TensorFlow Logistic Regression tutorial

tf_logistic_reg.py

#
#   tf_logistic_reg.py
#       date. 11/18/2015
#       date. 1/29/2017 - checked under tensorflow 0.12.1
#
#   The baseline is the code for 'Theano'.

from __future__ import print_function

import numpy as np
import tensorflow as tf

def prep_dataset():
    n_betl = np.array([59, 60, 62, 56, 63, 59, 62, 60])
    y_betl = np.array([6, 13, 18, 28, 52, 53, 61, 60])
    x1 = np.array([1.6907, 1.7242, 1.7552, 1.7842,
        1.8113, 1.8369, 1.8610, 1.8839])
    # prep data arrays by expanding tabulated data
    resp_expand = np.array([])
    x1_expand = np.array([])

    for i in range(len(n_betl)):
        ni = n_betl[i] ; yi = y_betl[i]
        resp_expand = np.append(resp_expand, np.repeat(0, (ni-yi)))
        resp_expand = np.append(resp_expand, np.repeat(1, yi))
        x1_expand = np.append(x1_expand, np.repeat(x1[i], ni))

    return x1_expand, resp_expand

trX, trY = prep_dataset()
mlen = len(trY)

def linear_model(X, w, b):
    return X * w + b

w = tf.Variable([0.])
b = tf.Variable([0.])

x = tf.placeholder(tf.float32, shape=(mlen))
y = tf.placeholder(tf.float32, shape=(mlen))

p_1 = linear_model(x, w, b)
x_entropy = tf.nn.sigmoid_cross_entropy_with_logits(p_1, y, name='xentropy')
loss = tf.reduce_mean(x_entropy, name='xentropy_mean')

train_step = tf.train.GradientDescentOptimizer(0.5).minimize(loss)
init_op = tf.global_variables_initializer()

# Train
with tf.Session() as sess:
    sess.run(init_op)

    for i in range(100001):
        sess.run(train_step, feed_dict={x: trX, y: trY})
        
        if i % 10000 == 0:
            print("%8d:(w, b)=(%8.4f, %8.4f)" % (i, sess.run(w), sess.run(b)))

# Regression parameter will be
#    (w, b) = 34.37, -60.71
# Step = 0
#       0:(w, b)=(  0.1048,   0.0525)
#     .  .  .
# Step = 100,000
#  100000:(w, b)=( 33.4332, -59.2280)
#