根据原始张量的元素索引生成新的张量流张量

Question

I have a question about tensorflow tensor.

If I have a NeuralNet like y=xw+b as an example.

then x is placeholder([7,7] dims), w is Variable([7,1]) and b is Variable([1,1])

So, y is tensorflow tensor with [7,1] dims.

then, in this case. can I make a new tensor like

new_y = [tf.reduce_sum(y[0:3]), tf.reduce_sum(y[3:5]), tf.reduce_sum(y[5:])]

and use it for training step?

If possible, how can I make it?

Answer 1

You should just make your label (y) in your reduced sum format (ie 3 bits), and train to that label. The neural net should be smart enough to adjust the weights to imitate your reduce_sum logic.