TensorFlow 1.x Deep Learning Cookbook

How to do it...

We proceed with the recipe as follows:

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

import tensorflow as tfimport numpy as npimport matplotlib.pyplot as plt

2. In neural networks, all the inputs are added linearly to generate activity; for effective training, the inputs should be normalized, so we define a function to normalize the input data:

def normalize(X):    """ Normalizes the array X"""    mean = np.mean(X)    std = np.std(X)    X = (X - mean)/std    return X

3. Now we load the Boston house price dataset using TensorFlow contrib datasets and separate it into X_train and Y_train. We can choose to normalize the data here:

# Databoston = tf.contrib.learn.datasets.load_dataset('boston')X_train, Y_train = boston.data[:,5], ...