1.创建图

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import tensorflow as tf
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m1 = tf.constant([[3,3]])
m2 = tf.constant([[2],[3]])
# 创建一个矩阵乘法
product = tf.matmul(m1,m2)
print(product)
Tensor("MatMul_1:0", shape=(1, 1), dtype=int32)

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# 定义一个会话，启动默认图
with tf.Session() as sess:
    result = sess.run(product)
    print(result)
[[15]]

2.变量

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import tensorflow as tf
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x = tf.Variable([1,2])
a = tf.constant([3,3])
# 创建一个减法op
sub = tf.subtract(x,a)
# 创建一个加法op
add = tf.add(x,a)

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    print(sess.run(sub))
    print(sess.run(add))
[[-2 -1]]
[[4 5]]

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# 创建一个变量，初始化为0
state = tf.Variable(0,name='counter')
new_value = tf.add(state,1)
update = tf.assign(state,new_value)

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    print(sess.run(state))
    for _ in range(5):
        sess.run(update)
        print(sess.run(state))
0
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3.Fetch & Feed

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import tensorflow as tf
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# Fetch
input1 = tf.constant(3.0)
input2 = tf.constant(2.0)
input3 = tf.constant(5.0)

add = tf.add(input2,input3)

mul = tf.multiply(input1,add)

with tf.Session() as sess:
    result = sess.run([mul,add])
    print(result)
[21.0, 7.0]

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# Feed
# 创建占位符
input1 = tf.placeholder(tf.float32)
input2 = tf.placeholder(tf.float32)
output = tf.multiply(input1,input2)

with tf.Session() as sess:
    # Feed的数据以字典的形式传入
    print(sess.run(output,feed_dict={input1:[7.],input2:[2.]}))
[14.]

4.梯度下降优化器

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import tensorflow as tf
import numpy as np
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# 使用numpy生成100个随即点
x_data = np.random.rand(100)
y_data = x_data*0.5 + 0.2

# 构造一个线性模型
b = tf.Variable(0.)
k = tf.Variable(0.)
y = k*x_data + b

# cost function, 最小均方算法
loss = tf.reduce_mean(tf.square(y-y_data))
# tensorflow提供了梯度下降优化器，不用手撸梯度下降算法，是不是很爽！！！
optimizer = tf.train.GradientDescentOptimizer(0.2)
# 最小化代价函数
train = optimizer.minimize(loss)

# 初始化变量
init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    for step in range(200):
        sess.run(train)
        if step%20==0:
            print(step,sess.run([k,b]))
0 [0.112656556, 0.18330559]
20 [0.33603606, 0.2910507]
40 [0.40696397, 0.25166383]
60 [0.44720966, 0.229315]
80 [0.47004583, 0.21663386]
100 [0.48300347, 0.20943834]
120 [0.4903559, 0.20535547]
140 [0.4945278, 0.20303877]
160 [0.496895, 0.20172425]
180 [0.49823818, 0.20097837]