giuseppebonaccorso / reuters-21578-classification Goto Github PK
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Home Page: http://www.bonaccorso.eu
License: MIT License
Text classification with Reuters-21578 datasets using Gensim Word2Vec and Keras LSTM
Home Page: http://www.bonaccorso.eu
License: MIT License
I am trying to run you codes, but got a memory error in Word2Vec conversion:
X = np.zeros(shape=(number_of_documents, document_max_num_words, num_features)).astype('float32')
MemoryError
I also note there are probably some typos, for example:
vector = zeros(len(target_categories)).astype(float32)
I change it into:
vector = np.zeros(len(target_categories)).astype('float32')
Can you show your implementation environment, such as keras version, operation system and RAM?
Thank you for your great sample. It is good tutorial for classifying text.
Would like to know about the loss function of LSTM model. For multiclass problem, categorical_crossentropy should be used while Why do you use binary_crossentropy ?
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