(self, in_size=28*28, hidden_size=128,
out_size=10, num_layers=3, batchnorm=False)
| 65 | class Net(nn.Module): |
| 66 | """Define a network that has num_layers of linear->norm->sigmoid transformations""" |
| 67 | def __init__(self, in_size=28*28, hidden_size=128, |
| 68 | out_size=10, num_layers=3, batchnorm=False): |
| 69 | super().__init__() |
| 70 | if batchnorm is False: |
| 71 | norm_layer = nn.Identity |
| 72 | else: |
| 73 | norm_layer = nn.BatchNorm1d |
| 74 | |
| 75 | layers = [] |
| 76 | layers.append(fc_layer(in_size, hidden_size, norm_layer)) |
| 77 | |
| 78 | for i in range(num_layers-1): |
| 79 | layers.append(fc_layer(hidden_size, hidden_size, norm_layer)) |
| 80 | |
| 81 | layers.append(nn.Linear(hidden_size, out_size)) |
| 82 | |
| 83 | self.layers = nn.Sequential(*layers) |
| 84 | |
| 85 | def forward(self, x): |
| 86 | x = torch.flatten(x, 1) |
nothing calls this directly
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