将 CNN 模型代码从 Keras 转换为 Pytorch
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【中文标题】将 CNN 模型代码从 Keras 转换为 Pytorch【英文标题】:To convert CNN model code from Keras to Pytorch 【发布时间】:2022-01-02 18:37:12 【问题描述】:我正在尝试。
这里是 Keras Sequential 层
model=Sequential()
model.add(Conv2D(filters=64, kernel_size = (3,3), activation="relu", input_shape=(28,28,1)))
model.add(Conv2D(filters=64, kernel_size = (3,3), activation="relu"))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(BatchNormalization())
model.add(Conv2D(filters=128, kernel_size = (3,3), activation="relu"))
model.add(Conv2D(filters=128, kernel_size = (3,3), activation="relu"))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(BatchNormalization())
model.add(Conv2D(filters=256, kernel_size = (3,3), activation="relu"))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Flatten())
model.add(BatchNormalization())
model.add(Dense(512,activation="relu"))
model.add(Dense(10,activation="softmax"))
model.compile(loss="categorical_crossentropy",optimizer=optimizer,metrics=["accuracy"])
如何在 pytorch 模型上初始化和编写转发代码?尤其是 Flatten 和 Dense 层。
任何评论都将不胜感激。
【问题讨论】:
【参考方案1】:我尝试在 PyTorch 中实现它,但检查参数的数量以确保这与您的 Keras 实现相同。我试图把它写得更容易理解和简单,这就是我写下所有激活函数的原因。我希望这可能会有所帮助。
import torch
import torch.nn as nn
class Net(nn.Module):
def __init__(self, num_classes=10):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=64, kernel_size=(3, 3), padding=(1, 1))
self.relu1 = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=(3, 3), padding=(1, 1))
self.relu2 = nn.ReLU(inplace=True)
self.pool1 = nn.MaxPool2d(kernel_size=(2, 2))
self.norm1 = nn.BatchNorm2d(num_features=64)
self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=(3, 3), padding=(1, 1))
self.relu3 = nn.ReLU(inplace=True)
self.conv4 = nn.Conv2d(in_channels=128, out_channels=128, kernel_size=(3, 3), padding=(1, 1))
self.relu4 = nn.ReLU(inplace=True)
self.pool2 = nn.MaxPool2d(kernel_size=(2, 2))
self.norm2 = nn.BatchNorm2d(num_features=128)
self.conv5 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=(3, 3), padding=(1, 1))
self.relu5 = nn.ReLU(inplace=True)
self.pool3 = nn.MaxPool2d(kernel_size=(2, 2))
self.norm3 = nn.BatchNorm2d(num_features=256)
self.fc1 = nn.Linear(in_features=256, out_features=512)
self.relu6 = nn.ReLU(inplace=True)
self.fc2 = nn.Linear(in_features=512, out_features=10)
self.act = nn.Softmax(dim=1)
def forward(self, x):
x = self.relu1(self.conv1(x))
x = self.relu2(self.conv2(x))
x = self.norm1(self.pool1(x))
x = self.relu3(self.conv3(x))
x = self.relu4(self.conv4(x))
x = self.norm2(self.pool2(x))
x = self.relu5(self.conv5(x))
x = self.norm3(self.pool3(x))
x = x.mean((2, 3), keepdim=True)
x = torch.flatten(x, 1)
x = self.relu6(self.fc1(x))
x = self.act(self.fc2(x),)
return x
if __name__ == '__main__':
model = Net(num_classes=10)
a = torch.randn(1, 3, 224, 224)
print("Output: ", model(a).shape)
print("Num. params: ", sum(p.numel() for p in model.parameters() if p.requires_grad))
输出
Output: torch.Size([1, 10])
Num. params: 692938
【讨论】:
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