street_view_house_numbers_classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "street view house numbers classification.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "am9bBqY9SJir",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#!pip uninstall tensorflow"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HgNycRxpSU_8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#!pip install tensorflow-gpu"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "acEMIzbC9Npp",
        "colab_type": "text"
      },
      "source": [
        "# Check for GPU Support"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-zJ3IapSScTy",
        "colab_type": "code",
        "outputId": "c2f5565e-0b9c-4748-d2d5-1ceeef3f4210",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "device_name = tf.test.gpu_device_name()\n",
        "if device_name != '/device:GPU:0':\n",
        "  raise SystemError('GPU device not found')\n",
        "print('Found GPU at: {}'.format(device_name))"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found GPU at: /device:GPU:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YU2fAiuN9Sqj",
        "colab_type": "text"
      },
      "source": [
        "# Connect to Google Drive and access the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pbxfxSIR3Amf",
        "colab_type": "code",
        "outputId": "e5690a62-81cc-4eb3-c131-ee6b7c34ef26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LKl6-Om_9YqW",
        "colab_type": "text"
      },
      "source": [
        "# Load the X_train, X_test, Y_train, Y_test, X_val and Y_val datasets from the h5py file\t"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dw7UYm7CUNmU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import h5py\n",
        "filename = '/content/gdrive/My Drive/Colab Notebooks/dataset/svhn/SVHN_single_grey1.h5'\n",
        "\n",
        "with h5py.File(filename, 'r') as f:\n",
        "    # Get the data\n",
        "    X_test = list(f['X_test'])\n",
        "    X_train = list(f['X_train'])\n",
        "    X_val = list(f['X_val'])\n",
        "    y_val = list(f['y_val'])\n",
        "    y_test = list(f['y_test'])\n",
        "    y_train = list(f['y_train'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TVtaOfhb9evU",
        "colab_type": "text"
      },
      "source": [
        "#Flatten the images for Keras\t& Normalize the inputs for X_train, X_test and X_val\t"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VaPmPCuroZxL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Images data pre-processing\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "import numpy as np\n",
        "from keras.utils import to_categorical\n",
        "\n",
        "X_train_scaled = [x/255 for x in X_train]\n",
        "X_val_scaled = [x/255 for x in X_val]\n",
        "X_test_scaled = [x/255 for x in X_test]\n",
        "\n",
        "X_train_scaled = np.asarray(X_train_scaled)\n",
        "X_val_scaled = np.asarray(X_val_scaled)\n",
        "X_test_scaled = np.asarray(X_test_scaled)\n",
        "\n",
        "X_train_scaled = X_train_scaled.reshape(len(X_train_scaled),32,32,1)\n",
        "X_val_scaled = X_val_scaled.reshape(len(X_val_scaled),32,32,1)\n",
        "X_test_scaled = X_test_scaled.reshape(len(X_test_scaled),32,32,1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ZBcRBrX9p9K",
        "colab_type": "text"
      },
      "source": [
        "#Convert the class matrices Y_train, Y_test and Y_val into one hot vectors\t"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f2dwHwFM9ktE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_val_scaled = np.asarray(y_val)\n",
        "y_test_scaled = np.asarray(y_test)\n",
        "y_train_scaled = np.asarray(y_train)\n",
        "\n",
        "y_val_scaled = to_categorical(y_val_scaled)\n",
        "y_test_scaled = to_categorical(y_test_scaled)\n",
        "y_train_scaled = to_categorical(y_train_scaled)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3DF0olzz9ti-",
        "colab_type": "text"
      },
      "source": [
        "#Print the train, test and val shapes\t"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZI_aEb9r9vAA",
        "colab_type": "code",
        "outputId": "663a815f-18fb-48a9-ff15-02153efcec00",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "print ('Shape of training data is ({:},{:})'.format(len(X_train_scaled), len(y_train_scaled)))\n",
        "print ('Shape of test data is ({:},{:})'.format(len(X_test_scaled), len(y_test_scaled)))\n",
        "print ('Shape of validation data is ({:},{:})'.format(len(X_val_scaled), len(y_val_scaled)))"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of training data is (42000,42000)\n",
            "Shape of test data is (18000,18000)\n",
            "Shape of validation data is (60000,60000)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uUp0ZJ1Q-bSs",
        "colab_type": "text"
      },
      "source": [
        "#Visualize the first 10 images in X_train and the corresponding Y_train labels\t"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YhUnhpBZ-czw",
        "colab_type": "code",
        "outputId": "d2a1406c-7956-4bb8-b82b-fba71eecd563",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "for i in range(10):\n",
        "  print ('Label is->{:}'.format(y_train[i]))\n",
        "  plt.imshow(X_train[i])\n",
        "  plt.show()"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Label is->2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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G+0At/8I3AYSu4qvuvqX+b/JAF0LMK5MGu7s/B4B3zhNC/Eowk7/ZP2tmu83sMTPjv6MI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\n7EIkwv8HBWiFlhK7ycwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Label is->6\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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A9l4w9l8B3F1/fDeAP18kP74E4N81eD16AFxbf9wB4HUAWxu9JhE/GromAAxA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\nULALkQgKdiESQcEuRCL8P3zU8OVik2NeAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Label is->7\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Label is->4\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UNtPkr+3kpUPUNvJaeHvlEi9guXw3L0a5so/Px+i9t6jt/Xf9NLj9/5TupWNaV8MZewAA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\nQiSCgl2IRPj/zvqQrsceieIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Label is->4\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Label is->0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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APNZ4/BiAP9wmPz4D4D81+XgMAnhn43EHgNcA3NnsYxLxo6nHBIABaG88LgJ4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\nIijYhUgEBbsQifD/AceC2SEGV+ZvAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Label is->3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Label is->0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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S1XmEXXckWm6qzmXW6fnwue4v8a+Uu/NcBs4TSXEpmLwGACWyzVgpp0otbPNIrr4lnd3d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\nP8KC7x3kNQaaAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Label is->7\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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en0S/Z95jAF4A8FcA9u2QH/8DwDMAnkY/2A6PwI970P+I/jSApwb/PjTqNQn8\nGOmaAPgV9Iu4Po3+G8sfXnfN/gjAiwD+J4DGjRxX36ATIhNy36ATIhsU7EJkgoJdiExQsAuRCQp2\nITJBwS5EJijYhcgEBbsQmfD/AaThVuDeezC8AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Label is->3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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O65JZpJYczdYCYGVeQ6/tRNiXgVler2/0Mn/Hta97G7X958GnqY2919sXkT03TfNsxNZT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\ngoJdiERQsAuRCAp2IRJBwS5EIvwzOFR5DRT1jfIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M1kNBeUZ_Oyt",
        "colab_type": "text"
      },
      "source": [
        "#In the train and test loop, define the hyperparameters for the model\t"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ULgMHRDcogGU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Lists to hold the Experiment details, acuracy and l2 regularization details for final comparision"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "30T4IjPFoYVs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_description = []\n",
        "model_accuracy = []\n",
        "model_l2_regularization = []\n",
        "model_lr = []\n",
        "model_dropout = []"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QJCF1RTw_Q5D",
        "colab_type": "text"
      },
      "source": [
        "#Create a Sequential model in Keras with input layer with the correct input shape, Hidden Layers, Output Layers and the activation functions\t"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kGO4DzdtopFP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D\n",
        "from keras import regularizers\n",
        "from keras.optimizers import Adam\n",
        "from keras.layers import Dropout\n",
        "from keras.constraints import maxnorm\n",
        "\n",
        "def build_model(l2_lambda=0.0001,\n",
        "                learning_rate=0.01,\n",
        "                dropout_rate=0.0\n",
        "                ):\n",
        "\n",
        "  l2 = None if l2_lambda is 0 else regularizers.l2(l2_lambda)\n",
        "\n",
        "  if l2 is None:\n",
        "    print ('Model is running without any L2 regularization')\n",
        "\n",
        "  model = Sequential()\n",
        "  model.add(Conv2D(64, (2,2), activation='relu', kernel_regularizer=l2, input_shape=(32,32,1)))\n",
        "  model.add(MaxPooling2D(2,2))\n",
        "\n",
        "  model.add(Conv2D(128, (2,2), activation='relu', kernel_regularizer=l2, ))\n",
        "  model.add(MaxPooling2D(2,2))\n",
        "\n",
        "  model.add(Flatten())\n",
        "\n",
        "  model.add(Dropout(dropout_rate))\n",
        "\n",
        "  model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "  model.summary()\n",
        "\n",
        "  # create optimizer\n",
        "  optimizer = Adam(lr=learning_rate)\n",
        "\n",
        "  #Define the optimizer to be used in this model\t\n",
        "  #Compile the model with the corresponding Loss and metrics to monitor\t\n",
        "  model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "snuuFjYSsM8r",
        "colab_type": "text"
      },
      "source": [
        "# Create Model - Low Lambda Value"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8u46QgF0sHAx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 781
        },
        "outputId": "0932d493-76c2-438a-f5cd-10bce14e1172"
      },
      "source": [
        "model_with_low_l2_lambda = build_model(0.001)\n",
        "\n",
        "#Fit the model\t\n",
        "history = model_with_low_l2_lambda.fit(X_train_scaled, y_train_scaled, epochs=10, validation_data=(X_val_scaled,y_val_scaled))"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_22\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_43 (Conv2D)           (None, 31, 31, 64)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_43 (MaxPooling (None, 15, 15, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_44 (Conv2D)           (None, 14, 14, 128)       32896     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_44 (MaxPooling (None, 7, 7, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_22 (Flatten)         (None, 6272)              0         \n",
            "_________________________________________________________________\n",
            "dropout_14 (Dropout)         (None, 6272)              0         \n",
            "_________________________________________________________________\n",
            "dense_29 (Dense)             (None, 10)                62730     \n",
            "=================================================================\n",
            "Total params: 95,946\n",
            "Trainable params: 95,946\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Train on 42000 samples, validate on 60000 samples\n",
            "Epoch 1/10\n",
            "42000/42000 [==============================] - 13s 315us/step - loss: 1.3637 - acc: 0.5793 - val_loss: 0.8370 - val_acc: 0.7795\n",
            "Epoch 2/10\n",
            "42000/42000 [==============================] - 12s 280us/step - loss: 0.8042 - acc: 0.7867 - val_loss: 0.7516 - val_acc: 0.8045\n",
            "Epoch 3/10\n",
            "42000/42000 [==============================] - 12s 285us/step - loss: 0.7472 - acc: 0.8025 - val_loss: 0.7206 - val_acc: 0.8134\n",
            "Epoch 4/10\n",
            "42000/42000 [==============================] - 12s 277us/step - loss: 0.7238 - acc: 0.8092 - val_loss: 0.7459 - val_acc: 0.7983\n",
            "Epoch 5/10\n",
            "42000/42000 [==============================] - 12s 285us/step - loss: 0.7093 - acc: 0.8115 - val_loss: 0.6981 - val_acc: 0.8192\n",
            "Epoch 6/10\n",
            "42000/42000 [==============================] - 12s 279us/step - loss: 0.6957 - acc: 0.8153 - val_loss: 0.7044 - val_acc: 0.8165\n",
            "Epoch 7/10\n",
            "42000/42000 [==============================] - 12s 277us/step - loss: 0.6868 - acc: 0.8206 - val_loss: 0.6829 - val_acc: 0.8190\n",
            "Epoch 8/10\n",
            "42000/42000 [==============================] - 11s 266us/step - loss: 0.6805 - acc: 0.8213 - val_loss: 0.6836 - val_acc: 0.8220\n",
            "Epoch 9/10\n",
            "42000/42000 [==============================] - 12s 277us/step - loss: 0.6737 - acc: 0.8221 - val_loss: 0.6950 - val_acc: 0.8152\n",
            "Epoch 10/10\n",
            "42000/42000 [==============================] - 13s 302us/step - loss: 0.6658 - acc: 0.8253 - val_loss: 0.6529 - val_acc: 0.8334\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nZ47DHIlAjUG",
        "colab_type": "text"
      },
      "source": [
        "#Visualizing Training Performance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vMKmrvnq59sl",
        "colab_type": "code",
        "outputId": "1c51a7b5-f833-4c8b-e7ea-a86d9c4e96e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "figure = plt.figure()\n",
        "\n",
        "ax1 = figure.add_subplot(211)\n",
        "ax1.plot(history.history['acc'])\n",
        "ax1.plot(history.history['val_acc'])\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend(labels=['Training','Validation'])\n",
        "\n",
        "ax1 = figure.add_subplot(212)\n",
        "ax1.plot(history.history['loss'])\n",
        "ax1.plot(history.history['val_loss'])\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend(labels=['Training','Validation'])\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZYwwkuY5AVZ8Hnj9m2rc7DSvwlUSXdM+8s4+axjZusqamjRkwVNWad+lHfSnu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\nirNEYIwxKc4SgTHGpLj/B21SqrR8wE7eAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "baTTVrzQAWB1",
        "colab_type": "text"
      },
      "source": [
        "#Low Lambda Model - Use model.evaluate() to return the score\t"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TrKgeGdXAWs5",
        "colab_type": "code",
        "outputId": "e6839a08-a534-41c8-ee6f-fd4bb7291a73",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "scores = model_with_low_l2_lambda.evaluate(X_test_scaled, y_test_scaled)\n",
        "m1_accuracy = model_with_low_l2_lambda.metrics_names[1], scores[1]*100\n",
        "print(\"%s: %.2f%%\" % (m1_accuracy))\n",
        "\n",
        "model_description.append('Model 1->Low L2 Lambda')\n",
        "model_accuracy.append(m1_accuracy)\n",
        "model_l2_regularization.append(.0001)\n",
        "model_lr.append(\"None\")\n",
        "model_dropout.append(\"None\")"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "18000/18000 [==============================] - 1s 79us/step\n",
            "acc: 82.01%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cU2BSAfVBHzf",
        "colab_type": "text"
      },
      "source": [
        "#Disable Regularization by setting appropriate value for Lambda and check the loss of the NN\t"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YJ7zQdiGApwi",
        "colab_type": "code",
        "outputId": "dc453841-82ea-4802-b658-4a3b17cf7fab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 799
        }
      },
      "source": [
        "model_without_l2_lambda = build_model(0)\n",
        "\n",
        "#Fit the model\t\n",
        "history = model_without_l2_lambda.fit(X_train_scaled, y_train_scaled, epochs=10, validation_data=(X_val_scaled,y_val_scaled))"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model is running without any L2 regularization\n",
            "Model: \"sequential_23\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_45 (Conv2D)           (None, 31, 31, 64)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_45 (MaxPooling (None, 15, 15, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_46 (Conv2D)           (None, 14, 14, 128)       32896     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_46 (MaxPooling (None, 7, 7, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_23 (Flatten)         (None, 6272)              0         \n",
            "_________________________________________________________________\n",
            "dropout_15 (Dropout)         (None, 6272)              0         \n",
            "_________________________________________________________________\n",
            "dense_30 (Dense)             (None, 10)                62730     \n",
            "=================================================================\n",
            "Total params: 95,946\n",
            "Trainable params: 95,946\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Train on 42000 samples, validate on 60000 samples\n",
            "Epoch 1/10\n",
            "42000/42000 [==============================] - 13s 306us/step - loss: 1.1594 - acc: 0.6381 - val_loss: 0.6811 - val_acc: 0.8115\n",
            "Epoch 2/10\n",
            "42000/42000 [==============================] - 11s 260us/step - loss: 0.6584 - acc: 0.8132 - val_loss: 0.6052 - val_acc: 0.8300\n",
            "Epoch 3/10\n",
            "42000/42000 [==============================] - 11s 271us/step - loss: 0.5940 - acc: 0.8321 - val_loss: 0.5565 - val_acc: 0.8459\n",
            "Epoch 4/10\n",
            "42000/42000 [==============================] - 11s 270us/step - loss: 0.5565 - acc: 0.8448 - val_loss: 0.5508 - val_acc: 0.8455\n",
            "Epoch 5/10\n",
            "42000/42000 [==============================] - 11s 266us/step - loss: 0.5277 - acc: 0.8512 - val_loss: 0.5029 - val_acc: 0.8626\n",
            "Epoch 6/10\n",
            "42000/42000 [==============================] - 11s 256us/step - loss: 0.5042 - acc: 0.8590 - val_loss: 0.5007 - val_acc: 0.8634\n",
            "Epoch 7/10\n",
            "42000/42000 [==============================] - 11s 262us/step - loss: 0.4843 - acc: 0.8634 - val_loss: 0.4715 - val_acc: 0.8708\n",
            "Epoch 8/10\n",
            "42000/42000 [==============================] - 11s 258us/step - loss: 0.4657 - acc: 0.8681 - val_loss: 0.4649 - val_acc: 0.8727\n",
            "Epoch 9/10\n",
            "42000/42000 [==============================] - 12s 276us/step - loss: 0.4480 - acc: 0.8729 - val_loss: 0.4501 - val_acc: 0.8764\n",
            "Epoch 10/10\n",
            "42000/42000 [==============================] - 11s 259us/step - loss: 0.4338 - acc: 0.8780 - val_loss: 0.4806 - val_acc: 0.8644\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MyJEHzFCDPYi",
        "colab_type": "text"
      },
      "source": [
        "# Zero Regularization - Use model.evaluate() to return the score\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bouS8mPqDTpq",
        "colab_type": "code",
        "outputId": "57a154d4-522a-4a0a-b20d-8ed39cd141ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "scores = model_without_l2_lambda.evaluate(X_test_scaled, y_test_scaled)\n",
        "m2_accuracy = model_without_l2_lambda.metrics_names[1], scores[1]*100\n",
        "print(\"%s: %.2f%%\" % (m1_accuracy))\n",
        "\n",
        "model_description.append('Model 2->Zero L2 Lambda')\n",
        "model_accuracy.append(m2_accuracy)\n",
        "model_l2_regularization.append(0)\n",
        "model_lr.append(\"None\")\n",
        "model_dropout.append(\"None\")"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "18000/18000 [==============================] - 1s 72us/step\n",
            "acc: 82.01%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FP3g5xF8DbaH",
        "colab_type": "text"
      },
      "source": [
        "#Increase the Regularization parameter (Lambda) and check how the loss is for the NN. Record findings\t\t"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IHerNflnDdZ8",
        "colab_type": "code",
        "outputId": "a6a24988-ce1d-44cd-b783-bb0327e7619e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 781
        }
      },
      "source": [
        "model_with_high_l2_lambda = build_model(0.05)\n",
        "\n",
        "#Fit the model\t\n",
        "history = model_with_high_l2_lambda.fit(X_train_scaled, y_train_scaled, epochs=10, validation_data=(X_val_scaled,y_val_scaled))"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_24\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_47 (Conv2D)           (None, 31, 31, 64)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_47 (MaxPooling (None, 15, 15, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_48 (Conv2D)           (None, 14, 14, 128)       32896     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_48 (MaxPooling (None, 7, 7, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_24 (Flatten)         (None, 6272)              0         \n",
            "_________________________________________________________________\n",
            "dropout_16 (Dropout)         (None, 6272)              0         \n",
            "_________________________________________________________________\n",
            "dense_31 (Dense)             (None, 10)                62730     \n",
            "=================================================================\n",
            "Total params: 95,946\n",
            "Trainable params: 95,946\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Train on 42000 samples, validate on 60000 samples\n",
            "Epoch 1/10\n",
            "42000/42000 [==============================] - 13s 307us/step - loss: 2.3885 - acc: 0.1001 - val_loss: 2.3026 - val_acc: 0.1000\n",
            "Epoch 2/10\n",
            "42000/42000 [==============================] - 12s 284us/step - loss: 2.3028 - acc: 0.0987 - val_loss: 2.3027 - val_acc: 0.1000\n",
            "Epoch 3/10\n",
            "42000/42000 [==============================] - 12s 276us/step - loss: 2.3027 - acc: 0.1003 - val_loss: 2.3026 - val_acc: 0.1000\n",
            "Epoch 4/10\n",
            "42000/42000 [==============================] - 11s 270us/step - loss: 2.3028 - acc: 0.1002 - val_loss: 2.3026 - val_acc: 0.1000\n",
            "Epoch 5/10\n",
            "42000/42000 [==============================] - 11s 273us/step - loss: 2.3027 - acc: 0.1001 - val_loss: 2.3026 - val_acc: 0.1000\n",
            "Epoch 6/10\n",
            "42000/42000 [==============================] - 11s 274us/step - loss: 2.3028 - acc: 0.0988 - val_loss: 2.3026 - val_acc: 0.1000\n",
            "Epoch 7/10\n",
            "42000/42000 [==============================] - 12s 293us/step - loss: 2.3028 - acc: 0.1000 - val_loss: 2.3027 - val_acc: 0.1000\n",
            "Epoch 8/10\n",
            "42000/42000 [==============================] - 11s 271us/step - loss: 2.3028 - acc: 0.1015 - val_loss: 2.3026 - val_acc: 0.1000\n",
            "Epoch 9/10\n",
            "42000/42000 [==============================] - 11s 272us/step - loss: 2.3028 - acc: 0.0999 - val_loss: 2.3027 - val_acc: 0.1000\n",
            "Epoch 10/10\n",
            "42000/42000 [==============================] - 12s 278us/step - loss: 2.3028 - acc: 0.0996 - val_loss: 2.3027 - val_acc: 0.1000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qh32l1SBDyFi",
        "colab_type": "text"
      },
      "source": [
        "#High Regularization - Use model.evaluate() to return the score\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4gV0IWsFDplA",
        "colab_type": "code",
        "outputId": "c2537b23-ee60-49dd-a1a4-247dc7391032",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "scores = model_with_high_l2_lambda.evaluate(X_test_scaled, y_test_scaled)\n",
        "m3_accuracy = model_with_high_l2_lambda.metrics_names[1], scores[1]*100\n",
        "print(\"%s: %.2f%%\" % (m1_accuracy))\n",
        "\n",
        "model_description.append('Model 3->High L2 Lambda')\n",
        "model_accuracy.append(m3_accuracy)\n",
        "model_l2_regularization.append(0.05)\n",
        "model_lr.append(\"None\")\n",
        "model_dropout.append(\"None\")"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "18000/18000 [==============================] - 1s 72us/step\n",
            "acc: 82.01%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fyMaN28TswmK",
        "colab_type": "text"
      },
      "source": [
        "# Hyper Parameter Tuning Using GridSearch "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zTxpzyPkExNe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "outputId": "0a4e77e6-a27c-4696-efe9-478daa4dba77"
      },
      "source": [
        "from keras.wrappers.scikit_learn import KerasClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "import time\n",
        "\n",
        "# define the grid search parameters\n",
        "learn_rate = [0.001,0.005,0.01,0.1]\n",
        "dropout_rate = [0.0,0.2,0.3]\n",
        "l2_lambda=[0.001,0.05,0.1]\n",
        "\n",
        "param_grid = dict(l2_lambda=l2_lambda,\n",
        "                  learning_rate=learn_rate,\n",
        "                  dropout_rate=dropout_rate)\n",
        "\n",
        "t0 = time.time()\n",
        "\n",
        "# create model\n",
        "model = KerasClassifier(build_fn=build_model, epochs=3, verbose=1)\n",
        "grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1)\n",
        "grid_result = grid.fit(X_train_scaled, y_train_scaled)\n",
        "\n",
        "t1 = time.time()\n",
        "\n",
        "print ('\\nTotal time taken is {:} sec'.format(t1-t0))"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/joblib/externals/loky/process_executor.py:706: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
            "  \"timeout or by a memory leak.\", UserWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_21\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_41 (Conv2D)           (None, 31, 31, 64)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_41 (MaxPooling (None, 15, 15, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_42 (Conv2D)           (None, 14, 14, 128)       32896     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_42 (MaxPooling (None, 7, 7, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_21 (Flatten)         (None, 6272)              0         \n",
            "_________________________________________________________________\n",
            "dropout_13 (Dropout)         (None, 6272)              0         \n",
            "_________________________________________________________________\n",
            "dense_28 (Dense)             (None, 10)                62730     \n",
            "=================================================================\n",
            "Total params: 95,946\n",
            "Trainable params: 95,946\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/3\n",
            "42000/42000 [==============================] - 8s 193us/step - loss: 1.3751 - acc: 0.5796\n",
            "Epoch 2/3\n",
            "42000/42000 [==============================] - 7s 164us/step - loss: 0.8082 - acc: 0.7846\n",
            "Epoch 3/3\n",
            "42000/42000 [==============================] - 7s 169us/step - loss: 0.7476 - acc: 0.8019\n",
            "\n",
            "Total time taken is 2519.1734795570374 sec\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BNnDOpKXUz3F",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 728
        },
        "outputId": "eddef6aa-ef98-49c3-f9f8-59c52250eede"
      },
      "source": [
        "# Hyper parameter tuning summarize results\n",
        "print(\"\\n\\nBest: %f using %s \\n\" % (grid_result.best_score_, grid_result.best_params_))\n",
        "means = grid_result.cv_results_['mean_test_score']\n",
        "stds = grid_result.cv_results_['std_test_score']\n",
        "params = grid_result.cv_results_['params']\n",
        "for mean, stdev, param in zip(means, stds, params):\n",
        "    print(\"%f (%f) with: %r\" % (mean, stdev, param))"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Best: 0.801262 using {'dropout_rate': 0.0, 'l2_lambda': 0.001, 'learning_rate': 0.01} \n",
            "\n",
            "0.798929 (0.009703) with: {'dropout_rate': 0.0, 'l2_lambda': 0.001, 'learning_rate': 0.001}\n",
            "0.799571 (0.004532) with: {'dropout_rate': 0.0, 'l2_lambda': 0.001, 'learning_rate': 0.005}\n",
            "0.801262 (0.008640) with: {'dropout_rate': 0.0, 'l2_lambda': 0.001, 'learning_rate': 0.01}\n",
            "0.799357 (0.007733) with: {'dropout_rate': 0.0, 'l2_lambda': 0.001, 'learning_rate': 0.1}\n",
            "0.099500 (0.002452) with: {'dropout_rate': 0.0, 'l2_lambda': 0.05, 'learning_rate': 0.001}\n",
            "0.098690 (0.002469) with: {'dropout_rate': 0.0, 'l2_lambda': 0.05, 'learning_rate': 0.005}\n",
            "0.101929 (0.000923) with: {'dropout_rate': 0.0, 'l2_lambda': 0.05, 'learning_rate': 0.01}\n",
            "0.099143 (0.002864) with: {'dropout_rate': 0.0, 'l2_lambda': 0.05, 'learning_rate': 0.1}\n",
            "0.100452 (0.002403) with: {'dropout_rate': 0.0, 'l2_lambda': 0.1, 'learning_rate': 0.001}\n",
            "0.100452 (0.002403) with: {'dropout_rate': 0.0, 'l2_lambda': 0.1, 'learning_rate': 0.005}\n",
            "0.098262 (0.001767) with: {'dropout_rate': 0.0, 'l2_lambda': 0.1, 'learning_rate': 0.01}\n",
            "0.099452 (0.003119) with: {'dropout_rate': 0.0, 'l2_lambda': 0.1, 'learning_rate': 0.1}\n",
            "0.795381 (0.004396) with: {'dropout_rate': 0.2, 'l2_lambda': 0.001, 'learning_rate': 0.001}\n",
            "0.788024 (0.008232) with: {'dropout_rate': 0.2, 'l2_lambda': 0.001, 'learning_rate': 0.005}\n",
            "0.793310 (0.005713) with: {'dropout_rate': 0.2, 'l2_lambda': 0.001, 'learning_rate': 0.01}\n",
            "0.801048 (0.003088) with: {'dropout_rate': 0.2, 'l2_lambda': 0.001, 'learning_rate': 0.1}\n",
            "0.100833 (0.002764) with: {'dropout_rate': 0.2, 'l2_lambda': 0.05, 'learning_rate': 0.001}\n",
            "0.100452 (0.002403) with: {'dropout_rate': 0.2, 'l2_lambda': 0.05, 'learning_rate': 0.005}\n",
            "0.100667 (0.001990) with: {'dropout_rate': 0.2, 'l2_lambda': 0.05, 'learning_rate': 0.01}\n",
            "0.099643 (0.002610) with: {'dropout_rate': 0.2, 'l2_lambda': 0.05, 'learning_rate': 0.1}\n",
            "0.100381 (0.003005) with: {'dropout_rate': 0.2, 'l2_lambda': 0.1, 'learning_rate': 0.001}\n",
            "0.100000 (0.002202) with: {'dropout_rate': 0.2, 'l2_lambda': 0.1, 'learning_rate': 0.005}\n",
            "0.099929 (0.002258) with: {'dropout_rate': 0.2, 'l2_lambda': 0.1, 'learning_rate': 0.01}\n",
            "0.100095 (0.002526) with: {'dropout_rate': 0.2, 'l2_lambda': 0.1, 'learning_rate': 0.1}\n",
            "0.788524 (0.009399) with: {'dropout_rate': 0.3, 'l2_lambda': 0.001, 'learning_rate': 0.001}\n",
            "0.790190 (0.004498) with: {'dropout_rate': 0.3, 'l2_lambda': 0.001, 'learning_rate': 0.005}\n",
            "0.790214 (0.011747) with: {'dropout_rate': 0.3, 'l2_lambda': 0.001, 'learning_rate': 0.01}\n",
            "0.784143 (0.007707) with: {'dropout_rate': 0.3, 'l2_lambda': 0.001, 'learning_rate': 0.1}\n",
            "0.098976 (0.002994) with: {'dropout_rate': 0.3, 'l2_lambda': 0.05, 'learning_rate': 0.001}\n",
            "0.100452 (0.002403) with: {'dropout_rate': 0.3, 'l2_lambda': 0.05, 'learning_rate': 0.005}\n",
            "0.100452 (0.002403) with: {'dropout_rate': 0.3, 'l2_lambda': 0.05, 'learning_rate': 0.01}\n",
            "0.099214 (0.003776) with: {'dropout_rate': 0.3, 'l2_lambda': 0.05, 'learning_rate': 0.1}\n",
            "0.101476 (0.001123) with: {'dropout_rate': 0.3, 'l2_lambda': 0.1, 'learning_rate': 0.001}\n",
            "0.098524 (0.002482) with: {'dropout_rate': 0.3, 'l2_lambda': 0.1, 'learning_rate': 0.005}\n",
            "0.098905 (0.003063) with: {'dropout_rate': 0.3, 'l2_lambda': 0.1, 'learning_rate': 0.01}\n",
            "0.100452 (0.002403) with: {'dropout_rate': 0.3, 'l2_lambda': 0.1, 'learning_rate': 0.1}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HuZXoNxApfEx",
        "colab_type": "text"
      },
      "source": [
        "# Results Comparision"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FJ2ORO0wRbz5",
        "colab_type": "text"
      },
      "source": [
        "#Set the best hyperparameters found in the previous step. Check the Network’s accuracy.\t"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "edQf85Y9C2lL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817
        },
        "outputId": "c9f61189-e77e-4e46-cc32-9d4502bf62e8"
      },
      "source": [
        "final_model = build_model(l2_lambda = 0.001,\n",
        "                          learning_rate = 0.01,\n",
        "                          dropout_rate = 0.0)\n",
        "\n",
        "#Fit the model\t\n",
        "history = final_model.fit(X_train_scaled, \n",
        "                          y_train_scaled, \n",
        "                          epochs=10, \n",
        "                          validation_data=(X_val_scaled,y_val_scaled))\n",
        "\n",
        "scores = final_model.evaluate(X_test_scaled, y_test_scaled)\n",
        "accuracy_final = final_model.metrics_names[1], scores[1]*100\n",
        "print(\"%s: %.2f%%\" % (accuracy_final))\n",
        "\n",
        "model_description.append('Final Model')\n",
        "model_accuracy.append(accuracy_final)\n",
        "model_l2_regularization.append(0.001)\n",
        "model_lr.append(0.01)\n",
        "model_dropout.append(0.0)"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_25\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_49 (Conv2D)           (None, 31, 31, 64)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_49 (MaxPooling (None, 15, 15, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_50 (Conv2D)           (None, 14, 14, 128)       32896     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_50 (MaxPooling (None, 7, 7, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_25 (Flatten)         (None, 6272)              0         \n",
            "_________________________________________________________________\n",
            "dropout_17 (Dropout)         (None, 6272)              0         \n",
            "_________________________________________________________________\n",
            "dense_32 (Dense)             (None, 10)                62730     \n",
            "=================================================================\n",
            "Total params: 95,946\n",
            "Trainable params: 95,946\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Train on 42000 samples, validate on 60000 samples\n",
            "Epoch 1/10\n",
            "42000/42000 [==============================] - 13s 308us/step - loss: 1.2266 - acc: 0.6408 - val_loss: 0.8013 - val_acc: 0.7952\n",
            "Epoch 2/10\n",
            "42000/42000 [==============================] - 11s 273us/step - loss: 0.7793 - acc: 0.7958 - val_loss: 0.7578 - val_acc: 0.8002\n",
            "Epoch 3/10\n",
            "42000/42000 [==============================] - 12s 274us/step - loss: 0.7303 - acc: 0.8097 - val_loss: 0.7194 - val_acc: 0.8139\n",
            "Epoch 4/10\n",
            "42000/42000 [==============================] - 12s 280us/step - loss: 0.6981 - acc: 0.8202 - val_loss: 0.6906 - val_acc: 0.8238\n",
            "Epoch 5/10\n",
            "42000/42000 [==============================] - 12s 276us/step - loss: 0.6776 - acc: 0.8257 - val_loss: 0.6625 - val_acc: 0.8318\n",
            "Epoch 6/10\n",
            "42000/42000 [==============================] - 12s 288us/step - loss: 0.6599 - acc: 0.8320 - val_loss: 0.7057 - val_acc: 0.8168\n",
            "Epoch 7/10\n",
            "42000/42000 [==============================] - 11s 271us/step - loss: 0.6518 - acc: 0.8341 - val_loss: 0.6382 - val_acc: 0.8426\n",
            "Epoch 8/10\n",
            "42000/42000 [==============================] - 11s 268us/step - loss: 0.6356 - acc: 0.8399 - val_loss: 0.6450 - val_acc: 0.8362\n",
            "Epoch 9/10\n",
            "42000/42000 [==============================] - 12s 275us/step - loss: 0.6257 - acc: 0.8427 - val_loss: 0.6233 - val_acc: 0.8458\n",
            "Epoch 10/10\n",
            "42000/42000 [==============================] - 12s 282us/step - loss: 0.6138 - acc: 0.8473 - val_loss: 0.6054 - val_acc: 0.8515\n",
            "18000/18000 [==============================] - 1s 77us/step\n",
            "acc: 83.47%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "soOyFjwgVhou",
        "colab_type": "text"
      },
      "source": [
        "# Summary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tRnacUA_pgwQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177
        },
        "outputId": "ce313883-2ee5-4eed-9600-c8686cb15167"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "results_df = pd.DataFrame(list(zip(model_description, \n",
        "                                   model_accuracy,\n",
        "                                   model_l2_regularization,                                    \n",
        "                                   model_lr,\n",
        "                                   model_dropout)),\n",
        "                          columns=['Description',                                   \n",
        "                                   'Accuracy',\n",
        "                                   'L2 Lambda',                                   \n",
        "                                   'Learning Rate',\n",
        "                                   'Dropout'])\n",
        "\n",
        "print ('\\n==============Final Results==========================')\n",
        "print (results_df.head(5))\n",
        "\n"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "==============Final Results==========================\n",
            "               Description                  Accuracy  ...  Learning Rate Dropout\n",
            "0   Model 1->Low L2 Lambda  (acc, 82.00555555555556)  ...           None    None\n",
            "1  Model 2->Zero L2 Lambda  (acc, 84.56666666666666)  ...           None    None\n",
            "2  Model 3->High L2 Lambda               (acc, 9.55)  ...           None    None\n",
            "3              Final Model  (acc, 83.46666666666667)  ...           0.01       0\n",
            "\n",
            "[4 rows x 5 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J3DvidsyVYCE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}