def model2(learning_rate=0.01, momentum=0.9, decay=1e-6):
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), input_shape=(28, 28, 1)))
model.add(
MaxPooling2D(pool_size=(2, 2),
strides=None,
padding='valid',
data_format=None))
model.add(Conv2D(16, kernel_size=(2, 2)))
model.add(
MaxPooling2D(pool_size=(2, 2),
strides=None,
padding='valid',
data_format=None))
model.add(Conv2D(16, kernel_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.2))
model.add(
Dense(num_classes,
activation='softmax',
kernel_regularizer=tf.keras.regularizers.l2(0.01)))
model.custom_name = 'model2'
sgd = tf.keras.optimizers.SGD(lr=learning_rate,
momentum=momentum,
decay=decay,
nesterov=False)
model.compile(optimizer=sgd,
loss='categorical_crossentropy',
metrics=['accuracy'])
print("Compiled model {} with lr={} momentum={}, decay={}".format(
model.custom_name, learning_rate, momentum, decay))
return model
def model3(learning_rate=0.01, momentum=0.9, decay=1e-6):
model = Sequential()
model.add(
Dense(128, activation='relu', input_shape=(features_train.shape[1], )))
model.add(Dropout(0.2))
model.add(Dense(64, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
model.custom_name = "model3"
sgd = tf.keras.optimizers.SGD(lr=learning_rate,
momentum=momentum,
decay=decay,
nesterov=False)
model.compile(optimizer=sgd,
loss='categorical_crossentropy',
metrics=['accuracy'])
print("Compiled model {} with lr={} momentum={}, decay={}".format(
model.custom_name, learning_rate, momentum, decay))
return model
