mnist3.py

# Importing the required Keras modules containing model and layers
import tensorflow as tf
from tensorflow.keras import layers

print(tf.__version__)
print("GPU Available: ", tf.test.is_gpu_available())
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

# Reshaping the array to 4-dims so that it can work with the Keras API
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)

x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
input_shape = (28, 28, 1)
# Making sure that the values are float so that we can get decimal points after division
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
# Normalizing the RGB codes by dividing it to the max RGB value.
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print('Number of images in x_train', x_train.shape[0])
print('Number of images in x_test', x_test.shape[0])

# Creating a Sequential Model and adding the layers
model = tf.keras.Sequential()
model.add(layers.Conv2D(28, kernel_size=(3, 3), input_shape=input_shape))
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
model.add(layers.Flatten())  # Flattening the 2D arrays for fully connected layers
model.add(layers.Dense(128, activation=tf.nn.relu))
model.add(layers.Dropout(0.2))
model.add(layers.Dense(10, activation=tf.nn.softmax))

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
model.fit(x=x_train, y=y_train, epochs=10)