虽然您不会像Keras的模型那样获得有关模型的详细信息。总之，仅打印模型将使您对所涉及的不同层及其规格有所了解。
例如：
from torchvision import modelsmodel = models.vgg16()print(model)
在这种情况下，输出将如下所示：
VGG ( (features): Sequential ( (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): ReLU (inplace) (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (3): ReLU (inplace) (4): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1)) (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (6): ReLU (inplace) (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (8): ReLU (inplace) (9): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1)) (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (11): ReLU (inplace) (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (13): ReLU (inplace) (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (15): ReLU (inplace) (16): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1)) (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (18): ReLU (inplace) (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (20): ReLU (inplace) (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (22): ReLU (inplace) (23): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1)) (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (25): ReLU (inplace) (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (27): ReLU (inplace) (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (29): ReLU (inplace) (30): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1)) ) (classifier): Sequential ( (0): Dropout (p = 0.5) (1): Linear (25088 -> 4096) (2): ReLU (inplace) (3): Dropout (p = 0.5) (4): Linear (4096 -> 4096) (5): ReLU (inplace) (6): Linear (4096 -> 1000) ))
正如Kashy所提到的，现在您可以使用该
state_dict方法来获取不同图层的权重。但是，使用此层列表可能会提供更多指导，即创建一个辅助函数来获得类似模型摘要的Keras！希望这可以帮助！
