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 message
.
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.
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