-
-
Save amankharwal/cb7f1b05b8e5e7e5e43385c6dd19e8a0 to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
from keras.models import Sequential | |
from keras.layers import LSTM | |
from keras.layers import Dropout | |
from keras.layers import Dense | |
from sklearn.metrics import mean_squared_error,r2_score | |
import matplotlib.pyplot as plt | |
import numpy as np | |
model = Sequential() | |
model.add(LSTM(100, input_shape=(train_x.shape[1], train_x.shape[2]))) | |
model.add(Dropout(0.1)) | |
model.add(Dense(1)) | |
model.compile(loss='mean_squared_error', optimizer='adam') | |
# Network fitting | |
history = model.fit(train_x, train_y, epochs=50, batch_size=70, validation_data=(test_x, test_y), verbose=2, shuffle=False) | |
# Loss history plot | |
plt.plot(history.history['loss']) | |
plt.plot(history.history['val_loss']) | |
plt.title('model loss') | |
plt.ylabel('loss') | |
plt.xlabel('epoch') | |
plt.legend(['train', 'test'], loc='upper right') | |
plt.show() | |
size = df_resample.shape[1] | |
# Prediction test | |
yhat = model.predict(test_x) | |
test_x = test_x.reshape((test_x.shape[0], size)) | |
# invert scaling for prediction | |
inv_yhat = np.concatenate((yhat, test_x[:, 1-size:]), axis=1) | |
inv_yhat = scaler.inverse_transform(inv_yhat) | |
inv_yhat = inv_yhat[:,0] | |
# invert scaling for actual | |
test_y = test_y.reshape((len(test_y), 1)) | |
inv_y = np.concatenate((test_y, test_x[:, 1-size:]), axis=1) | |
inv_y = scaler.inverse_transform(inv_y) | |
inv_y = inv_y[:,0] | |
# calculate RMSE | |
rmse = np.sqrt(mean_squared_error(inv_y, inv_yhat)) | |
print('Test RMSE: %.3f' % rmse) | |
aa=[x for x in range(500)] | |
plt.figure(figsize=(25,10)) | |
plt.plot(aa, inv_y[:500], marker='.', label="actual") | |
plt.plot(aa, inv_yhat[:500], 'r', label="prediction") | |
plt.ylabel(df.columns[0], size=15) | |
plt.xlabel('Time step for first 500 hours', size=15) | |
plt.legend(fontsize=15) | |
plt.show() |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment