Deep Learning with PyTorch

Creating the network

Let's look at the network architecture and then walk through the code:

class IMDBCnn(nn.Module):        def __init__(self,vocab,hidden_size,n_cat,bs=1,kernel_size=3,max_len=200):        super().__init__()        self.hidden_size = hidden_size        self.bs = bs    self.e = nn.Embedding(n_vocab,hidden_size)    self.cnn = nn.Conv1d(max_len,hidden_size,kernel_size)    self.avg = nn.AdaptiveAvgPool1d(10)        self.fc = nn.Linear(1000,n_cat)        self.softmax = nn.LogSoftmax(dim=-1)            def forward(self,inp):        bs = inp.size()[0]        if bs != self.bs:            self.bs = bs        e_out = self.e(inp)        cnn_o = self.cnn(e_out)         cnn_avg = self.avg(cnn_o)        cnn_avg = cnn_avg.view(self.bs,-1)        fc = F.dropout(self.fc(cnn_avg),p=0.5)        return self.softmax(fc)

In the preceding code, instead of an LSTM layer ...