MLP Exp-5

 import tensorflow as tf

import numpy as np

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Flatten

from tensorflow.keras.layers import Dense

from tensorflow.keras.layers import Activation

import matplotlib.pyplot as plt

(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

# Cast the records into float values

x_train = x_train.astype('float32')

x_test = x_test.astype('float32')

  

# normalize image pixel values by dividing 

# by 255

gray_scale = 255

x_train /= gray_scale

x_test /= gray_scale

print("Feature matrix:", x_train.shape)

print("Target matrix:", x_test.shape)

print("Feature matrix:", y_train.shape)

print("Target matrix:", y_test.shape)

fig, ax = plt.subplots(10, 10)

k = 0

for i in range(10):

    for j in range(10):

        ax[i][j].imshow(x_train[k].reshape(28, 28), 

                        aspect='auto')

        k += 1

plt.show()

model = Sequential([

    

    # reshape 28 row * 28 column data to 28*28 rows

    Flatten(input_shape=(28, 28)),

    

      # dense layer 1

    Dense(256, activation='sigmoid'),  

    

    # dense layer 2

    Dense(128, activation='sigmoid'), 

    

      # output layer

    Dense(10, activation='sigmoid'),  

])

model.compile(optimizer='adam',

              loss='sparse_categorical_crossentropy',

              metrics=['accuracy'])

results = model.evaluate(x_test,  y_test, verbose = 0)

print('test loss, test acc:', results)