吴裕雄 python 神经网络——TensorFlow 完整神经网络样例程序

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)

X = rdm.rand(128,2)

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)

    # 输出目前（未经训练）的参数取值。

    print(sess.run(w1))

    print(sess.run(w2))

    print("\n")

    # 训练模型。

    STEPS = 5000

    for i in range(STEPS):

        start = (i*batch_size) % 128

        end = (i*batch_size) % 128 + batch_size

        sess.run([train_step, y, y_], 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 step(s), cross entropy on all data is %g" % (i, total_cross_entropy))

    # 输出训练后的参数取值。

    print("\n")

    print(sess.run(w1))

    print(sess.run(w2))