model4 = Sequential()
model4.add(Embedding(total_words, 100, weights=[embedding_matrix], input_length=max_sequence_len-1, trainable=False))
model4.add(LSTM(150))
model4.add(Dropout(0.1))
model4.add(Dense(total_words, activation='softmax'))
model4.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

model4.summary()

history = train(model4)
viz_metrics(history)