I am trying to implement conv+max_pooling for encoding sentence (as the above figure indicates), yet the documentation for conv2d is kind of messy. Does anyone have experience about this?
Basically, the input is a tensor a with dimension (sent_len, EMB_SIZE):
a = self.lookup(x_sent).unsqueeze(1) // I am using batch_size = 1
The output should be a tensor with dimension (1, EMB_SIZE) ?
Anyone knows?
the output of CNN should be size of (batch_size, EMB_SIZE, seq_len),
then you may use transpose() to transpose it to (seq_len, batch_size, EMB_SIZE), after which you are ready for the input of LSTM.
Is this what you need?
Hi Jin,
I want to know what’s the code snippet for transforming a to the tensor after max_pooling — (conv2d and max_pooling code).
What you need is Embedding + Conv1d + MaxPool1d here.
Thanks for responding.
I implemented the model with conv2d and maxpooing APIs, similar to the Tensorflow implementation here: http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/
