Keras - K.print not working in Loss Function - tensorflow

Question

I have written a custom loss function adjusted_r2. I am trying to print the tensor values inside the function, but when Logs are printed, I don't see anything. Could somebody help me in this.
def coeff_determination(y_true, y_pred):
from keras import backend as K
SS_res = K.sum(K.square( y_true-y_pred ))
SS_tot = K.sum(K.square( y_true - K.mean(y_true) ) )
SS_res = K.print_tensor(SS_res, message='SS_res = ')
SS_tot = K.print_tensor(SS_tot, message='SS_tot = ')
r_squared = 1 - SS_res/(SS_tot + K.epsilon())
r_squared = K.print_tensor(r_squared, message='r_squared = ')
adj_r_squared = 1 -( (1-r_squared)*K.cast(K.shape(y_true)[0]-1,"float32")/K.cast((K.shape(y_true)[0]-n_features-1),"float32"))
adj_r_squared = K.print_tensor(adj_r_squared, message='adj_r_squared = ')
return -adj_r_squared
Logs are :
1/250 [..............................] - ETA: 51:44 - loss: -6.7060 - coeff_determination: -6.7060 - mean_squared_error: 40.5785
2/250 [..............................] - ETA: 42:56 - loss: -7.2036 - coeff_determination: -7.2036 - mean_squared_error: 48.8251
3/250 [..............................] - ETA: 41:30 - loss: -8.0279 - coeff_determination: -8.0279 - mean_squared_error: 48.1565
4/250 [..............................] - ETA: 40:48 - loss: -9.1016 - coeff_determination: -9.1016 - mean_squared_error: 51.9965

Answer 1

The K.print_tensor() function works when the tensors are evaluated (see documentation here). The tensors are not initialized when the custom loss function is being called. That is why you can not evaluate tensor values from within the loss function. The arguments to your custom loss function are tensors which work as placeholders, without having actual data attached to them.
The same problem has been also discussed in this thread.