diff --git a/docs/api/api_python/mindspore.ops.rst b/docs/api/api_python/mindspore.ops.rst index 5e34f1e283b..8508bc54f06 100644 --- a/docs/api/api_python/mindspore.ops.rst +++ b/docs/api/api_python/mindspore.ops.rst @@ -59,6 +59,7 @@ MindSpore中 `mindspore.ops` 接口与上一版本相比，新增、删除和支 mindspore.ops.binary_cross_entropy_with_logits mindspore.ops.cosine_embedding_loss mindspore.ops.cross_entropy + mindspore.ops.ctc_loss mindspore.ops.gaussian_nll_loss mindspore.ops.hinge_embedding_loss mindspore.ops.huber_loss diff --git a/docs/api/api_python/ops/mindspore.ops.func_ctc_loss.rst b/docs/api/api_python/ops/mindspore.ops.func_ctc_loss.rst new file mode 100644 index 00000000000..a2c83bd807b --- /dev/null +++ b/docs/api/api_python/ops/mindspore.ops.func_ctc_loss.rst @@ -0,0 +1,34 @@ +mindspore.ops.ctc_loss +====================== + +.. py:function:: mindspore.ops.ctc_loss(log_probs, targets, input_lengths, target_lengths, blank=0, reduction="mean", zero_infinity=False) + + 计算CTC（Connectist Temporal Classification）损失和梯度。 + + 关于CTCLoss算法详细介绍，请参考 `Connectionist Temporal Classification: Labeling Unsegmented Sequence Data withRecurrent Neural Networks `_ 。 + + 参数： + - **log_probs** (Tensor) - 输入Tensor，shape :math:`(T, N, C)` 。其中T表示输入长度，N表示批次大小，C是分类数，包含空白。 + - **targets** (Tensor) - 目标Tensor，shape :math:`(N, S)` 。其中S表示最大目标长度。 + - **input_lengths** (Union[tuple, Tensor]) - shape为N的Tensor或tuple。表示输入长度。 + - **target_lengths** (Union[tuple, Tensor]) - shape为N的Tensor或tuple。表示目标长度。 + - **blank** (int) - 空白标签。默认值：0。 + - **reduction** (str) - 对输出应用归约方法。可选值为"none"、"mean"或"sum"。默认值："mean"。 + - **zero_infinity** (bool) - 是否设置无限损失和相关梯度为零。默认值：False。 + + 返回： + - **neg_log_likelihood** (Tensor) - 对每一个输入节点可微调的损失值，shape是 :math:`(N)`。 + - **log_alpha** (Tensor) - shape为 :math:`(N, T, 2 * S + 1)` 的输入到输出的轨迹概率。 + + 异常： + - **TypeError** - `zero_infinity` 不是布尔值， `reduction` 不是字符串。 + - **TypeError** - `log_probs` 的数据类型不是float或double。 + - **TypeError** - `targets` 、 `input_lengths` 或 `target_lengths` 数据类型不是int32或int64。 + - **ValueError** - `log_probs` 的秩不是3。 + - **ValueError** - `targets` 的秩不是2。 + - **ValueError** - `input_lengths` 的shape大小不等于N。N是 `log_probs` 的批次大小。 + - **ValueError** - `target_lengths` 的shape大小不等于N。N是 `log_probs` 的批次大小。 + - **ValueError** - `targets` 、 `input_lengths` 或 `target_lengths` 的数据类型是不同的。 + - **ValueError** - `blank` 值不介于0到C之间。C是 `log_probs` 的分类数。 + - **RuntimeError** - `input_lengths` 的值大于T。T是 `log_probs` 的长度。 + - **RuntimeError** - `target_lengths[i]` 的取值范围不在0到 `input_length[i]` 之间。 diff --git a/docs/api/api_python_en/mindspore.ops.rst b/docs/api/api_python_en/mindspore.ops.rst index 324bc6b95a2..ac13c46dcbb 100644 --- a/docs/api/api_python_en/mindspore.ops.rst +++ b/docs/api/api_python_en/mindspore.ops.rst @@ -59,6 +59,7 @@ Loss Functions mindspore.ops.binary_cross_entropy_with_logits mindspore.ops.cosine_embedding_loss mindspore.ops.cross_entropy + mindspore.ops.ctc_loss mindspore.ops.gaussian_nll_loss mindspore.ops.hinge_embedding_loss mindspore.ops.huber_loss diff --git a/mindspore/python/mindspore/nn/loss/loss.py b/mindspore/python/mindspore/nn/loss/loss.py index fb22ab18256..191f2e1b9c7 100644 --- a/mindspore/python/mindspore/nn/loss/loss.py +++ b/mindspore/python/mindspore/nn/loss/loss.py @@ -2302,7 +2302,7 @@ class CTCLoss(LossBase): Recurrent Neural Networks `_ . Args: - blank (int): The blank tag. Default: 0. + blank (int): The blank label. Default: 0. reduction (str): Implements the reduction method to the output with 'none', 'mean', or 'sum'. Default: 'mean'. zero_infinity (bool): Whether to set infinite loss and correlation gradient to 0. Default: False. diff --git a/mindspore/python/mindspore/ops/function/nn_func.py b/mindspore/python/mindspore/ops/function/nn_func.py index 0f47dbed1a9..f35bb22b218 100644 --- a/mindspore/python/mindspore/ops/function/nn_func.py +++ b/mindspore/python/mindspore/ops/function/nn_func.py @@ -3974,26 +3974,27 @@ def ctc_loss(log_probs, targets, input_lengths, target_lengths, blank=0, reducti input_lengths (Union(Tuple, Tensor)): A tuple or Tensor of shape(N). It means the lengths of the input. target_lengths (Union(Tuple, Tensor)): A tuple or Tensor of shape(N). It means the lengths of the target. blank (int): The blank label. Default: 0. - reduction (string): Apply specific reduction method to the output: 'none', 'mean', or 'sum'. Default: 'mean'. - zero_infinity (bool): Whether to set infinite loss and correlation gradient to zero. Default: False. + reduction (string): Implements the reduction method to the output with 'none', 'mean', or 'sum'. + Default: 'mean'. + zero_infinity (bool): Whether to set infinite loss and correlation gradient to 0. Default: False. Returns: - neg_log_likelihood (Tensor), A loss value with shape (N), which is differentiable with respect to + neg_log_likelihood (Tensor), A loss value with shape :math:`(N)` , which is differentiable with respect to each input node. - log_alpha (Tensor), The probability of possible trace of input to target with shape (N, T, 2 * S + 1). + log_alpha (Tensor), The probability of possible trace of input to target with shape :math:`(N, T, 2 * S + 1)` . Raises: - TypeError: If `zero_infinity` is not a bool, reduction is not string. - TypeError: If the dtype of `log_probs` or `grad_out` is not float or double. + TypeError: If `zero_infinity` is not a bool, `reduction` is not string. + TypeError: If the dtype of `log_probs` is not float or double. TypeError: If the dtype of `targets`, `input_lengths` or `target_lengths` is not int32 or int64. ValueError: If the rank of `log_probs` is not 3. ValueError: If the rank of `targets` is not 2. - ValueError: If the shape of `input_lengths` does not match {batch_size|N}. - ValueError: If the shape of `target_lengths` does not match {batch_size|N}. - TypeError: If the types of `targets`, `input_lengths`, `grad_out` or `target_lengths` are different. - ValueError: If the value of `blank` is not in range [0, num_labels|C). - RuntimeError: If any value of `input_lengths` is larger than (num_labels|C). + ValueError: If the shape of `input_lengths` does not match N. N is batch size of `log_probs` . + ValueError: If the shape of `target_lengths` does not match N. N is batch size of `log_probs` . + TypeError: If the types of `targets`, `input_lengths` or `target_lengths` are different. + ValueError: If the value of `blank` is not in range [0, num_labels|C). C is number of classes of `log_probs` . + RuntimeError: If any value of `input_lengths` is larger than T. T is the length of `log_probs`. RuntimeError: If any target_lengths[i] is not in range [0, input_length[i]]. Supported Platforms: