import tensorflow as tf
import numpy as np
def add_layer(inputs, input_size, output_size, activation_function = None):
Weights = tf.Variable(tf.random_normal([input_size, output_size]))
biases = tf.Variable(tf.zeros([1, output_size]) + 0.1) #biases初始化为0.1的列向量
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
#create_real data
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
#define placeholder for inputs to network
xs = tf.placeholder(dtype=tf.float32,shape=[None, 1])
ys = tf.placeholder(dtype=tf.float32,shape=[None, 1])
#add hiden layer 输入层输出层一个神经元（因为只有一个属性），隐层十个神经元
l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)
#add output layer
prediction = add_layer(l1, 10, 1, activation_function=None)
#the error between predic and real data
loss = tf.reduce_mean(tf.reduce_sum(tf.square(prediction - ys), reduction_indices=[1])) #相当于转化为横向量
train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)
recuction_indices = [1] 可以理解为axis = 1,都是设置对某一维度进行操作，具体以下链接 Tensorflow 的reduce_sum()函数到底是什么意思，谁能解释下？ - 黄璞的回答 - 知乎https://www.zhihu.com/question/51325408/answer/125426642
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