Given batched RGB images as input, shape=(batch_size, width, height, 3)
And a multiclass target represented as one-hot, shape=(batch_size, width, height, n_classes)
And a model (Unet, DeepLab) with softmax activation in last layer.
I’m looking for weighted categorical-cross-entropy loss funciton in kera/tensorflow.
class_weight argument in
fit_generator doesn’t seems to work, and I didn’t find the answer here or in https://github.com/keras-team/keras/issues/2115.
def weighted_categorical_crossentropy(weights): # weights = [0.9,0.05,0.04,0.01] def wcce(y_true, y_pred): # y_true, y_pred shape is (batch_size, width, height, n_classes) loos = ?... return loss return wcce
I will answer my question:
def weighted_categorical_crossentropy(weights): # weights = [0.9,0.05,0.04,0.01] def wcce(y_true, y_pred): Kweights = K.constant(weights) if not K.is_tensor(y_pred): y_pred = K.constant(y_pred) y_true = K.cast(y_true, y_pred.dtype) return K.categorical_crossentropy(y_true, y_pred) * K.sum(y_true * Kweights, axis=-1) return wcce
loss = weighted_categorical_crossentropy(weights) optimizer = keras.optimizers.Adam(lr=0.01) model.compile(optimizer=optimizer, loss=loss)
Answered By – Mendi Barel