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I am using autoencoders to do anomaly detection. So, I have finished training my model and now I want to calculate the reconstruction loss for each entry in the dataset. so that I can assign anomalies to data points with high reconstruction loss.

This is my current code to calculate the reconstruction loss But this is really slow. By my estimation, it should take 5 hours to go through the dataset whereas training one epoch occurs in approx 55 mins. I feel that converting to tensor operation is bottlenecking the code, but I can't find a better way to do it.

I've tried changing the batch sizes but it does not make much of a difference. I have to use the convert to tensor part because K.eval is throwing an error if I do it normally.

python

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))) 

I was able to train in 55 mins per epoch. So I feel prediction should not take 5 hours per epoch. encoded_dataset is a variable that has the entire dataset in main memory as a data frame. I am using Azure VM instance. K.eval(loss_function(y_true,y_pred) is to find the loss for each row of the batch So y_true will be of size (batch_size,2000) and so will y_pred K.eval(loss_function(y_true,y_pred) will give me an output of

(batch_size,1) evaluating binary cross-entropy on each row of y _true and y_pred

1 Answer

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by (95.5k points)

The answer depends on how the loss function is implemented. Both the functions i.e. ae1.predict and K.eval(loss_function) will produce perfectly valid and identical results in TensorFlow under the hood. You could take the average of the loss over the batch before taking the gradient w.r.t. the loss, or take the gradient w.r.t. a vector of losses. The gradient operation in TensorFlow will perform the averaging of the losses for you.

You could define your own loss if Keras implements the loss function with reduce_mean that is built into the loss function. 

If you're using square loss i.e. replacing 'mean_squared_error' with lambda y_true, y_pred then the tf.square(y_pred - y_true) function will produce a square error instead of mean squared error(no difference to the gradient).

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