神经系统的搭建
import tensorflow as tf
import numpy as np
import matplotlib,pylab as plt
def add_layer(input,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(input,Weights)+biases
if activation_function is None:
outputs = Wx_plus_b
else:
outputs = activation_function(Wx_plus_b)
return outputs
#输入层到神经层
x_data = np.linspace(-1,1,300)[:,np.newaxis] #产生等差数列
noise = np.random.normal(0,0.05,x_data.shape)
y_data = np.square(x_data)-0.5+noise #对x_data进行平方
xs = tf.placeholder(tf.float32,[None,1])
ys = tf.placeholder(tf.float32,[None,1])
#神经层到输出层
l1 = add_layer(xs,1,10,activation_function=tf.nn.relu)
prediction = add_layer(l1,10,1,activation_function=None)
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1])) #将每个y_data-prediction值取平方,然后求和后去平均值
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