name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor, if :attr:`reduction` is ``'mean'`` or ``'sum'``, the out shape is :math:`[1]`, otherwise the shape is the same as `input` .The same dtype as input tensor.
Tensor, if :attr:`reduction` is ``'mean'`` or ``'sum'``, the out shape is :math:`[]`, otherwise the shape is the same as `input` .The same dtype as input tensor.
Examples:
...
...
@@ -1159,7 +1159,7 @@ def margin_ranking_loss(
other = paddle.to_tensor([[2, 1], [2, 4]], dtype='float32')
If `reduction` is ``'none'``, the output loss is the same shape as the input, and the loss at each point is calculated separately. There is no reduction to the result.
If `reduction` is ``'mean'``, the output loss is the shape of [1], and the output is the average of all losses.
If `reduction` is ``'mean'``, the output loss is the shape of [], and the output is the average of all losses.
If `reduction` is ``'sum'``, the output loss is the shape of [1], and the output is the sum of all losses.
If `reduction` is ``'sum'``, the output loss is the shape of [], and the output is the sum of all losses.
If `reduction` is ``'batchmean'``, the output loss is the shape of [N], N is the batch size, and the output is the sum of all losses divided by the batch size.
norm_by_times (bool, optional): Whether to normalize the gradients by the number of time-step, which is also the sequence's length. There is no need to normalize the gradients if reduction mode is 'mean'. Default: False.
Returns:
Tensor, The Connectionist Temporal Classification (CTC) loss between ``log_probs`` and ``labels``. If attr:`reduction` is ``'none'``, the shape of loss is [batch_size], otherwise, the shape of loss is [1]. Data type is the same as ``log_probs``.
Tensor, The Connectionist Temporal Classification (CTC) loss between ``log_probs`` and ``labels``. If attr:`reduction` is ``'none'``, the shape of loss is [batch_size], otherwise, the shape of loss is []. Data type is the same as ``log_probs``.
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor, The RNN-T loss between ``logprobs`` and ``labels``. If attr:`reduction` is ``'none'``, the shape of loss is [batch_size], otherwise, the shape of loss is [1]. Data type is the same as ``logprobs``.
Tensor, The RNN-T loss between ``logprobs`` and ``labels``. If attr:`reduction` is ``'none'``, the shape of loss is [batch_size], otherwise, the shape of loss is []. Data type is the same as ``logprobs``.
For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor, if :attr:`reduction` is ``'mean'`` or ``'sum'``, the out shape is :math:`[1]`, otherwise the shape is the same as ``logit``. The same dtype as ``logit`` tensor.
Tensor, if :attr:`reduction` is ``'mean'`` or ``'sum'``, the out shape is :math:`[]`, otherwise the shape is the same as ``logit``. The same dtype as ``logit`` tensor.
If `reduction` is ``'none'``, the output loss is the same shape as the input, and the loss at each point is calculated separately. There is no reduction to the result.
If `reduction` is ``'mean'``, the output loss is the shape of [1], and the output is the average of all losses.
If `reduction` is ``'mean'``, the output loss is the shape of [], and the output is the average of all losses.
If `reduction` is ``'sum'``, the output loss is the shape of [1], and the output is the sum of all losses.
If `reduction` is ``'sum'``, the output loss is the shape of [], and the output is the sum of all losses.
If `reduction` is ``'batchmean'``, the output loss is the shape of [N], N is the batch size, and the output is the sum of all losses divided by the batch size.
...
...
@@ -1012,7 +1012,7 @@ class KLDivLoss(Layer):
label (Tensor): ``(N, *)``, same shape as input.
output (Tensor): tensor with shape: [1] by default.
output (Tensor): tensor with shape: [] by default.
Examples:
.. code-block:: python
...
...
@@ -1024,20 +1024,20 @@ class KLDivLoss(Layer):
x = paddle.uniform(shape, min=-10, max=10).astype('float32')
# 'none' reduction, loss shape is same with X shape
kldiv_criterion = nn.KLDivLoss(reduction='none')
...
...
@@ -1090,7 +1090,7 @@ class MarginRankingLoss(Layer):
label: N-D Tensor, label have the same shape and dtype as `input`.
output: If :attr:`reduction` is ``'mean'`` or ``'sum'`` , the out shape is :math:`[1]`, otherwise the shape is the same as `input` .The same dtype as input tensor.
output: If :attr:`reduction` is ``'mean'`` or ``'sum'`` , the out shape is :math:`[]`, otherwise the shape is the same as `input` .The same dtype as input tensor.
Returns:
A callable object of MarginRankingLoss.
...
...
@@ -1108,7 +1108,7 @@ class MarginRankingLoss(Layer):
- norm_by_times (bool, optional): Whether to normalize the gradients by the number of time-step, which is also the sequence's length. There is no need to normalize the gradients if reduction mode is 'mean'. Default: False.
Returns:
Tensor, The Connectionist Temporal Classification (CTC) loss between ``log_probs`` and ``labels``. If attr:`reduction` is ``'none'``, the shape of loss is [batch_size], otherwise, the shape of loss is [1]. Data type is the same as ``log_probs``.
Tensor, The Connectionist Temporal Classification (CTC) loss between ``log_probs`` and ``labels``. If attr:`reduction` is ``'none'``, the shape of loss is [batch_size], otherwise, the shape of loss is []. Data type is the same as ``log_probs``.
label_lengths: Tensor of (batch) containing label length of each example
Returns:
Tensor, The RNN-T loss between ``logprobs`` and ``labels``. If attr:`reduction` is ``'none'``, the shape of loss is [batch_size], otherwise, the shape of loss is [1]. Data type is the same as ``logprobs``.
Tensor, The RNN-T loss between ``logprobs`` and ``labels``. If attr:`reduction` is ``'none'``, the shape of loss is [batch_size], otherwise, the shape of loss is []. Data type is the same as ``logprobs``.
x (Tensor): An N-D ``Tensor``. The data type is ``bool``, ``float16``, ``float32``, ``float64``, ``int32`` or ``int64``.
axes (list|tuple): The data type is ``int32`` . Axes that `starts` and `ends` apply to.
It's optional. If it is not provides, it will be treated as :math:`[0,1,...,len(starts)-1]`.
starts (list|tuple|Tensor): The data type is ``int32`` . If ``starts`` is a list or tuple, the elements of it should be integers or Tensors with shape [1]. If ``starts`` is an Tensor, it should be an 1-D Tensor. It represents starting indices of corresponding axis in ``axes``.
ends (list|tuple|Tensor): The data type is ``int32`` . If ``ends`` is a list or tuple, the elements of
it should be integers or Tensors with shape [1]. If ``ends`` is an Tensor, it should be an 1-D Tensor . It represents ending indices of corresponding axis in ``axes``.
strides (list|tuple|Tensor): The data type is ``int32`` . If ``strides`` is a list or tuple, the elements of
it should be integers or Tensors with shape [1]. If ``strides`` is an Tensor, it should be an 1-D Tensor . It represents slice step of corresponding axis in ``axes``.
starts (list|tuple|Tensor): The data type is ``int32`` . If ``starts`` is a list or tuple, the elements of it should be
integers or Tensors with shape []. If ``starts`` is an Tensor, it should be an 1-D Tensor.
It represents starting indices of corresponding axis in ``axes``.
ends (list|tuple|Tensor): The data type is ``int32`` . If ``ends`` is a list or tuple, the elements of it should be
integers or Tensors with shape []. If ``ends`` is an Tensor, it should be an 1-D Tensor.
It represents ending indices of corresponding axis in ``axes``.
strides (list|tuple|Tensor): The data type is ``int32`` . If ``strides`` is a list or tuple, the elements of it should be
integers or Tensors with shape []. If ``strides`` is an Tensor, it should be an 1-D Tensor.
It represents slice step of corresponding axis in ``axes``.
name(str, optional): The default value is None. Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name` .
...
...
@@ -4074,7 +4078,7 @@ def tensordot(x, y, axes=2, name=None):
y = paddle.arange(10, dtype=data_type)
z1 = paddle.tensordot(x, y, axes=1)
z2 = paddle.dot(x, y)
# z1 = z2 = [285.]
# z1 = z2 = 285.
# For two 2-d tensor x and y, the case axes=1 is equivalent to matrix multiplication.
