Neural network sample

import tensorflow as tf
from numpy.random import RandomState

batch_size = 8

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

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)
y = tf.matmul(a, w2)

y = tf.sigmoid(y)
cross_entropy = -tf.reduce_mean(y_ * tf.log(tf.clip_by_value(y, 1e-10, 1.0)) + (1-y_) * tf.log(tf.clip_by_value(1-y, 1e-10, 1.0)))
train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entropy)

rdm = RandomState(1)
dataset_size = 128
X = rdm.rand(dataset_size, 2)
Y = [[int(x1+x2 < 1)] for (x1,x2) in X]

with tf.Session() as sess:
    init_op = tf.global_variables_initializer()
    sess.run(init_op)

    print(sess.run(w1))
    print(sess.run(w2))

    STEPS = 5000
    for i in range(STEPS):
        start = (i*batch_size) % dataset_size
        end = min(start+batch_size, dataset_size)

        sess.run(train_step, 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("After %d training steps, cross entropy on all data is %g" %(i, total_cross_entropy))
            # print("After %d training steps, cross entropy on all data is"  %i)
            # print( total_cross_entropy)

    print(sess.run(w1))
    print(sess.run(w2))