I am confused about the input vector in LSTM model, the data I am using is the text data, e.g. 1,000 sentences. I have two questions about the LSTM input layer:
1.If I would tokenize those sentences into the vectors (we can call it sentence vectors), is there a way in Keras to make sentence vectors given a document? Should be word level, right?
2.The second question is the 3D Tensor type in LSTM. I have 1,000 sentences (samples) and time_step would be 1 if I want to LSTM read one document at each time step, is that correct? The last one is the input dimension, this input dimension is the word dimension (100) in each sentence or how many word observed in each time step (10)?
Thus the LSTM tensor should be (1000, 1, 10) or (1000, 1, 100)