| 104 | |
| 105 | # Binary classification |
| 106 | class NeuralNet1(nn.Module): |
| 107 | def __init__(self, input_size, hidden_size): |
| 108 | super(NeuralNet1, self).__init__() |
| 109 | self.linear1 = nn.Linear(input_size, hidden_size) |
| 110 | self.relu = nn.ReLU() |
| 111 | self.linear2 = nn.Linear(hidden_size, 1) |
| 112 | |
| 113 | def forward(self, x): |
| 114 | out = self.linear1(x) |
| 115 | out = self.relu(out) |
| 116 | out = self.linear2(out) |
| 117 | # sigmoid at the end |
| 118 | y_pred = torch.sigmoid(out) |
| 119 | return y_pred |
| 120 | |
| 121 | model = NeuralNet1(input_size=28*28, hidden_size=5) |
| 122 | criterion = nn.BCELoss() |
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