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November 1, 2019 15:30
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Iteration 2 of the myLinear model
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class myLinear(nn.Module): | |
def __init__(self, in_features, out_features, bias=True): | |
super().__init__() | |
self.in_features = in_features | |
self.out_features = out_features | |
self.bias = bias | |
self.weight = torch.nn.Parameter(torch.Tensor(out_features, in_features)) | |
if bias: | |
self.bias = torch.nn.Parameter(torch.Tensor(out_features)) | |
else: | |
self.register_parameter('bias', None) | |
self.reset_parameters() | |
def reset_parameters(self): | |
torch.nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5)) | |
if self.bias is not None: | |
fan_in, _ = torch.nn.init._calculate_fan_in_and_fan_out(self.weight) | |
bound = 1 / math.sqrt(fan_in) | |
torch.nn.init.uniform_(self.bias, -bound, bound) | |
def forward(self, input): | |
x, y = input.shape | |
if y != self.in_features: | |
print(f'Wrong Input Features. Please use tensor with {self.in_features} Input Features') | |
return 0 | |
output = input.matmul(weight.t()) | |
if bias is not None: | |
output += bias | |
ret = output | |
return ret | |
def extra_repr(self): | |
return 'in_features={}, out_features={}, bias={}'.format( | |
self.in_features, self.out_features, self.bias is not None | |
) |
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