我需要在Python中使用keras实现自定义损失功能。我已经在另一个website上找到了一个示例。但是,这并不完全容易理解。我对这些密集和输入工具不是很熟悉。你们中的任何人都可以评论我找到的代码并帮助理解它吗?此外,如何初始化input
,layer1
和layer2
?每当我更改代码中的某些内容时,都会收到一堆错误。
# Build a model
inputs = Input(shape=(128,))
layer1 = Dense(64, activation='relu')(inputs)
layer2 = Dense(64, activation='relu')(layer1)
predictions = Dense(10, activation='softmax')(layer2)
model = Model(inputs=inputs, outputs=predictions)
# Define custom loss
def custom_loss(layer):
# Create a loss function that adds the MSE loss to the mean of all squared activations of a specific layer
def loss(y_true,y_pred):
return K.mean(K.square(y_pred - y_true) + K.square(layer), axis=-1)
# Return a function
return loss
# Compile the model
model.compile(optimizer='adam',
loss=custom_loss(layer), # Call the loss function with the selected layer
metrics=['accuracy'])
# train
model.fit(data, labels)