tensorflow训练代码1

#coding :utf-8
#0导入模块，生成模拟数据集

import tensorflow as tf
import numpy as np

BATCH_SIZE = 8
seed = 24355

#基于seed产生随机数
rng = np.random.RandomState(seed)
#随机数返回32行2列的矩阵，表示32组 体积和重量 作为输入数据集
X = rng.rand(32,2)

#从X这个32行2列的矩阵中去除一行判断如果和小于1给Y赋值1 如果和不小于1给Y赋值0
#作为输入数据集标签（正确答案）
#print(X)
Y = [[int(x0 + x1 < 1)] for (x0, x1) in X]
print(“X:\n”,X)
print(“Y:\n”,Y)

#1 定义神经网络的输入，参数和输出，定义向前传播过程
x = tf.placeholder(tf.float32,shape = (None,2))
y_ = tf.placeholder(tf.float32,shape = (None,1))

w1 = tf.Variable(tf.random_normal([2,3],stddev = 1,seed = 1))
w2 = tf.Variable(tf.random_normal([3,1],stddev = 1,seed = 1))

a = tf.matmul(x,w1)
y = tf.matmul(a,w2)

#2 定义损失函数及反向传播方法
loss = tf.reduce_mean(tf.square(y-y_))
train_step = tf.train.GradientDescentOptimizer(0.001).minimize(loss)
#train_step = tf.train.AdamOptimizer(0.001).minimize(loss)
#train_step = tf.train.MomentumOptimizer(0.001).minimize(loss)

#3生成绘画，训练STEPS轮
with tf.Session() as sess:
inint_op = tf.global_variables_initializer()
sess.run(inint_op)

# 输出目前（未经训练）的参数取值
print("w1:\n",sess.run(w1))
print("w2:\n",sess.run(w2))
print("\n")# 训练模型
STEPS = 30000
for i in range(STEPS):start = (i * BATCH_SIZE)%32end = start + BATCH_SIZEsess.run(train_step,feed_dict = {x:X[start:end],y:Y[start:end]})if i % 500 == 0:total_loss = sess.run(loss,feed_dict={x:X,y_:Y})print("After %d training step(s),loss on all data is %g"%(i,total_loss))print("\n")
print("w1:\n",sess.run(w1))

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