Python Tensorflow:无法将tf.case与输入参数一起使用

我需要创建一个变量epsilon\u n，它根据当前步骤更改定义和值。由于我有两个以上的案例，似乎我不能使用tf.cond。我尝试使用tf.case，如下所示：

import tensorflow as tf

####
EPSILON_DELTA_PHASE1 = 33e-4
EPSILON_DELTA_PHASE2 = 2.5
####
step = tf.placeholder(dtype=tf.float32, shape=None)


def fn1(step):
    return tf.constant([1.])

def fn2(step):
    return tf.constant([1.+step*EPSILON_DELTA_PHASE1])

def fn3(step):
    return tf.constant([1.+step*EPSILON_DELTA_PHASE2])

epsilon_n = tf.case(
        pred_fn_pairs=[
            (tf.less(step, 3e4), lambda step: fn1(step)),
            (tf.less(step, 6e4), lambda step: fn2(step)),
            (tf.less(step, 1e5), lambda step: fn3(step))],
            default=lambda: tf.constant([1e5]),
        exclusive=False)

但是，我一直收到以下错误消息：

TypeError: <lambda>() missing 1 required positional argument: 'step'

---

我还是会犯同样的错误。Tensorflow文档中的示例说明了没有输入参数传递给可调用函数的情况。我在网上找不到足够的关于tf.case的信息！请提供帮助？

这里有一些您需要做的更改。 为了保持一致性，可以将所有返回值设置为变量

# Since step is a scalar, scalar shape [() or [], not None] much be provided 
step = tf.placeholder(dtype=tf.float32, shape=())


def fn1(step):
    return tf.constant([1.])

# Here you need to use Variable not constant, since you are modifying the value using placeholder
def fn2(step):
    return tf.Variable([1.+step*EPSILON_DELTA_PHASE1])

def fn3(step):
    return tf.Variable([1.+step*EPSILON_DELTA_PHASE2])

epsilon_n = tf.case(
    pred_fn_pairs=[
        (tf.less(step, 3e4), lambda : fn1(step)),
        (tf.less(step, 6e4), lambda : fn2(step)),
        (tf.less(step, 1e5), lambda : fn3(step))],
        default=lambda: tf.constant([1e5]),
    exclusive=False)

---

修正了小错误

# Since step is a scalar, scalar shape [() or [], not None] much be provided 
step = tf.placeholder(dtype=tf.float32, shape=())


def fn1(step):
    return tf.constant([1.])

# Here you need to use Variable not constant, since you are modifying the value using placeholder
def fn2(step):
    return tf.Variable([1.+step*EPSILON_DELTA_PHASE1])

def fn3(step):
    return tf.Variable([1.+step*EPSILON_DELTA_PHASE2])

epsilon_n = tf.case(
    pred_fn_pairs=[
        (tf.less(step, 3e4), lambda : fn1(step)),
        (tf.less(step, 6e4), lambda : fn2(step)),
        (tf.less(step, 1e5), lambda : fn3(step))],
        default=lambda: tf.constant([1e5]),
    exclusive=False)