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@ -22,36 +22,13 @@ from ..param_attr import ParamAttr
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from tensor import concat
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__all__ = [
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'fc',
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'embedding',
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'dynamic_lstm',
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'gru_unit',
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'linear_chain_crf',
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'crf_decoding',
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'cos_sim',
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'cross_entropy',
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'square_error_cost',
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'accuracy',
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'chunk_eval',
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'sequence_conv',
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'conv2d',
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'sequence_pool',
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'pool2d',
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'batch_norm',
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'beam_search_decode',
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'conv2d_transpose',
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'sequence_expand',
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'lstm_unit',
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'reduce_sum',
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'reduce_mean',
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'reduce_max',
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'reduce_min',
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'sequence_first_step',
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'sequence_last_step',
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'dropout',
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'split',
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'l2_normalize',
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'matmul',
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'fc', 'embedding', 'dynamic_lstm', 'gru_unit', 'linear_chain_crf',
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'crf_decoding', 'cos_sim', 'cross_entropy', 'square_error_cost', 'accuracy',
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'chunk_eval', 'sequence_conv', 'conv2d', 'sequence_pool', 'pool2d',
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'batch_norm', 'beam_search_decode', 'conv2d_transpose', 'sequence_expand',
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'lstm_unit', 'reduce_sum', 'reduce_mean', 'reduce_max', 'reduce_min',
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'sequence_first_step', 'sequence_last_step', 'dropout', 'split',
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'l2_normalize', 'matmul', 'warpctc'
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]
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@ -1721,29 +1698,29 @@ def l2_normalize(x, axis, epsilon=1e-12, name=None):
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def matmul(x, y, transpose_x=False, transpose_y=False, name=None):
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"""
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Applies matrix multipication to two tensors. Currently only rank 1 to rank
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Applies matrix multipication to two tensors. Currently only rank 1 to rank
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3 input tensors are supported.
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The actual behavior depends on the shapes of :math:`x`, :math:`y` and the
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The actual behavior depends on the shapes of :math:`x`, :math:`y` and the
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flag values of :attr:`transpose_x`, :attr:`transpose_y`. Specifically:
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- If a transpose flag is specified, the last two dimensions of the tensor
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are transposed. If the tensor is rank-1 of shape :math:`[D]`, then for
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:math:`x` it is treated as :math:`[1, D]` in nontransposed form and as
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:math:`[D, 1]` in transposed form, whereas for :math:`y` it is the
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opposite: It is treated as :math:`[D, 1]` in nontransposed form and as
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- If a transpose flag is specified, the last two dimensions of the tensor
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are transposed. If the tensor is rank-1 of shape :math:`[D]`, then for
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:math:`x` it is treated as :math:`[1, D]` in nontransposed form and as
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:math:`[D, 1]` in transposed form, whereas for :math:`y` it is the
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opposite: It is treated as :math:`[D, 1]` in nontransposed form and as
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:math:`[1, D]` in transposed form.
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- After transpose, the two tensors are 2-D or 3-D and matrix multipication
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- After transpose, the two tensors are 2-D or 3-D and matrix multipication
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performs in the following way.
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- If both are 2-D, they are multiplied like conventional matrices.
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- If either is 3-D, it is treated as a stack of matrices residing in the
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last two dimensions and a batched matrix multiply supporting broadcast
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- If either is 3-D, it is treated as a stack of matrices residing in the
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last two dimensions and a batched matrix multiply supporting broadcast
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applies on the two tensors.
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Also note that if the raw tensor :math:`x` or :math:`y` is rank-1 and
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nontransposed, the prepended or appended dimension :math:`1` will be
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Also note that if the raw tensor :math:`x` or :math:`y` is rank-1 and
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nontransposed, the prepended or appended dimension :math:`1` will be
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removed after matrix multipication.
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Args:
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@ -1751,7 +1728,7 @@ def matmul(x, y, transpose_x=False, transpose_y=False, name=None):
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y (Variable): The input variable which is a Tensor or LoDTensor.
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transpose_x (bool): Whether to transpose :math:`x` before multiplication.
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transpose_y (bool): Whether to transpose :math:`y` before multiplication.
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name(str|None): A name for this layer(optional). If set None, the layer
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name(str|None): A name for this layer(optional). If set None, the layer
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will be named automatically.
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Returns:
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@ -1788,3 +1765,56 @@ def matmul(x, y, transpose_x=False, transpose_y=False, name=None):
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attrs={'transpose_X': transpose_x,
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'transpose_Y': transpose_y})
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return out
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def warpctc(input, label, blank=0, norm_by_times=False, **kwargs):
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"""
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An operator integrating the open source Warp-CTC library
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(https://github.com/baidu-research/warp-ctc)
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to compute Connectionist Temporal Classification (CTC) loss.
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It can be aliased as softmax with CTC, since a native softmax activation is
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interated to the Warp-CTC library, to to normlize values for each row of the
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input tensor.
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Args:
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input(Variable): (LodTensor, default: LoDTensor<float>),
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the unscaled probabilities of variable-length sequences,
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which is a 2-D Tensor with LoD information.
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It's shape is [Lp, num_classes + 1], where Lp is the sum of all input
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sequences' length and num_classes is the true number of classes.
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(not including the blank label).
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label(Variable): (LodTensor, default: LoDTensor<int>), the ground truth
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of variable-length sequence, which is a 2-D Tensor with LoD
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information. It is of the shape [Lg, 1], where Lg is th sum of
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all labels' length.
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blank: (int, default: 0), the blank label index of Connectionist
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Temporal Classification (CTC) loss, which is in the
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half-opened interval [0, num_classes + 1).
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norm_by_times: (bool, default: false), whether to normalize
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the gradients by the number of time-step,which is also the
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sequence's length. There is no need to normalize the gradients
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if warpctc layer was follewed by a mean_op.
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Returns:
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Variable: The Connectionist Temporal Classification (CTC) loss,
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which is a 2-D Tensor of the shape [batch_size, 1].
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Examples:
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.. code-block:: python
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y = layers.data(name='y', shape=[11, 8], dtype='float32', lod_level=1)
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y_predict = layers.data(name='y_predict', shape=[11, 1], dtype='float32')
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cost = layers.warpctc(input=y_predict, label=y)
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"""
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helper = LayerHelper('warpctc', **kwargs)
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loss_out = helper.create_tmp_variable(dtype=input.dtype)
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grad_out = helper.create_tmp_variable(dtype=input.dtype)
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helper.append_op(
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type='warpctc',
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inputs={'Logits': [input],
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'Label': [label]},
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outputs={'WarpCTCGrad': [grad_out],
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'Loss': [loss_out]},
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attrs={'blank': blank,
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'norm_by_times': norm_by_times})
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return loss_out
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whs
8 years ago
committed by
GitHub