本文整理汇总了Python中utils.logits_to_log_prob方法的典型用法代码示例。如果您正苦于以下问题：Python utils.logits_to_log_prob方法的具体用法？Python utils.logits_to_log_prob怎么用？Python utils.logits_to_log_prob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utils的用法示例。
在下文中一共展示了utils.logits_to_log_prob方法的3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: char_predictions
# 需要导入模块: import utils [as 别名]
# 或者: from utils import logits_to_log_prob [as 别名]
def char_predictions(self, chars_logit):
"""Returns confidence scores (softmax values) for predicted characters.
Args:
chars_logit: chars logits, a tensor with shape
[batch_size x seq_length x num_char_classes]
Returns:
A tuple (ids, log_prob, scores), where:
ids - predicted characters, a int32 tensor with shape
[batch_size x seq_length];
log_prob - a log probability of all characters, a float tensor with
shape [batch_size, seq_length, num_char_classes];
scores - corresponding confidence scores for characters, a float
tensor
with shape [batch_size x seq_length].
"""
log_prob = utils.logits_to_log_prob(chars_logit)
ids = tf.to_int32(tf.argmax(log_prob, dimension=2), name='predicted_chars')
mask = tf.cast(
slim.one_hot_encoding(ids, self._params.num_char_classes), tf.bool)
all_scores = tf.nn.softmax(chars_logit)
selected_scores = tf.boolean_mask(all_scores, mask, name='char_scores')
scores = tf.reshape(selected_scores, shape=(-1, self._params.seq_length))
return ids, log_prob, scores 示例2: char_predictions
# 需要导入模块: import utils [as 别名]
# 或者: from utils import logits_to_log_prob [as 别名]
def char_predictions(self, chars_logit):
"""Returns confidence scores (softmax values) for predicted characters.
Args:
chars_logit: chars logits, a tensor with shape
[batch_size x seq_length x num_char_classes]
Returns:
A tuple (ids, log_prob, scores), where:
ids - predicted characters, a int32 tensor with shape
[batch_size x seq_length];
log_prob - a log probability of all characters, a float tensor with
shape [batch_size, seq_length, num_char_classes];
scores - corresponding confidence scores for characters, a float
tensor
with shape [batch_size x seq_length].
"""
log_prob = utils.logits_to_log_prob(chars_logit)
ids = tf.to_int32(tf.argmax(log_prob, axis=2), name='predicted_chars')
mask = tf.cast(
slim.one_hot_encoding(ids, self._params.num_char_classes), tf.bool)
all_scores = tf.nn.softmax(chars_logit)
selected_scores = tf.boolean_mask(all_scores, mask, name='char_scores')
scores = tf.reshape(selected_scores, shape=(-1, self._params.seq_length))
return ids, log_prob, scores 示例3: char_predictions
# 需要导入模块: import utils [as 别名]
# 或者: from utils import logits_to_log_prob [as 别名]
def char_predictions(self, chars_logit):
"""Returns confidence scores (softmax values) for predicted characters.
Args:
chars_logit: chars logits, a tensor with shape
[batch_size x seq_length x num_char_classes]
Returns:
A tuple (ids, log_prob, scores), where:
ids - predicted characters, a int32 tensor with shape
[batch_size x seq_length];
log_prob - a log probability of all characters, a float tensor with
shape [batch_size, seq_length, num_char_classes];
scores - corresponding confidence scores for characters, a float
tensor
with shape [batch_size x seq_length].
"""
log_prob = utils.logits_to_log_prob(chars_logit)
ids = tf.to_int32(tf.argmax(log_prob, axis=2), name='predicted_chars')
mask = tf.cast(
slim.one_hot_encoding(ids, self._params.num_char_classes), tf.bool)
all_scores = tf.nn.softmax(chars_logit)
selected_scores = tf.boolean_mask(all_scores, mask, name='char_scores')
scores = tf.reshape(selected_scores, shape=(-1, self._params.seq_length))
return ids, log_prob, scores