        "code = sess.run(layer_3, feed_dict={x: test, keep_prob: 1.})"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "metadata": {
        "id": "7c81DSyPro5d",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "a43acb5e-cb8f-4020-818b-1c148cc2f3c4",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528928285069,
          "user_tz": -120,
          "elapsed": 892,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        "code"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
              "        0.        ],\n",
              "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
              "        0.        ],\n",
              "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
              "        0.        ],\n",
              "       ...,\n",
              "       [0.64484525, 0.        , 0.        , ..., 0.        , 0.        ,\n",
              "        0.        ],\n",
              "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
              "        0.        ],\n",
              "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
              "        0.        ]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "metadata": {
        "id": "GCK8WW_Uro5g",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## From compressed to restored"
      ]
    },
    {
      "metadata": {
        "id": "LhbAUT6Rro5g",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 120
        },
        "outputId": "2df71cb6-0c55-4b9a-ae3b-81309641f691",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528928379363,
          "user_tz": -120,
          "elapsed": 8603,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "\n",
        "tf.reset_default_graph()\n",
        "# Network Parameters\n",
        "n_input = 64\n",
        "n_hidden_1 = 64  # 1st layer num features\n",
        "n_hidden_2 = 128  # 2nd layer num features\n",
        "n_hidden_3 = 256  # 3nd layer num features\n",
        "n_hidden_4 = 256  # 4nd layer num features\n",
        "n_hidden_5 = 128  # 4nd layer num features\n",
        "n_hidden_6 = 64  # 1st layer num features\n",
        "\n",
        "\n",
        "\n",
        "# Parameters\n",
        "learning_rate = 1e-3\n",
        "# tf Graph input\n",
        "x = tf.placeholder(\"float\", [None, n_input], name='x')\n",
        "y = tf.placeholder(\"float\", [None, n_input], name='y')\n",
        "lmd = tf.placeholder(\"float\",[], name = 'lambda')\n",
        "# Store layers weight & bias\n",
        "c = 0.1\n",
        "weights = {\n",
        "    'h1': tf.Variable(c*tf.random_normal([n_input, n_hidden_1]), name='W1'),\n",
        "    'h2': tf.Variable(c*tf.random_normal([n_hidden_1, n_hidden_2]), name='W2'),\n",
        "    'h3': tf.Variable(c*tf.random_normal([n_hidden_2, n_hidden_3]), name='W3'),\n",
        "    'h4': tf.Variable(c*tf.random_normal([n_hidden_3, n_hidden_4]), name='W4'),\n",
        "    'h5': tf.Variable(c*tf.random_normal([n_hidden_4, n_hidden_5]), name='W5'),\n",
        "    'h6': tf.Variable(c*tf.random_normal([n_hidden_5, n_hidden_6]), name='W6'),\n",
        "    'out': tf.Variable(c*tf.random_normal([n_hidden_6, n_input]))\n",
        "}\n",
        "biases = {\n",
        "    'b1': tf.Variable(c*tf.random_normal([n_hidden_1]), name='b1'),\n",
        "    'b2': tf.Variable(c*tf.random_normal([n_hidden_2]), name='b2'),\n",
        "    'b3': tf.Variable(c*tf.random_normal([n_hidden_3]), name='b3'),\n",
        "    'b4': tf.Variable(c*tf.random_normal([n_hidden_4]), name='b4'),\n",
        "    'b5': tf.Variable(c*tf.random_normal([n_hidden_5]), name='b5'),\n",
        "    'b6': tf.Variable(c*tf.random_normal([n_hidden_6]), name='b6'),\n",
        "    'out': tf.Variable(c*tf.random_normal([n_input]))\n",
        "}\n",
        "\n",
        "keep_prob = tf.placeholder(tf.float32)\n",
        "pre_layer_drop = tf.nn.dropout(x, keep_prob)\n",
        "layer_1 = tf.nn.relu(tf.add(tf.matmul(pre_layer_drop, weights['h1']), biases['b1']))\n",
        "layer_2 = tf.nn.relu(tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']))\n",
        "layer_3 = tf.nn.relu(tf.add(tf.matmul(layer_2, weights['h3']), biases['b3']))\n",
        "layer_4 = tf.nn.relu(tf.add(tf.matmul(layer_3, weights['h4']), biases['b4']))\n",
        "layer_5 = tf.nn.relu(tf.add(tf.matmul(layer_4, weights['h5']), biases['b5']))\n",
        "layer_6 = tf.nn.relu(tf.add(tf.matmul(layer_5, weights['h6']), biases['b6']))\n",
        "output = tf.add(tf.matmul(layer_6, weights['out']), biases['out'])\n",
        "\n",
        "loss = tf.reduce_mean(tf.pow(output - y, 2))\n",
        "loss2 = tf.reduce_mean(tf.abs(layer_3))\n",
        "floss = loss + lmd * loss2\n",
        "\n",
        "opt = tf.train.AdamOptimizer(learning_rate).minimize(floss)\n",
        "\n",
        "\n",
        "\n",
        "#################################### Decoder \n",
        "x_code = tf.placeholder(\"float\", [None, n_hidden_4], name='x')\n",
        "\n",
        "\n",
        "dec_layer_1 = tf.nn.relu(tf.add(tf.matmul(x_code, weights['h4']), biases['b4']))\n",
        "dec_layer_2 = tf.nn.relu(tf.add(tf.matmul(dec_layer_1, weights['h5']), biases['b5']))\n",
        "dec_layer_3 = tf.nn.relu(tf.add(tf.matmul(dec_layer_2, weights['h6']), biases['b6']))\n",
        "dec_output = tf.add(tf.matmul(dec_layer_3, weights['out']), biases['out'])\n",
        "#############################################\n",
        "\n",
        "\n",
        "init = tf.initialize_all_variables()\n",
        "\n",
        "# Add ops to save and restore all the variables.\n",
        "saver = tf.train.Saver()\n",
        "\n",
        "from sklearn.datasets import load_digits\n",
        "data = load_digits()\n",
        "\n",
        "idx = np.random.permutation(data.data.shape[0])\n",
        "idx_train = idx[:-100]\n",
        "idx_test = idx[-100:]\n",
        "\n",
        "import pickle\n",
        "from google.colab import files\n",
        "print \"Upload file ... digits_idx_part2_sparse.pkl\"\n",
        "uploaded = files.upload()\n",
        "import io\n",
        "idx_train,idx_test = pickle.load(io.StringIO(uploaded['digits_idx_part2_sparse.pkl'].decode('utf-8')))\n",
        "\n",
        "train = data.data[idx_train,:]/16.\n",
        "test = data.data[idx_test,:]/16.\n",
        "\n",
        "\n",
        "sess = tf.Session()\n",
        "sess.run(init)\n",
        "\n",
        "saver.restore(sess, \"autoencoder_digits_part2_sparse.ckpt\")\n",
        "print(\"Model restored.\")"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Upload file ... digits_idx_part2_sparse.pkl\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-c06d1161-a1bd-453a-9e12-97dcffdea842\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-c06d1161-a1bd-453a-9e12-97dcffdea842\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving digits_idx_part2_sparse.pkl to digits_idx_part2_sparse (1).pkl\n",
            "INFO:tensorflow:Restoring parameters from autoencoder_digits_part2_sparse.ckpt\n",
            "Model restored.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "3-t1EnkIro5j",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "2eb11c4c-f817-459d-b4d3-749090929d8b",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528928383661,
          "user_tz": -120,
          "elapsed": 805,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        "code = sess.run(layer_3, feed_dict={x: test, keep_prob: 1.})"
      ],
      "execution_count": 78,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kgM84jH6ro5n",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "3196637c-5735-4836-962b-3421f395c674",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528928385506,
          "user_tz": -120,
          "elapsed": 842,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        "res = sess.run(dec_output, feed_dict={x_code: code})"
      ],
      "execution_count": 79,
      "outputs": []
    },
    {
      "metadata": {
        "id": "R2Qm4zJ9ro5q",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 4977
        },
        "outputId": "3d34468d-cbd6-4f9e-961d-26c06910b205",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528928388927,
          "user_tz": -120,
          "elapsed": 2583,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        "for i in xrange(10):\n",
        "\n",
        "    plt.figure()\n",
        "    plt.imshow(test[i].reshape((8,8)),cmap='gray',interpolation='nearest')\n",
        "    plt.figure()\n",
        "    plt.imshow(res[i].reshape((8,8)),cmap='gray',interpolation='nearest')"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fec839c1c50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fec817b7550>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fec79752590>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fec7c9643d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPQAAAD4CAYAAADb7cuFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fec8216c490>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fec84a98b50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fec7ca38890>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fec7da67a50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPQAAAD4CAYAAADb7cuFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nS4UYaKkQAy0VYqClQgy0VMj/AkS0UlyDDY2VAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fec8392ab50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fec84d89890>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPQAAAD4CAYAAADb7cuFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fec817e85d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fec81734310>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPQAAAD4CAYAAADb7cuFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fec81687dd0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fec795852d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPQAAAD4CAYAAADb7cuFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYqClQgy0VIiBlgox0FIhBloq5H8AaIhi3f45M0EAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fec7d87be50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fec84d0af10>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPQAAAD4CAYAAADb7cuFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGGipkP8GDRNMgAmkQwAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fec7ced4210>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZJxHUAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fec794a04d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPQAAAD4CAYAAADb7cuFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fec79790590>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fec7ce59650>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Tw7y9syqro5t",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Manifold learning"
      ]
    },
    {
      "metadata": {
        "id": "wTpV_Fqcro5u",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 689
        },
        "outputId": "222bd1b5-5883-4d0c-c276-2f576a4bbf09",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528928597697,
          "user_tz": -120,
          "elapsed": 16815,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "\n",
        "tf.reset_default_graph()\n",
        "# Network Parameters\n",
        "n_input = 64\n",
        "n_hidden_1 = 32  # 1st layer num features\n",
        "n_hidden_2 = 16  # 2nd layer num features\n",
        "n_hidden_3 = 2  # 3nd layer num features\n",
        "n_hidden_4 = 2  # 4nd layer num features\n",
        "n_hidden_5 = 16 # 4nd layer num features\n",
        "n_hidden_6 = 32  # 1st layer num features\n",
        "\n",
        "\n",
        "\n",
        "# Parameters\n",
        "lr = 1e-3\n",
        "lr_decay_step = 100000\n",
        "lr_decay_factor = 0.5\n",
        "training_epochs = 50000\n",
        "display_step = 100\n",
        "batch_size=16\n",
        "# tf Graph input\n",
        "x = tf.placeholder(\"float\", [None, n_input], name='x')\n",
        "y = tf.placeholder(\"float\", [None, n_input], name='y')\n",
        "learning_rate = tf.placeholder(tf.float32, [], name='learning_rate')\n",
        "\n",
        "# Store layers weight & bias\n",
        "c = 0.1\n",
        "weights = {\n",
        "    'h1': tf.Variable(c*tf.random_normal([n_input, n_hidden_1]), name='W1'),\n",
        "    'h2': tf.Variable(c*tf.random_normal([n_hidden_1, n_hidden_2]), name='W2'),\n",
        "    'h3': tf.Variable(c*tf.random_normal([n_hidden_2, n_hidden_3]), name='W3'),\n",
        "    'h4': tf.Variable(c*tf.random_normal([n_hidden_3, n_hidden_4]), name='W4'),\n",
        "    'h5': tf.Variable(c*tf.random_normal([n_hidden_4, n_hidden_5]), name='W5'),\n",
        "    'h6': tf.Variable(c*tf.random_normal([n_hidden_5, n_hidden_6]), name='W6'),\n",
        "    'out': tf.Variable(c*tf.random_normal([n_hidden_6, n_input]))\n",
        "}\n",
        "biases = {\n",
        "    'b1': tf.Variable(c*tf.random_normal([n_hidden_1]), name='b1'),\n",
        "    'b2': tf.Variable(c*tf.random_normal([n_hidden_2]), name='b2'),\n",
        "    'b3': tf.Variable(c*tf.random_normal([n_hidden_3]), name='b3'),\n",
        "    'b4': tf.Variable(c*tf.random_normal([n_hidden_4]), name='b4'),\n",
        "    'b5': tf.Variable(c*tf.random_normal([n_hidden_5]), name='b5'),\n",
        "    'b6': tf.Variable(c*tf.random_normal([n_hidden_6]), name='b6'),\n",
        "    'out': tf.Variable(c*tf.random_normal([n_input]))\n",
        "}\n",
        "\n",
        "keep_prob = tf.placeholder(tf.float32)\n",
        "pre_layer_drop = tf.nn.dropout(x, keep_prob)\n",
        "layer_1 = tf.nn.relu(tf.add(tf.matmul(pre_layer_drop, weights['h1']), biases['b1']))\n",
        "layer_2 = tf.nn.relu(tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']))\n",
        "layer_3 = tf.nn.relu(tf.add(tf.matmul(layer_2, weights['h3']), biases['b3']))\n",
        "layer_4 = tf.nn.relu(tf.add(tf.matmul(layer_3, weights['h4']), biases['b4']))\n",
        "layer_5 = tf.nn.relu(tf.add(tf.matmul(layer_4, weights['h5']), biases['b5']))\n",
        "layer_6 = tf.nn.relu(tf.add(tf.matmul(layer_5, weights['h6']), biases['b6']))\n",
        "output = tf.add(tf.matmul(layer_6, weights['out']), biases['out'])\n",
        "\n",
        "loss = tf.reduce_mean(tf.pow(output - y, 2))\n",
        "opt = tf.train.AdamOptimizer(learning_rate).minimize(loss)\n",
        "\n",
        "init = tf.initialize_all_variables()\n",
        "\n",
        "# Add ops to save and restore all the variables.\n",
        "saver = tf.train.Saver()\n",
        "\n",
        "from sklearn.datasets import load_digits\n",
        "data = load_digits()\n",
        "\n",
        "idx = np.random.permutation(data.data.shape[0])\n",
        "idx_train = idx[:-100]\n",
        "idx_test = idx[-100:]\n",
        "\n",
        "\n",
        "import pickle\n",
        "from google.colab import files\n",
        "print \"Upload file ... digits_idx_part3_viz.pkl\"\n",
        "uploaded = files.upload()\n",
        "import io\n",
        "idx_train,idx_test = pickle.load(io.StringIO(uploaded['digits_idx_part3_viz.pkl'].decode('utf-8')))\n",
        "\n",
        "\n",
        "train = data.data[idx_train,:]/16.\n",
        "test = data.data[idx_test,:]/16.\n",
        "\n",
        "sess = tf.Session()\n",
        "sess.run(init)\n",
        "print \"Upload file ... autoencoder_digits_part3_viz.ckpt\"\n",
        "uploaded = files.upload()\n",
        "saver.restore(sess, \"autoencoder_digits_part3_viz.ckpt\")\n",
        "print(\"Model restored.\")\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "xs = test[np.random.randint(0,test.shape[0],1),:]\n",
        "rec = sess.run(output, feed_dict={x: xs, keep_prob: 1.})\n",
        "\n",
        "\n",
        "plt.imshow(xs.reshape((8, 8)),interpolation='nearest',cmap='gray')\n",
        "plt.colorbar()\n",
        "plt.show()\n",
        "plt.imshow(rec.reshape((8, 8)),interpolation='nearest',cmap='gray')\n",
        "plt.colorbar()\n",
        "plt.show()\n"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Upload file ... digits_idx_part3_viz.pkl\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-af504473-ce82-4fb5-9151-56c5c457da0a\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-af504473-ce82-4fb5-9151-56c5c457da0a\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving digits_idx_part3_viz.pkl to digits_idx_part3_viz (1).pkl\n",
            "Upload file ... autoencoder_digits_part3_viz.ckpt\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-1b3e266c-2f00-4341-a88e-48c3ef05fe08\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-1b3e266c-2f00-4341-a88e-48c3ef05fe08\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving autoencoder_digits_part3_viz.ckpt to autoencoder_digits_part3_viz (1).ckpt\n",
            "INFO:tensorflow:Restoring parameters from autoencoder_digits_part3_viz.ckpt\n",
            "Model restored.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fec818df950>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fec817c7610>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "wgWqlFlUro5x",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "25e2f435-9adc-458d-b6ef-713fd2116eeb",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528928608340,
          "user_tz": -120,
          "elapsed": 665,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        "xs = train\n",
        "representation = sess.run(layer_3, feed_dict={x: xs, keep_prob: 1.})"
      ],
      "execution_count": 84,
      "outputs": []
    },
    {
      "metadata": {
        "id": "KlqU4KN-ro5z",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d1502828-303d-4c83-ba65-1edd96a88eba",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528928610256,
          "user_tz": -120,
          "elapsed": 521,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        "representation.shape"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1697, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "metadata": {
        "id": "jpEtTE-Gro53",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "52fda744-969d-4993-da4b-1aed432c801a",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528928633530,
          "user_tz": -120,
          "elapsed": 21870,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        "from scipy.spatial import distance \n",
        "N=10\n",
        "vx = np.linspace(np.min(representation[:,0]),np.max(representation[:,0]),N)\n",
        "vy = np.linspace(np.min(representation[:,1]),np.max(representation[:,1]),N)\n",
        "\n",
        "def is_visited(x,l):\n",
        "    for item in l:\n",
        "        if np.abs(x-item)<1e-10:\n",
        "            return True\n",
        "    return False\n",
        "visited=[]\n",
        "idx_mat=np.zeros((N,N))       \n",
        "for i in xrange(N):\n",
        "    for j in xrange(N):\n",
        "        d = distance.cdist(np.array([vx[i],vy[j]])[np.newaxis,:], representation)\n",
        "        idx_sort = np.argsort(d)[0]\n",
        "        idx_not_visited=[tmp for tmp in idx_sort if not(is_visited(tmp,visited))]\n",
        "        if len(idx_not_visited)>0:\n",
        "            idx_mat[i,j] = idx_not_visited[0]\n",
        "            visited.append(idx_not_visited[0])"
      ],
      "execution_count": 86,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MIXtktc8ro55",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 593
        },
        "outputId": "341c54db-5dd3-4777-eee1-31631b1100c1",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1528928720658,
          "user_tz": -120,
          "elapsed": 6919,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          }
        }
      },
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "f, axarr = plt.subplots(N, N)\n",
        "\n",
        "for i in xrange(N):\n",
        "    for j in xrange(N):\n",
        "        axarr[i,j].imshow(xs[int(idx_mat[i,j])].reshape((8,8)),cmap='gray', interpolation='nearest')\n",
        "f.set_size_inches(10,10)"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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V1Ad8qTGJvkr98Ik5in9nHx4expUrV4rlSWKOfDX6jmHaMUeFQgG9vb0Akscc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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fec79606910>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "2rtK0Kicro58",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}