The end.

embeddings_RNN.py

@@ -160,12 +160,12 @@ def classification_embedings_rnn(tweets_train, tweets_train_labels_numeric, twee
     #model.add(LSTM(128, return_sequences=True))
     model.add(Bidirectional(LSTM(128, return_sequences=True)))
     #model.add(Dense(128, activation='relu', kernel_initializer=glorot_uniform(seed=RANDOM_SEED), activity_regularizer=regularizers.l2(0.0001)))
-    model.add(Dense(128, activation='relu', kernel_initializer=glorot_uniform(seed=RANDOM_SEED), kernel_regularizer=regularizers.l2(0.01)))
+    model.add(Dense(128, activation='relu', kernel_initializer=glorot_uniform(seed=RANDOM_SEED)))
     model.add(Dropout(0.5))
     model.add(MaxPooling1D(pool_size=2, strides=1, padding="same"))
     model.add(Flatten())
     #model.add(Dense(64, activation='relu', kernel_initializer=glorot_uniform(seed=RANDOM_SEED), activity_regularizer=regularizers.l2(0.001)))
-    model.add(Dense(64, activation='relu', kernel_initializer=glorot_uniform(seed=RANDOM_SEED), kernel_regularizer=regularizers.l2(0.01)))
+    model.add(Dense(64, activation='relu', kernel_initializer=glorot_uniform(seed=RANDOM_SEED)))
     model.add(Dropout(0.5))
     #model.add(Dropout(0.25))
     #model.add(Dense(16, activation='relu', kernel_initializer=glorot_uniform(seed=RANDOM_SEED), activity_regularizer=regularizers.l2(0.001)))

embeddings_lexicon_features_emotion_RNN.py

@@ -236,18 +236,18 @@ def classification_embedings_rnn(tweets_train, tweets_train_labels_numeric, twee
     #model.add(LSTM(128, return_sequences=True))
     model.add(Bidirectional(LSTM(128, return_sequences=True)))
     #model.add(Dense(128, activation='relu', kernel_initializer=glorot_uniform(seed=RANDOM_SEED), activity_regularizer=regularizers.l2(0.0001)))
-    model.add(Dense(128, activation='relu', kernel_initializer=glorot_uniform(seed=RANDOM_SEED), kernel_regularizer=regularizers.l2(0.01)))
+    model.add(Dense(128, activation='relu', kernel_initializer=glorot_uniform(seed=RANDOM_SEED)))
     model.add(Dropout(0.5))
     model.add(MaxPooling1D(pool_size=2, strides=1, padding="same"))
     model.add(Flatten())
     #model.add(Dense(64, activation='relu', kernel_initializer=glorot_uniform(seed=RANDOM_SEED), activity_regularizer=regularizers.l2(0.001)))
-    model.add(Dense(64, activation='relu', kernel_initializer=glorot_uniform(seed=RANDOM_SEED), kernel_regularizer=regularizers.l2(0.01)))
+    model.add(Dense(64, activation='relu', kernel_initializer=glorot_uniform(seed=RANDOM_SEED)))
     model.add(Dropout(0.5))
     #model.add(Dropout(0.25))
     #model.add(Dense(16, activation='relu', kernel_initializer=glorot_uniform(seed=RANDOM_SEED), activity_regularizer=regularizers.l2(0.001)))
     #model.add(Dropout(0.5))
     model.add(Dense(len(CLASSES), activation='softmax'))
-    model.add(ActivityRegularization(l1=0.0,l2=0.0001))
+    model.add(ActivityRegularization(l1=0.0,l2=0.001))
     
     # summarize the model
     print(model.summary())