Practical Convolutional Neural Networks

Step 4 – Constructing the CNN layers

Once we have defined the CNN hyperparameters, the next task is to implement the CNN network. As you can guess, for our task, we will construct a CNN network having three convolutional layers, a flattened layer and two fully connected layers (refer to LayersConstructor.py). Moreover, we need to define the weight and the bias as well. Furthermore, we will have implicit max-pooling layers too. At first, let's define the weight. In the following, we have the new_weights() method that asks for the image shape and returns the truncated normal shapes:

def new_weights(shape): 
    return tf.Variable(tf.truncated_normal(shape, stddev=0.05)) 

Then we define the biases using the new_biases() method:

def new_biases(length): ...