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3_mnist_keras.py
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3_mnist_keras.py
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import tensorflow as tf
batch_size = 128
no_classes = 10
epochs = 50
image_height, image_width = 28, 28
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
x_train = x_train.reshape(x_train.shape[0], image_height, image_width, 1)
x_test = x_test.reshape(x_test.shape[0], image_height, image_width, 1)
input_shape = (image_height, image_width, 1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
y_train = tf.keras.utils.to_categorical(y_train, no_classes)
y_test = tf.keras.utils.to_categorical(y_test, no_classes)
def simple_cnn(input_shape):
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Conv2D(
filters=64,
kernel_size=(3, 3),
activation='relu',
input_shape=input_shape
))
model.add(tf.keras.layers.Conv2D(
filters=128,
kernel_size=(3, 3),
activation='relu'
))
model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))
model.add(tf.keras.layers.Dropout(rate=0.3))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(units=1024, activation='relu'))
model.add(tf.keras.layers.Dropout(rate=0.3))
model.add(tf.keras.layers.Dense(units=no_classes, activation='softmax'))
model.compile(loss=tf.keras.losses.categorical_crossentropy,
optimizer=tf.keras.optimizers.Adam(),
metrics=['accuracy'])
return model
simple_cnn_model = simple_cnn(input_shape)
simple_cnn_model.fit(x=x_train, y=y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test))
train_loss, train_accuracy = simple_cnn_model.evaluate(
x_train, y_train, verbose=0)
print('Train data loss:', train_loss)
print('Train data accuracy:', train_accuracy)
test_loss, test_accuracy = simple_cnn_model.evaluate(
x_test, y_test, verbose=0)
print('Test data loss:', test_loss)
print('Test data accuracy:', test_accuracy)