test.py

import tensorflow as tf
import numpy as np
def add_layer(inputs, in_size, out_size, activation_function=None):  # inputs.shape=[None,1],in_size.shape=1,out_size=10 |inputs.shape=[None,10]，in_size.shape=10,out_size=1
    with tf.name_scope('layer'):
        with tf.name_scope('weights'):
            Weights = tf.Variable(tf.random_normal([in_size, out_size]), name='w')  # [1,10] | [10,1]
        with tf.name_scope('bias'):
            biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, name='b')  # [1,10]，这个1跟上面那个1貌似不是一个意思 | [1,1]
        with tf.name_scope('wx_plus_b'):
            Wx_plus_b = tf.matmul(inputs, Weights) + biases  # 注意这里顺序，inputs在前。[None,1]×[1,10]+[1,10]（python广播）=[None,10] | [None,10]×[10,1]+[1,1]（python广播）=[None,1]
        if activation_function is None:
            outputs = Wx_plus_b
        else:
            outputs = activation_function(Wx_plus_b)
        return outputs

# 训练集数据和标签
train_data_x = np.linspace(-1,1,300, dtype=np.float32)[:, np.newaxis]  # shape=(300,1)
noise = np.random.normal(0, 0.05, train_data_x.shape).astype(np.float32)
label_y = np.square(train_data_x) - 0.5 + noise
with tf.name_scope('inputs'):
    inputer_x = tf.placeholder(tf.float32, [None, 1], name='inputer_x')
    inputer_y = tf.placeholder(tf.float32, [None, 1], name='inputer_y')

# 设计的网络结构是1-10-1的
l1 = add_layer(inputer_x, 1, 10, activation_function=tf.nn.relu)
prediction = add_layer(l1, 10, 1, activation_function=None)
with tf.name_scope('loss'):
    loss = tf.reduce_mean(tf.reduce_sum(tf.square(inputer_y-prediction), reduction_indices=[1]))
with tf.name_scope('train_scope'):
    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

sess = tf.Session()

init = tf.global_variables_initializer()
sess.run(init)
writer = tf.summary.FileWriter("logs", sess.graph)  # 文件写在该.py文件同级，在命令行中，用tensorboard --logdir=.打开

for i in range(1000):
    # training
    sess.run(train_step, feed_dict={inputer_x: train_data_x, inputer_y: label_y})
    if i % 50 == 0:
        # to see the step improvement
        print(sess.run(loss, feed_dict={inputer_x: train_data_x, inputer_y: label_y}))