c3t4.py

import numpy as np
import tensorflow as tf

w1 = tf.Variable(tf.random_normal([2,3], stddev=1, seed=1), name='w1')
w2 = tf.Variable(tf.random_normal([3,1], stddev=1, seed=1), name='w2')

x = tf.placeholder(tf.float32, shape=[None,2], name='x-input')
y_ = tf.placeholder(tf.float32, shape=[None, 1], name='y-input')

a = tf.matmul(x, w1)
ypa = tf.matmul(a, w2)
y = tf.sigmoid(ypa)

cross_entropy = -tf.reduce_mean(y_ * tf.log(y))
train_op = tf.train.AdamOptimizer(0.001).minimize(cross_entropy)



np.random.seed(1)
dataset_size = 128
X = np.random.rand(dataset_size, 2)
Y = np.array([int(x1+x2<1) for (x1,x2) in X], dtype=np.float32)[None].T

batch_size = 8

with tf.Session() as sess:
    init_op = tf.global_variables_initializer()
    sess.run(init_op)
    print(w1.eval(sess))
    print(w2.eval(sess))

    STEPS = 5000
    for i in range(STEPS):
        start = (i * batch_size) % dataset_size
        end = min(start+batch_size, dataset_size)
        sess.run(train_op, feed_dict={x:X[start:end], y_:Y[start:end]})
        if i % 1000 == 0:
            total_cross_entropy = sess.run(cross_entropy, feed_dict={x:X, y_:Y})
            print(f'After {i} trainning step(s), cross entropy on all data is {total_cross_entropy:g}')


    print(w1.eval(sess))
    print(w2.eval(sess))