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Stop gradient when pool_type=='max'

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detection_output_fixbug
yangyaming 7 years ago
parent 25af35d8bb
commit 872b1c8884
1 changed files with 52 additions and 47 deletions
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99
python/paddle/v2/fluid/layers/nn.py
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@ -151,7 +151,7 @@ def embedding(input, size, is_sparse=False, param_attr=None, dtype='float32'):
Args:
input(Variable): Input to the function
size(tuple|list|None): Shape of the look up table parameter
size(tuple|list|None): Shape of the look up table parameter
is_sparse(bool): Boolean flag that specifying whether the input is sparse
param_attr(ParamAttr): Parameters for this layer
dtype(np.dtype|core.DataType|str): The type of data : float32, float_16, int etc
@ -366,9 +366,9 @@ def cross_entropy(input, label, **kwargs):
1) One-hot cross-entropy:
`soft_label = False`, `Label[i, 0]` indicates the class index for sample i:
.. math::
Y[i] = -\log(X[i, Label[i]])
2) Soft-label cross-entropy:
@ -386,15 +386,15 @@ def cross_entropy(input, label, **kwargs):
As a special case of 2), when each row of 'label' has only one
non-zero element which is equal to 1, soft-label cross-entropy degenerates
to a one-hot cross-entropy with one-hot label representation.
Args:
input (Variable|list): a 2-D tensor with shape [N x D], where N is the
batch size and D is the number of classes. This input is a probability
input (Variable|list): a 2-D tensor with shape [N x D], where N is the
batch size and D is the number of classes. This input is a probability
computed by the previous operator, which is almost always the result
of a softmax operator.
label (Variable|list): the ground truth which is a 2-D tensor. When
`soft_label` is set to `False`, `label` is a tensor<int64> with shape
[N x 1]. When `soft_label` is set to `True`, `label` is a
label (Variable|list): the ground truth which is a 2-D tensor. When
`soft_label` is set to `False`, `label` is a tensor<int64> with shape
[N x 1]. When `soft_label` is set to `True`, `label` is a
tensor<float/double> with shape [N x D].
soft_label (bool, via `**kwargs`): a flag indicating whether to interpretate
the given labels as soft labels, default `False`.
@ -403,7 +403,7 @@ def cross_entropy(input, label, **kwargs):
A 2-D tensor with shape [N x 1], the cross entropy loss.
Raises:
`ValueError`: 1) the 1st dimension of `input` and `label` are not equal; 2) when \
`ValueError`: 1) the 1st dimension of `input` and `label` are not equal; 2) when \
`soft_label == True`, and the 2nd dimension of `input` and `label` are not \
equal; 3) when `soft_label == False`, and the 2nd dimension of `label` is not 1.
@ -699,9 +699,9 @@ def conv2d(input,
def sequence_pool(input, pool_type, **kwargs):
"""
This function add the operator for sequence pooling.
It pools features of all time-steps of each instance, and is applied
on top of the input using pool_type mentioned in the parameters.
This function add the operator for sequence pooling.
It pools features of all time-steps of each instance, and is applied
on top of the input using pool_type mentioned in the parameters.
It supports four pool_type:
@ -730,7 +730,7 @@ def sequence_pool(input, pool_type, **kwargs):
Args:
input(variable): The input variable which is a LoDTensor.
pool_type (string): The pooling type of sequence_pool.
pool_type (string): The pooling type of sequence_pool.
It supports average, sum, sqrt and max.
Returns:
@ -740,7 +740,7 @@ def sequence_pool(input, pool_type, **kwargs):
.. code-block:: python
x = fluid.layers.data(name='x', shape=[7, 1],
x = fluid.layers.data(name='x', shape=[7, 1],
dtype='float32', lod_level=1)
avg_x = fluid.layers.sequence_pool(input=x, pool_type='average')
sum_x = fluid.layers.sequence_pool(input=x, pool_type='sum')
@ -759,6 +759,11 @@ def sequence_pool(input, pool_type, **kwargs):
"MaxIndex": max_index},
attrs={"pooltype": pool_type.upper()})
# when pool_type is max, variable max_index is initialized,
# so we stop the gradient explicitly here
if pool_type == 'max':
max_index.stop_gradient = True
return pool_out
@ -788,7 +793,7 @@ def sequence_first_step(input, **kwargs):
.. code-block:: python
x = fluid.layers.data(name='x', shape=[7, 1],
x = fluid.layers.data(name='x', shape=[7, 1],
dtype='float32', lod_level=1)
x_first_step = fluid.layers.sequence_first_step(input=x)
"""
@ -821,7 +826,7 @@ def sequence_last_step(input, **kwargs):
.. code-block:: python
x = fluid.layers.data(name='x', shape=[7, 1],
x = fluid.layers.data(name='x', shape=[7, 1],
dtype='float32', lod_level=1)
x_last_step = fluid.layers.sequence_last_step(input=x)
"""
@ -1240,17 +1245,17 @@ def lstm_unit(x_t,
def reduce_sum(input, dim=None, keep_dim=False):
"""
Computes the sum of tensor elements over the given dimension.
Computes the sum of tensor elements over the given dimension.
Args:
input (Variable): The input variable which is a Tensor or LoDTensor.
dim (int|None): The dimension along which the sum is performed. If
:attr:`None`, sum all elements of :attr:`input` and return a
Tensor variable with a single element, otherwise must be in the
range :math:`[-rank(input), rank(input))`. If :math:`dim < 0`,
dim (int|None): The dimension along which the sum is performed. If
:attr:`None`, sum all elements of :attr:`input` and return a
Tensor variable with a single element, otherwise must be in the
range :math:`[-rank(input), rank(input))`. If :math:`dim < 0`,
the dimension to reduce is :math:`rank + dim`.
keep_dim (bool): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
keep_dim (bool): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
than the :attr:`input` unless :attr:`keep_dim` is true.
Returns:
@ -1284,17 +1289,17 @@ def reduce_sum(input, dim=None, keep_dim=False):
def reduce_mean(input, dim=None, keep_dim=False):
"""
Computes the mean of tensor elements over the given dimension.
Computes the mean of tensor elements over the given dimension.
Args:
input (Variable): The input variable which is a Tensor or LoDTensor.
dim (int|None): The dimension along which the mean is computed. If
:attr:`None`, compute the mean over all elements of :attr:`input`
and return a Tensor variable with a single element, otherwise
must be in the range :math:`[-rank(input), rank(input))`. If
dim (int|None): The dimension along which the mean is computed. If
:attr:`None`, compute the mean over all elements of :attr:`input`
and return a Tensor variable with a single element, otherwise
must be in the range :math:`[-rank(input), rank(input))`. If
:math:`dim < 0`, the dimension to reduce is :math:`rank + dim`.
keep_dim (bool): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
keep_dim (bool): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
than the :attr:`input` unless :attr:`keep_dim` is true.
Returns:
@ -1328,22 +1333,22 @@ def reduce_mean(input, dim=None, keep_dim=False):
def reduce_max(input, dim=None, keep_dim=False):
"""
Computes the maximum of tensor elements over the given dimension.
Computes the maximum of tensor elements over the given dimension.
Args:
input (Variable): The input variable which is a Tensor or LoDTensor.
dim (int|None): The dimension along which the maximum is computed.
If :attr:`None`, compute the maximum over all elements of
:attr:`input` and return a Tensor variable with a single element,
otherwise must be in the range :math:`[-rank(input), rank(input))`.
dim (int|None): The dimension along which the maximum is computed.
If :attr:`None`, compute the maximum over all elements of
:attr:`input` and return a Tensor variable with a single element,
otherwise must be in the range :math:`[-rank(input), rank(input))`.
If :math:`dim < 0`, the dimension to reduce is :math:`rank + dim`.
keep_dim (bool): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
keep_dim (bool): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
than the :attr:`input` unless :attr:`keep_dim` is true.
Returns:
Variable: The reduced Tensor variable.
Examples:
.. code-block:: python
@ -1372,22 +1377,22 @@ def reduce_max(input, dim=None, keep_dim=False):
def reduce_min(input, dim=None, keep_dim=False):
"""
Computes the minimum of tensor elements over the given dimension.
Computes the minimum of tensor elements over the given dimension.
Args:
input (Variable): The input variable which is a Tensor or LoDTensor.
dim (int|None): The dimension along which the minimum is computed.
If :attr:`None`, compute the minimum over all elements of
:attr:`input` and return a Tensor variable with a single element,
otherwise must be in the range :math:`[-rank(input), rank(input))`.
dim (int|None): The dimension along which the minimum is computed.
If :attr:`None`, compute the minimum over all elements of
:attr:`input` and return a Tensor variable with a single element,
otherwise must be in the range :math:`[-rank(input), rank(input))`.
If :math:`dim < 0`, the dimension to reduce is :math:`rank + dim`.
keep_dim (bool): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
keep_dim (bool): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
than the :attr:`input` unless :attr:`keep_dim` is true.
Returns:
Variable: The reduced Tensor variable.
Examples:
.. code-block:: python

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