09. Generative Models/03. Sparse Autoencoder - MXNet.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Sparse Autoencoder - MXNet(From Scratch).ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "L3KIZndDdgt4",
        "colab_type": "code",
        "outputId": "8b9ff1c3-0a29-4902-f234-041cd2d80212",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 171
        }
      },
      "source": [
        "!pip install mxnet-cu100"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: mxnet-cu100 in /usr/local/lib/python3.6/dist-packages (1.4.1)\n",
            "Requirement already satisfied: numpy<1.15.0,>=1.8.2 in /usr/local/lib/python3.6/dist-packages (from mxnet-cu100) (1.14.6)\n",
            "Requirement already satisfied: requests>=2.20.0 in /usr/local/lib/python3.6/dist-packages (from mxnet-cu100) (2.21.0)\n",
            "Requirement already satisfied: graphviz<0.9.0,>=0.8.1 in /usr/local/lib/python3.6/dist-packages (from mxnet-cu100) (0.8.4)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20.0->mxnet-cu100) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20.0->mxnet-cu100) (2019.6.16)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20.0->mxnet-cu100) (3.0.4)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20.0->mxnet-cu100) (2.8)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1YN4PT_Idstj",
        "colab_type": "text"
      },
      "source": [
        "#MXNet (From Scratch)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CLGHa8i6WEaA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import mxnet as mx\n",
        "from mxnet import nd, autograd, gluon\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "mx.random.seed(1)\n",
        "\n",
        "ctx = mx.gpu()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "huuK9vLaWEcO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def transform(data, label):\n",
        "    return data.reshape((784)).astype('float32')/255.0\n",
        "\n",
        "train_mnist = gluon.data.vision.FashionMNIST(train = True, transform = transform)\n",
        "test_mnist = gluon.data.vision.FashionMNIST(train = False, transform = transform)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sAXcT8nSWEYo",
        "colab_type": "code",
        "outputId": "54f75c83-e945-4e2b-ea38-12a5b52cba6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "image = train_mnist[0]\n",
        "print(image.shape)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(784,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SIQSLxiFWEXg",
        "colab_type": "code",
        "outputId": "30f1f415-6232-48f1-d5c2-ed85485a7057",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "plt.imshow(image.reshape((28, 28)).asnumpy())\n",
        "plt.show()\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fVfep6mvFj/sAbAUwA8BSAKuKd1sF4NZqDZKIKu+0XvOLyBwAFwF4FUCHqu4rlvZj5GUB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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oF-WKS-xX73T",
        "colab_type": "text"
      },
      "source": [
        "##Creating data loader for training and testing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IVmMwE94WEWY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size = 128\n",
        "training_data = gluon.data.DataLoader(train_mnist, batch_size = batch_size, shuffle = True)\n",
        "testing_data = gluon.data.DataLoader(test_mnist, batch_size = batch_size, shuffle = False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5HUXvMyHYKoM",
        "colab_type": "text"
      },
      "source": [
        "##Building the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u7Vdh54QWESq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "weight_scale = .01\n",
        "\n",
        "# ============================ Defining Encoder =================================\n",
        "#  Allocate parameters for the first hidden layer of encoder\n",
        "eW1 = nd.random_normal(shape = (784, 256), scale = weight_scale, ctx = ctx)\n",
        "eb1 = nd.random_normal(shape = 256, scale = weight_scale, ctx = ctx)\n",
        "\n",
        "#  Allocate parameters for the ouput layer of encoder\n",
        "eW2 = nd.random_normal(shape = (256, 128), scale = weight_scale, ctx = ctx)\n",
        "eb2 = nd.random_normal(shape = 128, scale = weight_scale, ctx = ctx)\n",
        "# ===============================================================================\n",
        "\n",
        "\n",
        "# ============================ Defining Decoder =================================\n",
        "#  Allocate parameters for the first hidden layer of encoder\n",
        "dW1 = nd.random_normal(shape = (128, 256), scale = weight_scale, ctx = ctx)\n",
        "db1 = nd.random_normal(shape = 256, scale = weight_scale, ctx = ctx)\n",
        "\n",
        "#  Allocate parameters for the second hidden layer of encoder\n",
        "dW2 = nd.random_normal(shape = (256, 784), scale = weight_scale, ctx = ctx)\n",
        "db2 = nd.random_normal(shape = 784, scale = weight_scale, ctx = ctx)\n",
        "# ===============================================================================\n",
        "\n",
        "params = [eW1, eb1, eW2, eb2, dW1, db1, dW2, db2]\n",
        "\n",
        "sqrs = [] # for adam optimizer\n",
        "vs = [] # for adam optimizer\n",
        "for param in params:\n",
        "    param.attach_grad()\n",
        "    sqrs.append(param.zeros_like() + 1e-8) # adding 1e-8 for computation stability \n",
        "    vs.append(param.zeros_like() + 1e-8) # adding 1e-8 for computation stability \n",
        "\n",
        "def encode(inputs):\n",
        "    layer1 = nd.relu(nd.dot(inputs, eW1) + eb1)\n",
        "    return nd.relu(nd.dot(layer1, eW2) + eb2)\n",
        "\n",
        "def decode(encoded):\n",
        "    layer1 = nd.relu(nd.dot(encoded, dW1) + db1)\n",
        "    return nd.sigmoid(nd.dot(layer1, dW2) + db2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e7nGkrA6yFLj",
        "colab_type": "text"
      },
      "source": [
        "##Lets do some sample encoding and decoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PICPr0DpyABh",
        "colab_type": "code",
        "outputId": "b4c92042-9e0e-4b66-cf14-8db01a5b698d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "encoded = encode(image.as_in_context(ctx))\n",
        "decoded = decode(encoded)\n",
        "\n",
        "plt.imshow(decoded.reshape((28, 28)).asnumpy(), cmap = 'gray')\n",
        "plt.title('Generated')\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rG+rzsaaP+pZLfvTRR83cN4XTN/Szbt06Z+bbirp169Zm3qlTJzO3hvIAoHnz5s6sW7du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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HW4Jzb46EyyD",
        "colab_type": "text"
      },
      "source": [
        "##Defining our objective function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "avO1uf5fItMV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def objective(predictions, labels):\n",
        "    return nd.mean(nd.square(predictions - labels))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3qr1NTDkE7Kh",
        "colab_type": "text"
      },
      "source": [
        "##Defining adam parameters optimizer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GZJJjKSO5Eyr",
        "colab_type": "text"
      },
      "source": [
        "We will use adam optimizer because sgd performs very slow when a model has many layers, as in our model we have total 5 layers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hvFKa4WuI9c-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def adam(t, params, vs, sqrs, lr = 0.001):\n",
        "    beta1 = 0.9\n",
        "    beta2 = 0.999\n",
        "    eps = 1e-8\n",
        "\n",
        "    for param, v, sqr in zip(params, vs, sqrs):\n",
        "        grad = param.grad / batch_size\n",
        "\n",
        "        v[:] = beta1 * v + (1. - beta1) * grad\n",
        "        sqr[:] = beta2 * sqr + (1. - beta2) * nd.square(grad + 1e-8)\n",
        "\n",
        "        v_bias_corr = v / (1. - beta1 ** t)\n",
        "        sqr_bias_corr = sqr / (1. - beta2 ** t)\n",
        "\n",
        "        update = lr * v_bias_corr / (nd.sqrt(sqr_bias_corr + 1e-8) + eps)\n",
        "        param[:] = param - update"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9o5E8mEEAnSa",
        "colab_type": "text"
      },
      "source": [
        "##Initializing rho and defining KL Divergence"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tau4pr5IAtnc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "rho = nd.full((1, 128), 0.01, ctx = ctx) # shape is (1, 128), becuse 128 is the no of neurons of encoded layer(middel layer of autoencoder).\n",
        "\n",
        "def kl_divergence(p, q):\n",
        "#     p = nd.softmax(p)\n",
        "    q = nd.softmax(q)\n",
        "    s1 = nd.sum(p * nd.log(p / q))\n",
        "    s2 = nd.sum((1 - p) * nd.log((1 - p) / (1 - q)))\n",
        "    return s1 + s2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UlkEn5XxFPkd",
        "colab_type": "text"
      },
      "source": [
        "##Training the  model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kVao6lbWCizF",
        "colab_type": "code",
        "outputId": "61606550-573a-497b-cf10-10b4fef459b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "epochs = 20\n",
        "num_batches = 60000//batch_size\n",
        "learning_rate = .1\n",
        "losses = []\n",
        "t = 0\n",
        "\n",
        "for epoch in range(epochs):\n",
        "    cumulative_loss = 0\n",
        "    for features in training_data:\n",
        "        t += 1\n",
        "        features = features.as_in_context(ctx)\n",
        "        with autograd.record():\n",
        "            encoded = encode(features)\n",
        "            decoded = decode(encoded)\n",
        "            objective_loss = objective(decoded, features)\n",
        "            \n",
        "            rho_hat = nd.sum(encoded, axis = 0, keepdims = True) # summing accross batch\n",
        "            sparsity_loss = 0.1 * kl_divergence(rho, rho_hat)\n",
        "            \n",
        "            loss = objective_loss\n",
        "            \n",
        "        loss.backward()\n",
        "        adam(t, params, vs = vs, sqrs = sqrs, lr = learning_rate)\n",
        "        cumulative_loss += loss\n",
        "    loss = cumulative_loss.asscalar()/num_batches\n",
        "    losses.append(loss)\n",
        "    print(f'Epoch: {epoch} | Loss: {loss:.5f}')\n",
        "    \n",
        "    # Below we are generating a sample image from the first image of last batch\n",
        "    \n",
        "    encoded = encode(features[0])\n",
        "    decoded = decode(encoded)\n",
        "    \n",
        "    \n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.imshow(features[0].reshape((28, 28)).asnumpy())\n",
        "    plt.axis('off')\n",
        "    plt.title('Actual Image')\n",
        "    \n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.imshow(decoded.reshape((28, 28)).asnumpy())\n",
        "    plt.axis('off')\n",
        "    plt.title('Generated image')\n",
        "    \n",
        "    plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 0 | Loss: 0.13009\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5hOYWnbio3m8nhRo7dLkv0ePbOXuxft1OVvUjffzsbvujrNx0myorFc4160YX6eRqete9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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 1 | Loss: 0.07870\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gfQukeqhx/w7aN9RI2UubQ+ZjdAcuIpIoBXARkUQpgIuIJEoBXEQkURb280t0\nERFZGLoDFxFJlAK4iEiiFMBFRBKlAC4ikigFcBGRRCmAi4gkSgFcRCRRCuAiIolSABcRSZQCuIhI\nohTARUQSpQAuIpIoBXARkUQpgIuIJEoBXEQkUQrgIiKJUgAXEUmUAriISKIUwEVEEqUALiKSKAVw\nEZFEKYCLiCRKAVxEJFH/D+6zZctbGZMvAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 2 | Loss: 0.04377\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 3 | Loss: 0.03629\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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srCj5hQhCHJry5fyh87yvzNtHMNi9Vc7zNhFHSebU6KzPLNl3jGehTI+TbJ4h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\nESn/D4+CVY3SBo2BAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 4 | Loss: 0.03251\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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A0Yv8Q2SuAcBK3c+hWDX1sy1/T5jYDADzTS94Tq+R853h4l3juO878jhfZw6c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\nJIoMuBBCJMr/A/ffq8e35SimAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 5 | Loss: 0.02805\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 6 | Loss: 0.02512\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JzBkUqwfdaBLzvGduou23Dpzg2o5r66d92ZL3mGZSx7jF+BLiIksKkVrewPSW7u8d9fWk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\nFQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 7 | Loss: 0.02378\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 8 | Loss: 0.02288\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 9 | Loss: 0.02207\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rbp9SHe/5fEOlvqjsZiexuL/pr9vdHNwVlQ1by62735ni3+z4S0p0mvHrLR+K17+XJP9s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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 10 | Loss: 0.02114\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 11 | Loss: 0.02045\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fC+wIXu5Z+nrrK1V1rU9ijpenaF+W6jvU5NPj9zX9zvb3NngSc54kMZuBe7+Zlk+OlsyP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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 12 | Loss: 0.01987\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 13 | Loss: 0.01919\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+R7L6sbn0SXsg6ybEJqenQ5tJlh3g1SnVs7wEYOTv/e/zk7fznSiSofTtRmTYdsVXrIRa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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 14 | Loss: 0.01866\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 15 | Loss: 0.01803\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 16 | Loss: 0.01758\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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h+3N26unJpVQsE9f9MkpUwbPSWCe8ROzHtd6zznn2IIF6Mfq8s9/H0cArcCIiTzGBExF5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\nOBGRp5jAiYg8xQROROSp/wMRYr22a3wclQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 17 | Loss: 0.01718\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RuTOiss0viJPCktR9RnyH88Udz7h1XzM7TsqtOOQ3bl0vYbl5pM2tW3WuCR8diNfiLpry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\nAAeARBHAASBRBHAASNT/AbDVQC9w5t7IAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 18 | Loss: 0.01688\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 19 | Loss: 0.01658\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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up3XrwS9Wcfco722ycPFoR/vXHbiISKIUwEVEEqUALiKSKAVwEZFEKYl5LjoLQ7zv+tUP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\nLgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JJOc7DOepwEE",
        "colab_type": "text"
      },
      "source": [
        "##Lets generate more images from first 10 images of testing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dvvQC3oQYu5A",
        "colab_type": "code",
        "outputId": "8496b56e-e62e-447b-fae5-acac7b783501",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for i, features in enumerate(testing_data):\n",
        "    features = features.as_in_context(ctx)\n",
        "    encoded = encode(features)\n",
        "    decoded = decode(encoded)\n",
        "    \n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.imshow(features.reshape((28, 28)).asnumpy())\n",
        "    plt.title('Actual Image')\n",
        "    \n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.imshow(decoded.reshape((28, 28)).asnumpy())\n",
        "    plt.title('Generated image')\n",
        "    \n",
        "    plt.show()\n",
        "    if i == 9: break"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Ok+05+cxx/sx4jWLt9c3+vV3d/Ztt3/E0/GO/odf2Es9FlO5v7mszskJEuwyPtqxNDljb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\nIfx/Orr2Qx5hq68AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SY/bYsFL3k6Oll7jP2V8j1NDj9JDHCZxz5aML7f62LXZPLr3HY1at8g4VXiugElEu/0VV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\ng04IIYFAg04IIYHw/wGjgT3D63yfpwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qjOyXpMyTgVCjZHUZbe9z6uwGGVb5gULCuDLGD6d6Y18ArfX6+nrmef15Aaya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\nJyW4Q3ccx0kJ/x/9YzPpuEG0nAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EMJPjtLkdjiYwkA+7ccmZv3J19wyE83LvfOgsV/cX+VXOwfzvQC+jpavbzwSo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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Z+0mC/SwzexF4e/pYpKYotiVrDnqG7u6XFHgqo2O05FCh2Jas0UxREZGMUEIXEckIJXQR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\nfhGCHgseRgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuAbArWZ2PoBbs/dC9BLSbZErTmnQzWyfmf0wez0O4AEA2wC8GsCNWbMbAbym\nW50UohtIt0XeaCsOneQOAM8CcCeALWa2L/toP9Kfrd42VwG4CgD6EVmbSYgVZsm6XRjpfieFOAUt\nO0VJDgP4PIB3mtmCurBmZoisjmdm15nZTjPbWULkeZ4QK0gndLuc+MlpQiwnLRl0kiWkCv9pM/tC\nJj5Acmv2+VYAB7vTRSG6h3Rb5IlWolwI4OMAHjCzDzV8dDOAKwG8P/v/pa70sAUKk2F0hSX+wgxe\nmQDz1xbA+p/5nm0PFpxIgFiUSreiV1qlneibSNu56fCkFY/4J7JWDlOhYwskLCfd1u3asB/JUy+1\nHi1cnAojI2bH/fP8vbFzA9m+uTA1HQD6Dnl5834kRfl4KLOI5UicyJO6o1ax699/NPwgiaT+e4tp\nxPZbOxae89l1fjTK+FT4JOHYgP+4eGY6vMbFE34IUHkivJfKx/3IsMXSyjP0FwB4I4B7Sd6Tyd6D\nVNk/R/LNAH4B4HUd7ZkQ3Ue6LXLFKQ26md2BeHTnizvbHSGWD+m2yBvKFBVCiJwggy6EEDkhF/XQ\nPSyy0rgnj2TqojQ2E7aNHa+2wo7OZaawP3QcxZxX9Uhae64wg80udHAVZvwTYoXQqVmY9TWrb8zb\n3ne6PXRsUyCbqPqhwgOHQlndSU0HfEdl7EGVNw7vnov4X9F/xPGqRtomlbBfVojVQw9N3ew63/xN\nHA1DUJ8o+N5WOx46Rb2a7gBQmgj1oXBsyt/vZJO8uT56BM3QhRAiJ8igCyFETpBBF0KInCCDLoQQ\nOUEGXQghckIuolw4EVbFr5f8lOeak/ofi85I9oShALmOZWmjJMHAwdCTP/GMMCoIAGxXGNXBvJ1I\nM6C6UJGSCf98lKbDSA5Lhty21QEnQsRbVAHAkcNhxcfDB9e4bc9+LFT6WJRLaTK8WKz6oSdJLYzG\noLMATVL1ozaSKaeMB9uIkopMUZPZMNpncI0fLTS3LpRPJX7qf9+R8IBlJzIJAIpjYSkRTkXumebz\n2Nr6FpqhCyFEXpBBF0KInCCDLoQQOUEGXQghckI+nKKz3vLfkbZODeXCdKRMwPhE651Y6RrnnaCN\neuh9x8LzOHnUr//teXRiq8b3LPU6bHqhc56HjrpNk+HQAVou+nOr6mB4ouaGI+ntRx3nc8SfOLjb\nKXIeacw5J2rAcX4CcFenD1awP9n2lbAtY4NInHMWSZFPJsL1EQbX+HXlZzaEelwr+6ayfDzsW2nS\n92DS6ZvN+fXQrcnB7p1XD83QhRAiJ8igCyFETpBBF0KInCCDLoQQOeGUBp3kdpLfJHk/yZ+QfEcm\nv5bkXpL3ZH+v7H53hegc0m3X2jieAAAFZklEQVSRN1qJcqkCuNrMfkhyBMDdJG/JPvuwmX2ge91r\nDc9THFv92yu2X4gsvG2VSE2AXseLZukAhVk/GqF8IoxcmF0bi4hZVjqm22aG+uzC1O6k5EdRsD+M\nuOCknwLe50SugP6iFZWR1oPW+Pj+UBhZFAZLXLzFnMiyaFsvSiYS5dLOojKcDsuDDDzmn6+BjRsD\nWWXQn/uWx8L+Dhz1++Ve49mwHAAA2MxCuXdePFpZJHofgH3Z63GSDwDY1tLehVjFSLdF3mjrGTrJ\nHQCeBeDOTPR2kj8meT3J0cg2V5HcRXJXBf63kRArjXRb5IGWDTrJYQCfB/BOMzsB4G8BnAfgUqSz\nnA9625nZdWa208x2luD/VBRiJZFui7zQkkEnWUKq8J82sy8AgJkdMLOamdUBfAzAZd3rphDdQbot\n8sQpn6Ez9Uh8HMADZvahBvnW7BkkALwWwH3d6WILbFgXiCbO8h0p9VI45IpfihrsD2ddVol4UHuJ\nWJmCNpyldcdXV9nin5vj54YO0Lk1bdS47hId1+0mx1WzY+tJ+WDr6e3JXOu1yEvjjkMx4kurHT/h\nf+Bhra04nx7QmSM627Pg61o7DtS2+uWQeCUJAJQvXB/ISlP+3Lc07ZQSmYml/ofy2nSkHnq1qW+d\ncooCeAGANwK4l+Q9mew9AN5A8lKkhToeA/DWlo4oxOpBui1yRStRLncA8KYPX+18d4RYPqTbIm8o\nU1QIIXKCDLoQQuQEGXQhhMgJuVjgonb/g4Hs7AE/7breH8o3f2vc3++4L88tbSzSseWmnweywYNP\ncdsWZsISCoO/cBZYAJCDZUKeJBYRVR8Lx+4tegEAnBl2hP7x+o6HkRCDB1pf/GXJ0SwA6JYPcCJa\nYtt7wS+Rflm9tYiaVB6em3okCqn/YCifGfWjcpJKuN/+Pb7dsBOhPFpepMWolqA/i9pKCCHEqkMG\nXQghcoIMuhBC5AQZdCGEyAlstc5uRw5GHgLwi+ztRgCHl+3gy4fGtXKcY2abVuLADbrdC+dpseR1\nbL0wrpZ0e1kN+oIDk7vMbOeKHLyLaFynN3k+T3kdW57GpUcuQgiRE2TQhRAiJ6ykQb9uBY/dTTSu\n05s8n6e8ji0341qxZ+hCCCE6ix65CCFETlh2g07yFSR/RvJhktcs9/E7SbaA8EGS9zXI1pO8heRD\n2X93geHVDMntJL9J8n6SPyH5jkze82PrJnnRbel1741tnmU16CQLAP4awG8AuBjpyjAXL2cfOswN\nAF7RJLsGwK1mdj6AW7P3vUYVwNVmdjGA5wH4/ew65WFsXSFnun0DpNc9yXLP0C8D8LCZPWJmcwA+\nC+DVy9yHjmFmtwM42iR+NYAbs9c3AnjNsnaqA5jZPjP7YfZ6HMADALYhB2PrIrnRbel1741tnuU2\n6NsA7G54vyeT5YktDQsM7wewZSU7s1RI7gDwLAB3Imdj6zB51+1cXfu86rWcol3E0hCing0jIjkM\n4PMA3mlmC5aJ7/WxicXT69c+z3q93AZ9L4DtDe/PymR54gDJrQCQ/T+4wv1ZFCRLSJX+02b2hUyc\ni7F1ibzrdi6ufd71erkN+l0Azif5FJJlAK8HcPMy96Hb3Azgyuz1lQC+tIJ9WRQkCeDjAB4wsw81\nfNTzY+siedftnr/2p4NeL3tiEclXAvhLpOtSXW9mf76sHeggJD8D4Aqk1doOAHgvgJsAfA7A2Uir\n773OzJodTKsaki8E8G0A9wKYX9PrPUifN/b02LpJXnRbet17Y5tHmaJCCJET5BQVQoicIIMuhBA5\nQQZdCCFyggy6EELkBBl0IYTICTLoQgiRE2TQhRAiJ8igCyFETvj/Rdvi85TfYeQAAAAASUVORK5C\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dbB+Zhx2uhhFCnQVbL+q1sA9WNArEF42wyBvRTzPZftwoNVHJ29exo9C8blvU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\nuEF3HMfJCG7QHcdxMsL/ByIw2e/QI4zxAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WR1kfzUbc3mi/hbpR9kugutPjyHqlmm5fq8cR++hYR4/Fv5S8GfR3AiUNivob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\nEuTQhRCiJMihCyFESfj/LSHP/x39g1kAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HW/jihZqxNyGyKvz/2iSeMmO55bIAwI/SOSGyAG43s5+NS6lEJpdiWyrSMVfoZvYagDPG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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/jY6MsAliES0eeyeHAllhX6yEfehGjxWHcFYsIxcpROAt/c+XncrokSIQboED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\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FPxI_NMj4xBN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "486f857f-e6af-46b3-f810-9e94c1c2e3df"
      },
      "source": [
        "plt.plot(losses, label = 'Training Losses')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Rb0JkKIHMjFiff374uG17e2PfIBpaO9nX3MG+5th9Q0vf4w5e3XmQ/c1Ddy8V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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}