Single hidden layer model

Here, we'll put the basics of neural network into practice. We'll adapt the logistic regression TenserFlow code into a single hidden layer of neurons. Then, you'll learn the idea behind backpropagation to compute the weights, that is, train the net. Finally, you'll train your first true neural network in TensorFlow.

The TensorFlow code for this section should look familiar. It's just a slightly evolved version of the logistic regression code. Let's look at how to add a hidden layer of neurons that will compute nonlinear combinations of our input pixels.

You should start with a fresh Python session, execute the code to read in, and set up the data as in the logistic model. It's the same code, just copied to the new file:

import tensorflow as tf
import numpy as np
import math
from tqdm import tqdm
%autoindent
try:
    from tqdm import tqdm
except ImportError:
    def tqdm(x, *args, **kwargs):
        return x

You can always go back...