先定义变量再定义操作，再在一个session中进行初始化，进行计算。
import tensorflow as tf
a = 3
# Create a variable.
w = tf.Variable([[0.5,1.0]])
x = tf.Variable([[2.0],[1.0]])
y = tf.matmul(w, x)
#variables have to be explicitly initialized before you can run Ops
init_op = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init_op)
print (y.eval())
#tf需要先把数据转化为tf，tensor，再进行flow操作，也就是先定义变量再定义操作，再进行下面的初始化，后对初始化，创造了一个范围session，在范围中初始化，然后在范围的初始化后进行计算。
在TensorFlow中，数据类型用float32，因为在cpu还是在gpu上都是支持的
# float32
tf.zeros([3, 4], int32) ==> [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
# 'tensor' is [[1, 2, 3], [4, 5, 6]]
tf.zeros_like(tensor) ==> [[0, 0, 0], [0, 0, 0]]
tf.ones([2, 3], int32) ==> [[1, 1, 1], [1, 1, 1]]
# 'tensor' is [[1, 2, 3], [4, 5, 6]]
tf.ones_like(tensor) ==> [[1, 1, 1], [1, 1, 1]]
# Constant 1-D Tensor populated with value list.
tensor = tf.constant([1, 2, 3, 4, 5, 6, 7]) => [1 2 3 4 5 6 7]
# Constant 2-D tensor populated with scalar value -1.
tensor = tf.constant(-1.0, shape=[2, 3]) => [[-1. -1. -1.]
[-1. -1. -1.]]
tf.linspace(10.0, 12.0, 3, name="linspace") => [ 10.0 11.0 12.0]
# 'start' is 3
# 'limit' is 18
# 'delta' is 3
tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15]
建立一个高斯分布，指定均值mean，方差stddev，步骤还是先进行定义变量和计算方式在进行初始化，在设定范围进行初始化和计算。
norm = tf.random_normal([2, 3], mean=-1, stddev=4)
# Shuffle the first dimension of a tensor
c = tf.constant([[1, 2], [3, 4], [5, 6]])
shuff = tf.random_shuffle(c)
# Each time we run these ops, different results are generated
sess = tf.Session()
print (sess.run(norm))
print (sess.run(shuff))
小例子，0初始，循环加1，打印出来
state = tf.Variable(0)
new_value = tf.add(state, tf.constant(1))#相加
update = tf.assign(state, new_value)#更新，把new的值赋给前面的state的值，进行更新
#上面是骨架写好了没法计算，需要建立session区域，然后对全局变量初始化tf.global_variables_initializer()，然后循环跑骨架内容。仔细观察跑的时候的变量的形式。
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
print(sess.run(state))
for _ in range(3):
sess.run(update)
print(sess.run(state))
saver类，先存着计算步骤
#tf.train.Saver
w = tf.Variable([[0.5,1.0]])
x = tf.Variable([[2.0],[1.0]])
y = tf.matmul(w, x)
init_op = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init_op)
# Do some work with the model.
# Save the variables to disk.
save_path = saver.save(sess, "C://tensorflow//model//test")
print ("Model saved in file: ", save_path)
下面是np转化为tf格式的方法，建议还是直接用tf.Variable([[0.5,1.0]])，这样的，直接支持的，比较好。
import numpy as np
a = np.zeros((3,3))
ta = tf.convert_to_tensor(a)
with tf.Session() as sess:
print(sess.run(ta))
下面是另一种逻辑编程，先建立两个区域预留给数据用，后在生成数据feed_dict={input1:[7.], input2:[2.]}
input1 = tf.placeholder(tf.float32)#需要指定格式tf.float32
input2 = tf.placeholder(tf.float32)
output = tf.mul(input1, input2)
with tf.Session() as sess:
print(sess.run([output], feed_dict={input1:[7.], input2:[2.]}))