Keras custom loss function: Accessing current input pattern

Question

In Keras (with Tensorflow backend), is the current input pattern available to my custom loss function?

The current input pattern is defined as the input vector used

Answer 1

You can wrap the loss function as a inner function and pass your input tensor to it (as commonly done when passing additional arguments to the loss function).

def custom_loss_wrapper(input_tensor):
    def custom_loss(y_true, y_pred):
        return K.binary_crossentropy(y_true, y_pred) + K.mean(input_tensor)
    return custom_loss

input_tensor = Input(shape=(10,))
hidden = Dense(100, activation='relu')(input_tensor)
out = Dense(1, activation='sigmoid')(hidden)
model = Model(input_tensor, out)
model.compile(loss=custom_loss_wrapper(input_tensor), optimizer='adam')

You can verify that input_tensor and the loss value (mostly, the K.mean(input_tensor) part) will change as different X is passed to the model.

X = np.random.rand(1000, 10)
y = np.random.randint(2, size=1000)
model.test_on_batch(X, y)  # => 1.1974642

X *= 1000
model.test_on_batch(X, y)  # => 511.15466