Stateful Model Add

Author: lordtt13

lstm_regression_stacked_memory.py

@@ -0,0 +1,81 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Aug  1 23:47:37 2019
+
+@author: tanma
+"""
+
+import numpy
+import matplotlib.pyplot as plt
+from pandas import read_csv
+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
+
+def create_dataset(dataset, look_back=1):
+	dataX, dataY = [], []
+	for i in range(len(dataset)-look_back-1):
+		a = dataset[i:(i+look_back), 0]
+		dataX.append(a)
+		dataY.append(dataset[i + look_back, 0])
+	return numpy.array(dataX), numpy.array(dataY)
+
+numpy.random.seed(7)
+
+dataframe = read_csv('airline_passengers.csv', usecols=[1], engine='python')
+dataset = dataframe.values
+dataset = dataset.astype('float32')
+
+scaler = MinMaxScaler(feature_range=(0, 1))
+dataset = scaler.fit_transform(dataset)
+
+train_size = int(len(dataset) * 0.67)
+test_size = len(dataset) - train_size
+train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]
+
+look_back = 3
+trainX, trainY = create_dataset(train, look_back)
+testX, testY = create_dataset(test, look_back)
+
+trainX = numpy.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))
+testX = numpy.reshape(testX, (testX.shape[0], testX.shape[1], 1))
+
+batch_size = 1
+model = Sequential()
+model.add(LSTM(4, batch_input_shape=(batch_size, look_back, 1), stateful=True, return_sequences=True))
+model.add(LSTM(4, batch_input_shape=(batch_size, look_back, 1), stateful=True))
+model.add(Dense(1))
+model.compile(loss='mean_squared_error', optimizer='adam')
+for i in range(100):
+	model.fit(trainX, trainY, epochs=1, batch_size=batch_size, verbose=2, shuffle=False)
+	model.reset_states()
+
+trainPredict = model.predict(trainX, batch_size=batch_size)
+model.reset_states()
+testPredict = model.predict(testX, batch_size=batch_size)
+
+trainPredict = scaler.inverse_transform(trainPredict)
+trainY = scaler.inverse_transform([trainY])
+testPredict = scaler.inverse_transform(testPredict)
+testY = scaler.inverse_transform([testY])
+
+trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
+print('Train Score: %.2f RMSE' % (trainScore))
+testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0]))
+print('Test Score: %.2f RMSE' % (testScore))
+
+trainPredictPlot = numpy.empty_like(dataset)
+trainPredictPlot[:, :] = numpy.nan
+trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict
+
+testPredictPlot = numpy.empty_like(dataset)
+testPredictPlot[:, :] = numpy.nan
+testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict
+
+plt.plot(scaler.inverse_transform(dataset))
+plt.plot(trainPredictPlot)
+plt.plot(testPredictPlot)
+plt.show()