Pytorch手写数字分类 AlexNet卷积神经网络 MNIST 包含绘板识别程序

Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data\MNIST\raw\train-images-idx3-ubyte.gz 100%|████████████████████████████████████████████████████████████████████| 9912422/9912422 [00:02<00:00, 3632046.03it/s] Extracting ./data\MNIST\raw\train-images-idx3-ubyte.gz to ./data\MNIST\raw Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data\MNIST\raw\train-labels-idx1-ubyte.gz 100%|█████████████████████████████████████████████████████████████████████████| 28881/28881 [00:00<00:00, 446700.13it/s] Extracting ./data\MNIST\raw\train-labels-idx1-ubyte.gz to ./data\MNIST\raw Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data\MNIST\raw\t10k-images-idx3-ubyte.gz 100%|█████████████████████████████████████████████████████████████████████| 1648877/1648877 [00:02<00:00, 559002.47it/s] Extracting ./data\MNIST\raw\t10k-images-idx3-ubyte.gz to ./data\MNIST\raw Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data\MNIST\raw\t10k-labels-idx1-ubyte.gz 100%|██████████████████████████████████████████████████████████████████████████| 4542/4542 [00:00<00:00, 4496230.53it/s] Extracting ./data\MNIST\raw\t10k-labels-idx1-ubyte.gz to ./data\MNIST\raw Epoch [1/10], Train Loss: 0.3743 Test Loss: 0.0433, Test Accuracy: 0.9860 Epoch [2/10], Train Loss: 0.0544 Test Loss: 0.0342, Test Accuracy: 0.9895 Epoch [3/10], Train Loss: 0.0398 Test Loss: 0.0229, Test Accuracy: 0.9927 Epoch [4/10], Train Loss: 0.0304 Test Loss: 0.0167, Test Accuracy: 0.9944 Epoch [5/10], Train Loss: 0.0252 Test Loss: 0.0252, Test Accuracy: 0.9935 Epoch [6/10], Train Loss: 0.0213 Test Loss: 0.0194, Test Accuracy: 0.9934 Epoch [7/10], Train Loss: 0.0192 Test Loss: 0.0207, Test Accuracy: 0.9939 Epoch [8/10], Train Loss: 0.0160 Test Loss: 0.0151, Test Accuracy: 0.9956 Epoch [9/10], Train Loss: 0.0137 Test Loss: 0.0164, Test Accuracy: 0.9953 Epoch [10/10], Train Loss: 0.0140 Test Loss: 0.0163, Test Accuracy: 0.9952 Training finished!