Vladislav Ladenkov Vladislav Ladenkov - 1 year ago 243
Python Question

Tensor Flow tutorial logloss implementation

I need to study TF in the express way and i cant understant this part:

cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))

It's explained with this: First, tf.log computes the logarithm of each element of y. Next, we multiply each element of y_ with the corresponding element of tf.log(y). Then tf.reduce_sum adds the elements in the second dimension of y, due to the reduction_indices=[1] parameter. Finally, tf.reduce_mean computes the mean over all the examples in the batch.

Why it does this manipulations, which are marked bold? why do wee need another dimensiom? Thanks

Answer Source

There are two dimensions because cross_entropy computes values for a batch of training examples. Therefore, the dimension 0 is for a batch, and dimension 1 is for different classes of a specific example. For example, if there are 3 possible classes and batch size is 2, then y is a 2D tensor of size (2, 3).

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