本文整理汇总了Python中tensorflow.python.ops.array_ops.invert_permutation方法的典型用法代码示例。如果您正苦于以下问题:Python array_ops.invert_permutation方法的具体用法?Python array_ops.invert_permutation怎么用?Python array_ops.invert_permutation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.python.ops.array_ops
的用法示例。
在下文中一共展示了array_ops.invert_permutation方法的5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: _SparseReorderGrad
# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import invert_permutation [as 别名]
def _SparseReorderGrad(op, unused_output_indices_grad, output_values_grad):
"""Gradients for the SparseReorder op.
Args:
op: the SparseReorder op
unused_output_indices_grad: the incoming gradients of the output indices
output_values_grad: the incoming gradients of the output values
Returns:
Gradient for each of the 3 input tensors:
(input_indices, input_values, input_shape)
The gradients for input_indices and input_shape is None.
"""
input_indices = op.inputs[0]
input_shape = op.inputs[2]
num_entries = array_ops.shape(input_indices)[0]
entry_indices = math_ops.range(num_entries)
sp_unordered = sparse_tensor.SparseTensor(
input_indices, entry_indices, input_shape)
sp_ordered = sparse_ops.sparse_reorder(sp_unordered)
inverted_permutation = array_ops.invert_permutation(sp_ordered.values)
return (None,
array_ops.gather(output_values_grad, inverted_permutation),
None)
示例2: _TransposeGrad
# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import invert_permutation [as 别名]
def _TransposeGrad(op, grad):
"""Returns unshuffle(grad)."""
p = op.inputs[1]
return [array_ops.transpose(grad, array_ops.invert_permutation(p)), None]
示例3: _ProdGrad
# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import invert_permutation [as 别名]
def _ProdGrad(op, grad):
"""Gradient for Prod."""
# The gradient can be expressed by dividing the product by each entry of the
# input tensor, but this approach can't deal with zeros in the input.
# Here, we avoid this problem by composing the output as a product of two
# cumprod operations.
input_shape = array_ops.shape(op.inputs[0])
# Reshape reduction indices for the case where the parameter is a scalar
reduction_indices = array_ops.reshape(op.inputs[1], [-1])
# Expand grad to full input shape
output_shape_kept_dims = math_ops.reduced_shape(input_shape, op.inputs[1])
tile_scaling = _safe_shape_div(input_shape, output_shape_kept_dims)
grad = array_ops.reshape(grad, output_shape_kept_dims)
grad = array_ops.tile(grad, tile_scaling)
# Pack all reduced dimensions into a single one, so we can perform the
# cumprod ops. If the reduction dims list is empty, it defaults to float32,
# so we need to cast here. We put all the shape-related ops on CPU to avoid
# copying back and forth, and since listdiff is CPU only.
with ops.device("/cpu:0"):
reduced = math_ops.cast(reduction_indices, dtypes.int32)
idx = math_ops.range(0, array_ops.rank(op.inputs[0]))
other, _ = array_ops.setdiff1d(idx, reduced)
perm = array_ops.concat([reduced, other], 0)
reduced_num = math_ops.reduce_prod(array_ops.gather(input_shape, reduced))
other_num = math_ops.reduce_prod(array_ops.gather(input_shape, other))
permuted = array_ops.transpose(op.inputs[0], perm)
permuted_shape = array_ops.shape(permuted)
reshaped = array_ops.reshape(permuted, (reduced_num, other_num))
# Calculate product, leaving out the current entry
left = math_ops.cumprod(reshaped, axis=0, exclusive=True)
right = math_ops.cumprod(reshaped, axis=0, exclusive=True, reverse=True)
y = array_ops.reshape(left * right, permuted_shape)
# Invert the transpose and reshape operations.
# Make sure to set the statically known shape information through a reshape.
out = grad * array_ops.transpose(y, array_ops.invert_permutation(perm))
return array_ops.reshape(out, input_shape), None
示例4: _ProdGrad
# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import invert_permutation [as 别名]
def _ProdGrad(op, grad):
"""Gradient for Prod."""
# The gradient can be expressed by dividing the product by each entry of the
# input tensor, but this approach can't deal with zeros in the input.
# Here, we avoid this problem by composing the output as a product of two
# cumprod operations.
input_shape = array_ops.shape(op.inputs[0])
# Reshape reduction indices for the case where the parameter is a scalar
reduction_indices = array_ops.reshape(op.inputs[1], [-1])
# Expand grad to full input shape
output_shape_kept_dims = math_ops.reduced_shape(input_shape, op.inputs[1])
tile_scaling = _safe_shape_div(input_shape, output_shape_kept_dims)
grad = array_ops.reshape(grad, output_shape_kept_dims)
grad = array_ops.tile(grad, tile_scaling)
# Pack all reduced dimensions into a single one, so we can perform the
# cumprod ops. If the reduction dims list is empty, it defaults to float32,
# so we need to cast here. We put all the shape-related ops on CPU to avoid
# copying back and forth, and since listdiff is CPU only.
with ops.device("/cpu:0"):
reduced = math_ops.cast(reduction_indices, dtypes.int32)
idx = math_ops.range(0, array_ops.rank(op.inputs[0]))
other, _ = array_ops.setdiff1d(idx, reduced)
perm = array_ops.concat(0, [reduced, other])
reduced_num = math_ops.reduce_prod(array_ops.gather(input_shape, reduced))
other_num = math_ops.reduce_prod(array_ops.gather(input_shape, other))
permuted = array_ops.transpose(op.inputs[0], perm)
permuted_shape = array_ops.shape(permuted)
reshaped = array_ops.reshape(permuted, (reduced_num, other_num))
# Calculate product, leaving out the current entry
left = math_ops.cumprod(reshaped, axis=0, exclusive=True)
right = math_ops.cumprod(reshaped, axis=0, exclusive=True, reverse=True)
y = array_ops.reshape(left * right, permuted_shape)
# Invert the transpose and reshape operations.
# Make sure to set the statically known shape information through a reshape.
out = grad * array_ops.transpose(y, array_ops.invert_permutation(perm))
return array_ops.reshape(out, input_shape), None
示例5: _ProdGrad
# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import invert_permutation [as 别名]
def _ProdGrad(op, grad):
"""Gradient for Prod."""
# The gradient can be expressed by dividing the product by each entry of the
# input tensor, but this approach can't deal with zeros in the input.
# Here, we avoid this problem by composing the output as a product of two
# cumprod operations.
input_shape = array_ops.shape(op.inputs[0])
# Reshape reduction indices for the case where the parameter is a scalar
reduction_indices = array_ops.reshape(op.inputs[1], [-1])
# Expand grad to full input shape
output_shape_kept_dims = math_ops.reduced_shape(input_shape, op.inputs[1])
tile_scaling = _safe_shape_div(input_shape, output_shape_kept_dims)
grad = array_ops.reshape(grad, output_shape_kept_dims)
grad = array_ops.tile(grad, tile_scaling)
# Pack all reduced dimensions into a single one, so we can perform the
# cumprod ops. If the reduction dims list is empty, it defaults to float32,
# so we need to cast here. We put all the shape-related ops on CPU to avoid
# copying back and forth, and since listdiff is CPU only.
with ops.device("/cpu:0"):
rank = array_ops.rank(op.inputs[0])
reduction_indices = (reduction_indices + rank) % rank
reduced = math_ops.cast(reduction_indices, dtypes.int32)
idx = math_ops.range(0, rank)
other, _ = array_ops.setdiff1d(idx, reduced)
perm = array_ops.concat([reduced, other], 0)
reduced_num = math_ops.reduce_prod(array_ops.gather(input_shape, reduced))
other_num = math_ops.reduce_prod(array_ops.gather(input_shape, other))
permuted = array_ops.transpose(op.inputs[0], perm)
permuted_shape = array_ops.shape(permuted)
reshaped = array_ops.reshape(permuted, (reduced_num, other_num))
# Calculate product, leaving out the current entry
left = math_ops.cumprod(reshaped, axis=0, exclusive=True)
right = math_ops.cumprod(reshaped, axis=0, exclusive=True, reverse=True)
y = array_ops.reshape(left * right, permuted_shape)
# Invert the transpose and reshape operations.
# Make sure to set the statically known shape information through a reshape.
out = grad * array_ops.transpose(y, array_ops.invert_permutation(perm))
return array_ops.reshape(out, input_shape), None
开发者ID:PacktPublishing,项目名称:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda,代码行数:45,代码来源:math_grad.py