12/5/2023 0 Comments Pytorch sequential![]() ![]() 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 & Bias that was automatically set as follows. in the following illustration indicates the Sigmoid activation function. It can be converted to a little bit different form that is used more often in neural network documents. The above illustration would be easier to map between Pytorch code and network structure, but it may look a little bit different from what you normally see in the textbook or other documents. To use nn.Sequential module, you have to import torch as below.Ģ Inputs, 1 outputs and Activation Function NOTE : The Pytorch version that I am using for this tutorial is as follows. 2 Hidden Layers : 4 and 6 Neurons, and 1 Output Neurons.Creating a FeedForwardNetwork : 3 Layer.1 Hidden Layer : 3 neuron, 1 Output Layer.1 Hidden Layer : 2 neuron, 1 Output Layer.Creating a FeedForwardNetwork : 2 Layer.3 Inputs and 2 output (2 neuron) and Activation.2 Inputs and 3 output (3 neuron) and Activation.2 Inputs and 2 outputs (2 neuron) and Activation.2 Inputs and 1 output (1 neuron) and Activation.Creating a FeedForwardNetwork : 1 Layer.Nn.Sequential is a module that can pack multiple components into a complicated or multilayer network. ![]()
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