Resultados evaluación

results_evaluation

+Params: 
+
+vocab_embeddings = 100000
+max_lenght_tweet = 40
+
+Layers: 
+
+e = Embedding(feature_size, EMBEDDING_DIM, input_length=max_len_input, weights=[embedding_matrix], trainable=False)
+model.add(e)
+#number of features:_32 each vector of 200 dim is converted to a vector of 32 dim
+model.add(LSTM(128, dropout=0.2, recurrent_dropout=0.2))
+#model.add(Bidirectional(LSTM(2,dropout=0.2,recurrent_dropout=0.2,return_sequences=True)))
+model.add(Dense(32, activation='tanh'))
+model.add(Dropout(0.5))
+model.add(Dense(len(CLASSES), activation='softmax'))
+
+Results evaluation:
+
+Accuracy trainning: 53.418567
+
+*** Results RNN_LSTM ***
+Macro-Precision: 0.5226264521865173
+Macro-Recall: 0.5156404166549265
+Macro-F1: 0.5100454275009482
+Accuracy: 0.5155875299760192
+