However, for non-trivial neural networks such as a variational autoencoder, the Module approach is much easier to work with. If you’re new to PyTorch, the Sequential approach looks very appealing. The exact same network could be created using Sequential() like so: The Module approach for a 4-7-3 tanh network could look like: The difference between the two approaches is best described with a concrete example. The Module approach is more flexible than the Sequential but the Module approach requires more code. You can use tensor.nn.Module() or you can use tensor.nn.Sequential(). ], requires_grad=True)Somewhat confusingly, PyTorch has two different ways to create a simple neural network. Linear(in_features=3, out_features=2, bias=True) (0): Linear(in_features=3, out_features=2, bias=True) ![]() Tensor(], grad_fn=)ģ Inputs, 2 outputs and Activation Function Linear(in_features=2, out_features=3, bias=True) (0): Linear(in_features=2, out_features=3, bias=True) Tensor(, requires_grad=True)Ģ Inputs, 3 outputs and Activation Function Linear(in_features=2, out_features=2, bias=True) (0): Linear(in_features=2, out_features=2, bias=True) in the following illustration indicates the Sigmoid activation function. The above illustration can be converted to a little bit different form that is used more often in neural network documents. Print('Sigmoid(w x b) :\n',torch.nn.Sigmoid().forward(o))įor practice, let's try with another examples of input vector.Ģ Inputs, 2 outputs and Activation Function O = torch.mm(net.weight,x.t()) net.bias One is to verify the result of forward() function and clarify your understanding on how the network forward processing works. You can evaluate the network manually as shown below. Print('net.forward(x) :\n',net.forward(x)) You can evaluate the whole network using forward() function as shown below. Print('Activation function of network :\n',net) You can get access to the second component as follows. Linear(in_features=2, out_features=1, bias=True) => Network Structure of the first component : Print('Weight of network :\n',net.weight) Print('Network Structure of the first component :\n',net) You can get access to each of the component in the sequence using array index as shown below. (0): Linear(in_features=2, out_features=1, bias=True) ![]() ![]() You can print out overal network structure and Weight
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