本文整理匯總了Python中baselines.common.tf_util.argmax方法的典型用法代碼示例。如果您正苦於以下問題：Python tf_util.argmax方法的具體用法？Python tf_util.argmax怎麽用？Python tf_util.argmax使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類baselines.common.tf_util的用法示例。
在下文中一共展示了tf_util.argmax方法的9個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: sample_dtype
# 需要導入模塊: from baselines.common import tf_util [as 別名]
# 或者: from baselines.common.tf_util import argmax [as 別名]
def sample_dtype(self):
return tf.int32
# WRONG SECOND DERIVATIVES
# class CategoricalPd(Pd):
# def __init__(self, logits):
# self.logits = logits
# self.ps = tf.nn.softmax(logits)
# @classmethod
# def fromflat(cls, flat):
# return cls(flat)
# def flatparam(self):
# return self.logits
# def mode(self):
# return U.argmax(self.logits, axis=-1)
# def logp(self, x):
# return -tf.nn.sparse_softmax_cross_entropy_with_logits(self.logits, x)
# def kl(self, other):
# return tf.nn.softmax_cross_entropy_with_logits(other.logits, self.ps) \
# - tf.nn.softmax_cross_entropy_with_logits(self.logits, self.ps)
# def entropy(self):
# return tf.nn.softmax_cross_entropy_with_logits(self.logits, self.ps)
# def sample(self):
# u = tf.random_uniform(tf.shape(self.logits))
# return U.argmax(self.logits - tf.log(-tf.log(u)), axis=-1) 示例2: sample_dtype
# 需要導入模塊: from baselines.common import tf_util [as 別名]
# 或者: from baselines.common.tf_util import argmax [as 別名]
def sample_dtype(self):
return tf.int32
# WRONG SECOND DERIVATIVES
# class CategoricalPd(Pd):
# def __init__(self, logits):
# self.logits = logits
# self.ps = tf.nn.softmax(logits)
# @classmethod
# def fromflat(cls, flat):
# return cls(flat)
# def flatparam(self):
# return self.logits
# def mode(self):
# return U.argmax(self.logits, axis=1)
# def logp(self, x):
# return -tf.nn.sparse_softmax_cross_entropy_with_logits(self.logits, x)
# def kl(self, other):
# return tf.nn.softmax_cross_entropy_with_logits(other.logits, self.ps) \
# - tf.nn.softmax_cross_entropy_with_logits(self.logits, self.ps)
# def entropy(self):
# return tf.nn.softmax_cross_entropy_with_logits(self.logits, self.ps)
# def sample(self):
# u = tf.random_uniform(tf.shape(self.logits))
# return U.argmax(self.logits - tf.log(-tf.log(u)), axis=1) 示例3: mode
# 需要導入模塊: from baselines.common import tf_util [as 別名]
# 或者: from baselines.common.tf_util import argmax [as 別名]
def mode(self):
return tf.argmax(self.logits, axis=-1) 示例4: sample
# 需要導入模塊: from baselines.common import tf_util [as 別名]
# 或者: from baselines.common.tf_util import argmax [as 別名]
def sample(self):
u = tf.random_uniform(tf.shape(self.logits))
return tf.argmax(self.logits - tf.log(-tf.log(u)), axis=-1) 示例5: sample
# 需要導入模塊: from baselines.common import tf_util [as 別名]
# 或者: from baselines.common.tf_util import argmax [as 別名]
def sample(self):
u = tf.random_uniform(tf.shape(self.logits), dtype=self.logits.dtype)
return tf.argmax(self.logits - tf.log(-tf.log(u)), axis=-1) 示例6: pdfromlatent
# 需要導入模塊: from baselines.common import tf_util [as 別名]
# 或者: from baselines.common.tf_util import argmax [as 別名]
def pdfromlatent(self, latent_vector, init_scale=1.0, init_bias=0.0):
pdparam = fc(latent_vector, 'pi', self.size, init_scale=init_scale, init_bias=init_bias)
return self.pdfromflat(pdparam), pdparam
# WRONG SECOND DERIVATIVES
# class CategoricalPd(Pd):
# def __init__(self, logits):
# self.logits = logits
# self.ps = tf.nn.softmax(logits)
# @classmethod
# def fromflat(cls, flat):
# return cls(flat)
# def flatparam(self):
# return self.logits
# def mode(self):
# return U.argmax(self.logits, axis=-1)
# def logp(self, x):
# return -tf.nn.sparse_softmax_cross_entropy_with_logits(self.logits, x)
# def kl(self, other):
# return tf.nn.softmax_cross_entropy_with_logits(other.logits, self.ps) \
# - tf.nn.softmax_cross_entropy_with_logits(self.logits, self.ps)
# def entropy(self):
# return tf.nn.softmax_cross_entropy_with_logits(self.logits, self.ps)
# def sample(self):
# u = tf.random_uniform(tf.shape(self.logits))
# return U.argmax(self.logits - tf.log(-tf.log(u)), axis=-1) 開發者ID:quantumiracle，項目名稱:Reinforcement_Learning_for_Traffic_Light_Control，代碼行數:28，代碼來源:distributions.py
示例7: mode
# 需要導入模塊: from baselines.common import tf_util [as 別名]
# 或者: from baselines.common.tf_util import argmax [as 別名]
def mode(self):
return U.argmax(self.logits, axis=1) 示例8: sample
# 需要導入模塊: from baselines.common import tf_util [as 別名]
# 或者: from baselines.common.tf_util import argmax [as 別名]
def sample(self):
u = tf.random_uniform(tf.shape(self.logits))
return tf.argmax(self.logits - tf.log(-tf.log(u)), axis=1) 示例9: pdfromlatent
# 需要導入模塊: from baselines.common import tf_util [as 別名]
# 或者: from baselines.common.tf_util import argmax [as 別名]
def pdfromlatent(self, latent_vector, init_scale=1.0, init_bias=0.0):
pdparam = _matching_fc(latent_vector, 'pi', self.size, init_scale=init_scale, init_bias=init_bias)
return self.pdfromflat(pdparam), pdparam
# WRONG SECOND DERIVATIVES
# class CategoricalPd(Pd):
# def __init__(self, logits):
# self.logits = logits
# self.ps = tf.nn.softmax(logits)
# @classmethod
# def fromflat(cls, flat):
# return cls(flat)
# def flatparam(self):
# return self.logits
# def mode(self):
# return U.argmax(self.logits, axis=-1)
# def logp(self, x):
# return -tf.nn.sparse_softmax_cross_entropy_with_logits(self.logits, x)
# def kl(self, other):
# return tf.nn.softmax_cross_entropy_with_logits(other.logits, self.ps) \
# - tf.nn.softmax_cross_entropy_with_logits(self.logits, self.ps)
# def entropy(self):
# return tf.nn.softmax_cross_entropy_with_logits(self.logits, self.ps)
# def sample(self):
# u = tf.random_uniform(tf.shape(self.logits))
# return U.argmax(self.logits - tf.log(-tf.log(u)), axis=-1)