dimension = 120
model_users = Sequential()
model_users.add(Embedding(len(np.unique(users)), dimension))
model_users.add(Reshape((dimension,)))

model_movies = Sequential()
model_movies.add(Embedding(len(np.unique(movies)), dimension, input_length=1))
model_movies.add(Reshape((dimension,)))

model = Sequential()
model.add(Merge([model_users, model_movies], mode = 'concat'))
model.add(Dropout(0.1))
model.add(Dense(100, activation = 'relu'))
model.add(Dropout(0.1))
model.add(Dense(500, activation = 'sigmoid'))
model.add(Dropout(0.1))
model.add(Dense(dimension, activation = 'linear'))
model.add(Dropout(0.1))
model.add(Dense(5, activation = 'softmax'))
model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics=['accuracy'])
print(model.summary())

dimensions = len(movies_unique)

model = Sequential()
model.add(Masking(mask_value = 0, input_shape = (dimensions, dimensions)))
model.add(LSTM(32, return_sequences = True))
model.add(TimeDistributed(Dense(len(ratings_unique), activation = 'relu')))
model.add(Activation('softmax'))
model.compile(loss = 'mse', optimizer = 'adam', metrics = ['accuracy'])
print(model.summary())