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('conv2',nn.Conv2d(8,16,3,padding=1)),('relu2',nn.ReLU()),('pool2',nn.MaxPool2d(2)),\n      ('flatten',nn.Flatten()),('hidden',nn.Linear(784,32)),('relu3',nn.ReLU()),('logits',nn.Linear(32,10))]))\nsource_model=make_cnn();source_model.load_state_dict(source_state);source_model.eval()\nassert sum(p.numel() for p in source_model.parameters())==26698\nprint('已读取独立包内的 26698 参数 CNN；PyTorch',torch.__version__)\nprint('当前模型 SHA-256:',model_sha256)\n\n```","sha256":"4e8403f272270dbf8b62b8561945ef255842c8d5a118f15592e3f54af0326e72","chapter":{"id":"course","title":"准备：本实验的固定材料 · 实现代码"},"unit":{"id":"notebook-blocks/97d6413de80249e1986f2ac8fa35a0db","title":"准备：本实验的固定材料 · 实现代码"},"page":{"id":"reading-opening","title":"准备：本实验的固定材料 · 实现代码","number":1},"sectionTitle":"准备：本实验的固定材料 · 实现代码","currentQuestion":"准备：本实验的固定材料 · 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