Lstm Network On Pre Trained Word Embedding Gensim

I am new to deep learning. I am trying to make very basic LSTM network on word embedding feature. I have written the following code for the model but I am unable to run it. fro

Solution 1:

You should try using Embedding layer before LSTM layer. Also, since you have pre-trained vectors of 300-dimensions for 3019 comments, you can initialize the weights for embedding layer with this matrix.

inp_layer = Input((maxlen,))
x = Embedding(max_features, embed_size, weights=[trainDataVecs])(x)
x = LSTM(50, dropout=0.1)(x)

Here, maxlen is the maximum length of your comments, max_features is the maximum number of unique words or vocabulary size of your dataset, and embed_size is dimensions of your vectors, which is 300 in your case.

Note that shape of trainDataVecs should be (max_features, embed_size), so if you have pre-trained word vectors loaded into trainDataVecs, this should work.