卷积神经网络损失

Question

While Calculating the Loss Function. Can i manually Calculate Loss like

Loss = tf.reduce_mean(tf.square(np.array(Prediction) - np.array(Y)))

and then Optimize this Loss using Adam Optimizer

Answer 1

No, actually you need to use tensor Variable for Loss , not use numpy.array ( np.array(Prediction) ).

Since tensorflow will eval these tensors in tensorflow engine.

Answer 2

No. Tensorflow loss functions typically accept tensors as input and also outputs a tensor. So np.array() wouldn't work.

In case of CNNs, you'd generally come across loss functions like cross-entropy, softmax corss-entropy, sigmoid cross-entropy etc. These are already in-built in tf.losses module. So you can use them directly. The loss function that you're trying to apply looks like a Mean-squared loss. This is built in tf.losses as well. tf.losses.mean_squared_error.

Having said that, I've also implemented a few loss functions like cross-entropy using hand-coded formula such as: -tf.reduce_mean(tf.reduce_sum(targets * logProb)) . This works equally fine, as long as the inputs targets and logProb are computed as tensors and not as numpy arrays.