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Uses the assignment syntax to get deterministic ordering of parameters. The ordering of parameters using the constructor syntax is non-deterministic because kwargs use dict() in Python 3.5 and earlier.
60 lines
2.1 KiB
Python
60 lines
2.1 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 .module import Module
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class Linear(Module):
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"""Applies a linear transformation to the incoming data, y = Ax + b
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The input is a 2D mini-batch of samples, each of size in_features
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The output will be a 2D Tensor of size mini-batch x out_features
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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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Input Shape: [*, in_features] : Input can be of shape minibatch x in_features
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Output Shape:[*, out_features] : Output is of shape minibatch x out_features
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Members:
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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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# TODO: Bilinear
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# TODO: PartialLinear - maybe in sparse?
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