## TensorFlow基本使用操作

TensorFlow基本模型

import  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())

TensorFlow基本数据类型

# 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]

random_shuffle算子及random_normal算子

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))
[[-0.30886292  3.11809683  3.29861784]
[-7.09597015 -1.89811802  1.75282788]]
[[3 4]
[5 6]
[1 2]]

state = tf.Variable(0)
update = tf.assign(state, new_value)
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))

#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)

numpy与TensorFlow互转

import numpy as np
a = np.zeros((3,3))
ta = tf.convert_to_tensor(a)
with tf.Session() as sess:
print(sess.run(ta))

TensorFlow占坑操作

input1 = tf.placeholder(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.]}))