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mindspore.ops.RNNTLoss

class mindspore.ops.RNNTLoss(blank_label=0)

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) - Tensor of shape $(B,U-1)$. Data type is int32.

• input_lengths (Tensor) - Tensor of shape $(B,)$. Data type is int32.

• label_lengths (Tensor) - Tensor of shape $(B,)$. Data type is int32.

Outputs:

• costs (Tensor) - Tensor of shape $(B,)$. Data type is int32.

• grads (Tensor) - Has the same shape and dtype 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)