(self)
| 107 | np.random.seed(self.args.seed) |
| 108 | |
| 109 | def fit(self): |
| 110 | self.prepare_data() |
| 111 | self.model = self.model(nfeat=self.nfeat_dim, |
| 112 | nhid=self.args.nhid, |
| 113 | nclass=self.nclass, |
| 114 | dropout=self.args.dropout) |
| 115 | print(self.model.parameters) |
| 116 | self.model = self.model.to(self.device) |
| 117 | |
| 118 | self.optimizer = th.optim.Adam(self.model.parameters(), lr=self.args.lr) |
| 119 | self.criterion = th.nn.CrossEntropyLoss() |
| 120 | |
| 121 | self.model_param = sum(param.numel() for param in self.model.parameters()) |
| 122 | print('# model parameters:', self.model_param) |
| 123 | self.convert_tensor() |
| 124 | |
| 125 | start = time() |
| 126 | self.train() |
| 127 | self.train_time = time() - start |
| 128 | |
| 129 | @classmethod |
| 130 | def set_description(cls, desc): |
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