output = output.view(output.shape[0], -1) return self.fc(output) def score(self, x, y): y_pred = self.forward(x) acc = (y_pred.argmax(dim=1) == y).sum().item() / len(y) return acc if __name__ == "__main__": model = VGG16() model = model.cuda() loss = nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters(), lr=0.0003) # load data mnist_train, mnist_test = download_data_fashion_mnist(resize=224) params = { "epoch_num": 2, "data_num": len(mnist_train), "batch_size": 16, "gpu": True, "model": model, "loss": loss, "optimizer": optimizer, "draw": True, "evaluate": model.score, # "accum_step": 8, } train_iter, test_iter = load_data_fashion_mnist(batch_size=params["batch_size"],
def score(self, x, y): y_pred = self.forward(x) acc = (y_pred.argmax(dim=1) == y).sum().item() / len(y) return acc if __name__ == "__main__": # define model model = LeNet5() model = model.cuda() # loss and optimizer loss = nn.CrossEntropyLoss() optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9) # load data mnist_train, mnist_test = download_data_fashion_mnist() params = { "epoch_num": 2, "model": model, "loss": loss, "data_num": len(mnist_train), "optimizer": optimizer, "draw": True, "gpu": True, "batch_size": 256, "evaluate": model.score, "test_iter": Data.DataLoader(mnist_test, batch_size=len(mnist_test), num_workers=8, pin_memory=True), # "save_fig": True, # "save_path": "../result/BN对比试验/img/"
y_pred = self.forward(x) return (y_pred.argmax(dim=1) == y).sum().item() / len(y) if __name__ == "__main__": # define model model = DropoutModel2() # change to cuda if cuda is available if torch.cuda.is_available(): model = model.cuda() # loss loss = nn.CrossEntropyLoss() # optimizer optimizer = torch.optim.SGD(model.parameters(), lr=0.3) # download data train_mnist, test_mnist = download_data_fashion_mnist() # parameter params = { "model": model, "loss": loss, "epoch_num": 10, "data_num": len(train_mnist), "batch_size": 512, "optimizer": optimizer, # "test_iter": Data.DataLoader(test_mnist, len(test_mnist), shuffle=True), "test_iter": Data.DataLoader(test_mnist, batch_size=512, shuffle=True), # "evaluate": model.score, "draw": True, "gpu": True, # "sample_rate": 0.5,