##### Analyze, predict the next step in a sequence.
 Analyze, predict the next step in a sequence. Antonio0608 Unladen Swallow Posts: 1 Threads: 1 Joined: Jul 2020 Reputation: Jul-23-2020, 05:53 PM 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? ```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): 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,1,trainX.shape)) testX = numpy.reshape(testX, (testX.shape,1,testX.shape)) # 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,testX))```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.)) Reply

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