如何禁用某些图层的培训？
我正在尝试标准方式：
def get_model():
weight_decay=0.0
inputs = Input(shape=(INPUT_IMAGE_H, INPUT_IMAGE_W, 3))
# Block 1
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1', kernel_regularizer=l2(weight_decay))(inputs)
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2', kernel_regularizer=l2(weight_decay))(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
# Block 2
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1', kernel_regularizer=l2(weight_decay))(x)
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2', kernel_regularizer=l2(weight_decay))(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
# Block 3
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1', kernel_regularizer=l2(weight_decay))(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2', kernel_regularizer=l2(weight_decay))(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3', kernel_regularizer=l2(weight_decay))(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
# Block 4
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1', kernel_regularizer=l2(weight_decay))(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2', kernel_regularizer=l2(weight_decay))(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3', kernel_regularizer=l2(weight_decay))(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
# Block 5
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1', kernel_regularizer=l2(weight_decay))(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2', kernel_regularizer=l2(weight_decay))(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3', kernel_regularizer=l2(weight_decay))(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
# Convolutional layers transfered from fully-connected layers
x = Conv2D(4096, (7, 7), activation='relu', padding='same', name='fc1', kernel_regularizer=l2(weight_decay))(x)
x = Dropout(0.5)(x)
x = Conv2D(4096, (1, 1), activation='relu', padding='same', name='fc2', kernel_regularizer=l2(weight_decay))(x)
x = Dropout(0.5)(x)
# Classifying layer
x = Conv2D(NUMBER_OF_CLASSES, (1, 1), kernel_initializer='he_normal', activation='sigmoid', padding='valid', strides=(1, 1), kernel_regularizer=l2(weight_decay))(x)
x = BilinearUpSampling2D(size=(32, 32))(x)
model = Model(inputs, x)
weights_path = os.path.expanduser(os.path.join('~', '.keras/models/fcn_vgg16_weights_tf_dim_ordering_tf_kernels.h5'))
model.load_weights(weights_path, by_name=True)
#Freeze layers
for layer in model.layers[-8]:
layer.trainable = False
model.compile(optimizer=Adadelta(), loss='binary_crossentropy')
print('len(model.layers)', len(model.layers)) #
print(model.summary()) #
return model
model = get_model()
但是得到错误：
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-23-849aac962bb2> in <module>()
71 return model
72
---> 73 model = get_model()
<ipython-input-23-849aac962bb2> in get_model()
61
62 #Freeze layers
---> 63 for layer in model.layers[-8]:
64 layer.trainable = False
65
TypeError: 'Conv2D' object is not iterable
它也不会像这样工作：
def get_model():
weight_decay=0.0
#inputs = Input((None, None, 3)) # will not work with BilinearUpSampling2D
#inputs = Input((INPUT_IMAGE_H, INPUT_IMAGE_W, 3))
inputs = Input(shape=(INPUT_IMAGE_H, INPUT_IMAGE_W, 3))
# Block 1
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1', kernel_regularizer=l2(weight_decay))(inputs)
x.trainable = False
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2', kernel_regularizer=l2(weight_decay))(x)
x.trainable = False
x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
# Block 2
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1', kernel_regularizer=l2(weight_decay))(x)
x.trainable = False
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2', kernel_regularizer=l2(weight_decay))(x)
x.trainable = False
x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
# Block 3
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1', kernel_regularizer=l2(weight_decay))(x)
x.trainable = False
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2', kernel_regularizer=l2(weight_decay))(x)
x.trainable = False
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3', kernel_regularizer=l2(weight_decay))(x)
x.trainable = False
x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
# Block 4
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1', kernel_regularizer=l2(weight_decay))(x)
x.trainable = False
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2', kernel_regularizer=l2(weight_decay))(x)
x.trainable = False
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3', kernel_regularizer=l2(weight_decay))(x)
x.trainable = False
x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
# Block 5
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1', kernel_regularizer=l2(weight_decay))(x)
x.trainable = False
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2', kernel_regularizer=l2(weight_decay))(x)
x.trainable = False
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3', kernel_regularizer=l2(weight_decay))(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
# Convolutional layers transfered from fully-connected layers
x = Conv2D(4096, (7, 7), activation='relu', padding='same', name='fc1', kernel_regularizer=l2(weight_decay))(x)
x = Dropout(0.5)(x)
x = Conv2D(4096, (1, 1), activation='relu', padding='same', name='fc2', kernel_regularizer=l2(weight_decay))(x)
x = Dropout(0.5)(x)
# Classifying layer
x = Conv2D(NUMBER_OF_CLASSES, (1, 1), kernel_initializer='he_normal', activation='sigmoid', padding='valid', strides=(1, 1), kernel_regularizer=l2(weight_decay))(x)
x = BilinearUpSampling2D(size=(32, 32))(x)
model = Model(inputs, x)
weights_path = os.path.expanduser(os.path.join('~', '.keras/models/fcn_vgg16_weights_tf_dim_ordering_tf_kernels.h5'))
model.load_weights(weights_path, by_name=True)
model.compile(optimizer=Adadelta(), loss='binary_crossentropy')
print('len(model.layers)', len(model.layers)) #
print(model.summary()) #
return model
model = get_model()
正如我在模型摘要中所看到的，所有权重都是可训练的：
Total params: 134,272,835
Trainable params: 134,272,835
Non-trainable params: 0