Loss function stops working when y_pred is modified

General Discussion
#1

I am trying to create a custom loss function but as soon as I try to create a copy of the y_pred (model predictions) tensor, the loss function stops working.

This function is working

def custom_loss(y_true, y_pred):
    y_true = tf.cast(y_true, dtype=y_pred.dtype)

    loss = binary_crossentropy(y_true, y_pred)

    return loss

The output is

Epoch 1/10
 26/26 [==============================] - 5s 169ms/step - loss: 56.1577 - accuracy: 0.7867 - val_loss: 14.7032 - val_accuracy: 0.9185
 Epoch 2/10
 26/26 [==============================] - 4s 159ms/step - loss: 18.6890 - accuracy: 0.8762 - val_loss: 9.4140 - val_accuracy: 0.9185
 Epoch 3/10
 26/26 [==============================] - 4s 158ms/step - loss: 13.7425 - accuracy: 0.8437 - val_loss: 7.7499 - val_accuracy: 0.9185
 Epoch 4/10
 26/26 [==============================] - 4s 159ms/step - loss: 10.5267 - accuracy: 0.8510 - val_loss: 6.1037 - val_accuracy: 0.9185
 Epoch 5/10
 26/26 [==============================] - 4s 160ms/step - loss: 7.5695 - accuracy: 0.8544 - val_loss: 3.9937 - val_accuracy: 0.9185
 Epoch 6/10
 26/26 [==============================] - 4s 159ms/step - loss: 5.1320 - accuracy: 0.8538 - val_loss: 2.6940 - val_accuracy: 0.9185
 Epoch 7/10
 26/26 [==============================] - 4s 160ms/step - loss: 3.3265 - accuracy: 0.8557 - val_loss: 1.6613 - val_accuracy: 0.9185
 Epoch 8/10
 26/26 [==============================] - 4s 160ms/step - loss: 2.1421 - accuracy: 0.8538 - val_loss: 1.0443 - val_accuracy: 0.9185
 Epoch 9/10
 26/26 [==============================] - 4s 160ms/step - loss: 1.3384 - accuracy: 0.8601 - val_loss: 0.5159 - val_accuracy: 0.9184
 Epoch 10/10
 26/26 [==============================] - 4s 173ms/step - loss: 0.6041 - accuracy: 0.8895 - val_loss: 0.3164 - val_accuracy: 0.9185
 testing
 **********Testing model**********
 training AUC : 0.6204090733263475
 testing AUC: 0.6196677312833667

But this is not working

def custom_loss(y_true, y_pred):
    y_true = tf.cast(y_true, dtype=y_pred.dtype)

    y_p = tf.identity(y_pred)

    loss = binary_crossentropy(y_true, y_p)

    return loss

I am getting this output

Epoch 1/10
26/26 [==============================] - 11s 179ms/step - loss: 1.3587 - accuracy: 0.9106 - val_loss: 1.2569 - val_accuracy: 0.9185
Epoch 2/10
26/26 [==============================] - 4s 159ms/step - loss: 1.2572 - accuracy: 0.9185 - val_loss: 1.2569 - val_accuracy: 0.9185
Epoch 3/10
26/26 [==============================] - 4s 158ms/step - loss: 1.2572 - accuracy: 0.9185 - val_loss: 1.2569 - val_accuracy: 0.9185
Epoch 4/10
26/26 [==============================] - 4s 158ms/step - loss: 1.2572 - accuracy: 0.9185 - val_loss: 1.2569 - val_accuracy: 0.9185
Epoch 5/10
26/26 [==============================] - 4s 158ms/step - loss: 1.2572 - accuracy: 0.9185 - val_loss: 1.2569 - val_accuracy: 0.9185
Epoch 6/10
26/26 [==============================] - 4s 158ms/step - loss: 1.2572 - accuracy: 0.9185 - val_loss: 1.2569 - val_accuracy: 0.9185
Epoch 7/10
26/26 [==============================] - 4s 159ms/step - loss: 1.2572 - accuracy: 0.9185 - val_loss: 1.2569 - val_accuracy: 0.9185
Epoch 8/10
26/26 [==============================] - 4s 159ms/step - loss: 1.2572 - accuracy: 0.9185 - val_loss: 1.2569 - val_accuracy: 0.9185
Epoch 9/10
26/26 [==============================] - 4s 160ms/step - loss: 1.2572 - accuracy: 0.9185 - val_loss: 1.2569 - val_accuracy: 0.9185
Epoch 10/10
26/26 [==============================] - 4s 159ms/step - loss: 1.2572 - accuracy: 0.9185 - val_loss: 1.2569 - val_accuracy: 0.9185
testing
**********Testing model**********
training AUC : 0.5
testing AUC : 0.5

Is there a problem with tf.identity() which is causing the issue?
Or is there any other way to copy tensors which I should be using?