tensorflow学习笔记（三十五）：control flow

tf.cond(pred, fn1, fn2, name=None)

等价于:

res = fn1() if pred else fn2()

注意：pred不能是 python bool， pred是个标量Tensor i.e. tf.placeholder(dtype=tf.bool, shape=[])
官网例子

z = tf.mul(a, b)
result = tf.cond(x < y, lambda: tf.add(x, z), lambda: tf.square(y))

tf.case(pred_fn_pairs, default, exclusive=False, name=’case’)

pred_fn_pairs:以下两种形式都是正确的
1. [(pred_1, fn_1), (pred_2, fn_2)]
2. {pred_1:fn_1, pred_2:fn_2}

tf.case()等价于:

if pred_1:
  return fn_1()
elif pred_2:
  return fn_2()
else:
  return default()

• exclusive: 如果为True，那么pred至多有一个为True，如果有多余一个，会报错。如果False，则不会检查所有条件。

import tensorflow as tf

x = tf.constant(0)
y = tf.constant(1)
z = tf.constant(2)

def f1(): return tf.constant(17)
def f2(): return tf.constant(23)
def f3(): return tf.constant(-1)

r = tf.case({tf.less(x, y): f2, tf.less(x, z): f1},
         default=f3, exclusive=False)

with tf.Session() as sess:
    tf.global_variables_initializer().run()
    print(sess.run(r))

tf.group() 与 tf.tuple()

如果我们有很多 tensor 或 op想要一起run，这时这两个函数就是一个很好的帮手了。

w = tf.Variable(1)
mul = tf.multiply(w, 2)
add = tf.add(w, 2)
group = tf.group(mul, add)
tuple = tf.tuple([mul, add])
# sess.run(group)和sess.run(tuple)都会求Tensor(add)
#Tensor(mul)的值。区别是，tf.group()返回的是`op`
#tf.tuple()返回的是list of tensor。
#这样就会导致，sess.run(tuple)的时候，会返回 Tensor(mul),Tensor(add)的值.
#而 sess.run(group)不会

tf.identity()

http://stackoverflow.com/questions/34877523/in-tensorflow-what-is-tf-identity-used-for

tf.while_loop()

tf.while_loop(cond, body, loop_vars, shape_invariants=None, parallel_iterations=10, back_prop=True, swap_memory=False, name=None)

while_loop可以这么理解

loop_vars = [...]
while cond(*loop_vars):
    loop_vars = body(*loop_vars)    

示例：

import tensorflow as tf

a = tf.get_variable("a", dtype=tf.int32, shape=[], initializer=tf.ones_initializer())
b = tf.constant(2)

f = tf.constant(6)

# Definition of condition and body
def cond(a, b, f):
    return a < 3

def body(a, b, f):
    # do some stuff with a, b
    a = a + 1
    return a, b, f
# Loop, 返回的tensor while 循环后的 a，b，f
a, b, f = tf.while_loop(cond, body, [a, b, f])

with tf.Session() as sess:
    tf.global_variables_initializer().run()
    res = sess.run([a, b, f])
    print(res)