Hands-On Deep Learning Algorithms with Python

Defining the convolutional network

Our network architecture consists of two convolutional layers. Each convolutional layer is followed by one pooling layer, and we use a fully connected layer that is followed by an output layer; that is, conv1->pooling->conv2->pooling2->fully connected layer-> output layer.

First, we define the first convolutional layer and pooling layer.

The weights are actually the filters in the convolutional layers. So, the weight matrix will be initialized as [ filter_shape[0], filter_shape[1], number_of_input_channel, filter_size ].

We use a 5 x 5 filter. Since we use grayscale images, the number of input channels will be 1 and we set the filter size as 32. So, the weight matrix of the first convolution layer ...