def generate_data():
    X=[]
    Y=[]
    for i in range(50000):
        start=random.randint(1,100)
        d=random.randrange(-1,2,2) #-1 or 1
        param=[(start),(start+d),(start+d+d)]
        X.append(np.array(param))
        if d<0:
            Y.append([1,0])

        elif len(Y)>2 and d>0 and Y[-1][1]==1 and Y[-2][1]==1:
            Y.append([1,0])
        elif d>0:
            Y.append([0,1])
    X=np.array(X)
    Y=np.array(Y)
    return X,Y
X,Y = generate_data()
X=np.asarray(X,'float32')
Y=np.asarray(Y,'float32')
X=np.reshape(X,(1,len(X),3))
Y=np.reshape(Y,(1,len(Y),2))

model=Sequential()
model.add(LSTM(20, input_shape=(50000,3), return_sequences=True))
model.add(Dense(2))
model.add(Activation('softmax'))

model.compile(loss='categorical_crossentropy', optimizer=RMSprop(), metrics=['accuracy'])
history = model.fit(X, Y,batch_size=100, nb_epoch=250, verbose=2)

model.add(CuDNNLSTM(20, input_shape=(50000,3), return_sequences=True))