Word vectors as input I have a corpus on which I want to perform sentiment analysis using LSTM and word embeddings. I have converted the words in the documents to word vectors using Word2Vec. My question is how to input these word vectors as input to Keras? I don't want to use the embeddings provided by Keras.
You can just skip the Embedding layer and use a normal input layer with n input nodes where n is the dimensions of your word2vec embeddings. The rest is the same as you would with an embedding layer, just pass a sequence of n dimensional vectors as the input, potentially padded or truncated depending on your model.