Implementing a One-Layer Neural Network

We have all the tools to implement a neural network that operates on real data. We will create a neural network with one layer that operates on the Iris dataset.

Getting ready

In this section, we will implement a neural network with one hidden layer. It will be important to understand that a fully connected neural network is based mostly on matrix multiplication. As such, the dimensions of the data and matrix are very important to get lined up correctly.

Since this is a regression problem, we will use the mean squared error as the loss function.

How to do it…

1. To create the computational graph, we'll start by loading the necessary libraries:

   import matplotlib.pyplot as plt
   import numpy as np
   import tensorflow as tf
   from sklearn import datasets

2. Now we'll load the Iris data and store the pedal length as the target value. Then we'll start a graph session:

   iris = datasets.load_iris()
   x_vals = np.array([x[0:3] for x in iris.data])
   y_vals = np.array([x[3] for x in...