logistic regression example code

logistic_regression.py

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

samples = 1000
data = [[float(i)*0.01] for i in range(-samples, samples)]
label = [[1 if i[0] > 2.5 else 0] for i in data]

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

weight = tf.Variable(-0.9)
bias = tf.Variable(0.2)
y = x * weight + bias

loss = tf.losses.sigmoid_cross_entropy(y_, y)
train_op = tf.train.GradientDescentOptimizer(5e-1).minimize(loss)

pred = tf.nn.sigmoid(y)

accuracy = tf.metrics.accuracy(y_, tf.round(pred))


with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    sess.run(tf.local_variables_initializer())
    for i in range(100):
        _, _loss, _acc  = sess.run([train_op, loss, accuracy], {x: data, y_: label})
        print('step {}'.format(i))
        print('loss: {}'.format(_loss))
        print('accuracy: {}'.format(_acc))

        _pred = sess.run(pred, {x: data})
        plt.scatter(data, _pred, 1, 'b')
    plt.scatter(data, label, 1, 'r')