Keras’ backend has
print_tensor which enables you to do this. You can use it this way:
import keras.backend as K def loss_fn(y_true, y_pred): y_true = K.print_tensor(y_true, message="y_true = ") y_pred = K.print_tensor(y_pred, message="y_pred = ") ...
The function returns an identical tensor. When that tensor is evaluated, it will print its content, preceded by
From the Keras docs:
Note that print_tensor returns a new tensor identical to x which should be used in the following code. Otherwise the print operation is not taken into account during evaluation.
So, make sure to use the tensor afterwards.