model_lstm_model_7.py

model = Sequential();
model.add(Embedding(len(word_dict), max_cap, input_length=max_cap));
model.add(LSTM(60, return_sequences=True, recurrent_dropout=0.5));
model.add(Dropout(0.5))
model.add(LSTM(60, recurrent_dropout=0.5));
model.add(Dense(60, activation='relu'));
model.add(Dense(2, activation='softmax'));
print(model.summary());

optimizer = Adam(lr=0.01, decay=0.001);
model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])
# fit model
model.fit(X_train, Y_train, batch_size=64, epochs=10, validation_data=(X_dev, Y_dev))