convolution layer

model.py

class CNNModel(nn.Module):
    def __init__(self, embedding_dim, kernel_size, hidden_dim, vocab_size):
        super().__init__()
        self._kernel_size = kernel_size
        self._hidden_dim = hidden_dim
        self._word_embeddings = nn.Embedding(vocab_size, embedding_dim)
        self._conv = nn.Conv2d(1, hidden_dim, kernel_size=(kernel_size, embedding_dim))

    def forward(self, sample):
        embeds = self._word_embeddings(sample)
        # Converts the vector to a shape Conv2d can work with
        conv_in = embeds.view(1, 1, len(sample), -1)
        conv_out = self._conv(conv_in)
        conv_out = F.relu(conv_out)