tensroflow建立一个完整的单层网络

传送门
学习这一部分代码还是收获了很多的，虽然还是有的地方不是很理解，但是之后看的代码多了就会理解了。首先，写一下我的心得体会。
1、add_layer()函数是作者定义的函数，很方便，可以参考
2、变量定义的方式也很厉害，是numpy部分内容。可以定义x_data.shape大小的正态分布noise。

x_data = np.linspace(-1,1,300)[:, np.newaxis]noise = np.random.normal(0, 0.05, x_data.shape)y_data = np.square(x_data) - 0.5 + noise

其中是啥意思，不是很理解，可以看看numpy手册

x_data = np.linspace(-1,1,300)[:, np.newaxis]

3、placeholder定义占位符，可以用none的形式定义随机长度的输入

xs = tf.placeholder(tf.float32, [None, 1])

4、定义训练，设置训练步长

train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

5、对于有placeholder的输入，session的run要加上feed_dict，同时，读取一个loss变量值也要加上这一个东西

sess.run(train_step, feed_dict={xs: x_data, ys: y_data})    if i % 50 == 0:        # to see the step improvement        print(sess.run(loss, feed_dict={xs: x_data, ys: y_data}))

完整代码如下：

# View more python learning tutorial on my Youtube and Youku channel!!!# Youtube video tutorial: https://www.youtube.com/channel/UCdyjiB5H8Pu7aDTNVXTTpcg# Youku video tutorial: http://i.youku.com/pythontutorial"""Please note, this code is only for python 3+. If you are using python 2+, please modify the code accordingly."""from __future__ import print_functionimport tensorflow as tfimport numpy as npdef add_layer(inputs, in_size, out_size, activation_function=None):    # add one more layer and return the output of this layer    Weights = tf.Variable(tf.random_normal([in_size, out_size]))    biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)    Wx_plus_b = tf.matmul(inputs, Weights) + biases    if activation_function is None:        outputs = Wx_plus_b    else:        outputs = activation_function(Wx_plus_b)    return outputs# Make up some real datax_data = np.linspace(-1,1,300)[:, np.newaxis]noise = np.random.normal(0, 0.05, x_data.shape)y_data = np.square(x_data) - 0.5 + noise# define placeholder for inputs to networkxs = tf.placeholder(tf.float32, [None, 1])ys = tf.placeholder(tf.float32, [None, 1])# add hidden layerl1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)# add output layerprediction = add_layer(l1, 10, 1, activation_function=None)# the error between prediction and real dataloss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),                     reduction_indices=[1]))train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)# important step# tf.initialize_all_variables() no long valid from# 2017-03-02 if using tensorflow >= 0.12if int((tf.__version__).split('.')[1]) < 12:    init = tf.initialize_all_variables()else:    init = tf.global_variables_initializer()sess = tf.Session()sess.run(init)for i in range(1000):    # training    sess.run(train_step, feed_dict={xs: x_data, ys: y_data})    if i % 50 == 0:        # to see the step improvement        print(sess.run(loss, feed_dict={xs: x_data, ys: y_data}))