本文整理匯總了Python中tensorflow.python.util.deprecation.deprecated_argument_lookup方法的典型用法代碼示例。如果您正苦於以下問題：Python deprecation.deprecated_argument_lookup方法的具體用法？Python deprecation.deprecated_argument_lookup怎麽用？Python deprecation.deprecated_argument_lookup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tensorflow.python.util.deprecation的用法示例。
在下文中一共展示了deprecation.deprecated_argument_lookup方法的3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: reverse_sequence
# 需要導入模塊: from tensorflow.python.util import deprecation [as 別名]
# 或者: from tensorflow.python.util.deprecation import deprecated_argument_lookup [as 別名]
def reverse_sequence(input,
seq_lengths,
seq_axis=None,
batch_axis=None,
name=None,
seq_dim=None,
batch_dim=None):
seq_axis = deprecation.deprecated_argument_lookup("seq_axis", seq_axis,
"seq_dim", seq_dim)
batch_axis = deprecation.deprecated_argument_lookup("batch_axis", batch_axis,
"batch_dim", batch_dim)
return gen_array_ops.reverse_sequence(
input=input,
seq_lengths=seq_lengths,
seq_dim=seq_axis,
batch_dim=batch_axis,
name=name)
# pylint: enable=redefined-builtin 示例2: reverse_sequence
# 需要導入模塊: from tensorflow.python.util import deprecation [as 別名]
# 或者: from tensorflow.python.util.deprecation import deprecated_argument_lookup [as 別名]
def reverse_sequence(input,
seq_lengths,
seq_axis=None,
batch_axis=None,
name=None,
seq_dim=None,
batch_dim=None):
seq_axis = deprecation.deprecated_argument_lookup("seq_axis", seq_axis,
"seq_dim", seq_dim)
batch_axis = deprecation.deprecated_argument_lookup("batch_axis", batch_axis,
"batch_dim", batch_dim)
return gen_array_ops.reverse_sequence(
input=input,
seq_lengths=seq_lengths,
seq_dim=seq_axis,
batch_dim=batch_axis,
name=name)
# pylint: enable=redefined-builtin 開發者ID:PacktPublishing，項目名稱:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda，代碼行數:22，代碼來源:array_ops.py
示例3: cosine_distance
# 需要導入模塊: from tensorflow.python.util import deprecation [as 別名]
# 或者: from tensorflow.python.util.deprecation import deprecated_argument_lookup [as 別名]
def cosine_distance(predictions,
labels=None,
axis=None,
weights=1.0,
scope=None,
dim=None):
"""Adds a cosine-distance loss to the training procedure.
Note that the function assumes that `predictions` and `labels` are already
unit-normalized.
Args:
predictions: An arbitrary matrix.
labels: A `Tensor` whose shape matches 'predictions'
axis: The dimension along which the cosine distance is computed.
weights: Coefficients for the loss a scalar, a tensor of shape
[batch_size] or a tensor whose shape matches `predictions`.
scope: The scope for the operations performed in computing the loss.
dim: The old (deprecated) name for `axis`.
Returns:
A scalar `Tensor` representing the loss value.
Raises:
ValueError: If `predictions` shape doesn't match `labels` shape, or
`weights` is `None`.
"""
axis = deprecated_argument_lookup(
"axis", axis, "dim", dim)
if axis is None:
raise ValueError("You must specify 'axis'.")
with ops.name_scope(scope, "cosine_distance_loss",
[predictions, labels, weights]) as scope:
predictions.get_shape().assert_is_compatible_with(labels.get_shape())
predictions = math_ops.cast(predictions, dtypes.float32)
labels = math_ops.cast(labels, dtypes.float32)
radial_diffs = math_ops.multiply(predictions, labels)
losses = 1 - math_ops.reduce_sum(
radial_diffs, axis=[
axis,
])
return compute_weighted_loss(losses, weights, scope=scope)