Why do we need to worry about the batch dimension when specifying a model in Tensorflow?

Question

It seems a bit cumbersome to take into account the batch dimension for every layer in a neural network. Why don't we have some functionality in Tensorflow that can just set the batch size for an entire model?

Answer 1

In tensorflow you do not have to take into account the batch size.

In the MNIST Tutorial it's explained how tensorflow handles batches of every size.

Quoting the tutorial:

x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])

The input images x will consist of a 2d tensor of floating point numbers. Here we assign it a shape of [None, 784], where 784 is the dimensionality of a single flattened MNIST image, and None indicates that the first dimension, corresponding to the batch size, can be of any size.