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@ -66,6 +66,8 @@ __all__ = [
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'row_conv',
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'multiplex',
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'layer_norm',
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'softmax_with_cross_entropy',
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'smooth_l1',
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]
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@ -3091,3 +3093,122 @@ def multiplex(inputs, index):
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'Ids': index},
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outputs={'Out': [out]})
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return out
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def softmax_with_cross_entropy(logits, label, soft_label=False):
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"""
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**Softmax With Cross Entropy Operator.**
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Cross entropy loss with softmax is used as the output layer extensively. This
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operator computes the softmax normalized values for each row of the input
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tensor, after which cross-entropy loss is computed. This provides a more
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numerically stable gradient.
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Because this operator performs a softmax on logits internally, it expects
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unscaled logits. This operator should not be used with the output of
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softmax operator since that would produce incorrect results.
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When the attribute soft_label is set false, this operators expects mutually
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exclusive hard labels, each sample in a batch is in exactly one class with a
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probability of 1.0. Each sample in the batch will have a single label.
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The equation is as follows:
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1) Hard label (one-hot label, so every sample has exactly one class)
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.. math::
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loss_j = -\\text{logit}_{label_j} +
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\\log\\left(\\sum_{i=0}^{K}\\exp(\\text{logit}_i)\\right), j = 1,..., K
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2) Soft label (each sample can have a distribution over all classes)
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.. math::
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loss_j = -\\sum_{i=0}^{K}\\text{label}_i
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\\left(\\text{logit}_i - \\log\\left(\\sum_{i=0}^{K}
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\\exp(\\text{logit}_i)\\right)\\right), j = 1,...,K
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Args:
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logits (Variable): The unscaled log probabilities, which is a 2-D tensor
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with shape [N x K]. N is the batch_size, and K is the class number.
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label (Variable): The ground truth which is a 2-D tensor. If soft_label
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is set to false, Label is a Tensor<int64> with shape [N x 1]. If
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soft_label is set to true, Label is a Tensor<float/double> with
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soft_label (bool): A flag to indicate whether to interpretate the given
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labels as soft labels. By default, `soft_label` is set to False.
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Returns:
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Variable: The cross entropy loss is a 2-D tensor with shape [N x 1].
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Examples:
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.. code-block:: python
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data = fluid.layers.data(name='data', shape=[128], dtype='float32')
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label = fluid.layers.data(name='label', shape=[1], dtype='int64')
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fc = fluid.layers.fc(input=data, size=100)
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out = fluid.layers.softmax_with_cross_entropy(logits=fc, label=label)
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"""
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helper = LayerHelper('softmax_with_cross_entropy', **locals())
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softmax = helper.create_tmp_variable(dtype=logits.dtype)
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loss = helper.create_tmp_variable(dtype=logits.dtype)
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helper.append_op(
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type='softmax_with_cross_entropy',
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inputs={'Logits': logits,
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'Label': label},
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outputs={'Softmax': softmax,
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'Loss': loss},
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attrs={'soft_label': soft_label})
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return loss
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def smooth_l1(x, y, inside_weight=None, outside_weight=None, sigma=None):
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"""
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**Smooth L1 Loss Operator. **
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This operator computes the smooth l1 loss for X and Y.
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The operator takes the first dimension of X and Y as batch size.
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For each instance, it computes the smooth l1 loss element by element first
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and then sums all the losses. So the shape of Out is [batch_size, 1].
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Args:
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x (Variable): A tensor with rank at least 2. The input value of smooth
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l1 loss op with shape [batch_size, dim1, ..., dimN].
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y (Variable): A tensor with rank at least 2. The target value of smooth
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l1 loss op with same shape as x.
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inside_weight (Variable|None): A tensor with rank at least 2. This
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input is optional and should have same shape with x. If provided,
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the result of (x - y) will be multiplied by this tensor element by
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element.
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outside_weight (Variable|None): A tensor with rank at least 2. This
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input is optional and should have same shape with x. If provided,
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the out smooth l1 loss will be multiplied by this tensor element
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by element.
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sigma (float|None): Hyper parameter of smooth l1 loss op. A float scalar
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with default value 1.0.
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Returns:
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Variable: A tensor with rank be 2. The output smooth l1 loss with
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shape [batch_size, 1].
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Examples:
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.. code-block:: python
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data = fluid.layers.data(name='data', shape=[128], dtype='float32')
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label = fluid.layers.data(name='label', shape=[100], dtype='int64')
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fc = fluid.layers.fc(input=data, size=100)
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out = fluid.layers.smooth_l1(logits=fc, label=label)
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"""
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helper = LayerHelper('smooth_l1_loss', **locals())
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diff = helper.create_tmp_variable(dtype=x.dtype)
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loss = helper.create_tmp_variable(dtype=x.dtype)
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helper.append_op(
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type='smooth_l1_loss',
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inputs={
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'X': x,
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'Y': y,
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'InsideWeight': inside_weight,
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'OutsideWeight': outside_weight
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},
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outputs={'Diff': diff,
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'Out': loss},
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attrs={'sigma': sigma})
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return loss
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qingqing01
7 years ago
committed by
Yi Wang