Different loss function for validation set in Keras

Question

I have unbalanced training dataset, thats why I built custom weighted categorical cross entropy loss function. But the problem is my validation set is balanced one and I want to use the regular categorical cross entropy loss. So can I pass different loss function for validation set within Keras? I mean the wighted one for training and regular one for validation set?

def weighted_loss(y_pred, y_ture):
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return loss

model.compile(loss= weighted_loss, metric='accuracy')

Answer 1

You can try the backend function K.in_train_phase(), which is used by the Dropout and BatchNormalization layers to implement different behaviors in training and validation.

def custom_loss(y_true, y_pred):
    weighted_loss = ... # your implementation of weighted crossentropy loss
    unweighted_loss = K.sparse_categorical_crossentropy(y_true, y_pred)
    return K.in_train_phase(weighted_loss, unweighted_loss)

The first argument of K.in_train_phase() is the tensor used in training phase, and the second is the one used in test phase.

For example, if we set weighted_loss to 0 (just to verify the effect of K.in_train_phase() function):

def custom_loss(y_true, y_pred):
    weighted_loss = 0 * K.sparse_categorical_crossentropy(y_true, y_pred)
    unweighted_loss = K.sparse_categorical_crossentropy(y_true, y_pred)
    return K.in_train_phase(weighted_loss, unweighted_loss)

model = Sequential([Dense(100, activation='relu', input_shape=(100,)), Dense(1000, activation='softmax')])
model.compile(optimizer='adam', loss=custom_loss)
model.outputs[0]._uses_learning_phase = True  # required if no dropout or batch norm in the model

X = np.random.rand(1000, 100)
y = np.random.randint(1000, size=1000)
model.fit(X, y, validation_split=0.1)

Epoch 1/10
900/900 [==============================] - 1s 868us/step - loss: 0.0000e+00 - val_loss: 6.9438

As you can see, the loss in training phase is indeed the one multiplied by 0.

Note that if there's no dropout or batch norm in your model, you'll need to manually "turn on" the _uses_learning_phase boolean switch, otherwise K.in_train_phase() will have no effect by default.