Tensorflow 基本概念

import tensorflow as tf

a = tf.constant([[1, 2, 3, 4, 5], [1, 2, 3, 4, 5]], dtype=tf.float32)
b = tf.constant([[1, 2, 3, 4, 5], [1, 2, 3, 4, 5]], dtype=tf.float32)
result = a+b
print(result)
with tf.Session() as session:
b = session.run(result)
print(b)

import tensorflow as tf

# 新建一个计算图
g1 = tf.Graph()
with g1.as_default():
v = tf.get_variable("v", [1], initializer=tf.zeros_initializer())  # 设置初始值为0
# 新建另一个计算图
g2 = tf.Graph()
with g2.as_default():
v1 = tf.get_variable("v1", [1], initializer=tf.ones_initializer())  # 设置初始值为1
result = v1 + 1

# 新建一个session对话
with tf.Session(graph=g1) as sess:
tf.global_variables_initializer().run()
with tf.variable_scope("", reuse=True):
print(sess.run(tf.get_variable("v")))

# 新建另一个session对话
with tf.Session(graph=g2) as sess:
tf.global_variables_initializer().run()
with tf.variable_scope("", reuse=True):
print(sess.run(tf.get_variable("v1")))
print(sess.run(result))

import tensorflow as tf

a = tf.constant([1.0, 2.0], name="a")
b = tf.constant([2.0, 3.0], name="b")
result = a + b
print(result)
with tf.Session() as sess:
#即便是一个常量，也需要session通过运算得到。
print(sess.run(result))
print(result.eval())
sess.close()

# 创建一个会话。
sess = tf.Session()
#

sess = tf.InteractiveSession () 可为交互式会话。

# 使用会话得到之前计算的结果。
print(sess.run(result))

# 关闭会话使得本次运行中使用到的资源可以被释放。
sess.close()

config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=True)
#配置会话
sess1 = tf.InteractiveSession(config=config)
sess2 = tf.Session(config=config)

import tensorflow as tf

# 初始化随机值
init_w1 = tf.random_normal([2, 3], stddev=1, seed=1)
w1 = tf.Variable(init_w1)
init_w2 = tf.random_normal([3, 1], stddev=1, seed=1)
w2 = tf.Variable(init_w2)
x = tf.constant([[0.7, 0.9]])
a = tf.matmul(x, w1)
y = tf.matmul(a, w2)
sess = tf.Session()
sess.run(w1.initializer)
sess.run(w2.initializer)
print(sess.run(y))
sess.close()

# 新的输入类型
x = tf.placeholder(tf.float32, shape=(1, 2), name="input")
a = tf.matmul(x, w1)
y = tf.matmul(a, w2)
sess = tf.Session()
init_op = tf.global_variables_initializer()
sess.run(init_op)

print(sess.run(y, feed_dict={x: [[0.7, 0.9]]}))
# 批量input
x = tf.placeholder(tf.float32, shape=(3, 2), name="input")
a = tf.matmul(x, w1)
y = tf.matmul(a, w2)

sess = tf.Session()
# 使用tf.global_variables_initializer()来初始化所有的变量
init_op = tf.global_variables_initializer()
sess.run(init_op)

print(sess.run(y, feed_dict={x: [[0.7, 0.9], [0.1, 0.4], [0.5, 0.8]]}))