leNet_CNN_mnist.py

import tensorflow as tf
from tensorflow.keras import datasets, layers, models, optimizers


#define the convnet 
class LeNet:
	@staticmethod
	def build(input_shape, classes):
		model = models.Sequential()
		# CONV => RELU => POOL
		model.add(layers.Convolution2D(20, (5, 5), activation='relu',
			input_shape=input_shape))
		model.add(layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
		# CONV => RELU => POOL
		model.add(layers.Convolution2D(50, (5, 5), activation='relu'))
		model.add(layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
		# Flatten => RELU layers
		model.add(layers.Flatten())
		model.add(layers.Dense(500, activation='relu'))
		# a softmax classifier
		model.add(layers.Dense(classes, activation="softmax"))
		return model



# network and training
EPOCHS = 5
BATCH_SIZE = 128
VERBOSE = 1
OPTIMIZER = tf.keras.optimizers.Adam()
VALIDATION_SPLIT=0.90

IMG_ROWS, IMG_COLS = 28, 28 # input image dimensions
INPUT_SHAPE = (IMG_ROWS, IMG_COLS, 1)
NB_CLASSES = 10  # number of outputs = number of digits

# data: shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = datasets.mnist.load_data()

# reshape
X_train = X_train.reshape((60000, 28, 28, 1))
X_test = X_test.reshape((10000, 28, 28, 1))

# normalize
X_train, X_test = X_train / 255.0, X_test / 255.0

# cast
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')

print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
y_train = tf.keras.utils.to_categorical(y_train, NB_CLASSES)
y_test = tf.keras.utils.to_categorical(y_test, NB_CLASSES)

# initialize the optimizer and model
model = LeNet.build(input_shape=INPUT_SHAPE, classes=NB_CLASSES)
model.compile(loss="categorical_crossentropy", optimizer=OPTIMIZER,
	metrics=["accuracy"])
model.summary()

# use TensorBoard, princess Aurora!
callbacks = [
  # Write TensorBoard logs to `./logs` directory
  tf.keras.callbacks.TensorBoard(log_dir='./logs')
]

# fit 
history = model.fit(X_train, y_train, 
		batch_size=BATCH_SIZE, epochs=EPOCHS, 
		verbose=VERBOSE, validation_split=VALIDATION_SPLIT,
		callbacks=callbacks)

score = model.evaluate(X_test, y_test, verbose=VERBOSE)
print("\nTest score:", score[0])
print('Test accuracy:', score[1])