From 61581f5e1f7831e205e31cfb33a21e18574425a8 Mon Sep 17 00:00:00 2001 From: lixiaoping Date: Wed, 23 Dec 2020 09:26:42 +0800 Subject: [PATCH 1/2] update homework/class_2/lixiaoping/model.py. --- homework/class_2/lixiaoping/model.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/homework/class_2/lixiaoping/model.py b/homework/class_2/lixiaoping/model.py index c86885d..ace641e 100644 --- a/homework/class_2/lixiaoping/model.py +++ b/homework/class_2/lixiaoping/model.py @@ -7,7 +7,7 @@ class ConvNet(nn.Module): super(ConvNet, self).__init__() self.conv1 = nn.Sequential( # convolution - nn.Conv2d(3, 16, kernel_size=5, stride=1, padding=2), + nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=2), # batch normalization nn.BatchNorm2d(16), # ReLU activation @@ -16,19 +16,19 @@ class ConvNet(nn.Module): nn.MaxPool2d(kernel_size=2, stride=2)) self.conv2 = nn.Sequential( - nn.Conv2d(16, 16, kernel_size=5, stride=1, padding=2), - nn.BatchNorm2d(16), + nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=2), + nn.BatchNorm2d(32), nn.ReLU(), nn.AvgPool2d(kernel_size=2, stride=2) ) self.conv3 = nn.Sequential ( - nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2), - nn.BatchNorm2d(32), + nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=2), + nn.BatchNorm2d(64), nn.Softplus(), nn.MaxPool2d(kernel_size=2, stride=2)) - self.fc = nn.Linear(4 * 4 * 32, num_classes) + self.fc = nn.Linear(5 * 5 * 64, num_classes) # forward channel def forward(self, x): -- Gitee From aaa3e80d9227e147678cdcd57c5ee683c951447c Mon Sep 17 00:00:00 2001 From: lixiaoping Date: Wed, 23 Dec 2020 09:28:24 +0800 Subject: [PATCH 2/2] update homework/class_2/lixiaoping/train.py. --- homework/class_2/lixiaoping/train.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/homework/class_2/lixiaoping/train.py b/homework/class_2/lixiaoping/train.py index 0d8e499..4c50706 100644 --- a/homework/class_2/lixiaoping/train.py +++ b/homework/class_2/lixiaoping/train.py @@ -50,7 +50,7 @@ def train(train_loader, test_loader, model, learning_rate, num_epochs): if __name__ == '__main__': # hyper parameters - num_epochs = 10 + num_epochs = 30 num_classes = 10 batch_size = 32 learning_rate = 0.01 -- Gitee