Deep_Neural_Networks_With_Batch_Normalization.ipynb

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  "metadata": {
    "colab": {
      "name": "Deep Neural Networks With Batch Normalization.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SX_6Uq3XkOEK"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **This tutorial is divided into three parts; they are:**\n",
        "\n",
        "1. BatchNormalization in Keras\n",
        "\n",
        "\n",
        "2. BatchNormalization in Models\n",
        "\n",
        "\n",
        "3. BatchNormalization Case Study"
      ],
      "metadata": {
        "id": "NHdfpq79kSzX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# BatchNormalization in Keras\n"
      ],
      "metadata": {
        "id": "-mSycgSykjNW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Keras provides support for batch normalization via the BatchNormalization layer.\n",
        "\n",
        "For example:\n",
        "\n",
        "bn = BatchNormalization()"
      ],
      "metadata": {
        "id": "cZgyy28ekp4m"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. The layer will transform inputs so that they are standardized, meaning that they will have a mean of zero and a standard deviation of one.\n",
        "\n",
        "2. During training, the layer will keep track of statistics for each input variable and use them to standardize the data.\n",
        "\n",
        "3. Further, the standardized output can be scaled using the learned parameters of Beta and Gamma that define the new mean and standard deviation for the output of the transform. The layer can be configured to control whether these additional parameters will be used or not via the “center” and “scale” attributes respectively. By default, they are enabled.\n",
        "\n",
        "4. The statistics used to perform the standardization, e.g. the mean and standard deviation of each variable, are updated for each mini batch and a running average is maintained.\n",
        "\n",
        "5. A “momentum” argument allows you to control how much of the statistics from the previous mini batch to include when the update is calculated. By default, this is kept high with a value of 0.99. This can be set to 0.0 to only use statistics from the current mini-batch, as described in the original paper.\n",
        "\n",
        "bn = BatchNormalization(momentum=0.0)"
      ],
      "metadata": {
        "id": "EDXWxBbmkzT7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **BatchNormalization in Models**\n",
        "\n",
        "Batch normalization can be used at most points in a model and with most types of deep learning neural networks."
      ],
      "metadata": {
        "id": "REPibyiQlDdl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Input and Hidden Layer Inputs**"
      ],
      "metadata": {
        "id": "-XIPuPjulHtC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The BatchNormalization layer can be added to your model to standardize raw input variables or the outputs of a hidden layer.\n",
        "\n",
        "Batch normalization is not recommended as an alternative to proper data preparation for your model.\n",
        "\n",
        "Nevertheless, when used to standardize the raw input variables, the layer must specify the input_shape argument; for example:\n",
        "\n",
        "...\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(BatchNormalization(input_shape=(2,)))\n",
        "\n",
        "..."
      ],
      "metadata": {
        "id": "k5CaOB7jlHxE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "When used to standardize the outputs of a hidden layer, the layer can be added to the model just like any other layer.\n",
        "\n",
        "...\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "...\n",
        "\n",
        "model.add(BatchNormalization())\n",
        "..."
      ],
      "metadata": {
        "id": "Th9sftq2lH3o"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Use Before or After the Activation Function"
      ],
      "metadata": {
        "id": "c2eMqDjklk4u"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. The BatchNormalization normalization layer can be used to standardize inputs before or after the activation function of the previous layer.\n",
        "\n",
        "2. The original paper that introduced the method suggests adding batch normalization before the activation function of the previous layer, for example:\n",
        "\n",
        "...\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Dense(32))\n",
        "\n",
        "model.add(BatchNormalization())\n",
        "\n",
        "model.add(Activation('relu'))\n",
        "\n",
        "..."
      ],
      "metadata": {
        "id": "O_su837Tlpx6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Some reported experiments suggest better performance when adding the batch normalization layer after the activation function of the previous layer; for example\n",
        "\n",
        "...\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Dense(32, activation='relu'))\n",
        "\n",
        "model.add(BatchNormalization())\n",
        "...\n"
      ],
      "metadata": {
        "id": "zTl0FCkbl2Od"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# MLP Batch Normalization"
      ],
      "metadata": {
        "id": "RY3VLwnImGvE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "7yU4SqlomVZe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The example below adds batch normalization after the activation function between Dense hidden layers.\n",
        "\n",
        "# example of batch normalization for an mlp\n",
        "from keras.layers import Dense\n",
        "\n",
        "from keras.layers import BatchNormalization\n",
        "...\n",
        "\n",
        "model.add(Dense(32, activation='relu'))\n",
        "\n",
        "model.add(BatchNormalization())\n",
        "\n",
        "model.add(Dense(1))\n",
        "..."
      ],
      "metadata": {
        "id": "90bZfNVymK6m"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# CNN Batch Normalization"
      ],
      "metadata": {
        "id": "pccnWkIQmWWb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The example below adds batch normalization after the activation function between a convolutional and max pooling layers.\n",
        "\n",
        "# example of batch normalization for an cnn\n",
        "\n",
        "from keras.layers import Dense\n",
        "\n",
        "from keras.layers import Conv2D\n",
        "\n",
        "from keras.layers import MaxPooling2D\n",
        "\n",
        "from keras.layers import BatchNormalization\n",
        "\n",
        "...\n",
        "\n",
        "\n",
        "model.add(Conv2D(32, (3,3), activation='relu'))\n",
        "\n",
        "model.add(Conv2D(32, (3,3), activation='relu'))\n",
        "\n",
        "model.add(BatchNormalization())\n",
        "\n",
        "model.add(MaxPooling2D())\n",
        "\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "..."
      ],
      "metadata": {
        "id": "0YVhq4lomWgd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **RNN Batch Normalization**\n",
        "\n",
        "The example below adds batch normalization after the activation function between an LSTM and Dense hidden layers."
      ],
      "metadata": {
        "id": "G04HUqtzmpey"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# example of batch normalization for a lstm\n",
        "from keras.layers import Dense\n",
        "\n",
        "from keras.layers import LSTM\n",
        "\n",
        "from keras.layers import BatchNormalization\n",
        "\n",
        "...\n",
        "\n",
        "model.add(LSTM(32))\n",
        "\n",
        "model.add(BatchNormalization())\n",
        "\n",
        "model.add(Dense(1))\n",
        "\n",
        "..."
      ],
      "metadata": {
        "id": "IgvKUZWFm0Th"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# BatchNormalization Case Study"
      ],
      "metadata": {
        "id": "4BfRYEamm5dv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Binary Classification Problem"
      ],
      "metadata": {
        "id": "oSDMaSiPnG9m"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will use a standard binary classification problem that defines two two-dimensional concentric circles of observations, one circle for each class.\n",
        "\n",
        "Each observation has two input variables with the same scale and a class output value of either 0 or 1. This dataset is called the “circles” dataset because of the shape of the observations in each class when plotted.\n",
        "\n",
        "We can use the make_circles() function to generate observations from this problem. We will add noise to the data and seed the random number generator so that the same samples are generated each time the code is run."
      ],
      "metadata": {
        "id": "zbkqRLJXnKWB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# scatter plot of the circles dataset with points colored by class\n",
        "from sklearn.datasets import make_circles\n",
        "from numpy import where\n",
        "from keras.models import Sequential\n",
        "from tensorflow.keras.optimizers import SGD\n",
        "# example of batch normalization for an mlp\n",
        "from keras.layers import Dense\n",
        "from keras.layers import BatchNormalization,Activation\n",
        "from matplotlib import pyplot\n"
      ],
      "metadata": {
        "id": "eWiHAasJnOF6"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# generate circles\n",
        "X, y = make_circles(n_samples=1000, noise=0.1, random_state=1)\n",
        "# select indices of points with each class label\n",
        "for i in range(2):\n",
        "\tsamples_ix = where(y == i)\n",
        "\tpyplot.scatter(X[samples_ix, 0], X[samples_ix, 1], label=str(i))\n",
        "pyplot.legend()\n",
        "pyplot.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "P-Xi98KYnU9d",
        "outputId": "38f97a50-170f-4db3-fa36-202013d8436e"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Running the example creates a scatter plot showing the concentric circles shape of the observations in each class.\n",
        "\n",
        "We can see the noise in the dispersal of the points making the circles less obvious."
      ],
      "metadata": {
        "id": "KWeAIM9ZnaCs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is a good test problem because the classes cannot be separated by a line, e.g. are not linearly separable, requiring a nonlinear method such as a neural network to address."
      ],
      "metadata": {
        "id": "4QenAaiOndxY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Multilayer Perceptron Model"
      ],
      "metadata": {
        "id": "cf0DgDBpnhN4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can develop a Multilayer Perceptron model, or MLP, as a baseline for this problem.\n",
        "\n",
        "First, we will split the 1,000 generated samples into a train and test dataset, with 500 examples in each. This will provide a sufficiently large sample for the model to learn from and an equally sized (fair) evaluation of its performance."
      ],
      "metadata": {
        "id": "svW31fDonmux"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# split into train and test\n",
        "n_train = 500\n",
        "trainX, testX = X[:n_train, :], X[n_train:, :]\n",
        "trainy, testy = y[:n_train], y[n_train:]"
      ],
      "metadata": {
        "id": "UTFeI6AtnVtT"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will define a simple MLP model. The network must have two inputs in the visible layer for the two variables in the dataset.\n",
        "\n",
        "The model will have a single hidden layer with 50 nodes, chosen arbitrarily, and use the rectified linear activation function (ReLU) and the He random weight initialization method. The output layer will be a single node with the sigmoid activation function, capable of predicting a 0 for the outer circle and a 1 for the inner circle of the problem.\n",
        "\n",
        "The model will be trained using stochastic gradient descent with a modest learning rate of 0.01 and a large momentum of 0.9, and the optimization will be directed using the binary cross entropy loss function."
      ],
      "metadata": {
        "id": "oLdnekuDnuJn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# define model\n",
        "model = Sequential()\n",
        "model.add(Dense(50, input_dim=2, activation='relu', kernel_initializer='he_uniform'))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "opt = SGD(lr=0.01, momentum=0.9)\n",
        "model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g7k8hhIknr7u",
        "outputId": "1114da0a-1bb8-46d3-dc5d-858494417196"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(SGD, self).__init__(name, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# fit model\n",
        "history = model.fit(trainX, trainy, validation_data=(testX, testy), epochs=100, verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jOsvk3ksnwbu",
        "outputId": "9d20cd69-37f1-455e-9a2c-f62d35c85a43"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "16/16 [==============================] - 0s 7ms/step - loss: 0.3988 - accuracy: 0.8260 - val_loss: 0.3793 - val_accuracy: 0.8380\n",
            "Epoch 2/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3976 - accuracy: 0.8200 - val_loss: 0.3812 - val_accuracy: 0.8420\n",
            "Epoch 3/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.4001 - accuracy: 0.8320 - val_loss: 0.3777 - val_accuracy: 0.8440\n",
            "Epoch 4/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3938 - accuracy: 0.8260 - val_loss: 0.3766 - val_accuracy: 0.8520\n",
            "Epoch 5/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4007 - accuracy: 0.8260 - val_loss: 0.3788 - val_accuracy: 0.8440\n",
            "Epoch 6/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3953 - accuracy: 0.8220 - val_loss: 0.3774 - val_accuracy: 0.8440\n",
            "Epoch 7/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3964 - accuracy: 0.8220 - val_loss: 0.3771 - val_accuracy: 0.8540\n",
            "Epoch 8/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3955 - accuracy: 0.8180 - val_loss: 0.3761 - val_accuracy: 0.8480\n",
            "Epoch 9/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3971 - accuracy: 0.8240 - val_loss: 0.3749 - val_accuracy: 0.8420\n",
            "Epoch 10/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3934 - accuracy: 0.8140 - val_loss: 0.3766 - val_accuracy: 0.8480\n",
            "Epoch 11/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3913 - accuracy: 0.8240 - val_loss: 0.3733 - val_accuracy: 0.8480\n",
            "Epoch 12/100\n",
            "16/16 [==============================] - 0s 8ms/step - loss: 0.3913 - accuracy: 0.8220 - val_loss: 0.3756 - val_accuracy: 0.8420\n",
            "Epoch 13/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3917 - accuracy: 0.8200 - val_loss: 0.3733 - val_accuracy: 0.8460\n",
            "Epoch 14/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3906 - accuracy: 0.8320 - val_loss: 0.3715 - val_accuracy: 0.8560\n",
            "Epoch 15/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3908 - accuracy: 0.8260 - val_loss: 0.3726 - val_accuracy: 0.8480\n",
            "Epoch 16/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3913 - accuracy: 0.8260 - val_loss: 0.3759 - val_accuracy: 0.8460\n",
            "Epoch 17/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3908 - accuracy: 0.8200 - val_loss: 0.3715 - val_accuracy: 0.8560\n",
            "Epoch 18/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3887 - accuracy: 0.8240 - val_loss: 0.3705 - val_accuracy: 0.8520\n",
            "Epoch 19/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3875 - accuracy: 0.8240 - val_loss: 0.3717 - val_accuracy: 0.8540\n",
            "Epoch 20/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3884 - accuracy: 0.8220 - val_loss: 0.3716 - val_accuracy: 0.8460\n",
            "Epoch 21/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3885 - accuracy: 0.8300 - val_loss: 0.3700 - val_accuracy: 0.8480\n",
            "Epoch 22/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3866 - accuracy: 0.8300 - val_loss: 0.3710 - val_accuracy: 0.8500\n",
            "Epoch 23/100\n",
            "16/16 [==============================] - 0s 6ms/step - loss: 0.3871 - accuracy: 0.8240 - val_loss: 0.3699 - val_accuracy: 0.8540\n",
            "Epoch 24/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3861 - accuracy: 0.8220 - val_loss: 0.3716 - val_accuracy: 0.8440\n",
            "Epoch 25/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3871 - accuracy: 0.8260 - val_loss: 0.3675 - val_accuracy: 0.8560\n",
            "Epoch 26/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3878 - accuracy: 0.8300 - val_loss: 0.3682 - val_accuracy: 0.8480\n",
            "Epoch 27/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3854 - accuracy: 0.8260 - val_loss: 0.3697 - val_accuracy: 0.8480\n",
            "Epoch 28/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3862 - accuracy: 0.8220 - val_loss: 0.3696 - val_accuracy: 0.8440\n",
            "Epoch 29/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3840 - accuracy: 0.8260 - val_loss: 0.3685 - val_accuracy: 0.8440\n",
            "Epoch 30/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3857 - accuracy: 0.8200 - val_loss: 0.3693 - val_accuracy: 0.8420\n",
            "Epoch 31/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3874 - accuracy: 0.8320 - val_loss: 0.3675 - val_accuracy: 0.8460\n",
            "Epoch 32/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3871 - accuracy: 0.8300 - val_loss: 0.3698 - val_accuracy: 0.8500\n",
            "Epoch 33/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3861 - accuracy: 0.8180 - val_loss: 0.3693 - val_accuracy: 0.8540\n",
            "Epoch 34/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3826 - accuracy: 0.8300 - val_loss: 0.3649 - val_accuracy: 0.8560\n",
            "Epoch 35/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3828 - accuracy: 0.8200 - val_loss: 0.3670 - val_accuracy: 0.8460\n",
            "Epoch 36/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3845 - accuracy: 0.8320 - val_loss: 0.3659 - val_accuracy: 0.8500\n",
            "Epoch 37/100\n",
            "16/16 [==============================] - 0s 8ms/step - loss: 0.3839 - accuracy: 0.8340 - val_loss: 0.3651 - val_accuracy: 0.8520\n",
            "Epoch 38/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3835 - accuracy: 0.8200 - val_loss: 0.3669 - val_accuracy: 0.8500\n",
            "Epoch 39/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3839 - accuracy: 0.8280 - val_loss: 0.3660 - val_accuracy: 0.8460\n",
            "Epoch 40/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3829 - accuracy: 0.8320 - val_loss: 0.3690 - val_accuracy: 0.8460\n",
            "Epoch 41/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3881 - accuracy: 0.8180 - val_loss: 0.3682 - val_accuracy: 0.8460\n",
            "Epoch 42/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3858 - accuracy: 0.8220 - val_loss: 0.3665 - val_accuracy: 0.8440\n",
            "Epoch 43/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3823 - accuracy: 0.8280 - val_loss: 0.3656 - val_accuracy: 0.8480\n",
            "Epoch 44/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3833 - accuracy: 0.8260 - val_loss: 0.3654 - val_accuracy: 0.8440\n",
            "Epoch 45/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3861 - accuracy: 0.8320 - val_loss: 0.3684 - val_accuracy: 0.8420\n",
            "Epoch 46/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3877 - accuracy: 0.8280 - val_loss: 0.3665 - val_accuracy: 0.8500\n",
            "Epoch 47/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3825 - accuracy: 0.8300 - val_loss: 0.3630 - val_accuracy: 0.8480\n",
            "Epoch 48/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3860 - accuracy: 0.8400 - val_loss: 0.3667 - val_accuracy: 0.8460\n",
            "Epoch 49/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3924 - accuracy: 0.8260 - val_loss: 0.3728 - val_accuracy: 0.8520\n",
            "Epoch 50/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3918 - accuracy: 0.8240 - val_loss: 0.3638 - val_accuracy: 0.8540\n",
            "Epoch 51/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3824 - accuracy: 0.8280 - val_loss: 0.3654 - val_accuracy: 0.8460\n",
            "Epoch 52/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3824 - accuracy: 0.8280 - val_loss: 0.3641 - val_accuracy: 0.8500\n",
            "Epoch 53/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3815 - accuracy: 0.8320 - val_loss: 0.3627 - val_accuracy: 0.8580\n",
            "Epoch 54/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3796 - accuracy: 0.8300 - val_loss: 0.3639 - val_accuracy: 0.8520\n",
            "Epoch 55/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3811 - accuracy: 0.8260 - val_loss: 0.3636 - val_accuracy: 0.8440\n",
            "Epoch 56/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3880 - accuracy: 0.8380 - val_loss: 0.3666 - val_accuracy: 0.8460\n",
            "Epoch 57/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3807 - accuracy: 0.8400 - val_loss: 0.3638 - val_accuracy: 0.8440\n",
            "Epoch 58/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3823 - accuracy: 0.8360 - val_loss: 0.3667 - val_accuracy: 0.8460\n",
            "Epoch 59/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3830 - accuracy: 0.8300 - val_loss: 0.3624 - val_accuracy: 0.8560\n",
            "Epoch 60/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3803 - accuracy: 0.8240 - val_loss: 0.3635 - val_accuracy: 0.8460\n",
            "Epoch 61/100\n",
            "16/16 [==============================] - 0s 7ms/step - loss: 0.3795 - accuracy: 0.8400 - val_loss: 0.3643 - val_accuracy: 0.8480\n",
            "Epoch 62/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3840 - accuracy: 0.8240 - val_loss: 0.3651 - val_accuracy: 0.8420\n",
            "Epoch 63/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3897 - accuracy: 0.8220 - val_loss: 0.3670 - val_accuracy: 0.8480\n",
            "Epoch 64/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3828 - accuracy: 0.8180 - val_loss: 0.3622 - val_accuracy: 0.8460\n",
            "Epoch 65/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3853 - accuracy: 0.8320 - val_loss: 0.3643 - val_accuracy: 0.8480\n",
            "Epoch 66/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3843 - accuracy: 0.8200 - val_loss: 0.3631 - val_accuracy: 0.8440\n",
            "Epoch 67/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3785 - accuracy: 0.8340 - val_loss: 0.3615 - val_accuracy: 0.8500\n",
            "Epoch 68/100\n",
            "16/16 [==============================] - 0s 7ms/step - loss: 0.3808 - accuracy: 0.8220 - val_loss: 0.3653 - val_accuracy: 0.8440\n",
            "Epoch 69/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3821 - accuracy: 0.8240 - val_loss: 0.3594 - val_accuracy: 0.8560\n",
            "Epoch 70/100\n",
            "16/16 [==============================] - 0s 6ms/step - loss: 0.3794 - accuracy: 0.8400 - val_loss: 0.3629 - val_accuracy: 0.8500\n",
            "Epoch 71/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3810 - accuracy: 0.8180 - val_loss: 0.3634 - val_accuracy: 0.8460\n",
            "Epoch 72/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3807 - accuracy: 0.8300 - val_loss: 0.3588 - val_accuracy: 0.8560\n",
            "Epoch 73/100\n",
            "16/16 [==============================] - 0s 6ms/step - loss: 0.3797 - accuracy: 0.8220 - val_loss: 0.3613 - val_accuracy: 0.8500\n",
            "Epoch 74/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3814 - accuracy: 0.8340 - val_loss: 0.3613 - val_accuracy: 0.8480\n",
            "Epoch 75/100\n",
            "16/16 [==============================] - 0s 7ms/step - loss: 0.3793 - accuracy: 0.8260 - val_loss: 0.3606 - val_accuracy: 0.8440\n",
            "Epoch 76/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3793 - accuracy: 0.8300 - val_loss: 0.3617 - val_accuracy: 0.8460\n",
            "Epoch 77/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3834 - accuracy: 0.8320 - val_loss: 0.3628 - val_accuracy: 0.8480\n",
            "Epoch 78/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3839 - accuracy: 0.8200 - val_loss: 0.3651 - val_accuracy: 0.8500\n",
            "Epoch 79/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3869 - accuracy: 0.8280 - val_loss: 0.3663 - val_accuracy: 0.8400\n",
            "Epoch 80/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3852 - accuracy: 0.8380 - val_loss: 0.3653 - val_accuracy: 0.8460\n",
            "Epoch 81/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3798 - accuracy: 0.8440 - val_loss: 0.3632 - val_accuracy: 0.8440\n",
            "Epoch 82/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3817 - accuracy: 0.8180 - val_loss: 0.3599 - val_accuracy: 0.8500\n",
            "Epoch 83/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3840 - accuracy: 0.8240 - val_loss: 0.3609 - val_accuracy: 0.8520\n",
            "Epoch 84/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3870 - accuracy: 0.8280 - val_loss: 0.3606 - val_accuracy: 0.8500\n",
            "Epoch 85/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3833 - accuracy: 0.8260 - val_loss: 0.3577 - val_accuracy: 0.8540\n",
            "Epoch 86/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3804 - accuracy: 0.8280 - val_loss: 0.3644 - val_accuracy: 0.8420\n",
            "Epoch 87/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3846 - accuracy: 0.8280 - val_loss: 0.3610 - val_accuracy: 0.8420\n",
            "Epoch 88/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3858 - accuracy: 0.8240 - val_loss: 0.3607 - val_accuracy: 0.8520\n",
            "Epoch 89/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3773 - accuracy: 0.8300 - val_loss: 0.3610 - val_accuracy: 0.8480\n",
            "Epoch 90/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3780 - accuracy: 0.8340 - val_loss: 0.3614 - val_accuracy: 0.8500\n",
            "Epoch 91/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3774 - accuracy: 0.8320 - val_loss: 0.3592 - val_accuracy: 0.8520\n",
            "Epoch 92/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3782 - accuracy: 0.8320 - val_loss: 0.3586 - val_accuracy: 0.8500\n",
            "Epoch 93/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3764 - accuracy: 0.8260 - val_loss: 0.3592 - val_accuracy: 0.8520\n",
            "Epoch 94/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3760 - accuracy: 0.8340 - val_loss: 0.3588 - val_accuracy: 0.8500\n",
            "Epoch 95/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3798 - accuracy: 0.8280 - val_loss: 0.3607 - val_accuracy: 0.8460\n",
            "Epoch 96/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3981 - accuracy: 0.8260 - val_loss: 0.3604 - val_accuracy: 0.8500\n",
            "Epoch 97/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3824 - accuracy: 0.8260 - val_loss: 0.3591 - val_accuracy: 0.8500\n",
            "Epoch 98/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3797 - accuracy: 0.8340 - val_loss: 0.3574 - val_accuracy: 0.8560\n",
            "Epoch 99/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3783 - accuracy: 0.8300 - val_loss: 0.3626 - val_accuracy: 0.8460\n",
            "Epoch 100/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3770 - accuracy: 0.8360 - val_loss: 0.3592 - val_accuracy: 0.8460\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "At the end of the run, the model is evaluated on the train and test dataset and the accuracy is reported."
      ],
      "metadata": {
        "id": "RFynp9npq3Qc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluate the model\n",
        "_, train_acc = model.evaluate(trainX, trainy, verbose=0)\n",
        "_, test_acc = model.evaluate(testX, testy, verbose=0)\n",
        "print('Train: %.3f, Test: %.3f' % (train_acc, test_acc))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LrChw8T5q34Y",
        "outputId": "3c127c64-748b-481c-f891-2cf50f21db8b"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train: 0.832, Test: 0.846\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally, line plots are created showing model accuracy on the train and test sets at the end of each training epoch providing learning curves.\n",
        "\n",
        "This plot of learning curves is useful as it gives an idea of how quickly and how well the model has learned the problem."
      ],
      "metadata": {
        "id": "J8C6W9d9rTap"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# plot history\n",
        "pyplot.plot(history.history['accuracy'], label='train')\n",
        "pyplot.plot(history.history['val_accuracy'], label='test')\n",
        "pyplot.legend()\n",
        "pyplot.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "bwRMKjE1q6hI",
        "outputId": "f2ccece3-a17c-428a-95c1-da95f0dce6b5"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# MLP With Batch Normalization"
      ],
      "metadata": {
        "id": "0VRzD-7grgff"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The model introduced in the previous section can be updated to add batch normalization.\n",
        "\n",
        "The expectation is that the addition of batch normalization would accelerate the training process, offering similar or better classification accuracy of the model in fewer training epochs. Batch normalization is also reported as providing a modest form of regularization, meaning that it may also offer a small reduction in generalization error demonstrated by a small increase in classification accuracy on the holdout test dataset.\n",
        "\n",
        "A new BatchNormalization layer can be added to the model after the hidden layer before the output layer. Specifically, after the activation function of the prior hidden layer."
      ],
      "metadata": {
        "id": "S_dDCQXBsRCQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# define model\n",
        "model = Sequential()\n",
        "model.add(Dense(50, input_dim=2, activation='relu', kernel_initializer='he_uniform'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "opt = SGD(lr=0.01, momentum=0.9)\n",
        "model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u-Oh6ntQrVk5",
        "outputId": "99132f12-2785-4372-9d1c-bb93ef68b067"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(SGD, self).__init__(name, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# fit model\n",
        "history = model.fit(trainX, trainy, validation_data=(testX, testy), epochs=100, verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ogsZFTQfzmLl",
        "outputId": "9141ecf2-4043-4820-b74c-e0c8dd42298f"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "16/16 [==============================] - 0s 7ms/step - loss: 0.4398 - accuracy: 0.8060 - val_loss: 0.3606 - val_accuracy: 0.8440\n",
            "Epoch 2/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4446 - accuracy: 0.7980 - val_loss: 0.3580 - val_accuracy: 0.8640\n",
            "Epoch 3/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4070 - accuracy: 0.8040 - val_loss: 0.3685 - val_accuracy: 0.8480\n",
            "Epoch 4/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4027 - accuracy: 0.8200 - val_loss: 0.3591 - val_accuracy: 0.8480\n",
            "Epoch 5/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4105 - accuracy: 0.8260 - val_loss: 0.3679 - val_accuracy: 0.8380\n",
            "Epoch 6/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4800 - accuracy: 0.7760 - val_loss: 0.3624 - val_accuracy: 0.8540\n",
            "Epoch 7/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4491 - accuracy: 0.7940 - val_loss: 0.3645 - val_accuracy: 0.8420\n",
            "Epoch 8/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4422 - accuracy: 0.8120 - val_loss: 0.3668 - val_accuracy: 0.8460\n",
            "Epoch 9/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4513 - accuracy: 0.7980 - val_loss: 0.3766 - val_accuracy: 0.8440\n",
            "Epoch 10/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4514 - accuracy: 0.7920 - val_loss: 0.3691 - val_accuracy: 0.8300\n",
            "Epoch 11/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4334 - accuracy: 0.8040 - val_loss: 0.3688 - val_accuracy: 0.8440\n",
            "Epoch 12/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4278 - accuracy: 0.8120 - val_loss: 0.3641 - val_accuracy: 0.8340\n",
            "Epoch 13/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4500 - accuracy: 0.8020 - val_loss: 0.3666 - val_accuracy: 0.8480\n",
            "Epoch 14/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4246 - accuracy: 0.8180 - val_loss: 0.3610 - val_accuracy: 0.8460\n",
            "Epoch 15/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4273 - accuracy: 0.8160 - val_loss: 0.3620 - val_accuracy: 0.8500\n",
            "Epoch 16/100\n",
            "16/16 [==============================] - 0s 8ms/step - loss: 0.4737 - accuracy: 0.7880 - val_loss: 0.3696 - val_accuracy: 0.8420\n",
            "Epoch 17/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4580 - accuracy: 0.7780 - val_loss: 0.3742 - val_accuracy: 0.8500\n",
            "Epoch 18/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4112 - accuracy: 0.8060 - val_loss: 0.3676 - val_accuracy: 0.8380\n",
            "Epoch 19/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4156 - accuracy: 0.8280 - val_loss: 0.3641 - val_accuracy: 0.8300\n",
            "Epoch 20/100\n",
            "16/16 [==============================] - 0s 7ms/step - loss: 0.4260 - accuracy: 0.8080 - val_loss: 0.3808 - val_accuracy: 0.8380\n",
            "Epoch 21/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4317 - accuracy: 0.8000 - val_loss: 0.3616 - val_accuracy: 0.8520\n",
            "Epoch 22/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.4225 - accuracy: 0.8060 - val_loss: 0.3734 - val_accuracy: 0.8480\n",
            "Epoch 23/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4291 - accuracy: 0.8020 - val_loss: 0.3608 - val_accuracy: 0.8580\n",
            "Epoch 24/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.4458 - accuracy: 0.8020 - val_loss: 0.3756 - val_accuracy: 0.8320\n",
            "Epoch 25/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4168 - accuracy: 0.8200 - val_loss: 0.3616 - val_accuracy: 0.8560\n",
            "Epoch 26/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4220 - accuracy: 0.8180 - val_loss: 0.3707 - val_accuracy: 0.8340\n",
            "Epoch 27/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4085 - accuracy: 0.8080 - val_loss: 0.3644 - val_accuracy: 0.8500\n",
            "Epoch 28/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4184 - accuracy: 0.8120 - val_loss: 0.3618 - val_accuracy: 0.8500\n",
            "Epoch 29/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4494 - accuracy: 0.7720 - val_loss: 0.3613 - val_accuracy: 0.8460\n",
            "Epoch 30/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4118 - accuracy: 0.8200 - val_loss: 0.3663 - val_accuracy: 0.8400\n",
            "Epoch 31/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4514 - accuracy: 0.7900 - val_loss: 0.3603 - val_accuracy: 0.8400\n",
            "Epoch 32/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4543 - accuracy: 0.8020 - val_loss: 0.3707 - val_accuracy: 0.8360\n",
            "Epoch 33/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4132 - accuracy: 0.8100 - val_loss: 0.3599 - val_accuracy: 0.8480\n",
            "Epoch 34/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.4112 - accuracy: 0.8200 - val_loss: 0.3618 - val_accuracy: 0.8500\n",
            "Epoch 35/100\n",
            "16/16 [==============================] - 0s 7ms/step - loss: 0.4069 - accuracy: 0.8020 - val_loss: 0.3601 - val_accuracy: 0.8600\n",
            "Epoch 36/100\n",
            "16/16 [==============================] - 0s 6ms/step - loss: 0.4088 - accuracy: 0.8160 - val_loss: 0.3659 - val_accuracy: 0.8460\n",
            "Epoch 37/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4261 - accuracy: 0.7920 - val_loss: 0.3598 - val_accuracy: 0.8580\n",
            "Epoch 38/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4175 - accuracy: 0.8040 - val_loss: 0.3602 - val_accuracy: 0.8500\n",
            "Epoch 39/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4553 - accuracy: 0.7980 - val_loss: 0.3653 - val_accuracy: 0.8380\n",
            "Epoch 40/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4264 - accuracy: 0.7820 - val_loss: 0.3617 - val_accuracy: 0.8400\n",
            "Epoch 41/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4502 - accuracy: 0.7920 - val_loss: 0.3706 - val_accuracy: 0.8420\n",
            "Epoch 42/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4663 - accuracy: 0.7860 - val_loss: 0.3647 - val_accuracy: 0.8540\n",
            "Epoch 43/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4256 - accuracy: 0.7940 - val_loss: 0.3650 - val_accuracy: 0.8360\n",
            "Epoch 44/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4324 - accuracy: 0.8060 - val_loss: 0.3608 - val_accuracy: 0.8500\n",
            "Epoch 45/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4421 - accuracy: 0.7800 - val_loss: 0.3793 - val_accuracy: 0.8360\n",
            "Epoch 46/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4446 - accuracy: 0.7920 - val_loss: 0.3692 - val_accuracy: 0.8400\n",
            "Epoch 47/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4251 - accuracy: 0.8040 - val_loss: 0.3758 - val_accuracy: 0.8360\n",
            "Epoch 48/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4173 - accuracy: 0.8100 - val_loss: 0.3735 - val_accuracy: 0.8400\n",
            "Epoch 49/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4167 - accuracy: 0.8340 - val_loss: 0.3634 - val_accuracy: 0.8560\n",
            "Epoch 50/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.4262 - accuracy: 0.8060 - val_loss: 0.3702 - val_accuracy: 0.8340\n",
            "Epoch 51/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4107 - accuracy: 0.8240 - val_loss: 0.3657 - val_accuracy: 0.8400\n",
            "Epoch 52/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4122 - accuracy: 0.7960 - val_loss: 0.3631 - val_accuracy: 0.8500\n",
            "Epoch 53/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4290 - accuracy: 0.7900 - val_loss: 0.3687 - val_accuracy: 0.8480\n",
            "Epoch 54/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4221 - accuracy: 0.8000 - val_loss: 0.3591 - val_accuracy: 0.8540\n",
            "Epoch 55/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4048 - accuracy: 0.8400 - val_loss: 0.3586 - val_accuracy: 0.8580\n",
            "Epoch 56/100\n",
            "16/16 [==============================] - 0s 6ms/step - loss: 0.4382 - accuracy: 0.7880 - val_loss: 0.3719 - val_accuracy: 0.8340\n",
            "Epoch 57/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4370 - accuracy: 0.8120 - val_loss: 0.3611 - val_accuracy: 0.8520\n",
            "Epoch 58/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4113 - accuracy: 0.8140 - val_loss: 0.3651 - val_accuracy: 0.8400\n",
            "Epoch 59/100\n",
            "16/16 [==============================] - 0s 8ms/step - loss: 0.4126 - accuracy: 0.8080 - val_loss: 0.3592 - val_accuracy: 0.8600\n",
            "Epoch 60/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4284 - accuracy: 0.8060 - val_loss: 0.3601 - val_accuracy: 0.8520\n",
            "Epoch 61/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4387 - accuracy: 0.8100 - val_loss: 0.3699 - val_accuracy: 0.8360\n",
            "Epoch 62/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4109 - accuracy: 0.8040 - val_loss: 0.3628 - val_accuracy: 0.8380\n",
            "Epoch 63/100\n",
            "16/16 [==============================] - 0s 8ms/step - loss: 0.4210 - accuracy: 0.8120 - val_loss: 0.3615 - val_accuracy: 0.8440\n",
            "Epoch 64/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4405 - accuracy: 0.7880 - val_loss: 0.3671 - val_accuracy: 0.8440\n",
            "Epoch 65/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4438 - accuracy: 0.7980 - val_loss: 0.3707 - val_accuracy: 0.8440\n",
            "Epoch 66/100\n",
            "16/16 [==============================] - 0s 6ms/step - loss: 0.4047 - accuracy: 0.8220 - val_loss: 0.3712 - val_accuracy: 0.8500\n",
            "Epoch 67/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4068 - accuracy: 0.8100 - val_loss: 0.3632 - val_accuracy: 0.8420\n",
            "Epoch 68/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4063 - accuracy: 0.8220 - val_loss: 0.3613 - val_accuracy: 0.8360\n",
            "Epoch 69/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4203 - accuracy: 0.8160 - val_loss: 0.3669 - val_accuracy: 0.8380\n",
            "Epoch 70/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4324 - accuracy: 0.8140 - val_loss: 0.3596 - val_accuracy: 0.8500\n",
            "Epoch 71/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3992 - accuracy: 0.8040 - val_loss: 0.3678 - val_accuracy: 0.8400\n",
            "Epoch 72/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4472 - accuracy: 0.7800 - val_loss: 0.3654 - val_accuracy: 0.8420\n",
            "Epoch 73/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4254 - accuracy: 0.8100 - val_loss: 0.3589 - val_accuracy: 0.8540\n",
            "Epoch 74/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4381 - accuracy: 0.8140 - val_loss: 0.3685 - val_accuracy: 0.8380\n",
            "Epoch 75/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4309 - accuracy: 0.7940 - val_loss: 0.3617 - val_accuracy: 0.8400\n",
            "Epoch 76/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4413 - accuracy: 0.8100 - val_loss: 0.3625 - val_accuracy: 0.8480\n",
            "Epoch 77/100\n",
            "16/16 [==============================] - 0s 8ms/step - loss: 0.4433 - accuracy: 0.8020 - val_loss: 0.3584 - val_accuracy: 0.8580\n",
            "Epoch 78/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.4087 - accuracy: 0.8200 - val_loss: 0.3632 - val_accuracy: 0.8400\n",
            "Epoch 79/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4345 - accuracy: 0.8060 - val_loss: 0.3638 - val_accuracy: 0.8540\n",
            "Epoch 80/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4118 - accuracy: 0.8280 - val_loss: 0.3713 - val_accuracy: 0.8420\n",
            "Epoch 81/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4167 - accuracy: 0.8000 - val_loss: 0.3587 - val_accuracy: 0.8580\n",
            "Epoch 82/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4470 - accuracy: 0.7920 - val_loss: 0.3707 - val_accuracy: 0.8460\n",
            "Epoch 83/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.4378 - accuracy: 0.7960 - val_loss: 0.3603 - val_accuracy: 0.8400\n",
            "Epoch 84/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4508 - accuracy: 0.7920 - val_loss: 0.3732 - val_accuracy: 0.8480\n",
            "Epoch 85/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4237 - accuracy: 0.8000 - val_loss: 0.3573 - val_accuracy: 0.8520\n",
            "Epoch 86/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4414 - accuracy: 0.7900 - val_loss: 0.3838 - val_accuracy: 0.8300\n",
            "Epoch 87/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.4248 - accuracy: 0.8200 - val_loss: 0.3613 - val_accuracy: 0.8540\n",
            "Epoch 88/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.4117 - accuracy: 0.7980 - val_loss: 0.3609 - val_accuracy: 0.8580\n",
            "Epoch 89/100\n",
            "16/16 [==============================] - 0s 8ms/step - loss: 0.4195 - accuracy: 0.8120 - val_loss: 0.3699 - val_accuracy: 0.8380\n",
            "Epoch 90/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.4153 - accuracy: 0.8080 - val_loss: 0.3639 - val_accuracy: 0.8420\n",
            "Epoch 91/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4296 - accuracy: 0.8120 - val_loss: 0.3695 - val_accuracy: 0.8340\n",
            "Epoch 92/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4741 - accuracy: 0.7760 - val_loss: 0.3599 - val_accuracy: 0.8540\n",
            "Epoch 93/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3965 - accuracy: 0.8380 - val_loss: 0.3631 - val_accuracy: 0.8500\n",
            "Epoch 94/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.4337 - accuracy: 0.7820 - val_loss: 0.3578 - val_accuracy: 0.8540\n",
            "Epoch 95/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4474 - accuracy: 0.7920 - val_loss: 0.3606 - val_accuracy: 0.8460\n",
            "Epoch 96/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4225 - accuracy: 0.8060 - val_loss: 0.3631 - val_accuracy: 0.8400\n",
            "Epoch 97/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4129 - accuracy: 0.8220 - val_loss: 0.3587 - val_accuracy: 0.8480\n",
            "Epoch 98/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4425 - accuracy: 0.7920 - val_loss: 0.3654 - val_accuracy: 0.8380\n",
            "Epoch 99/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4290 - accuracy: 0.8040 - val_loss: 0.3582 - val_accuracy: 0.8520\n",
            "Epoch 100/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4554 - accuracy: 0.7880 - val_loss: 0.3621 - val_accuracy: 0.8440\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluate the model\n",
        "_, train_acc = model.evaluate(trainX, trainy, verbose=0)\n",
        "_, test_acc = model.evaluate(testX, testy, verbose=0)\n",
        "print('Train: %.3f, Test: %.3f' % (train_acc, test_acc))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HXnombkQzr0u",
        "outputId": "d93f909b-70df-44c8-89db-db8dea4e1c17"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train: 0.844, Test: 0.844\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot history\n",
        "pyplot.plot(history.history['accuracy'], label='train')\n",
        "pyplot.plot(history.history['val_accuracy'], label='test')\n",
        "pyplot.legend()\n",
        "pyplot.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "ks9jjnjOz3Id",
        "outputId": "716ea97b-4ad4-4ce9-ce7c-7f9c7dee0adf"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Running the example first prints the classification accuracy of the model on the train and test dataset.\n",
        "\n",
        "Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.\n",
        "\n",
        "In this case, we can see comparable performance of the model on both the train and test set of about 84% accuracy, very similar to what we saw in the previous section, if not a little bit better."
      ],
      "metadata": {
        "id": "uIGtdppS0Zuk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A graph of the learning curves is also created showing classification accuracy on both the train and test sets for each training epoch.\n",
        "\n",
        "In this case, we can see that the model has learned the problem faster than the model in the previous section without batch normalization. Specifically, we can see that classification accuracy on the train and test datasets leaps above 80% within the first 20 epochs, as opposed to 30-to-40 epochs in the model without batch normalization.\n",
        "\n",
        "The plot also shows the effect of batch normalization during training. We can see lower performance on the training dataset than the test dataset: scores on the training dataset that are lower than the performance of the model at the end of the training run. This is likely the effect of the input collected and updated each mini-batch."
      ],
      "metadata": {
        "id": "_qApaBdq0dLj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Batch normalization is applied prior to the activation function of the hidden layer**"
      ],
      "metadata": {
        "id": "w8uXDJ3I0kem"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "SR9EKG2W0Y7v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can also try a variation of the model where batch normalization is applied prior to the activation function of the hidden layer, instead of after the activation function."
      ],
      "metadata": {
        "id": "zYp-gPmv0hRg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# define model\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Dense(50, input_dim=2, kernel_initializer='he_uniform'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "opt = SGD(lr=0.01, momentum=0.9)\n",
        "model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZZS8f4xI1IMc",
        "outputId": "7bf569de-0428-45d5-a927-276aa8350073"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(SGD, self).__init__(name, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# fit model\n",
        "history = model.fit(trainX, trainy, validation_data=(testX, testy), epochs=100, verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Fhp4c5L1_D6",
        "outputId": "4a341142-644e-4864-8e44-4973245178e7"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "16/16 [==============================] - 1s 27ms/step - loss: 0.7586 - accuracy: 0.5080 - val_loss: 0.7125 - val_accuracy: 0.4960\n",
            "Epoch 2/100\n",
            "16/16 [==============================] - 0s 11ms/step - loss: 0.6874 - accuracy: 0.5300 - val_loss: 0.7030 - val_accuracy: 0.5080\n",
            "Epoch 3/100\n",
            "16/16 [==============================] - 0s 11ms/step - loss: 0.6809 - accuracy: 0.5300 - val_loss: 0.6951 - val_accuracy: 0.5100\n",
            "Epoch 4/100\n",
            "16/16 [==============================] - 0s 16ms/step - loss: 0.6755 - accuracy: 0.5440 - val_loss: 0.6844 - val_accuracy: 0.5220\n",
            "Epoch 5/100\n",
            "16/16 [==============================] - 0s 8ms/step - loss: 0.6751 - accuracy: 0.5440 - val_loss: 0.6783 - val_accuracy: 0.5240\n",
            "Epoch 6/100\n",
            "16/16 [==============================] - 0s 11ms/step - loss: 0.6666 - accuracy: 0.5920 - val_loss: 0.6755 - val_accuracy: 0.5780\n",
            "Epoch 7/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.6678 - accuracy: 0.5660 - val_loss: 0.6674 - val_accuracy: 0.5720\n",
            "Epoch 8/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.6634 - accuracy: 0.5760 - val_loss: 0.6636 - val_accuracy: 0.5600\n",
            "Epoch 9/100\n",
            "16/16 [==============================] - 0s 8ms/step - loss: 0.6643 - accuracy: 0.5940 - val_loss: 0.6592 - val_accuracy: 0.5800\n",
            "Epoch 10/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.6543 - accuracy: 0.5700 - val_loss: 0.6542 - val_accuracy: 0.5920\n",
            "Epoch 11/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.6475 - accuracy: 0.6500 - val_loss: 0.6516 - val_accuracy: 0.6040\n",
            "Epoch 12/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.6454 - accuracy: 0.6540 - val_loss: 0.6467 - val_accuracy: 0.6040\n",
            "Epoch 13/100\n",
            "16/16 [==============================] - 0s 6ms/step - loss: 0.6387 - accuracy: 0.6440 - val_loss: 0.6420 - val_accuracy: 0.6280\n",
            "Epoch 14/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.6422 - accuracy: 0.6380 - val_loss: 0.6385 - val_accuracy: 0.6320\n",
            "Epoch 15/100\n",
            "16/16 [==============================] - 0s 6ms/step - loss: 0.6482 - accuracy: 0.6440 - val_loss: 0.6322 - val_accuracy: 0.6760\n",
            "Epoch 16/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.6352 - accuracy: 0.6900 - val_loss: 0.6289 - val_accuracy: 0.6700\n",
            "Epoch 17/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.6184 - accuracy: 0.7040 - val_loss: 0.6261 - val_accuracy: 0.6380\n",
            "Epoch 18/100\n",
            "16/16 [==============================] - 0s 8ms/step - loss: 0.6267 - accuracy: 0.6720 - val_loss: 0.6213 - val_accuracy: 0.6800\n",
            "Epoch 19/100\n",
            "16/16 [==============================] - 0s 8ms/step - loss: 0.6306 - accuracy: 0.6960 - val_loss: 0.6177 - val_accuracy: 0.7160\n",
            "Epoch 20/100\n",
            "16/16 [==============================] - 0s 7ms/step - loss: 0.6296 - accuracy: 0.6960 - val_loss: 0.6164 - val_accuracy: 0.6940\n",
            "Epoch 21/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.6141 - accuracy: 0.7280 - val_loss: 0.6097 - val_accuracy: 0.7500\n",
            "Epoch 22/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.6225 - accuracy: 0.6980 - val_loss: 0.6065 - val_accuracy: 0.7440\n",
            "Epoch 23/100\n",
            "16/16 [==============================] - 0s 6ms/step - loss: 0.6149 - accuracy: 0.7120 - val_loss: 0.6033 - val_accuracy: 0.7640\n",
            "Epoch 24/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.6196 - accuracy: 0.6500 - val_loss: 0.6016 - val_accuracy: 0.7420\n",
            "Epoch 25/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.6264 - accuracy: 0.7260 - val_loss: 0.5950 - val_accuracy: 0.7920\n",
            "Epoch 26/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.6015 - accuracy: 0.7040 - val_loss: 0.6002 - val_accuracy: 0.6980\n",
            "Epoch 27/100\n",
            "16/16 [==============================] - 0s 8ms/step - loss: 0.6035 - accuracy: 0.7080 - val_loss: 0.5897 - val_accuracy: 0.7880\n",
            "Epoch 28/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.6025 - accuracy: 0.6740 - val_loss: 0.5887 - val_accuracy: 0.7820\n",
            "Epoch 29/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.6187 - accuracy: 0.6680 - val_loss: 0.5875 - val_accuracy: 0.7500\n",
            "Epoch 30/100\n",
            "16/16 [==============================] - 0s 8ms/step - loss: 0.6050 - accuracy: 0.7020 - val_loss: 0.5862 - val_accuracy: 0.7780\n",
            "Epoch 31/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5972 - accuracy: 0.7040 - val_loss: 0.5804 - val_accuracy: 0.7780\n",
            "Epoch 32/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.6017 - accuracy: 0.6900 - val_loss: 0.5752 - val_accuracy: 0.8320\n",
            "Epoch 33/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5860 - accuracy: 0.7160 - val_loss: 0.5799 - val_accuracy: 0.7640\n",
            "Epoch 34/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5847 - accuracy: 0.7260 - val_loss: 0.5730 - val_accuracy: 0.8020\n",
            "Epoch 35/100\n",
            "16/16 [==============================] - 0s 8ms/step - loss: 0.5974 - accuracy: 0.7020 - val_loss: 0.5699 - val_accuracy: 0.7820\n",
            "Epoch 36/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5884 - accuracy: 0.7180 - val_loss: 0.5667 - val_accuracy: 0.8140\n",
            "Epoch 37/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5716 - accuracy: 0.7500 - val_loss: 0.5633 - val_accuracy: 0.8280\n",
            "Epoch 38/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.6009 - accuracy: 0.6860 - val_loss: 0.5588 - val_accuracy: 0.7900\n",
            "Epoch 39/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5986 - accuracy: 0.7000 - val_loss: 0.5663 - val_accuracy: 0.7720\n",
            "Epoch 40/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.6048 - accuracy: 0.6660 - val_loss: 0.5577 - val_accuracy: 0.8260\n",
            "Epoch 41/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5887 - accuracy: 0.7000 - val_loss: 0.5570 - val_accuracy: 0.7920\n",
            "Epoch 42/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5782 - accuracy: 0.7220 - val_loss: 0.5532 - val_accuracy: 0.8180\n",
            "Epoch 43/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5782 - accuracy: 0.7220 - val_loss: 0.5539 - val_accuracy: 0.8060\n",
            "Epoch 44/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5783 - accuracy: 0.7180 - val_loss: 0.5499 - val_accuracy: 0.7860\n",
            "Epoch 45/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5666 - accuracy: 0.7340 - val_loss: 0.5453 - val_accuracy: 0.8300\n",
            "Epoch 46/100\n",
            "16/16 [==============================] - 0s 7ms/step - loss: 0.5811 - accuracy: 0.7220 - val_loss: 0.5456 - val_accuracy: 0.8080\n",
            "Epoch 47/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5707 - accuracy: 0.6860 - val_loss: 0.5445 - val_accuracy: 0.8020\n",
            "Epoch 48/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5530 - accuracy: 0.7620 - val_loss: 0.5389 - val_accuracy: 0.8260\n",
            "Epoch 49/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5802 - accuracy: 0.7020 - val_loss: 0.5362 - val_accuracy: 0.8300\n",
            "Epoch 50/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5603 - accuracy: 0.7340 - val_loss: 0.5370 - val_accuracy: 0.8160\n",
            "Epoch 51/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5619 - accuracy: 0.7500 - val_loss: 0.5324 - val_accuracy: 0.8320\n",
            "Epoch 52/100\n",
            "16/16 [==============================] - 0s 8ms/step - loss: 0.5634 - accuracy: 0.7440 - val_loss: 0.5318 - val_accuracy: 0.8160\n",
            "Epoch 53/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5476 - accuracy: 0.7520 - val_loss: 0.5282 - val_accuracy: 0.8220\n",
            "Epoch 54/100\n",
            "16/16 [==============================] - 0s 6ms/step - loss: 0.5701 - accuracy: 0.7080 - val_loss: 0.5297 - val_accuracy: 0.8120\n",
            "Epoch 55/100\n",
            "16/16 [==============================] - 0s 6ms/step - loss: 0.5801 - accuracy: 0.7320 - val_loss: 0.5242 - val_accuracy: 0.8280\n",
            "Epoch 56/100\n",
            "16/16 [==============================] - 0s 8ms/step - loss: 0.5762 - accuracy: 0.7120 - val_loss: 0.5205 - val_accuracy: 0.8140\n",
            "Epoch 57/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5571 - accuracy: 0.7460 - val_loss: 0.5240 - val_accuracy: 0.8260\n",
            "Epoch 58/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5660 - accuracy: 0.7320 - val_loss: 0.5177 - val_accuracy: 0.8500\n",
            "Epoch 59/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5616 - accuracy: 0.7300 - val_loss: 0.5174 - val_accuracy: 0.8280\n",
            "Epoch 60/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5816 - accuracy: 0.6920 - val_loss: 0.5238 - val_accuracy: 0.8020\n",
            "Epoch 61/100\n",
            "16/16 [==============================] - 0s 8ms/step - loss: 0.5708 - accuracy: 0.6900 - val_loss: 0.5170 - val_accuracy: 0.8140\n",
            "Epoch 62/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5388 - accuracy: 0.7420 - val_loss: 0.5149 - val_accuracy: 0.8160\n",
            "Epoch 63/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5442 - accuracy: 0.7560 - val_loss: 0.5126 - val_accuracy: 0.8260\n",
            "Epoch 64/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5581 - accuracy: 0.7260 - val_loss: 0.5138 - val_accuracy: 0.8120\n",
            "Epoch 65/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5670 - accuracy: 0.7140 - val_loss: 0.5115 - val_accuracy: 0.8120\n",
            "Epoch 66/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5496 - accuracy: 0.7260 - val_loss: 0.5160 - val_accuracy: 0.7960\n",
            "Epoch 67/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5452 - accuracy: 0.7380 - val_loss: 0.5095 - val_accuracy: 0.8240\n",
            "Epoch 68/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5354 - accuracy: 0.7420 - val_loss: 0.5033 - val_accuracy: 0.8360\n",
            "Epoch 69/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5304 - accuracy: 0.7540 - val_loss: 0.5056 - val_accuracy: 0.8060\n",
            "Epoch 70/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5715 - accuracy: 0.6960 - val_loss: 0.5009 - val_accuracy: 0.8340\n",
            "Epoch 71/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5469 - accuracy: 0.7540 - val_loss: 0.5023 - val_accuracy: 0.8220\n",
            "Epoch 72/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5487 - accuracy: 0.7320 - val_loss: 0.5021 - val_accuracy: 0.8120\n",
            "Epoch 73/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5698 - accuracy: 0.7240 - val_loss: 0.4997 - val_accuracy: 0.8240\n",
            "Epoch 74/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5770 - accuracy: 0.7100 - val_loss: 0.4963 - val_accuracy: 0.8260\n",
            "Epoch 75/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5405 - accuracy: 0.7380 - val_loss: 0.4984 - val_accuracy: 0.8180\n",
            "Epoch 76/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5377 - accuracy: 0.7560 - val_loss: 0.4954 - val_accuracy: 0.8100\n",
            "Epoch 77/100\n",
            "16/16 [==============================] - 0s 8ms/step - loss: 0.5954 - accuracy: 0.6920 - val_loss: 0.5004 - val_accuracy: 0.8220\n",
            "Epoch 78/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5287 - accuracy: 0.7640 - val_loss: 0.4912 - val_accuracy: 0.8200\n",
            "Epoch 79/100\n",
            "16/16 [==============================] - 0s 6ms/step - loss: 0.5852 - accuracy: 0.6860 - val_loss: 0.4916 - val_accuracy: 0.8200\n",
            "Epoch 80/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5593 - accuracy: 0.7220 - val_loss: 0.5001 - val_accuracy: 0.8040\n",
            "Epoch 81/100\n",
            "16/16 [==============================] - 0s 8ms/step - loss: 0.5725 - accuracy: 0.6960 - val_loss: 0.4869 - val_accuracy: 0.8400\n",
            "Epoch 82/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5487 - accuracy: 0.7440 - val_loss: 0.4897 - val_accuracy: 0.8180\n",
            "Epoch 83/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5667 - accuracy: 0.7060 - val_loss: 0.4986 - val_accuracy: 0.8180\n",
            "Epoch 84/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5873 - accuracy: 0.7000 - val_loss: 0.4896 - val_accuracy: 0.8140\n",
            "Epoch 85/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5706 - accuracy: 0.7180 - val_loss: 0.4878 - val_accuracy: 0.8420\n",
            "Epoch 86/100\n",
            "16/16 [==============================] - 0s 7ms/step - loss: 0.5664 - accuracy: 0.6960 - val_loss: 0.4962 - val_accuracy: 0.8120\n",
            "Epoch 87/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5778 - accuracy: 0.7060 - val_loss: 0.4833 - val_accuracy: 0.8320\n",
            "Epoch 88/100\n",
            "16/16 [==============================] - 0s 8ms/step - loss: 0.5609 - accuracy: 0.7360 - val_loss: 0.4989 - val_accuracy: 0.8200\n",
            "Epoch 89/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5544 - accuracy: 0.7100 - val_loss: 0.4885 - val_accuracy: 0.8220\n",
            "Epoch 90/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5258 - accuracy: 0.7420 - val_loss: 0.4846 - val_accuracy: 0.8100\n",
            "Epoch 91/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5278 - accuracy: 0.7420 - val_loss: 0.4831 - val_accuracy: 0.8520\n",
            "Epoch 92/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5183 - accuracy: 0.7620 - val_loss: 0.4824 - val_accuracy: 0.8300\n",
            "Epoch 93/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5461 - accuracy: 0.7400 - val_loss: 0.4824 - val_accuracy: 0.8160\n",
            "Epoch 94/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5546 - accuracy: 0.6960 - val_loss: 0.4825 - val_accuracy: 0.8420\n",
            "Epoch 95/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5381 - accuracy: 0.7400 - val_loss: 0.4806 - val_accuracy: 0.8240\n",
            "Epoch 96/100\n",
            "16/16 [==============================] - 0s 8ms/step - loss: 0.5338 - accuracy: 0.7320 - val_loss: 0.4802 - val_accuracy: 0.8200\n",
            "Epoch 97/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5516 - accuracy: 0.7080 - val_loss: 0.4771 - val_accuracy: 0.8200\n",
            "Epoch 98/100\n",
            "16/16 [==============================] - 0s 6ms/step - loss: 0.6051 - accuracy: 0.6500 - val_loss: 0.4834 - val_accuracy: 0.8280\n",
            "Epoch 99/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5761 - accuracy: 0.7340 - val_loss: 0.4757 - val_accuracy: 0.8240\n",
            "Epoch 100/100\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5663 - accuracy: 0.7100 - val_loss: 0.4737 - val_accuracy: 0.8480\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluate the model\n",
        "_, train_acc = model.evaluate(trainX, trainy, verbose=1)\n",
        "_, test_acc = model.evaluate(testX, testy, verbose=1)\n",
        "print('Train: %.3f, Test: %.3f' % (train_acc, test_acc))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oa1Vkrqs2FRu",
        "outputId": "e433d618-df8b-4389-c479-07896013ee81"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16/16 [==============================] - 0s 4ms/step - loss: 0.4763 - accuracy: 0.8300\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.4737 - accuracy: 0.8480\n",
            "Train: 0.830, Test: 0.848\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot history\n",
        "pyplot.plot(history.history['accuracy'], label='train')\n",
        "pyplot.plot(history.history['val_accuracy'], label='test')\n",
        "pyplot.legend()\n",
        "pyplot.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "qmmQtBfr2Qss",
        "outputId": "02aa769f-e5b5-4906-be76-90f6adf55fc5"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "-qaVR5nN2Tcf"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}