Python tensorflow.python.ops.gen_nn_ops._top_kv2() Examples
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code examples of tensorflow.python.ops.gen_nn_ops._top_kv2().
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Example #1
| Source File: nn_ops.py From lambda-packs with MIT License | 5 votes |
|
def top_k(input, k=1, sorted=True, name=None):
"""Finds values and indices of the `k` largest entries for the last dimension.
If the input is a vector (rank-1), finds the `k` largest entries in the vector
and outputs their values and indices as vectors. Thus `values[j]` is the
`j`-th largest entry in `input`, and its index is `indices[j]`.
For matrices (resp. higher rank input), computes the top `k` entries in each
row (resp. vector along the last dimension). Thus,
values.shape = indices.shape = input.shape[:-1] + [k]
If two elements are equal, the lower-index element appears first.
Args:
input: 1-D or higher `Tensor` with last dimension at least `k`.
k: 0-D `int32` `Tensor`. Number of top elements to look for along the last
dimension (along each row for matrices).
sorted: If true the resulting `k` elements will be sorted by the values in
descending order.
name: Optional name for the operation.
Returns:
values: The `k` largest elements along each last dimensional slice.
indices: The indices of `values` within the last dimension of `input`.
"""
return gen_nn_ops._top_kv2(input, k=k, sorted=sorted, name=name)
Example #2
| Source File: nn_ops.py From auto-alt-text-lambda-api with MIT License | 5 votes |
|
def top_k(input, k=1, sorted=True, name=None):
"""Finds values and indices of the `k` largest entries for the last dimension.
If the input is a vector (rank-1), finds the `k` largest entries in the vector
and outputs their values and indices as vectors. Thus `values[j]` is the
`j`-th largest entry in `input`, and its index is `indices[j]`.
For matrices (resp. higher rank input), computes the top `k` entries in each
row (resp. vector along the last dimension). Thus,
values.shape = indices.shape = input.shape[:-1] + [k]
If two elements are equal, the lower-index element appears first.
Args:
input: 1-D or higher `Tensor` with last dimension at least `k`.
k: 0-D `int32` `Tensor`. Number of top elements to look for along the last
dimension (along each row for matrices).
sorted: If true the resulting `k` elements will be sorted by the values in
descending order.
name: Optional name for the operation.
Returns:
values: The `k` largest elements along each last dimensional slice.
indices: The indices of `values` within the last dimension of `input`.
"""
return gen_nn_ops._top_kv2(input, k=k, sorted=sorted, name=name)
Example #3
| Source File: nn_ops.py From deep_image_model with Apache License 2.0 | 5 votes |
|
def top_k(input, k=1, sorted=True, name=None):
"""Finds values and indices of the `k` largest entries for the last dimension.
If the input is a vector (rank-1), finds the `k` largest entries in the vector
and outputs their values and indices as vectors. Thus `values[j]` is the
`j`-th largest entry in `input`, and its index is `indices[j]`.
For matrices (resp. higher rank input), computes the top `k` entries in each
row (resp. vector along the last dimension). Thus,
values.shape = indices.shape = input.shape[:-1] + [k]
If two elements are equal, the lower-index element appears first.
Args:
input: 1-D or higher `Tensor` with last dimension at least `k`.
k: 0-D `int32` `Tensor`. Number of top elements to look for along the last
dimension (along each row for matrices).
sorted: If true the resulting `k` elements will be sorted by the values in
descending order.
name: Optional name for the operation.
Returns:
values: The `k` largest elements along each last dimensional slice.
indices: The indices of `values` within the last dimension of `input`.
"""
return gen_nn_ops._top_kv2(input, k=k, sorted=sorted, name=name)
Example #4
| Source File: nn_ops.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
|
def top_k(input, k=1, sorted=True, name=None):
"""Finds values and indices of the `k` largest entries for the last dimension.
If the input is a vector (rank-1), finds the `k` largest entries in the vector
and outputs their values and indices as vectors. Thus `values[j]` is the
`j`-th largest entry in `input`, and its index is `indices[j]`.
For matrices (resp. higher rank input), computes the top `k` entries in each
row (resp. vector along the last dimension). Thus,
values.shape = indices.shape = input.shape[:-1] + [k]
If two elements are equal, the lower-index element appears first.
Args:
input: 1-D or higher `Tensor` with last dimension at least `k`.
k: 0-D `int32` `Tensor`. Number of top elements to look for along the last
dimension (along each row for matrices).
sorted: If true the resulting `k` elements will be sorted by the values in
descending order.
name: Optional name for the operation.
Returns:
values: The `k` largest elements along each last dimensional slice.
indices: The indices of `values` within the last dimension of `input`.
"""
return gen_nn_ops._top_kv2(input, k=k, sorted=sorted, name=name)
Example #5
| Source File: nn_ops.py From keras-lambda with MIT License | 5 votes |
|
def top_k(input, k=1, sorted=True, name=None):
"""Finds values and indices of the `k` largest entries for the last dimension.
If the input is a vector (rank-1), finds the `k` largest entries in the vector
and outputs their values and indices as vectors. Thus `values[j]` is the
`j`-th largest entry in `input`, and its index is `indices[j]`.
For matrices (resp. higher rank input), computes the top `k` entries in each
row (resp. vector along the last dimension). Thus,
values.shape = indices.shape = input.shape[:-1] + [k]
If two elements are equal, the lower-index element appears first.
Args:
input: 1-D or higher `Tensor` with last dimension at least `k`.
k: 0-D `int32` `Tensor`. Number of top elements to look for along the last
dimension (along each row for matrices).
sorted: If true the resulting `k` elements will be sorted by the values in
descending order.
name: Optional name for the operation.
Returns:
values: The `k` largest elements along each last dimensional slice.
indices: The indices of `values` within the last dimension of `input`.
"""
return gen_nn_ops._top_kv2(input, k=k, sorted=sorted, name=name)
