Training day

Now, we arrive at the fun part—the neural network. The complete code to train this model is available at the following link: https://github.com/mlwithtf/mlwithtf/blob/master/chapter_02/training.py

To train the model, we'll import several more modules:

 import sys, os
 import tensorflow as tf
 import numpy as np
 sys.path.append(os.path.realpath('..'))
 import data_utils
 import logmanager 

Then, we will define a few parameters for the training process:

 batch_size = 128
 num_steps = 10000
 learning_rate = 0.3
 data_showing_step = 500

After that, we will use the data_utils package to load the dataset that was downloaded in the previous section:

 dataset, image_size, num_of_classes, num_of_channels =  
 data_utils.prepare_not_mnist_dataset(root_dir="..")
 dataset = data_utils.reformat(dataset, image_size, num_of_channels,   
 num_of_classes)
 print('Training set', dataset.train_dataset.shape,  
 dataset.train_labels.shape)
 print('Validation set', dataset.valid_dataset.shape,  
 dataset...