tensorflow笔记3 变量
import tensorflow as tf
# 创建一个变量, 初始化为标量 0.
state = tf.Variable(0, name="counter")
# 创建一个 op, 其作用是使 state 增加 1
one = tf.constant(1)
new_value = tf.add(state, one)
update = tf.assign(state, new_value)
# 启动图后, 变量必须先经过`初始化` (init) op 初始化,
# 首先必须增加一个`初始化` op 到图中.
init_op = tf.initialize_all_variables()
# 启动图, 运行 op
with tf.Session() as sess:
# 运行 'init' op
sess.run(init_op)
# 打印 'state' 的初始值
print(sess.run(state))
# 运行 op, 更新 'state', 并打印 'state'
for _ in range(3):
sess.run(update)
print(sess.run(state))
0
1
2
3
input1 = tf.constant(3.0)
input2 = tf.constant(2.0)
input3 = tf.constant(5.0)
intermed = tf.add(input2, input3)
mul = tf.multiply(input1, intermed)
with tf.Session() as sess:
result = sess.run([mul, intermed])
print(result)
[21.0, 7.0]
input1 = tf.placeholder(tf.float32)
input2 = tf.placeholder(tf.float32)
output = tf.multiply(input1, input2)
with tf.Session() as sess:
print(sess.run([output], feed_dict={input1:[7.], input2:[2.]}))
[array([14.], dtype=float32)]