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I am trying to use SSIM as loss value for my Keras Sequential model. The output value is a set of 6 images - (6, 32, 28, 3). I want to implement a custom loss function for the model.fit() function but cannot understand how to implement this. I am attaching what I have tried before-

def ssim_loss(y_true, y_pred):
    return 1-tf.reduce_mean(tf.image.ssim_multiscale(y_true, y_pred, 2.0))
def ssim_loss(y_true, y_pred):
  loss = 0
  for i in range(6):
    loss += tf.reduce_mean(tf.image.ssim_multiscale(y_true[i], y_pred[i], 2.0))
  return loss

y_true shape is same as y_pred which I have checked. These both solutions do not work. I do not know how to access each element of y_pred. My input shape is - (6, 192, 56, 3). Please help. TIA

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