TensorFlow实现神经网络
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
# 1.生成训练样本
dataset_size=128
X=np.random.RandomState(1).uniform(0,1,(dataset_size,2))
Y=[[int(x1+x2<1)] for (x1,x2) in X]
for i in range(len(X)):
if Y[i][0]==1:
plt.scatter(X[i][0],X[i][1],c='r')
else:
plt.scatter(X[i][0],X[i][1],c='k')
plt.show()
# 2.定义训练数据batch大小
batchsize=8
# 3.定义神经网络参数
w1=tf.Variable(tf.random_normal([2,3],mean=0,stddev=1,seed=1))
w2=tf.Variable(tf.random_normal([3,1],mean=0,stddev=1,seed=1))
x=tf.placeholder(tf.float32,shape=(None,2),name='x-input')
y_=tf.placeholder(tf.float32,shape=(None,1),name='y-input')
# 前向传播
a=tf.matmul(x,w1)
y=tf.matmul(a,w2)
# 损失函数
cross_entropy=-tf.reduce_mean(y_*tf.log(tf.clip_by_value(y,1e-10,1.0)))
train_step=tf.train.AdamOptimizer(0.001).minimize(cross_entropy)
# 创建会话运行tensorflow
with tf.Session() as sess:
# 初始化变量
init_op=tf.initialize_all_variables()
sess.run(init_op)
STEPS=5000
print('Start training>............')
for i in range(STEPS):
start=(i*batchsize)%dataset_size
end=min(start+batchsize,dataset_size)
# 训练
sess.run(train_step,feed_dict={x:X[start:end],y_:Y[start:end]})
# 计算交叉熵并输出
if i%1000==0:
total_cross_entropy=sess.run(cross_entropy,feed_dict={x:X,y_:Y})
print('第%d次训练,总体交叉熵为:%f'%(i,total_cross_entropy))