Actualización código

embeddings_RNN.py

@@ -121,10 +121,8 @@ def classification_embedings_rnn(tweets_train, tweets_train_labels_numeric, twee
                 embedding_matrix[index] = embedding_vector
                 embedding_matrix[index] = embedding_vector
 
 
     #max_len_input = 30
     #max_len_input = 30
-    
     train_features_pad = sequence.pad_sequences(corpus_train_index, maxlen=max_len_input, padding="post", truncating="post", dtype=type(corpus_train_index[0][0]))
     train_features_pad = sequence.pad_sequences(corpus_train_index, maxlen=max_len_input, padding="post", truncating="post", dtype=type(corpus_train_index[0][0]))
     
     
-    
     # define RNN model
     # define RNN model
     model = Sequential()
     model = Sequential()
     
     
@@ -139,7 +137,7 @@ def classification_embedings_rnn(tweets_train, tweets_train_labels_numeric, twee
     model.add(Dense(len(CLASSES), activation='softmax'))
     model.add(Dense(len(CLASSES), activation='softmax'))
     
     
     # compile the model
     # compile the model
-    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])
+    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc'])
     
     
     # summarize the model
     # summarize the model
     print(model.summary())
     print(model.summary())
@@ -175,6 +173,5 @@ def main ():
     tweets_dev_classified_labels =  classification_embedings_rnn(tweets_train, tweets_train_labels_numeric, tweets_dev)
     tweets_dev_classified_labels =  classification_embedings_rnn(tweets_train, tweets_train_labels_numeric, tweets_dev)
     calculate_quality_performamnce(tweets_dev_labels, tweets_dev_classified_labels, "RNN_LSTM")
     calculate_quality_performamnce(tweets_dev_labels, tweets_dev_classified_labels, "RNN_LSTM")
 
 
-
 if __name__ == '__main__':
 if __name__ == '__main__':
     main()
     main()