Building Machine Learning Systems with Python - Third Edition

Useful operations

In all the previous TensorFlow models, we encountered functions that create layers in TensorFlow. There are a few layers that are more or less inescapable.

The first one is tf.dense, connecting all input to a new layer. We saw them in the auto-encoder example, and they take as an inputs parameter a tensor (variable, placeholder...) and then units the number of output units. By default, it also has bias, meaning that the layer computes inputs * weights + bias.

Another important layer that we will see later is conv2d. It computes a convolution on an image, and this times it takes the filters that will indicate the number of nodes in the output layer. It is what defines convolutional neural networks. Here is the usual formula ...