Analyze, predict the next step in a sequence.

[Antonio0608]

Hi, I am communicating through a translator. Do not swear too much for this.
I have a question.
How can you implement a neural network. To analyze and predict the next step of the sequence.
Not a great example.
Sequence:
112233112233112233 ......
or
111211312111211312 ......
You can take any sequence.
But here's how to teach a neural network to predict the next step in a sequence.
Even in such simple sequences as in the example.
I have the following code.
But this is a neural network. Doesn't predict the next step. And repeats the previous ones.
How can this be fixed?

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import numpy import pandas as pd import math from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM from sklearn.preprocessing import MinMaxScaler from sklearn.metrics import mean_squared_error from sklearn.metrics import accuracy_score # convert an array of values into a dataset matrix def create_dataset(dataset, look_back):         dataX, dataY = [], []         for i in range(len(dataset)-look_back-1):                 xset = []                 for j in range(dataset.shape[1]):                         a = dataset[i:(i+look_back), j]                         xset.append(a)                 dataX.append(xset)                 dataY.append(dataset[i + look_back,0])         return numpy.array(dataX), numpy.array(dataY) # fix random seed for reproducibility numpy.random.seed(1) # load the dataset file='test123456.xlsx' xl=pd.ExcelFile(file) dataframe = xl.parse('Sheet1') dataset = dataframe.values dataset = dataset.astype('float32') # normalize the dataset scaler = MinMaxScaler(feature_range=(0,1)) dataset = scaler.fit_transform(dataset) # split into train and test sets train_size = int(len(dataset) * 0.75) test_size = len(dataset) - train_size train, test = dataset[0:train_size,:],dataset[train_size:len(dataset),:] # reshape into X=t and Y=t+1 look_back = 1 trainX, trainY = create_dataset(train, look_back) testX, testY = create_dataset(test, look_back) # reshape input to be [samples, time steps, features] trainX = numpy.reshape(trainX, (trainX.shape[0],1,trainX.shape[1])) testX = numpy.reshape(testX, (testX.shape[0],1,testX.shape[1]))                            # create and fit the LSTM network model = Sequential() model.add(LSTM(8, input_shape=(1, look_back))) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='Adam') model.fit(trainX, trainY, epochs=10000, batch_size=1, verbose=2) # make predictions trainPredict = model.predict(trainX) testPredict = model.predict(testX) # invert predictions trainPredict = scaler.inverse_transform(trainPredict) trainY = scaler.inverse_transform([trainY]) testPredict = scaler.inverse_transform(testPredict) testY = scaler.inverse_transform([testY]) # print("X=%s, Predicted=%s" % (testPredict[-1],testX[-1])) print("X=%s, Predicted=%s" % (testPredict[0],testX[0]))

By changing the settings of this neural network. Does not improve results.
I will be grateful for any help.
P.s. I'm learning the language.))