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- import torch
- import torch.nn as nn
- from torch.autograd import Variable
- from torch.nn import functional as F
- class TextCNN(nn.Module):
- def __init__(self, batch_size, output_size, in_channels, out_channels, kernel_heights,
- stride, padding, keep_probab, vocab_size, embedding_dim, weights):
- super(TextCNN, self).__init__()
- """
- Arguments
- ---------
- batch_size : Size of each batch which is same as the batch_size of the data returned by the TorchText BucketIterator
- output_size : Number of labels
- in_channels : Number of input channels. Here it is 1 as the input data has dimension = (batch_size, num_seq, embedding_length)
- out_channels : Number of output channels after convolution operation performed on the input matrix
- kernel_heights : A list consisting of 3 different kernel_heights. Convolution will be performed 3 times and finally results from each kernel_height will be concatenated.
- stride: The number of tokens that the slide conv window moves over for next input
- padding:
- keep_probab : Probability of retaining an activation node during dropout operation
- vocab_size : Size of the vocabulary containing unique words
- embedding_dim : Embedding dimension of GloVe word embeddings
- weights : Pre-trained GloVe word_embeddings which we will use to create our word_embedding look-up table
- """
- self.batch_size = batch_size
- self.output_size = output_size
- self.out_channels = out_channels
- self.kernel_heights = kernel_heights
- self.stride = stride
- self.padding = padding
- self.vocab_size = vocab_size
- self.embedding_dim = embedding_dim
- self.word_embeddings = nn.Embedding(vocab_size, embedding_dim)
- self.word_embeddings.weight = nn.Parameter(weights, requires_grad=False)
- # Define convlutions
- self.conv1 = nn.Conv2d(in_channels, out_channels, (kernel_heights[0], embedding_dim), stride, padding)
- self.conv2 = nn.Conv2d(in_channels, out_channels, (kernel_heights[1], embedding_dim), stride, padding)
- self.conv3 = nn.Conv2d(in_channels, out_channels, (kernel_heights[2], embedding_dim), stride, padding)
- self.dropout = nn.Dropout(keep_probab)
- self.label = nn.Linear(len(kernel_heights)*out_channels, output_size)
- def conv_block(self, input, conv_layer):
- """
- Parameters
- ----------
- input: Batch of tokens
- conv_layer: convolution layer to be applied
- Returns
- -------
- Outputs fully connected max pooling on a layer and filter maximum activation.
- """
- conv_out = conv_layer(input) # (batch_size, out_channels, dim, 1)
- activation = F.relu(conv_out.squeeze(3)) # (batch_size, out_channels, dim1)
- max_out = F.max_pool1d(activation, activation.size()[2]).squeeze(2) # (batch_size, out_channels)
- return max_out
- def forward(self, input_text, batch_size=None):
- """
- Define how model is going to be run from input to output.
- Parameters
- ----------
- input_text: input_sentences of shape = (batch_size, num_sequences)
- batch_size : default = None. Used only for prediction on a single sentence after training (batch_size = 1)
- Returns
- -------
- Output of the linear layer containing logits for pos & neg class.
- logits.size() = (batch_size, output_size)
- """
- data_in = self.word_embeddings(input_text) # (batch_size, num_seq, embedding_length)
- data_in = data_in.unsqueeze(1) # (batch_size, 1, num_seq, embedding_length)
- max_out1 = self.conv_block(data_in, self.conv1)
- max_out2 = self.conv_block(data_in, self.conv2)
- max_out3 = self.conv_block(data_in, self.conv3)
- out = torch.cat((max_out1, max_out2, max_out3), 1) # (batch_size, num_kernels*out_channels)
- out = self.dropout(out) # (batch_size, num_kernels*out_channels)
- logits = self.label(out)
- return logits # (len(kernel_heights)*out_channels, output_size)
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