Keras Tensorflow Custom loss function debug

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I'm having trouble debugging my custom loss function in keras. 

def custom_loss_wrapper(input_tensor):
   def custom_loss(y_true, y_pred):
      diff = y_pred - y_true
      diff = kb.print_tensor(diff)
      print(input_tensor)
   return kb.square(diff)
return custom_loss

model.compile(optimizer='adam',loss=custom_loss_wrapper(model.input)) 
model.fit(x=training_set,y=target_set,epochs=100)

Output:

Tensor("lstm_29_input:0", shape=(?, 1, 5), dtype=float32)
Epoch 1/100 61550/61550 [==============================] - 13s 217us/step - loss: 0.0049
etc.

In particular, I cant seem to make prints of any kind works while the model is training. I tried tf.Print and Theano Print but to no avail. When I try with a normal print it gets printed only once (when it gets compiled i presume). Also, I tried to access the values of input_tensor (here I tried with various methods like kb.eval, converting to NumPy array,etc.) and it seems that my input_tensor is only a placeholder tensor. That again, contains no value because custom_loss is being executed during the compilation I presume. How I do access to input_tensor at runtime?

keras tensorflow python
1 Answers

You can define your model.fit as a loop displaying all the required fields like below - 

for epoch in range(1,5):
        model.fit(x, y, batch_size=64, epochs= epoch, initial_epoch = (epoch-1), verbose=1, validation_split=0.2, shuffle=True)
        inputs = model.model._feed_inputs + model.model._feed_targets + model.model._feed_sample_weights
        print(model.input)
        print(model.total_loss)
        print(model.trainable_weights)
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