logistic_regression_log_loss

Example Python program logistic_regression_log_loss.py Python version 3.x or newer. To check the Python version use:

python --version

Modules

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

Code

Python example

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

samples = 1000
data = [1e-2*float(i) for i in range(-samples, samples)]
label = [1 if i>3.14 else 0 for i in range(-samples, samples)]
plt.scatter(data, label, 1, 'r')

x = tf.placeholder(tf.float32)
y_ = tf.placeholder(tf.float32)

w = tf.Variable(0.0, dtype=tf.float32)
b = tf.Variable(0.0, dtype=tf.float32)

y = tf.nn.sigmoid(w*x + b)

loss = tf.losses.log_loss(y_, y)
train_op = tf.train.GradientDescentOptimizer(1e-2).minimize(loss)

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    for i in range(10000):
        _, _loss = sess.run([train_op, loss], {x: data, y_: label})
        if i%100 == 0:
            _pred = sess.run(y, {x: data})
            plt.scatter(data, label, 1, 'b')
            plt.scatter(data, _pred, 1, 'r')
            print('step: {}, loss: {}'.format(i, _loss))

Useful Links

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