通过tensorflow 建立神经网络

import tensorflow as tf

import numpy as np

#添加神经层

def add_layer(inputs,in_size,out_size,activation_function=None):

    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

#设置输入数据

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

#设置噪音数据

noise = np.random.normal(0,0.05,x_data.shape)

#设置预期输出

y_data = np.square(x_data)-0.5 + noise

#设置传入变量

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

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

#第一层，隐藏层，1个输入，10个输出（10个神经元）

l1 = add_layer(xs,1,10,activation_function=tf.nn.relu)

#输出层，一个输出

prediction = add_layer(l1,10,1,activation_function=None)

#误差/代价

loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1]))

#最优化过程

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

init = tf.initialize_all_variables()

sess = tf.Session()

sess.run(init)

训练和输出

for i in range(1000):

    sess.run(train_step,feed_dict={xs:x_data,ys:y_data})

    if i % 50 == 0:

        print sess.run(loss,feed_dict={xs:x_data,ys:y_data})