SLP EXP-4

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Dense

from tensorflow.keras.activations import hard_sigmoid

import matplotlib.pyplot as plt

from keras.callbacks import History

import pandas as pd

import numpy as np

history=History()

if _name_ == "_main_":

    # Load the Pima diabetes dataset from CSV

    # and convert into a NumPy matrix suitable for

    # extraction into X, y format needed for TensorFlow

    diabetes = pd.read_csv('/home/user/Downloads/diabetes.csv').values

    # Extract the feature columns and outcome response

    # into appropriate variables

    X = diabetes[:, 0:8].astype(np.float32)

    y = diabetes[:, 8].astype(np.float32)

    # Create the 'Perceptron' using the Keras API

    model = Sequential()

    model.add(Dense(1, input_shape=(8,), activation=hard_sigmoid, kernel_initializer='glorot_uniform'))

    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

    # Train the perceptron using stochastic gradient descent

    # with a validation split of 20%

    history=model.fit(X, y, epochs=500, batch_size=25, verbose=1, validation_split=0.2)

    acc_train = history.history['accuracy']

    acc_val = history.history['val_accuracy']

    epochs = range(0,500)

    plt.plot(epochs, acc_train, 'g', label='Training accuracy')

    plt.plot(epochs, acc_val, 'b', label='Validation accuracy')

    plt.title('Training and Validation accuracy')

    plt.xlabel('Epochs')

    plt.ylabel('Accuracy')

    plt.legend()

    plt.show()

   

    # Evaluate the model accuracy

    _, accuracy = model.evaluate(X, y)

    print("%0.3f" % accuracy)