我目前正在尝试在 Keras 中实现自定义损失函数。但是，由于我的模型是 VGG 和我的自定义网络的组合，我无法获得子模型的输出。
这是网络定义：
model = VGG19(weights='imagenet', include_top=False)
#model.summary()
def change_model(model, new_input_shape=(None, 40, 40, 3)):
''' Change the input size of the provided network'''
# replace input shape of first layer
model._layers[0].batch_input_shape = new_input_shape
# rebuild model architecture by exporting and importing via json
new_model = keras.models.model_from_json(model.to_json())
# copy weights from old model to new one
for layer in new_model.layers:
try:
layer.set_weights(model.get_layer(name=layer.name).get_weights())
print("Loaded layer {}".format(layer.name))
except:
print("Could not transfer weights for layer {}".format(layer.name))
return new_model
new_model = change_model(model,new_input_shape=(None, 1024, 1024, 3))
new_model.summary()
for layer in new_model.layers:
layer.trainable = False
vector_1 = new_model.get_layer("block4_conv4").output
def create_detector_network(kernel_reg = 0.):
input = Input(shape=(128, 128, 512))
x = Conv2D(128, kernel_size=3, strides=1, name='detect_1', padding='same', kernel_regularizer=regularizers.l2(kernel_reg))(input)
x = BatchNormalization()(x)
x = Conv2D(1+pow(8,2), kernel_size=1, strides=1, name='detect_2', kernel_regularizer=regularizers.l2(kernel_reg))(x)
x = BatchNormalization()(x)
prob = Activation('softmax')(x)
prob = Lambda(lambda x: x[:,:, :, :-1], output_shape= (128, 128, 64))(prob) #x[:, :, :-1]
prob = keras.layers.UpSampling2D(size=(8, 8), data_format=None, interpolation='nearest')(prob)
prob = Conv2D(1, kernel_size=1, strides=1, name='reduce_dim')(prob)
return Model(input, [prob, x])
detector_model = create_detector_network()
detector_model.summary()
output = detector_model(vector_1)
full_model = Model(inputs=new_model.input, outputs=output)
总结可以在这里看到： 我的网络：
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_4 (InputLayer) (None, 128, 128, 512) 0
_________________________________________________________________
detect_1 (Conv2D) (None, 128, 128, 128) 589952
_________________________________________________________________
batch_normalization_3 (Batch (None, 128, 128, 128) 512
_________________________________________________________________
detect_2 (Conv2D) (None, 128, 128, 65) 8385
_________________________________________________________________
batch_normalization_4 (Batch (None, 128, 128, 65) 260
_________________________________________________________________
activation_2 (Activation) (None, 128, 128, 65) 0
_________________________________________________________________
lambda_2 (Lambda) (None, 128, 128, 64) 0
_________________________________________________________________
up_sampling2d_2 (UpSampling2 (None, 1024, 1024, 64) 0
_________________________________________________________________
reduce_dim (Conv2D) (None, 1024, 1024, 1) 65
=================================================================
Total params: 599,174
Trainable params: 598,788
Non-trainable params: 386
_________________________________________________________________
VGG + 我的网络：
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_7 (InputLayer) (None, 1024, 1024, 3) 0
_________________________________________________________________
block1_conv1 (Conv2D) (None, 1024, 1024, 64) 1792
_________________________________________________________________
block1_conv2 (Conv2D) (None, 1024, 1024, 64) 36928
_________________________________________________________________
block1_pool (MaxPooling2D) (None, 512, 512, 64) 0
_________________________________________________________________
block2_conv1 (Conv2D) (None, 512, 512, 128) 73856
_________________________________________________________________
block2_conv2 (Conv2D) (None, 512, 512, 128) 147584
_________________________________________________________________
block2_pool (MaxPooling2D) (None, 256, 256, 128) 0
_________________________________________________________________
block3_conv1 (Conv2D) (None, 256, 256, 256) 295168
_________________________________________________________________
block3_conv2 (Conv2D) (None, 256, 256, 256) 590080
_________________________________________________________________
block3_conv3 (Conv2D) (None, 256, 256, 256) 590080
_________________________________________________________________
block3_conv4 (Conv2D) (None, 256, 256, 256) 590080
_________________________________________________________________
block3_pool (MaxPooling2D) (None, 128, 128, 256) 0
_________________________________________________________________
block4_conv1 (Conv2D) (None, 128, 128, 512) 1180160
_________________________________________________________________
block4_conv2 (Conv2D) (None, 128, 128, 512) 2359808
_________________________________________________________________
block4_conv3 (Conv2D) (None, 128, 128, 512) 2359808
_________________________________________________________________
block4_conv4 (Conv2D) (None, 128, 128, 512) 2359808
_________________________________________________________________
model_7 (Model) (None, 128, 128, 65) 599109
=================================================================
Total params: 11,184,261
Trainable params: 598,723
Non-trainable params: 10,585,538
_________________________________________________________________
这就是我训练我的网络的方式：
losses = {
"detect_2": "mse",
"reduce_dim": "mse",
}
full_model.compile(optimizer='adam',
loss=losses,
loss_weights={'prob': 1.0, 'main': 0})
full_model.summary()
history = full_model.fit_generator(
train_generator,
steps_per_epoch=100,
epochs=7,
validation_data=validation_generator,
validation_steps=80)
现在，我想使用层 'detect_2' 和 'reduce_dim' 的输出并从中计算损失/准确性。但是，当我运行我的代码时，我收到以下错误：
ValueError: Unknown entry in loss dictionary: "detect_2". Only expected the following keys: ['model_7', 'model_7']
显然，某处一定有错误，因为字典不能有两次相同的键。那么有人能告诉我我需要改变什么才能得到图层的输出吗？