TensorFlow 1.x Deep Learning Cookbook

How to do it...

Here is how we proceed with the recipe:

1. The first step is to import all the packages that we will need:

import tensorflow as tf import numpy as np import matplotlib.pyplot as plt

2. We need to normalize the feature data since the data ranges of all the features are varied. We define a normalize function for it. Also, here we combine the bias to weights by adding another input always fixed to one value; to do this we define the function append_bias_reshape(). This technique is sometimes used to simplify programming:

def normalize(X)    """ Normalizes the array X """    mean = np.mean(X)    std = np.std(X)    X = (X - mean)/std    return Xdef append_bias_reshape(features,labels):    m = features.shape[0]    n = features.shape[1] x = np.reshape(np.c_[np.ones(m),features],[m,n ...