本文整理汇总了Python中tensorflow.compat.v2.reduce_min方法的典型用法代码示例。如果您正苦于以下问题:Python v2.reduce_min方法的具体用法?Python v2.reduce_min怎么用?Python v2.reduce_min使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.compat.v2
的用法示例。
在下文中一共展示了v2.reduce_min方法的6个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: select_actor_action
# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import reduce_min [as 别名]
def select_actor_action(self, env_output, agent_output):
oracle_next_action = env_output.observation[constants.ORACLE_NEXT_ACTION]
oracle_next_action_indices = tf.where(
tf.equal(env_output.observation[constants.CONN_IDS],
oracle_next_action))
oracle_next_action_idx = tf.reduce_min(oracle_next_action_indices)
assert self._mode, 'mode must be set.'
if self._mode == 'train':
if self._loss_type == common.CE_LOSS:
# This is teacher-forcing mode, so choose action same as oracle action.
action_idx = oracle_next_action_idx
elif self._loss_type == common.AC_LOSS:
# Choose next pano from probability distribution over next panos
action_idx = tfp.distributions.Categorical(
logits=agent_output.policy_logits).sample()
else:
raise ValueError('Unsupported loss type {}'.format(self._loss_type))
else:
# In non-train modes, choose greedily.
action_idx = tf.argmax(agent_output.policy_logits, axis=-1)
action_val = env_output.observation[constants.CONN_IDS][action_idx]
return common.ActorAction(
chosen_action_idx=int(action_idx.numpy()),
oracle_next_action_idx=int(oracle_next_action_idx.numpy())), int(
action_val.numpy())
示例2: select_actor_action
# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import reduce_min [as 别名]
def select_actor_action(self, env_output, agent_output):
oracle_next_action = env_output.observation[constants.ORACLE_NEXT_ACTION]
oracle_next_action_indices = tf.where(
tf.equal(env_output.observation[constants.CONN_IDS],
oracle_next_action))
oracle_next_action_idx = tf.reduce_min(oracle_next_action_indices)
if self._loss_type == common.CE_LOSS:
# This is teacher-forcing mode, so choose action same as oracle action.
action_idx = oracle_next_action_idx
elif self._loss_type == common.AC_LOSS:
# Choose next pano from probability distribution over next panos
action_idx = tfp.distributions.Categorical(
logits=agent_output.policy_logits).sample()
else:
raise ValueError('Unsupported loss type {}'.format(self._loss_type))
action_val = env_output.observation[constants.CONN_IDS][action_idx]
return common.ActorAction(
chosen_action_idx=int(action_idx.numpy()),
oracle_next_action_idx=int(oracle_next_action_idx.numpy())), int(
action_val.numpy())
示例3: amin
# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import reduce_min [as 别名]
def amin(a, axis=None, keepdims=None):
return _reduce(tf.reduce_min, a, axis=axis, dtype=None, keepdims=keepdims,
promote_int=None, tf_bool_fn=tf.reduce_all, preserve_bool=True)
# TODO(wangpeng): Remove this workaround once b/157232284 is fixed
示例4: call
# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import reduce_min [as 别名]
def call(self, multi_objectives: tf.Tensor) -> tf.Tensor:
return tf.reduce_min(
(multi_objectives - self._reference_point) * self._weights, axis=1)
示例5: select_actor_action
# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import reduce_min [as 别名]
def select_actor_action(self, env_output, agent_output):
# Agent_output is unused here.
oracle_next_action = env_output.observation[constants.ORACLE_NEXT_ACTION]
oracle_next_action_indices = tf.where(
tf.equal(env_output.observation[constants.CONN_IDS],
oracle_next_action))
oracle_next_action_idx = tf.reduce_min(oracle_next_action_indices)
assert self._mode, 'mode must be set.'
action_idx = oracle_next_action_idx
action_val = env_output.observation[constants.CONN_IDS][action_idx]
return common.ActorAction(
chosen_action_idx=int(action_idx.numpy()),
oracle_next_action_idx=int(oracle_next_action_idx.numpy())), int(
action_val)
示例6: estimate_tails
# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import reduce_min [as 别名]
def estimate_tails(func, target, shape, dtype):
"""Estimates approximate tail quantiles.
This runs a simple Adam iteration to determine tail quantiles. The
objective is to find an `x` such that:
```
func(x) == target
```
For instance, if `func` is a CDF and the target is a quantile value, this
would find the approximate location of that quantile. Note that `func` is
assumed to be monotonic. When each tail estimate has passed the optimal value
of `x`, the algorithm does 10 additional iterations and then stops.
This operation is vectorized. The tensor shape of `x` is given by `shape`, and
`target` must have a shape that is broadcastable to the output of `func(x)`.
Arguments:
func: A callable that computes cumulative distribution function, survival
function, or similar.
target: The desired target value.
shape: The shape of the `tf.Tensor` representing `x`.
dtype: The `tf.dtypes.Dtype` of the computation (and the return value).
Returns:
A `tf.Tensor` representing the solution (`x`).
"""
with tf.name_scope("estimate_tails"):
dtype = tf.as_dtype(dtype)
shape = tf.convert_to_tensor(shape, tf.int32)
target = tf.convert_to_tensor(target, dtype)
def loop_cond(tails, m, v, count):
del tails, m, v # unused
return tf.reduce_min(count) < 10
def loop_body(tails, m, v, count):
with tf.GradientTape(watch_accessed_variables=False) as tape:
tape.watch(tails)
loss = abs(func(tails) - target)
grad = tape.gradient(loss, tails)
m = .5 * m + .5 * grad # Adam mean estimate.
v = .9 * v + .1 * tf.square(grad) # Adam variance estimate.
tails -= .5 * m / (tf.sqrt(v) + 1e-7)
# Start counting when the gradient flips sign (note that this assumes
# `tails` is initialized to zero).
count = tf.where(
tf.math.logical_or(count > 0, tails * grad > 0),
count + 1, count)
return tails, m, v, count
init_tails = tf.zeros(shape, dtype=dtype)
init_m = tf.zeros(shape, dtype=dtype)
init_v = tf.ones(shape, dtype=dtype)
init_count = tf.zeros(shape, dtype=tf.int32)
return tf.while_loop(
loop_cond, loop_body, (init_tails, init_m, init_v, init_count),
back_prop=False)[0]