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 Analyze, predict the next step in a sequence.
#1
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[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.))
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