# Understanding pytorch

Hi, I am new to pytorch and trying to understand the pytorch tutorial. https://pytorch.org/tutorials/beginner/blitz/neural_networks_tutorial.html#sphx-glr-beginner-blitz-neural-networks-tutorial-py

I dont understand how an object’s variable can accept arguments or act as a function as given in x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2)) where ‘self.conv1(x)’ is acting as a function?

class Net(nn.Module):

``````def __init__(self):
super(Net, self).__init__()
# 1 input image channel, 6 output channels, 5x5 square convolution
# kernel
self.conv1 = nn.Conv2d(1, 6, 5)
self.conv2 = nn.Conv2d(6, 16, 5)
# an affine operation: y = Wx + b
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)

def forward(self, x):
# Max pooling over a (2, 2) window
x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
# If the size is a square you can only specify a single number
x = F.max_pool2d(F.relu(self.conv2(x)), 2)
x = x.view(-1, self.num_flat_features(x))
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x

def num_flat_features(self, x):
size = x.size()[1:]  # all dimensions except the batch dimension
num_features = 1
for s in size:
num_features *= s
return num_features
``````

net = Net()
print(net)

Each module derives from `nn.Module`, while implements the `__call__` function. This function is called, when you can directly an object e.g. as `model(x)`.
You can see in the linked source code that some maintenance will be performed (e.g. hooks will be called) and finally `forward` will be executed, which defines the actual forward pass of this module.

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