64 lines
2.6 KiB
Diff
64 lines
2.6 KiB
Diff
--- /usr/local/lib/python3.5/dist-packages/torch/nn/modules/linear.py
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+++ /usr/local/lib/python3.5/dist-packages/torch/nn/modules/linear.py
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@@ -1,19 +1,17 @@
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class Linear(Module):
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r"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b`
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-
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- This module supports :ref:`TensorFloat32<tf32_on_ampere>`.
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Args:
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in_features: size of each input sample
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out_features: size of each output sample
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- bias: If set to ``False``, the layer will not learn an additive bias.
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+ bias: If set to False, the layer will not learn an additive bias.
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Default: ``True``
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Shape:
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- - Input: :math:`(N, *, H_{in})` where :math:`*` means any number of
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- additional dimensions and :math:`H_{in} = \text{in\_features}`
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- - Output: :math:`(N, *, H_{out})` where all but the last dimension
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- are the same shape as the input and :math:`H_{out} = \text{out\_features}`.
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+ - Input: :math:`(N, *, \text{in\_features})` where :math:`*` means any number of
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+ additional dimensions
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+ - Output: :math:`(N, *, \text{out\_features})` where all but the last dimension
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+ are the same shape as the input.
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Attributes:
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weight: the learnable weights of the module of shape
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@@ -33,12 +31,9 @@
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>>> print(output.size())
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torch.Size([128, 30])
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"""
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- __constants__ = ['in_features', 'out_features']
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- in_features: int
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- out_features: int
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- weight: Tensor
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+ __constants__ = ['bias']
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- def __init__(self, in_features: int, out_features: int, bias: bool = True) -> None:
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+ def __init__(self, in_features, out_features, bias=True):
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super(Linear, self).__init__()
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self.in_features = in_features
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self.out_features = out_features
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@@ -49,17 +44,18 @@
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self.register_parameter('bias', None)
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self.reset_parameters()
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- def reset_parameters(self) -> None:
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+ def reset_parameters(self):
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init.kaiming_uniform_(self.weight, a=math.sqrt(5))
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if self.bias is not None:
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fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
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bound = 1 / math.sqrt(fan_in)
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init.uniform_(self.bias, -bound, bound)
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- def forward(self, input: Tensor) -> Tensor:
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+ @weak_script_method
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+ def forward(self, input):
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return F.linear(input, self.weight, self.bias)
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- def extra_repr(self) -> str:
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+ def extra_repr(self):
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return 'in_features={}, out_features={}, bias={}'.format(
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self.in_features, self.out_features, self.bias is not None
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)
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