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- for i in range(0, encoded_dataset.shape[0], batch_size):
- y_true = tf.convert_to_tensor(encoded_dataset[i:i+batch_size].values,
- np.float32)
- y_pred= tf.convert_to_tensor(ae1.predict(encoded_dataset[i:i+batch_size].values),
- np.float32)
- # Append the batch losses (numpy array) to the list
- reconstruction_loss_transaction.append(K.eval(loss_function( y_true, y_pred)))
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