import tensorflow as tf
a=tf.constant([1.0,2.0])
b=tf.constant([3.0,4.0])
result=a+b
print(result)
#Tensor("add:0", shape=(2,), dtype=float32)
import tensorflow as tf
a=tf.constant([[1.0,2.0]]) #定义一个2 阶张量等于[[1.0,2.0]]
b=tf.constant([[3.0],[4.0]])#定义一个2 阶张量等于[[3.0],[4.0]]
result=tf.matmul(a,b)#实现xw 矩阵乘法
with tf.Session() as sess:
print(sess.run(result))
#[[ 11.]]
#前向传播
import tensorflow as tf
#用placeholder实现输入定义（sess.run中喂一组数据）
x=tf.placeholder(tf.float32,shape=(1,2))
w1=tf.Variable(tf.random_normal([2,3],stddev=1,seed=1))
w2=tf.Variable(tf.random_normal([3,1],stddev=1,seed=1))
#定义向前传播的过程
a=tf.matmul(x,w1)
y=tf.matmul(a,w2)
#用会话计算结果
with tf.Session() as sess:
init=tf.global_variables_initializer()
sess.run(init)
print('w1:\n', sess.run(w1))
print('w2:\n', sess.run(w2))
print('y:',sess.run(y,feed_dict={x:[[0.7,0.5]]}))
'''
w1:
[[-0.81131822 1.48459876 0.06532937]
[-2.4427042 0.0992484 0.59122431]]
w2:
[[-0.81131822]
[ 1.48459876]
[ 0.06532937]]
y: [[ 3.0904665]]
'''
#向前传播
import tensorflow as tf
x=tf.constant([[0.7,0.5]])
w1=tf.Variable(tf.random_normal([2,3],stddev=1,seed=1))
w2=tf.Variable(tf.random_normal([3,1],stddev=1,seed=1))
#定义向前传播的过程
a=tf.matmul(x,w1)
y=tf.matmul(a,w2)
#用会话计算结果
with tf.Session() as sess:
init=tf.global_variables_initializer()
sess.run(init)
print('w1:\n',sess.run(w1))
print('w2:\n', sess.run(w2))
print('a:\n', sess.run(a))
print('y:\n',sess.run(y))
'''
w1:
[[-0.81131822 1.48459876 0.06532937]
[-2.4427042 0.0992484 0.59122431]]
w2:
[[-0.81131822]
[ 1.48459876]
[ 0.06532937]]
a:
[[-1.78927493 1.08884335 0.34134272]]
y:
[[ 3.0904665]]
'''
import tensorflow as tf
import numpy as np
BATCH_SIZE=8
SEED=23455
#基于seed产生随机数
rdm=np.random.RandomState(SEED)
#从X这个32行2列的矩阵中 取出一行 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
#作为输入数据集的标签（正确答案）
X=rdm.rand(32,2)
Y_=[[int((x0+x1)<1)] for (x0,x1) in X]
print ("X:\n",X)
print ("Y_:\n",Y_)
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)
loss=tf.reduce_mean(tf.square(y_-y))
train_step=tf.train.AdamOptimizer(0.001).minimize(loss)
with tf.Session() as sess:
init=tf.global_variables_initializer()
sess.run(init)
print('w1:\n', sess.run(w1))
print('w2:\n', sess.run(w2))
#训练模型
step=3000
for i in range(step):
start=(i*BATCH_SIZE)%32
end=start+BATCH_SIZE
sess.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 steps, loss_mse on all data is %g" % (i, total_loss))
print('w1:\n', sess.run(w1))
print('w2:\n', sess.run(w2))
'''
X:
[[ 0.83494319 0.11482951]
[ 0.66899751 0.46594987]
[ 0.60181666 0.58838408]
[ 0.31836656 0.20502072]
[ 0.87043944 0.02679395]
[ 0.41539811 0.43938369]
[ 0.68635684 0.24833404]
[ 0.97315228 0.68541849]
[ 0.03081617 0.89479913]
[ 0.24665715 0.28584862]
[ 0.31375667 0.47718349]
[ 0.56689254 0.77079148]
[ 0.7321604 0.35828963]
[ 0.15724842 0.94294584]
[ 0.34933722 0.84634483]
[ 0.50304053 0.81299619]
[ 0.23869886 0.9895604 ]
[ 0.4636501 0.32531094]
[ 0.36510487 0.97365522]
[ 0.73350238 0.83833013]
[ 0.61810158 0.12580353]
[ 0.59274817 0.18779828]
[ 0.87150299 0.34679501]
[ 0.25883219 0.50002932]
[ 0.75690948 0.83429824]
[ 0.29316649 0.05646578]
[ 0.10409134 0.88235166]
[ 0.06727785 0.57784761]
[ 0.38492705 0.48384792]
[ 0.69234428 0.19687348]
[ 0.42783492 0.73416985]
[ 0.09696069 0.04883936]]
Y_:
[[1], [0], [0], [1], [1], [1], [1], [0], [1], [1], [1], [0], [0], [0], [0], [0], [0], [1], [0], [0], [1], [1], [0], [1], [0], [1], [1], [1], [1], [1], [0], [1]]
w1:
[[-0.81131822 1.48459876 0.06532937]
[-2.4427042 0.0992484 0.59122431]]
w2:
[[-0.81131822]
[ 1.48459876]
[ 0.06532937]]
After 0 training steps, loss_mse on all data is 5.20999
After 500 training steps, loss_mse on all data is 0.617026
After 1000 training steps, loss_mse on all data is 0.392288
After 1500 training steps, loss_mse on all data is 0.386432
After 2000 training steps, loss_mse on all data is 0.384254
After 2500 training steps, loss_mse on all data is 0.383676
w1:
[[-0.40074909 1.02251101 1.00135291]
[-2.13084817 -0.23977895 1.12739885]]
w2:
[[-0.4457432 ]
[ 1.04927158]
[-0.5386759 ]]
'''
