Go directly into RNN layers without using Embeddings and preprocessing methods.

Hello, We are currently working on an NLP research project. We have already obtained our way of preprocessing, such as give a long document a specified vector representer. So, what the training set now is a fixed length decimal vector, and the output should be different labels. However, as far as I know, Passage has its own preprocessing methods, and is kind of "required" for a training input. Could I have any way to reduce Passage's own preprocessing procedures? such as tokenizer and Embedding layer.

For example the layers looks like this

layers = [
            Embedding(size=256, n_features=tokenizer.n_features),
            GatedRecurrent(size=256, seq_output=True),
            GatedRecurrent(size=256, seq_output=False), # activation='t_rectify',
            Dense(size=1, activation='sigmoid')
        ]

So, is there any way to remove the first layer, so that the training inputs can directly go to RNN layers.

layers = [
            # Embedding(size=256, n_features=tokenizer.n_features),
            GatedRecurrent(size=256, seq_output=True),
            GatedRecurrent(size=256, seq_output=False), # activation='t_rectify',
            Dense(size=1, activation='sigmoid')
        ]

Well, if I do so, it will, of course, give error msg:

line 41, in __init__
    self.params = flatten([l.params for l in layers])
AttributeError: 'GatedRecurrent' object has no attribute 'params'

Thank you.

IndicoDataSolutions / Passage

Go directly into RNN layers without using Embeddings and preprocessing methods. #33