def LeNetModel(input_tensor):
layer1_conv = Conv2D(filters=6,
kernel_size=[5, 5],
strides=(1, 1),
activation='relu',
padding='SAME')(input_tensor)
layer2_pooling = MaxPooling2D(pool_size=[3, 3],
strides=[2, 2],
padding='SAME')(layer1_conv)
layer3_conv = Conv2D(filters=6,
kernel_size=[5, 5],
strides=(1, 1),
activation='relu',
padding='SAME')(layer2_pooling)
layer4_pooling = MaxPooling2D(pool_size=[3, 3],
strides=[2, 2],
padding='SAME')(layer3_conv)
layer5_conv = Conv2D(filters=16,
kernel_size=[5, 5],
strides=[1, 1],
activation='relu',
padding='SAME')(layer4_pooling)
shape = layer5_conv.get_shape().as_list()
flatten = Flatten()(layer5_conv)
flatten.set_shape([None, shape[1] * shape[2] * shape[3]])
layer6_fc1 = Dense(units=120, activation='relu')(flatten)
layer7_fc2 = Dense(units=84, activation='relu')(layer6_fc1)
layer8_fc3 = Dense(units=10, activation='relu')(layer7_fc2)
return layer8_fc3
def VGG16Model(input_tensor):
# input_tensor = Lambda(lambda x: ktf.resize_images(tf.assert_type(input_tensor, tf.float32), 224, 224, 'channels_last'))(input_tensor)
# Block1
x = Conv2D(64, (3, 3), strides=[1, 1], activation='relu', padding='same', name='block1_conv1')(input_tensor)
x = Conv2D(64, (3, 3), strides=[1, 1], activation='relu', padding='same', name='block1_conv2')(x)
x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same', name='block1_pooling')(x)
# Block2
x = Conv2D(64, (3, 3), strides=[1, 1], activation='relu', padding='same', name='block2_conv1')(x)
x = Conv2D(64, (3, 3), strides=[1, 1], activation='relu', padding='same', name='block2_conv2')(x)
x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same', name='block2_pooling')(x)
# Block3
x = Conv2D(128, (3, 3), strides=[1, 1], activation='relu', padding='same', name='block3_conv1')(x)
x = Conv2D(128, (3, 3), strides=[1, 1], activation='relu', padding='same', name='block3_conv2')(x)
x = Conv2D(128, (3, 3), strides=[1, 1], activation='relu', padding='same', name='block3_conv3')(x)
x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same', name='block3_pooling')(x)
# Block4
x = Conv2D(256, (3, 3), strides=[1, 1], activation='relu', padding='same', name='block4_conv1')(x)
x = Conv2D(256, (3, 3), strides=[1, 1], activation='relu', padding='same', name='block4_conv2')(x)
x = Conv2D(256, (3, 3), strides=[1, 1], activation='relu', padding='same', name='block4_conv3')(x)
x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same', name='block4_pooling')(x)
# Block5
x = Conv2D(512, (3, 3), strides=[1, 1], activation='relu', padding='same', name='block5_conv1')(x)
x = Conv2D(512, (3, 3), strides=[1, 1], activation='relu', padding='same', name='block5_conv2')(x)
x = Conv2D(512, (3, 3), strides=[1, 1], activation='relu', padding='same', name='block5_conv3')(x)
x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same', name='block5_pooling')(x)
shape = x.get_shape().as_list()
flatten = Flatten()(x)
flatten.set_shape([None, shape[1]*shape[2]*shape[3]])
layer6_fc1 = Dense(units=1024, activation='relu')(flatten)
layer7_fc2 = Dense(units=512, activation='relu')(layer6_fc1)
layer8_fc3 = Dense(units=10, activation='relu')(layer7_fc2)
return layer8_fc3
