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=0):
	dataX, dataY = [], []
	for i in range(len(dataset)-look_back):
		a = dataset[i:(i+look_back), 0]
		dataX.append(a)
		dataY.append(dataset[i + look_back, 0])
	return numpy.array(dataX), numpy.array(dataY)
# fix random seed for reproducibility
numpy.random.seed(7)
# load the dataset
file='test1.xlsx'
xl=pd.ExcelFile(file)
dataframe = xl.parse('Sheet1')
dataset = dataframe.values
dataset = dataset.astype('float32')
# normalize the dataset
scaler = MinMaxScaler(feature_range=(1, 3))
dataset = scaler.fit_transform(dataset)
# split into train and test sets
train_size = int(len(dataset) * 0.80)
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(4, input_shape=(1, look_back)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam',metrics=['accuracy'])
model.fit(trainX, trainY, epochs=2, 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])
# calculate root mean squared error
trainScore = math.sqrt(mean_squared_error(trainY[0],trainPredict[:,0]))
print('Test Score: %.2f RMSE' % (trainScore))
testScore = math.sqrt(mean_squared_error(testY[0],testPredict[:,0]))
print('Test Score: %.2f RMSE' % (testScore))
#
print(len(dataset))
print(len(testX)+(len(trainX)))
print(len(testY.T)+(len(trainY.T)))

 Epoch 1/2
 - 57s - loss: 1.1863 - accuracy: 1.0000
Epoch 2/2
 - 57s - loss: 0.7782 - accuracy: 1.0000
Test Score: 0.87 RMSE
Test Score: 0.84 RMSE
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