Deep Learning with PyTorch

Creating a fully connected model

A simple model may end in overfitting, so let's include dropout in the model. Dropout will help avoid overfitting. In the following code, we are creating our model:

class FullyConnectedModel(nn.Module):        def __init__(self,in_size,out_size,training=True):        super().__init__()        self.fc = nn.Linear(in_size,out_size)    def forward(self,inp):        out = F.dropout(inp, training=self.training)        out = self.fc(out)        return out# The size of the output from the selected convolution feature fc_in_size = 131072fc = FullyConnectedModel(fc_in_size,classes)if is_cuda:    fc = fc.cuda()

Once the model is created, we can train the model.