In the prior recipes, we have noted that the number of parameters we are fitting far exceeds the equivalent linear models. In this recipe, we will attempt to improve our logistic model of low birthweight with using a neural network.
For this recipe, we will load the low birth-weight data and use a neural network with two hidden fully connected layers with sigmoid activations to fit the probability of a low birth-weight.
We start by loading the libraries and initializing our computational graph:
import matplotlib.pyplot as plt import numpy as np import tensorflow as tf import requests sess = tf.Session()
Now we will load, extract, and normalize our data just like as in the prior recipe, except that we are going to using the low birthweight indicator variable as our target instead of the actual birthweight:
birthdata_url = 'https://www.umass.edu/statdata/statdata/data/lowbwt.dat'' birth_file = requests.get(birthdata_url) birth_data...