class Model(nn.Module): def init(self): super(Model, self).init() 中super(Model, self).init() 是调用的nn.Module吗?
时间: 2024-04-02 15:31:32 浏览: 98
不是完全正确。应该是调用nn.Module的构造函数,而不是init()方法。在Python中,super()函数返回一个临时对象,该对象可以访问父类的方法和属性。在这个例子中,super(Model, self)返回了一个临时对象,该对象可以访问nn.Module类的构造函数。通过调用这个临时对象的init()方法,可以调用nn.Module类的构造函数,以确保子类继承了nn.Module类的属性和方法。因此,super(Model, self).init()调用的是nn.Module类的构造函数。
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class Model(nn.Module): def __init__(self): super(Model, self).__init__()
这是一个Python类,该类继承了PyTorch中的nn.Module类,用于构建神经网络模型。该类中有一个构造函数__init__,用于初始化模型的参数和层。
在这个例子中,构造函数中首先调用了父类nn.Module的构造函数,即super(Model, self).__init__(),以确保子类继承了父类的属性和方法。然后可以定义模型的各个层,例如卷积层、池化层、全连接层等。
通过继承nn.Module类,该类可以利用PyTorch提供的优化器和损失函数等工具来训练和优化模型。
class Model(nn.Module): def __init__(self): super().__init__() self.conv1 = nn.Conv2d(1, 20, 5)
self.pool = nn.MaxPool2d(2, 2) self.conv2 = nn.Conv2d(20, 50, 5) self.fc1 = nn.Linear(4*4*50, 500) self.fc2 = nn.Linear(500, 10) def forward(self, x): x = self.pool(F.relu(self.conv1(x))) x = self.pool(F.relu(self.conv2(x))) x = x.view(-1, 4*4*50) x = F.relu(self.fc1(x)) x = self.fc2(x) return x
This is a PyTorch model for classifying handwritten digits using a convolutional neural network. The model consists of two convolutional layers followed by two fully connected layers. The first convolutional layer has 20 filters of size 5x5 and takes in a grayscale image of size 28x28. The second convolutional layer has 50 filters of size 5x5. The output of the convolutional layers is passed through a max pooling layer with a kernel size of 2x2. The output of the max pooling layer is then flattened and passed through two fully connected layers with ReLU activation. The final output is a probability distribution over the 10 possible digit classes.
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