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117 lines
3.9 KiB
Python
117 lines
3.9 KiB
Python
import math
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import torch
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from torch.nn.parameter import Parameter
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from .. import functional as F
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from .module import Module
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class Linear(Module):
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r"""Applies a linear transformation to the incoming data: :math:`y = Ax + b`
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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. Default: True
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Shape:
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- Input: :math:`(N, in\_features)`
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- Output: :math:`(N, out\_features)`
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Attributes:
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weight: the learnable weights of the module of shape (out_features x in_features)
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bias: the learnable bias of the module of shape (out_features)
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Examples::
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>>> m = nn.Linear(20, 30)
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>>> input = autograd.Variable(torch.randn(128, 20))
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>>> output = m(input)
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>>> print(output.size())
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"""
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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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self.weight = Parameter(torch.Tensor(out_features, in_features))
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if bias:
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self.bias = Parameter(torch.Tensor(out_features))
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else:
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self.register_parameter('bias', None)
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self.reset_parameters()
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def reset_parameters(self):
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stdv = 1. / math.sqrt(self.weight.size(1))
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self.weight.data.uniform_(-stdv, stdv)
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if self.bias is not None:
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self.bias.data.uniform_(-stdv, stdv)
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def forward(self, input):
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if self.bias is None:
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return self._backend.Linear()(input, self.weight)
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else:
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return self._backend.Linear()(input, self.weight, self.bias)
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def __repr__(self):
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return self.__class__.__name__ + ' (' \
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+ str(self.in_features) + ' -> ' \
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+ str(self.out_features) + ')'
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class Bilinear(Module):
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r"""Applies a bilinear transformation to the incoming data: :math:`y = x_1 * A * x_2 + b`
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Args:
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in1_features: size of each first input sample
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in2_features: size of each second 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. Default: True
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Shape:
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- Input: :math:`(N, in1\_features)`, :math:`(N, in2\_features)`
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- Output: :math:`(N, out\_features)`
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Attributes:
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weight: the learnable weights of the module of shape (out_features x in1_features x in2_features)
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bias: the learnable bias of the module of shape (out_features)
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Examples::
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>>> m = nn.Bilinear(20, 30, 40)
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>>> input1 = autograd.Variable(torch.randn(128, 20))
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>>> input1 = autograd.Variable(torch.randn(128, 30))
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>>> output = m(input1, input2)
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>>> print(output.size())
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"""
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def __init__(self, in1_features, in2_features, out_features, bias=True):
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super(Bilinear, self).__init__()
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self.in1_features = in1_features
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self.in2_features = in2_features
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self.out_features = out_features
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self.weight = Parameter(torch.Tensor(out_features, in1_features, in2_features))
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if bias:
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self.bias = Parameter(torch.Tensor(out_features))
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else:
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self.register_parameter('bias', None)
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self.reset_parameters()
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def reset_parameters(self):
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stdv = 1. / math.sqrt(self.weight.size(1))
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self.weight.data.uniform_(-stdv, stdv)
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if self.bias is not None:
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self.bias.data.uniform_(-stdv, stdv)
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def forward(self, input1, input2):
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return F.bilinear(input1, input2, self.weight, self.bias)
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def __repr__(self):
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return self.__class__.__name__ + ' (' \
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+ 'in1_features=' + str(self.in1_features) \
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+ ', in2_features=' + str(self.in2_features) \
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+ ', out_features=' + str(self.out_features) + ')'
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# TODO: PartialLinear - maybe in sparse?
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