Tensorflow 初步接触

上手ing,二话不说先安装python,以及tensorflow,ide选择PyCharm,倒腾了半天

然后上手敲代码
搭建简单神经网络的：准备 前传 反传 迭代

导入库

import tensorflow as tf;
import numpy as np;
BATCH_SIZE=8;
seed=23455

随机生成一批数据，

rng=np.random.RandomState(seed)
X=rng.rand(32,2);
Y=[[int((x0+x1)<1)] for (x0,x1) in X]
print("X:\n",X);
print("Y:\n",Y);

待输入的参数

x=tf.placeholder(tf.float32,shape=(None,2))
y_=tf.placeholder(tf.float32,shape=(None,1))

定义神经网络的参数

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

具体运算

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

定义损失函数，反向传播方法

loss=tf.reduce_mean(tf.square(y-y_))
train_step=tf.train.GradientDescentOptimizer(0.001).minimize(loss)

生成会话，训练STEPS次数

with tf.Session() as sess:
init_op=tf.global_variables_initializer()
sess.run(init_op)
print("w1:\n",sess.run(w1))
print("w2:\n",sess.run(w2))
STEPS=3000
for i in range(STEPS):
start=(i*BATCH_SIZE)%32
end=start+BATCH_SIZE

   sess.run(train_step,feed_dict={x:X[start:end],y_:Y[strat: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))