Building the network

Multiple deep learning frameworks have already implemented APIs for loading the F-MNIST dataset, including TensorFlow. For our implementation, we will be using Keras, another popular deep learning framework that is integrated with TensorFlow. The Keras datasets module provides a highly convenient interface for loading the datasets as numpy arrays.

Finally, we can start coding! For this exercise, we only need one Python module, which we will call cnn.py. Open up your favorite text editor or IDE, and let's get started.

Our first step is to declare the modules that we are going to use:

import logging
import os
import sys

logger = logging.getLogger(__name__)

import tensorflow as tf
import numpy as np
from keras.datasets import fashion_mnist
from keras.utils import np_utils

The following describes what each module is for and how we will use it:

We will implement our CNN as a class called SimpleCNN. The __init__ constructor takes a number of parameters:

class SimpleCNN(object):

    def __init__(self, learning_rate, num_epochs, beta, batch_size):
        self.learning_rate = learning_rate
        self.num_epochs = num_epochs
        self.beta = beta
        self.batch_size = batch_size
        self.save_dir = "saves"
        self.logs_dir = "logs"
        os.makedirs(self.save_dir, exist_ok=True)
        os.makedirs(self.logs_dir, exist_ok=True)
        self.save_path = os.path.join(self.save_dir, "simple_cnn")
        self.logs_path = os.path.join(self.logs_dir, "simple_cnn")

The parameters our SimpleCNN is initialized with are described here:

Moreover, save_dir and save_path refer to the locations where we will store our network's parameters. logs_dir and logs_path refer to the locations where the statistics of the training run will be stored (we will show how we can retrieve these logs later).