H4_InitialModel&HyperparameterTuning.ipynb

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  "metadata": {
    "colab": {
      "name": "H4_InitialModel&HyperparameterTuning.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "VvFa1ESkKa3n",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        },
        "outputId": "c5cbc01f-6c03-4042-8b68-125468a0bb66"
      },
      "source": [
        "!pip install scikit-optimize"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting scikit-optimize\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f4/44/60f82c97d1caa98752c7da2c1681cab5c7a390a0fdd3a55fac672b321cac/scikit_optimize-0.5.2-py2.py3-none-any.whl (74kB)\n",
            "\r\u001b[K     |████▍                           | 10kB 17.4MB/s eta 0:00:01\r\u001b[K     |████████▉                       | 20kB 3.0MB/s eta 0:00:01\r\u001b[K     |█████████████▏                  | 30kB 4.4MB/s eta 0:00:01\r\u001b[K     |█████████████████▋              | 40kB 2.9MB/s eta 0:00:01\r\u001b[K     |██████████████████████          | 51kB 3.6MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▍     | 61kB 4.3MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▊ | 71kB 4.9MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 81kB 5.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from scikit-optimize) (1.16.5)\n",
            "Requirement already satisfied: scipy>=0.14.0 in /usr/local/lib/python3.6/dist-packages (from scikit-optimize) (1.3.1)\n",
            "Requirement already satisfied: scikit-learn>=0.19.1 in /usr/local/lib/python3.6/dist-packages (from scikit-optimize) (0.21.3)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.19.1->scikit-optimize) (0.14.0)\n",
            "Installing collected packages: scikit-optimize\n",
            "Successfully installed scikit-optimize-0.5.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xVdu6Cz2F1k7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "54cdc633-1bfe-42d2-b746-8f11c30c33d8"
      },
      "source": [
        "# Importing Libraries\n",
        "\n",
        "import tensorflow\n",
        "from tensorflow.python.keras import backend \n",
        "\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
        "from keras import optimizers\n",
        "from keras.utils import np_utils\n",
        "from keras.optimizers import Adam\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pylab as plt\n",
        "\n",
        "import skopt\n",
        "from skopt import gbrt_minimize, gp_minimize\n",
        "from skopt.utils import use_named_args\n",
        "from skopt.space import Real, Categorical, Integer\n",
        "\n",
        "import gzip\n",
        "import os\n",
        "import sys\n",
        "import struct\n",
        "import numpy as np"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
            "  warnings.warn(msg, category=DeprecationWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WlwSlVU2GFdA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Extracting Files online\n",
        "def read_image(fi):\n",
        "    channel = 1\n",
        "    magic, n, rows, columns = struct.unpack(\">IIII\", fi.read(16))\n",
        "    assert magic == 0x00000803\n",
        "    assert rows == 28\n",
        "    assert columns == 28\n",
        "    rawbuffer = fi.read()\n",
        "    assert len(rawbuffer) == n * rows * columns * channel\n",
        "    rawdata = np.frombuffer(rawbuffer, dtype='>u1', count=n*rows*columns*channel)\n",
        "    return rawdata.reshape(n, rows, columns, channel).astype(np.float32) / 255.0\n",
        "\n",
        "def read_label(fi):\n",
        "    magic, n = struct.unpack(\">II\", fi.read(8))\n",
        "    assert magic == 0x00000801\n",
        "    rawbuffer = fi.read()\n",
        "    assert len(rawbuffer) == n\n",
        "    return np.frombuffer(rawbuffer, dtype='>u1', count=n)\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    os.system('wget -N http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz')\n",
        "    os.system('wget -N http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz')\n",
        "    os.system('wget -N http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz')\n",
        "    os.system('wget -N http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz')\n",
        "    \n",
        "    np.savez_compressed(\n",
        "        'mnist',\n",
        "        train_x=read_image(gzip.open('train-images-idx3-ubyte.gz', 'rb')),\n",
        "        train_y=read_label(gzip.open('train-labels-idx1-ubyte.gz', 'rb')),\n",
        "        test_x=read_image(gzip.open('t10k-images-idx3-ubyte.gz', 'rb')),\n",
        "        test_y=read_label(gzip.open('t10k-labels-idx1-ubyte.gz', 'rb'))\n",
        "    )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NVgDB-b5GNOj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 82
        },
        "outputId": "72d867da-9dc3-49c5-ceae-6d2d38d6f5aa"
      },
      "source": [
        "# Load the dataset\n",
        "\n",
        "data = np.load('mnist.npz')\n",
        "\n",
        "X_train = data['train_x']\n",
        "y_train = data['train_y']\n",
        "\n",
        "X_test = data['test_x']\n",
        "y_test = data['test_y']\n",
        "\n",
        "y_train = np_utils.to_categorical(y_train, 10)\n",
        "y_test = np_utils.to_categorical(y_test, 10)\n",
        "\n",
        "print(X_train.shape, X_train.dtype)\n",
        "print(y_train.shape, y_train.dtype)\n",
        "print(X_test.shape, X_test.dtype)\n",
        "print(y_test.shape, y_test.dtype)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 28, 28, 1) float32\n",
            "(60000, 10) float32\n",
            "(10000, 28, 28, 1) float32\n",
            "(10000, 10) float32\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8AgmHrMTGlsb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build Initial Model\n",
        "\n",
        "def build_model():\n",
        "  model = Sequential()\n",
        "  # add Convolutional layers\n",
        "  model.add(Conv2D(filters=32, kernel_size=(3,3), activation='relu', padding='same',\n",
        "                     input_shape=(28,28,1)))\n",
        "  model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "  model.add(Conv2D(filters=64, kernel_size=(3,3), activation='sigmoid', padding='same'))\n",
        "  model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "  model.add(Conv2D(filters=64, kernel_size=(3,3), activation='relu', padding='same'))\n",
        "  model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "  model.add(Flatten())\n",
        "  # Densely connected layers\n",
        "  model.add(Dense(128, activation='relu'))\n",
        "  model.add(Dense(10,activation='softmax',name=\"output_layer\"))\n",
        "  adam = Adam(lr=1e-2, decay= 1e-6)\n",
        "  model.compile(optimizer = adam, loss='categorical_crossentropy', metrics=[\"accuracy\"])\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j4b9nOhvGtu0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "7f065561-3438-42cd-cb0f-e0f020e85993"
      },
      "source": [
        "# Fit and Evaluate the Model\n",
        "\n",
        "initial_model = build_model()\n",
        "initial_model.summary()\n",
        "\n",
        "initial_cnn_model = initial_model.fit(X_train, y_train, batch_size=128, epochs =4, validation_split=.15)\n",
        "\n",
        "loss, accuracy = initial_model.evaluate(X_test,y_test)\n",
        "print('Accuracy = ', accuracy)\n",
        "print('Loss = ', loss)\n",
        "\n",
        "y_Predict = initial_model.predict(X_test)\n",
        "\n",
        "print(\"Misclassification = \", (np.argmax(y_Predict, axis=1) != np.argmax(y_test,axis=1)).sum())\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4267: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3576: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n",
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_1 (Conv2D)            (None, 28, 28, 32)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 14, 14, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 7, 7, 64)          36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 3, 3, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 576)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 128)               73856     \n",
            "_________________________________________________________________\n",
            "output_layer (Dense)         (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 130,890\n",
            "Trainable params: 130,890\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3005: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "Train on 51000 samples, validate on 9000 samples\n",
            "Epoch 1/4\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n",
            "51000/51000 [==============================] - 13s 260us/step - loss: 0.4158 - acc: 0.8725 - val_loss: 0.0992 - val_acc: 0.9712\n",
            "Epoch 2/4\n",
            "51000/51000 [==============================] - 6s 118us/step - loss: 0.1033 - acc: 0.9674 - val_loss: 0.0713 - val_acc: 0.9799\n",
            "Epoch 3/4\n",
            "51000/51000 [==============================] - 6s 117us/step - loss: 0.0749 - acc: 0.9766 - val_loss: 0.0773 - val_acc: 0.9783\n",
            "Epoch 4/4\n",
            "51000/51000 [==============================] - 6s 118us/step - loss: 0.0682 - acc: 0.9787 - val_loss: 0.0739 - val_acc: 0.9781\n",
            "10000/10000 [==============================] - 1s 99us/step\n",
            "Accuracy =  0.9786\n",
            "Loss =  0.06633278900738805\n",
            "Misclassification =  214\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F4YCt-p5HD_Z",
        "colab_type": "text"
      },
      "source": [
        "## Hyperparameter Optimization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bgbm_clhHIg2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dim_learning_rate = Real(low=1e-4, high=1e-1, prior='log-uniform',\n",
        "                         name='learning_rate')\n",
        "dim_num_dense_layers = Integer(low=1, high=3, name='num_dense_layers')\n",
        "dim_num_input_nodes = Integer(low=32, high=128, name='num_input_nodes')\n",
        "dim_num_dense_nodes = Integer(low=32, high=128, name='num_dense_nodes')\n",
        "dim_activation = Categorical(categories=['tanh','sigmoid','relu', 'softmax'],name='activation')\n",
        "dim_batch_size = Integer(low=64, high=128, name='batch_size')\n",
        "dim_adam_decay = Real(low=1e-6,high=1e-2,name=\"adam_decay\")\n",
        "\n",
        "dimensions = [dim_learning_rate,\n",
        "              dim_num_dense_layers,\n",
        "              dim_num_input_nodes,\n",
        "              dim_num_dense_nodes,\n",
        "              dim_activation,\n",
        "              dim_batch_size,\n",
        "              dim_adam_decay\n",
        "             ]\n",
        "default_parameters = [1e-2, 1,32, 128, 'relu',64, 1e-6]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WUto2JudIGd0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Model for Hyperparameter optimization\n",
        "\n",
        "from keras.optimizers import Adam\n",
        "def build_model(learning_rate, num_dense_layers,num_input_nodes,num_dense_nodes, \n",
        "                 activation, adam_decay):\n",
        "  model = Sequential()\n",
        "  # add Convolutional layers\n",
        "  model.add(Conv2D(filters=32, kernel_size=(3,3), activation=activation, padding='same',\n",
        "                     input_shape=(28,28,1)))\n",
        "  model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "  model.add(Conv2D(filters=64, kernel_size=(3,3), activation=activation, padding='same'))\n",
        "  model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "  model.add(Conv2D(filters=64, kernel_size=(3,3), activation=activation, padding='same'))\n",
        "  model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "  model.add(Flatten())\n",
        "  # Densely connected layers\n",
        "  model.add(Dense(num_input_nodes, activation=activation))\n",
        "  for i in range(num_dense_layers):\n",
        "        name = 'layer_dense_{0}'.format(i+1)\n",
        "        model.add(Dense(num_dense_nodes,\n",
        "                 activation=activation,\n",
        "                        name=name\n",
        "                 ))\n",
        "  model.add(Dense(10,activation='softmax',name=\"output_layer\"))\n",
        "  #setup our optimizer and compile\n",
        "  adam = Adam(lr=learning_rate, decay= adam_decay)\n",
        "  model.compile(optimizer = adam, loss='categorical_crossentropy', metrics=[\"accuracy\"])\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WCS7tO60JA8s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Perform optimization\n",
        "\n",
        "@use_named_args(dimensions=dimensions)\n",
        "def fitness(learning_rate, num_dense_layers, num_input_nodes, \n",
        "            num_dense_nodes,activation, batch_size,adam_decay):\n",
        "\n",
        "    model = build_model(learning_rate=learning_rate,\n",
        "                         num_dense_layers=num_dense_layers,\n",
        "                         num_input_nodes=num_input_nodes,\n",
        "                         num_dense_nodes=num_dense_nodes,\n",
        "                         activation=activation,\n",
        "                         adam_decay=adam_decay\n",
        "                        )\n",
        "    \n",
        "\n",
        "    #named blackbox becuase it represents the structure\n",
        "    blackbox = model.fit(x=X_train,\n",
        "                        y=y_train,\n",
        "                        epochs=3,\n",
        "                        batch_size=batch_size,\n",
        "                        validation_split=0.15,\n",
        "                        )\n",
        "    \n",
        "    #return the validation accuracy for the last epoch.\n",
        "    count = 0\n",
        "    train_accuracy = blackbox.history['acc'][-1]\n",
        "    train_loss = blackbox.history['loss'][-1]\n",
        "    val_accuracy = blackbox.history['val_acc'][-1]\n",
        "    val_loss = blackbox.history['val_loss'][-1]\n",
        "  \n",
        "    # Print the classification accuracy.\n",
        "    print(\"Training Accuracy: {0:.2%}\".format(train_accuracy))\n",
        "    print(\"Training Loss: {0:.2%}\".format(train_loss))\n",
        "    print(\"Validation Accuracy: {0:.2%}\".format(val_accuracy))\n",
        "    print(\"Validation Loss: {0:.2%}\".format(val_loss))\n",
        "    print()\n",
        "\n",
        "\n",
        "    # Delete the Keras model with these hyper-parameters from memory.\n",
        "    del model\n",
        "    \n",
        "    # Clear the Keras session, otherwise it will keep adding new\n",
        "    # models to the same TensorFlow graph each time we create\n",
        "    # a model with a different set of hyper-parameters.\n",
        "    backend.clear_session()\n",
        "    tensorflow.reset_default_graph()\n",
        "    \n",
        "    return val_accuracy"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MPk0_5UlJPpB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "backend.clear_session()\n",
        "tensorflow.reset_default_graph()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oJQfbP4IJOir",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "49c672b0-a10f-4a7a-831c-27a2451e5c4d"
      },
      "source": [
        "gp_result = gp_minimize(func=fitness,\n",
        "                            dimensions=dimensions,\n",
        "                            n_calls=15,\n",
        "                            noise= 0.01,\n",
        "                            n_jobs=-1,\n",
        "                            kappa = 5,\n",
        "                            x0=default_parameters)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 51000 samples, validate on 9000 samples\n",
            "Epoch 1/3\n",
            "51000/51000 [==============================] - 9s 185us/step - loss: 0.1131 - acc: 0.9669 - val_loss: 0.0795 - val_acc: 0.9790\n",
            "Epoch 2/3\n",
            "51000/51000 [==============================] - 9s 178us/step - loss: 0.0852 - acc: 0.9756 - val_loss: 0.0694 - val_acc: 0.9791\n",
            "Epoch 3/3\n",
            "51000/51000 [==============================] - 9s 179us/step - loss: 0.0815 - acc: 0.9760 - val_loss: 0.1085 - val_acc: 0.9718\n",
            "Training Accuracy: 97.60%\n",
            "Training Loss: 8.15%\n",
            "Validation Accuracy: 97.18%\n",
            "Validation Loss: 10.85%\n",
            "\n",
            "Train on 51000 samples, validate on 9000 samples\n",
            "Epoch 1/3\n",
            "51000/51000 [==============================] - 8s 151us/step - loss: 0.0534 - acc: 0.9837 - val_loss: 0.0618 - val_acc: 0.9838\n",
            "Epoch 2/3\n",
            "51000/51000 [==============================] - 7s 134us/step - loss: 0.0511 - acc: 0.9850 - val_loss: 0.0670 - val_acc: 0.9840\n",
            "Epoch 3/3\n",
            "51000/51000 [==============================] - 7s 138us/step - loss: 0.0526 - acc: 0.9849 - val_loss: 0.0732 - val_acc: 0.9820\n",
            "Training Accuracy: 98.49%\n",
            "Training Loss: 5.26%\n",
            "Validation Accuracy: 98.20%\n",
            "Validation Loss: 7.32%\n",
            "\n",
            "Train on 51000 samples, validate on 9000 samples\n",
            "Epoch 1/3\n",
            "51000/51000 [==============================] - 7s 147us/step - loss: 0.0630 - acc: 0.9827 - val_loss: 0.0748 - val_acc: 0.9811\n",
            "Epoch 2/3\n",
            "51000/51000 [==============================] - 7s 139us/step - loss: 0.0606 - acc: 0.9831 - val_loss: 0.0719 - val_acc: 0.9846\n",
            "Epoch 3/3\n",
            "51000/51000 [==============================] - 7s 142us/step - loss: 0.0578 - acc: 0.9837 - val_loss: 0.0610 - val_acc: 0.9848\n",
            "Training Accuracy: 98.37%\n",
            "Training Loss: 5.78%\n",
            "Validation Accuracy: 98.48%\n",
            "Validation Loss: 6.10%\n",
            "\n",
            "Train on 51000 samples, validate on 9000 samples\n",
            "Epoch 1/3\n",
            "51000/51000 [==============================] - 8s 155us/step - loss: 0.0564 - acc: 0.9841 - val_loss: 0.0711 - val_acc: 0.9836\n",
            "Epoch 2/3\n",
            "51000/51000 [==============================] - 7s 146us/step - loss: 0.0610 - acc: 0.9838 - val_loss: 0.0986 - val_acc: 0.9772\n",
            "Epoch 3/3\n",
            "51000/51000 [==============================] - 8s 150us/step - loss: 0.0483 - acc: 0.9857 - val_loss: 0.0738 - val_acc: 0.9812\n",
            "Training Accuracy: 98.57%\n",
            "Training Loss: 4.83%\n",
            "Validation Accuracy: 98.12%\n",
            "Validation Loss: 7.38%\n",
            "\n",
            "Train on 51000 samples, validate on 9000 samples\n",
            "Epoch 1/3\n",
            "51000/51000 [==============================] - 7s 130us/step - loss: 0.0397 - acc: 0.9886 - val_loss: 0.0816 - val_acc: 0.9800\n",
            "Epoch 2/3\n",
            "51000/51000 [==============================] - 6s 118us/step - loss: 0.0343 - acc: 0.9905 - val_loss: 0.0762 - val_acc: 0.9846\n",
            "Epoch 3/3\n",
            "51000/51000 [==============================] - 6s 118us/step - loss: 0.0319 - acc: 0.9908 - val_loss: 0.0745 - val_acc: 0.9839\n",
            "Training Accuracy: 99.08%\n",
            "Training Loss: 3.19%\n",
            "Validation Accuracy: 98.39%\n",
            "Validation Loss: 7.45%\n",
            "\n",
            "Train on 51000 samples, validate on 9000 samples\n",
            "Epoch 1/3\n",
            "51000/51000 [==============================] - 6s 115us/step - loss: 0.0326 - acc: 0.9906 - val_loss: 0.0788 - val_acc: 0.9817\n",
            "Epoch 2/3\n",
            "51000/51000 [==============================] - 5s 106us/step - loss: 0.0324 - acc: 0.9908 - val_loss: 0.0744 - val_acc: 0.9844\n",
            "Epoch 3/3\n",
            "51000/51000 [==============================] - 5s 107us/step - loss: 0.0349 - acc: 0.9902 - val_loss: 0.0926 - val_acc: 0.9828\n",
            "Training Accuracy: 99.02%\n",
            "Training Loss: 3.49%\n",
            "Validation Accuracy: 98.28%\n",
            "Validation Loss: 9.26%\n",
            "\n",
            "Train on 51000 samples, validate on 9000 samples\n",
            "Epoch 1/3\n",
            "51000/51000 [==============================] - 7s 141us/step - loss: 0.0721 - acc: 0.9826 - val_loss: 0.0829 - val_acc: 0.9836\n",
            "Epoch 2/3\n",
            "51000/51000 [==============================] - 7s 139us/step - loss: 0.0526 - acc: 0.9859 - val_loss: 0.0701 - val_acc: 0.9853\n",
            "Epoch 3/3\n",
            "51000/51000 [==============================] - 7s 139us/step - loss: 0.0532 - acc: 0.9863 - val_loss: 0.0716 - val_acc: 0.9839\n",
            "Training Accuracy: 98.63%\n",
            "Training Loss: 5.32%\n",
            "Validation Accuracy: 98.39%\n",
            "Validation Loss: 7.16%\n",
            "\n",
            "Train on 51000 samples, validate on 9000 samples\n",
            "Epoch 1/3\n",
            "51000/51000 [==============================] - 7s 131us/step - loss: 0.0317 - acc: 0.9907 - val_loss: 0.0726 - val_acc: 0.9861\n",
            "Epoch 2/3\n",
            "51000/51000 [==============================] - 6s 120us/step - loss: 0.0310 - acc: 0.9912 - val_loss: 0.0908 - val_acc: 0.9842\n",
            "Epoch 3/3\n",
            "51000/51000 [==============================] - 6s 120us/step - loss: 0.0345 - acc: 0.9907 - val_loss: 0.0743 - val_acc: 0.9846\n",
            "Training Accuracy: 99.07%\n",
            "Training Loss: 3.45%\n",
            "Validation Accuracy: 98.46%\n",
            "Validation Loss: 7.43%\n",
            "\n",
            "Train on 51000 samples, validate on 9000 samples\n",
            "Epoch 1/3\n",
            "51000/51000 [==============================] - 6s 126us/step - loss: 0.0293 - acc: 0.9918 - val_loss: 0.0809 - val_acc: 0.9851\n",
            "Epoch 2/3\n",
            "51000/51000 [==============================] - 6s 116us/step - loss: 0.0364 - acc: 0.9905 - val_loss: 0.0804 - val_acc: 0.9858\n",
            "Epoch 3/3\n",
            "51000/51000 [==============================] - 6s 117us/step - loss: 0.0367 - acc: 0.9902 - val_loss: 0.0901 - val_acc: 0.9846\n",
            "Training Accuracy: 99.02%\n",
            "Training Loss: 3.67%\n",
            "Validation Accuracy: 98.46%\n",
            "Validation Loss: 9.01%\n",
            "\n",
            "Train on 51000 samples, validate on 9000 samples\n",
            "Epoch 1/3\n",
            "51000/51000 [==============================] - 6s 127us/step - loss: 0.0316 - acc: 0.9916 - val_loss: 0.0840 - val_acc: 0.9840\n",
            "Epoch 2/3\n",
            "51000/51000 [==============================] - 6s 120us/step - loss: 0.0360 - acc: 0.9908 - val_loss: 0.0872 - val_acc: 0.9838\n",
            "Epoch 3/3\n",
            "51000/51000 [==============================] - 6s 117us/step - loss: 0.0378 - acc: 0.9905 - val_loss: 0.0922 - val_acc: 0.9838\n",
            "Training Accuracy: 99.05%\n",
            "Training Loss: 3.78%\n",
            "Validation Accuracy: 98.38%\n",
            "Validation Loss: 9.22%\n",
            "\n",
            "Train on 51000 samples, validate on 9000 samples\n",
            "Epoch 1/3\n",
            "51000/51000 [==============================] - 8s 161us/step - loss: 0.0694 - acc: 0.9839 - val_loss: 0.1582 - val_acc: 0.9744\n",
            "Epoch 2/3\n",
            "51000/51000 [==============================] - 8s 147us/step - loss: 0.0604 - acc: 0.9857 - val_loss: 0.0995 - val_acc: 0.9801\n",
            "Epoch 3/3\n",
            "51000/51000 [==============================] - 8s 150us/step - loss: 0.0563 - acc: 0.9869 - val_loss: 0.0897 - val_acc: 0.9839\n",
            "Training Accuracy: 98.69%\n",
            "Training Loss: 5.63%\n",
            "Validation Accuracy: 98.39%\n",
            "Validation Loss: 8.97%\n",
            "\n",
            "Train on 51000 samples, validate on 9000 samples\n",
            "Epoch 1/3\n",
            "51000/51000 [==============================] - 6s 111us/step - loss: 0.0309 - acc: 0.9920 - val_loss: 0.0854 - val_acc: 0.9856\n",
            "Epoch 2/3\n",
            "51000/51000 [==============================] - 5s 107us/step - loss: 0.0235 - acc: 0.9933 - val_loss: 0.0836 - val_acc: 0.9852\n",
            "Epoch 3/3\n",
            "51000/51000 [==============================] - 5s 106us/step - loss: 0.0203 - acc: 0.9944 - val_loss: 0.0871 - val_acc: 0.9842\n",
            "Training Accuracy: 99.44%\n",
            "Training Loss: 2.03%\n",
            "Validation Accuracy: 98.42%\n",
            "Validation Loss: 8.71%\n",
            "\n",
            "Train on 51000 samples, validate on 9000 samples\n",
            "Epoch 1/3\n",
            "51000/51000 [==============================] - 7s 130us/step - loss: 0.0219 - acc: 0.9936 - val_loss: 0.0951 - val_acc: 0.9834\n",
            "Epoch 2/3\n",
            "51000/51000 [==============================] - 6s 122us/step - loss: 0.0224 - acc: 0.9938 - val_loss: 0.0867 - val_acc: 0.9857\n",
            "Epoch 3/3\n",
            "51000/51000 [==============================] - 6s 125us/step - loss: 0.0299 - acc: 0.9926 - val_loss: 0.0962 - val_acc: 0.9844\n",
            "Training Accuracy: 99.26%\n",
            "Training Loss: 2.99%\n",
            "Validation Accuracy: 98.44%\n",
            "Validation Loss: 9.62%\n",
            "\n",
            "Train on 51000 samples, validate on 9000 samples\n",
            "Epoch 1/3\n",
            "51000/51000 [==============================] - 9s 183us/step - loss: 0.0802 - acc: 0.9818 - val_loss: 0.0956 - val_acc: 0.9811\n",
            "Epoch 2/3\n",
            "51000/51000 [==============================] - 9s 171us/step - loss: 0.0587 - acc: 0.9862 - val_loss: 0.0990 - val_acc: 0.9821\n",
            "Epoch 3/3\n",
            "51000/51000 [==============================] - 9s 172us/step - loss: 0.0615 - acc: 0.9863 - val_loss: 0.1021 - val_acc: 0.9831\n",
            "Training Accuracy: 98.63%\n",
            "Training Loss: 6.15%\n",
            "Validation Accuracy: 98.31%\n",
            "Validation Loss: 10.21%\n",
            "\n",
            "Train on 51000 samples, validate on 9000 samples\n",
            "Epoch 1/3\n",
            "51000/51000 [==============================] - 10s 190us/step - loss: 0.0656 - acc: 0.9856 - val_loss: 0.1155 - val_acc: 0.9802\n",
            "Epoch 2/3\n",
            "51000/51000 [==============================] - 9s 183us/step - loss: 0.0529 - acc: 0.9873 - val_loss: 0.1040 - val_acc: 0.9798\n",
            "Epoch 3/3\n",
            "51000/51000 [==============================] - 9s 178us/step - loss: 0.0612 - acc: 0.9857 - val_loss: 0.1117 - val_acc: 0.9822\n",
            "Training Accuracy: 98.57%\n",
            "Training Loss: 6.12%\n",
            "Validation Accuracy: 98.22%\n",
            "Validation Loss: 11.17%\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FwIA5gVXJY-P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "outputId": "e1ff2f8e-d6b2-459a-e528-542cd8fd2639"
      },
      "source": [
        "# Various Hyper Parameters and Accuracy achieved\n",
        "\n",
        "new_dataframe = [pd.DataFrame(gp_result.x_iters, columns = [\"learning rate\",\"hidden layers\",\"input layer nodes\",\"hidden layer nodes\",\n",
        "                                           \"activation function\",\"batch size\",\"adam learning rate decay\"]),\n",
        "(pd.Series(gp_result.func_vals*100, name=\"accuracy\"))]\n",
        "new_dataframe = pd.concat(new_dataframe,axis=1)\n",
        "new_dataframe"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>learning rate</th>\n",
              "      <th>hidden layers</th>\n",
              "      <th>input layer nodes</th>\n",
              "      <th>hidden layer nodes</th>\n",
              "      <th>activation function</th>\n",
              "      <th>batch size</th>\n",
              "      <th>adam learning rate decay</th>\n",
              "      <th>accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.010000</td>\n",
              "      <td>1</td>\n",
              "      <td>32</td>\n",
              "      <td>128</td>\n",
              "      <td>relu</td>\n",
              "      <td>64</td>\n",
              "      <td>0.000001</td>\n",
              "      <td>97.177778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.045077</td>\n",
              "      <td>2</td>\n",
              "      <td>62</td>\n",
              "      <td>77</td>\n",
              "      <td>sigmoid</td>\n",
              "      <td>94</td>\n",
              "      <td>0.008518</td>\n",
              "      <td>98.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.000122</td>\n",
              "      <td>2</td>\n",
              "      <td>125</td>\n",
              "      <td>86</td>\n",
              "      <td>relu</td>\n",
              "      <td>80</td>\n",
              "      <td>0.000491</td>\n",
              "      <td>98.477778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.005567</td>\n",
              "      <td>2</td>\n",
              "      <td>127</td>\n",
              "      <td>126</td>\n",
              "      <td>sigmoid</td>\n",
              "      <td>78</td>\n",
              "      <td>0.008573</td>\n",
              "      <td>98.122221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.000574</td>\n",
              "      <td>1</td>\n",
              "      <td>118</td>\n",
              "      <td>80</td>\n",
              "      <td>relu</td>\n",
              "      <td>107</td>\n",
              "      <td>0.003878</td>\n",
              "      <td>98.388889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.003470</td>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>75</td>\n",
              "      <td>relu</td>\n",
              "      <td>127</td>\n",
              "      <td>0.005748</td>\n",
              "      <td>98.277779</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.093264</td>\n",
              "      <td>3</td>\n",
              "      <td>80</td>\n",
              "      <td>79</td>\n",
              "      <td>softmax</td>\n",
              "      <td>80</td>\n",
              "      <td>0.002575</td>\n",
              "      <td>98.388890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.000700</td>\n",
              "      <td>3</td>\n",
              "      <td>113</td>\n",
              "      <td>122</td>\n",
              "      <td>tanh</td>\n",
              "      <td>103</td>\n",
              "      <td>0.001856</td>\n",
              "      <td>98.455555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.052584</td>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "      <td>43</td>\n",
              "      <td>softmax</td>\n",
              "      <td>108</td>\n",
              "      <td>0.001423</td>\n",
              "      <td>98.455555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.000451</td>\n",
              "      <td>2</td>\n",
              "      <td>90</td>\n",
              "      <td>39</td>\n",
              "      <td>sigmoid</td>\n",
              "      <td>105</td>\n",
              "      <td>0.006394</td>\n",
              "      <td>98.377778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.009964</td>\n",
              "      <td>1</td>\n",
              "      <td>54</td>\n",
              "      <td>45</td>\n",
              "      <td>relu</td>\n",
              "      <td>71</td>\n",
              "      <td>0.008534</td>\n",
              "      <td>98.388888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.086820</td>\n",
              "      <td>3</td>\n",
              "      <td>33</td>\n",
              "      <td>128</td>\n",
              "      <td>tanh</td>\n",
              "      <td>127</td>\n",
              "      <td>0.002111</td>\n",
              "      <td>98.422223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.000100</td>\n",
              "      <td>3</td>\n",
              "      <td>32</td>\n",
              "      <td>32</td>\n",
              "      <td>softmax</td>\n",
              "      <td>128</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>98.444444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.000124</td>\n",
              "      <td>1</td>\n",
              "      <td>112</td>\n",
              "      <td>121</td>\n",
              "      <td>tanh</td>\n",
              "      <td>65</td>\n",
              "      <td>0.008948</td>\n",
              "      <td>98.311111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.100000</td>\n",
              "      <td>3</td>\n",
              "      <td>32</td>\n",
              "      <td>32</td>\n",
              "      <td>sigmoid</td>\n",
              "      <td>64</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>98.222222</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    learning rate  hidden layers  ...  adam learning rate decay   accuracy\n",
              "0        0.010000              1  ...                  0.000001  97.177778\n",
              "1        0.045077              2  ...                  0.008518  98.200000\n",
              "2        0.000122              2  ...                  0.000491  98.477778\n",
              "3        0.005567              2  ...                  0.008573  98.122221\n",
              "4        0.000574              1  ...                  0.003878  98.388889\n",
              "5        0.003470              1  ...                  0.005748  98.277779\n",
              "6        0.093264              3  ...                  0.002575  98.388890\n",
              "7        0.000700              3  ...                  0.001856  98.455555\n",
              "8        0.052584              1  ...                  0.001423  98.455555\n",
              "9        0.000451              2  ...                  0.006394  98.377778\n",
              "10       0.009964              1  ...                  0.008534  98.388888\n",
              "11       0.086820              3  ...                  0.002111  98.422223\n",
              "12       0.000100              3  ...                  0.010000  98.444444\n",
              "13       0.000124              1  ...                  0.008948  98.311111\n",
              "14       0.100000              3  ...                  0.010000  98.222222\n",
              "\n",
              "[15 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iWDPGIqtJsCh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "87396b18-3d00-4a01-8ac6-a432259bcb43"
      },
      "source": [
        "# Model's Best hyperparameters\n",
        "new_dataframe['accuracy'].max()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "98.47777848773532"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3PdsC5zpKBwZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 842
        },
        "outputId": "a1503259-74a9-4d6b-f100-acfd20358846"
      },
      "source": [
        "# Data Visualization\n",
        "%matplotlib inline\n",
        "\n",
        "fig, axes = plt.subplots(nrows=4, ncols=2, figsize=(12,14))\n",
        "new_dataframe.plot(x='learning rate', y='accuracy', ax=axes[0,0])\n",
        "new_dataframe.plot(x='hidden layers', y='accuracy', ax=axes[0,1])\n",
        "new_dataframe.plot(x='input layer nodes', y='accuracy', ax=axes[1,0])\n",
        "new_dataframe.plot(x='hidden layer nodes', y='accuracy', ax=axes[1,1])\n",
        "new_dataframe.plot(x='activation function', y='accuracy', ax=axes[2,0])\n",
        "new_dataframe.plot(x='batch size', y='accuracy', ax=axes[2,1])\n",
        "new_dataframe.plot(x='adam learning rate decay', y='accuracy', ax=axes[3,0])"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6eec43dd30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LyDeX2Wnpw6TK5qSkqh57iOfojMxOa51/szWXzWR2gi/jSjZedC6LyUDvHriS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\nQDyd8TTvrouA7Vrr4mbOU3g69W1a6z9FosGdoU9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fsSb2uo5I7LSiDCjqoVYURVEURVGU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HN2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\nGyihFhERERFpg/8Px/DKIO5F7HMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x1008 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VWS_XP33Oc6d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}