We've previously talked about a layer of a deep neural network consisting of multiple units (which we've been calling neurons) of a linear function, combined with some nonlinearity such as relu. In a convolutional layer, each unit is a filter, combined with a nonlinearity. For example, a convolutional layer might be defined in Keras as follows:
from keras.layers import Conv2DConv2D(64, kernel_size=(3,3), activation="relu", name="conv_1")
In this layer, there are 64 separate units, each a 3 x 3 x 3 filter. After the convolution operation is done, each unit adds a bias and a nonlinearity to the output as we did in traditional fully connected layers (more on that term in just a moment).
Before moving on, let's quickly ...
