05. Preventing Overfitting/03. DropConnect - MXNet.ipynb

{
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  "metadata": {
    "colab": {
      "name": "DropConnect - 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": "498a59f0-6893-491e-f1e3-18834c017d35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155
        }
      },
      "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: 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: requests>=2.20.0 in /usr/local/lib/python3.6/dist-packages (from mxnet-cu100) (2.21.0)\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: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20.0->mxnet-cu100) (2019.3.9)\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",
            "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"
          ],
          "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",
        "data_ctx = mx.cpu()\n",
        "model_ctx = mx.gpu()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "huuK9vLaWEcO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "outputId": "ec3b997e-e90a-4df7-b53b-6429938ffc5a"
      },
      "source": [
        "def transform(data, label):\n",
        "    return data.astype('float32')/255.0, label.astype('float32')\n",
        "\n",
        "train_mnist = gluon.data.vision.MNIST(train = True, transform = transform)\n",
        "test_mnist = gluon.data.vision.MNIST(train = False, transform = transform)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading /root/.mxnet/datasets/mnist/train-images-idx3-ubyte.gz from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/mnist/train-images-idx3-ubyte.gz...\n",
            "Downloading /root/.mxnet/datasets/mnist/train-labels-idx1-ubyte.gz from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/mnist/train-labels-idx1-ubyte.gz...\n",
            "Downloading /root/.mxnet/datasets/mnist/t10k-images-idx3-ubyte.gz from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/mnist/t10k-images-idx3-ubyte.gz...\n",
            "Downloading /root/.mxnet/datasets/mnist/t10k-labels-idx1-ubyte.gz from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/mnist/t10k-labels-idx1-ubyte.gz...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sAXcT8nSWEYo",
        "colab_type": "code",
        "outputId": "28e8241d-35c8-4a9e-a188-8323fc8b7796",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "image, label = train_mnist[798]\n",
        "print(image.shape, label.shape)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(28, 28, 1) ()\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SIQSLxiFWEXg",
        "colab_type": "code",
        "outputId": "fde444c1-0b1a-483c-d60f-7ce821ec6663",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "plt.imshow(image.reshape((28, 28)).asnumpy())\n",
        "plt.title(label)\n",
        "plt.show()\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Jn5G0soE+3sf2aHEhRrZHJZ2v4Zt6fKWkJcXyEkkPNtjLewzLtO3tppVXw8/d0E13HxED\nv0m6UFNX/P9X0t800UObvj4k6dnitr7p3iTdramXgbs1dW3kSklHSFolaaOkH0qaO0S9/YuktZKe\n01TQ5jfU29maekn/nKQ1xe3Cpp+7kr4aed74eC+QFBf8gKQIP5AU4QeSIvxAUoQfSIrwA0kRfiCp\n/wcdBFP3ZvnQXQAAAABJRU5ErkJggg==\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 = 64\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": "markdown",
      "metadata": {
        "id": "n6LpOyKS9gKf",
        "colab_type": "text"
      },
      "source": [
        "###Understanding dropout function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u7Vdh54QWESq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "weight_scale = .01\n",
        "\n",
        "#  Allocate parameters for the first hidden layer\n",
        "W1 = nd.random_normal(shape = (784, 256), scale=weight_scale, ctx=model_ctx)\n",
        "b1 = nd.random_normal(shape = 256, scale=weight_scale, ctx=model_ctx)\n",
        "\n",
        "#  Allocate parameters for the second hidden layer\n",
        "W2 = nd.random_normal(shape = (256, 128), scale=weight_scale, ctx=model_ctx)\n",
        "b2 = nd.random_normal(shape = 128, scale=weight_scale, ctx=model_ctx)\n",
        "\n",
        "#  Allocate parameters for the output layer\n",
        "W3 = nd.random_normal(shape=(128, 10), scale=weight_scale, ctx=model_ctx)\n",
        "b3 = nd.random_normal(shape=10, scale=weight_scale, ctx=model_ctx)\n",
        "\n",
        "params = [W1, b1, W2, b2, W3, b3]\n",
        "\n",
        "for param in params:\n",
        "    param.attach_grad()\n",
        "    \n",
        "def dropconnect(weight, drop_prob = 0.5):\n",
        "    # drop_prob is what is the probability that a each neuron will get droped\n",
        "    keep_prob = 1 - drop_prob\n",
        "    mask = nd.random.uniform(0, 1, weight.shape).as_in_context(model_ctx) < keep_prob\n",
        "    scale = 1/keep_prob if keep_prob > 0.0 else 0.0\n",
        "    return weight * mask * scale\n",
        "\n",
        "def neuralnetworks(inputs):\n",
        "    layer1 = nd.relu(nd.dot(inputs, dropconnect(W1, 0.2)) + b1)\n",
        "    layer2 = nd.relu(nd.dot(layer1, dropconnect(W2, 0.2)) + b2)\n",
        "    return nd.softmax(nd.dot(layer2, dropconnect(W3, 0.2)) + b3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "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 categoricalcrossentropy(predictions, labels):\n",
        "    return -nd.sum(labels * nd.log(predictions))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3qr1NTDkE7Kh",
        "colab_type": "text"
      },
      "source": [
        "##Defining our parameters optimizer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hvFKa4WuI9c-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def sgd(params, lr):\n",
        "    for param in params:\n",
        "        param[:] = param - lr * param.grad"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lbHoiGM3ZrQY",
        "colab_type": "text"
      },
      "source": [
        "##Defining our accuracy function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-3osTW97ZuHR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def accuracy(predictions, labels):\n",
        "    return nd.mean(predictions.argmax(1) == labels.argmax(1))"
      ],
      "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": "91119fa5-a71e-4497-9802-2af25f263cab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        }
      },
      "source": [
        "epochs = 10\n",
        "num_batches = 60000/batch_size\n",
        "learning_rate = .001\n",
        "losses = []\n",
        "accs = []\n",
        "\n",
        "for epoch in range(epochs):\n",
        "    cumulative_loss = 0\n",
        "    cumulative_acc = 0\n",
        "    for features, labels in training_data:\n",
        "        features = features.as_in_context(model_ctx).reshape((-1, 784))\n",
        "        labels = labels.as_in_context(model_ctx).one_hot(10)\n",
        "        with autograd.record():\n",
        "            output = neuralnetworks(features)\n",
        "            loss = categoricalcrossentropy(output, labels)\n",
        "        loss.backward()\n",
        "        sgd(params, learning_rate)\n",
        "        cumulative_loss += loss\n",
        "        cumulative_acc += accuracy(output, labels)\n",
        "    print(f'Epoch: {epoch} Loss: {cumulative_loss.asscalar()/num_batches} Accuracy: {cumulative_acc.asscalar()/num_batches}')\n",
        "    losses.append(cumulative_loss.asscalar()/num_batches)\n",
        "    accs.append(cumulative_acc.asscalar()/num_batches)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 0 Loss: 95.8028 Accuracy: 0.46055\n",
            "Epoch: 1 Loss: 26.080225 Accuracy: 0.8785\n",
            "Epoch: 2 Loss: 17.2777625 Accuracy: 0.9205833333333333\n",
            "Epoch: 3 Loss: 12.753277083333334 Accuracy: 0.9419333333333333\n",
            "Epoch: 4 Loss: 10.328601041666667 Accuracy: 0.9530166666666666\n",
            "Epoch: 5 Loss: 8.5542140625 Accuracy: 0.9609166666666666\n",
            "Epoch: 6 Loss: 7.36605 Accuracy: 0.9667\n",
            "Epoch: 7 Loss: 6.646780729166666 Accuracy: 0.9692666666666667\n",
            "Epoch: 8 Loss: 6.008209375 Accuracy: 0.9727333333333333\n",
            "Epoch: 9 Loss: 5.419719270833333 Accuracy: 0.9753666666666667\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JJOc7DOepwEE",
        "colab_type": "text"
      },
      "source": [
        "##Lets see test accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dvvQC3oQYu5A",
        "colab_type": "code",
        "outputId": "a0a374ac-9962-493d-ce18-fe661dc85f50",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "acc = 0.0\n",
        "batches = 0\n",
        "for features, labels in testing_data:\n",
        "    features = features.as_in_context(model_ctx).reshape((-1, 784))\n",
        "    labels = labels.as_in_context(model_ctx).one_hot(10)\n",
        "    predictions = neuralnetworks(features)\n",
        "    acc += accuracy(predictions, labels)\n",
        "    batches += 1\n",
        "print(f'Test Accuracy: {acc.asscalar()/batches}')"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test Accuracy: 0.9713375796178344\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hIBgjWVBYu3t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot(losses = losses, accs = accs, model = neuralnetworks, samples = 5):\n",
        "    plt.plot(losses, 'r')\n",
        "    plt.title('Loss during Training')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.show()\n",
        "    \n",
        "    plt.plot(accs, 'g')\n",
        "    plt.title('Accuracy during Training')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.show()\n",
        "    \n",
        "    for i, batch in enumerate(test_mnist):\n",
        "        features = batch[0].as_in_context(model_ctx).reshape((1, 784))\n",
        "        labels = batch[1]\n",
        "        predictions = model(features).argmax(1).asscalar()\n",
        "        plt.imshow(features.reshape((28, 28)).asnumpy())\n",
        "        plt.title(f'Predicted: {predictions}, Target: {labels}')\n",
        "        plt.show()\n",
        "        if i == samples:\n",
        "            break"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mkorWu1EYu2b",
        "colab_type": "code",
        "outputId": "d9d94882-670e-4d50-bafa-8d272b654214",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3477
        }
      },
      "source": [
        "plot(samples = 10)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kZg2h3EnqNknXSdqcPa6V1Fbp4urOmDFumDOzhlHuENO3geuBqdnjJ9k6665QgDvvhL17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\nZiU5IMzMrCQHhJmZleSAMDOzkv4/k+EOSHThP1IAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Q2HNgD49teMydzGZW8RwKePhNM7M2DgXcyWxm1sahQDLS2vTR05kyakrepZiZ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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5EzdwzL6F9fMv+W5hfYiKf4VOW3xGxdped/v7+q3kLb9Zohx+s0Q5/GaJcvjNEuXwmyXK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\n+M0S5fCbJcrhN0uUw2+WqP8HMSMTgWJpncQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8Zoly+M0S5fCbJcrhN0uUw2+WKJ/n3wZsnLRTxdrIl6e0sZPX2vzsmoq12FB++TaNLn//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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Ov1miHH6zRPmQXmuqB859dcXa/Yd8tanzvmHt+Iq1S884pvC+ox4sPlx4S+A1v1miHH6z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\nawvz0N0dgQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4zTLl8JtlyuE3y5R/ursVfnlPaXnLGr9tPHu/E0vry3fu3nH+rc/7eVP3X/L9XavW7txr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\nZcrhN8uUw2+Wqf8HUhwCDoDKAlkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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F+YCh5psnaUDSwHa2NlWsmbVOXeGXNJoi+N+IiOvT5HWSJqX2ScD6oeaNiP6I6IuIvtGM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\nMuXwm2XK4TfLlMNvlimH3yxTDr9Zpv4fvKOx8+BAmnEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NMuXwm2XK4TfLlMNvlimH3yxT/kqvJb00+SPJ+uP7XJysz1v9ZtXa0Bffbqgnaw1v+c0y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\nDr9Zphx+s0w5/GaZ+n8ipiWeX9R7+gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Op/o0pPjEx5pD9ipb++R3f1Y47RaDij/WPHmLVwrrQzW4sL6p2sCGwvoeN59ZWN/py7+t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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}