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)