mindspore.ops.RNNTLoss

class mindspore.ops.RNNTLoss(*args, **kwargs)[source]

Computes the RNNTLoss and its gradient with respect to the softmax outputs.

Parameters

blank_label (int) – blank label. Default: 0.

Inputs:

  • acts (Tensor) - Tensor of shape \((B, T, U, V)\). Data type must be float16 or float32.

  • labels (Tensor[int32]) - Tensor of shape \((B, U-1)\).

  • input_lengths (Tensor[int32]) - Tensor of shape \((B,)\).

  • label_lengths (Tensor[int32]) - Tensor of shape \((B,)\).

Outputs:

  • costs (Tensor[int32]) - Tensor of shape \((B,)\).

  • grads (Tensor[int32]) - Has the same shape as acts.

Raises

  • TypeError – If acts, labels, input_lengths or label_lengths is not a Tensor.

  • TypeError – If dtype of acts is neither float16 nor float32.

  • TypeError – If dtype of labels, input_lengths or label_lengths is not int32.

Supported Platforms:

Ascend

Examples

>>> B, T, U, V = 1, 2, 3, 5
>>> blank = 0
>>> acts = np.random.random((B, T, U, V)).astype(np.float32)
>>> labels = np.array([[1, 2]]).astype(np.int32)
>>> input_length = np.array([T] * B).astype(np.int32)
>>> label_length = np.array([len(l) for l in labels]).astype(np.int32)
>>> rnnt_loss = ops.RNNTLoss(blank_label=0)
>>> costs, grads = rnnt_loss(Tensor(acts), Tensor(labels), Tensor(input_length), Tensor(label_length))
>>> print(costs.shape)
(1,)
>>> print(grads.shape)
(1, 2, 3, 5)