MAPE_, RMSE_ = sess.run([g.MAPE, g.RMSE],
feed_dict={
g.x: training_X,
g.y: training_Y
})
print('Tr_MAPE:' + str(MAPE_) + ', Tr_RMSE:' + str(RMSE_))
# tensorboard:
summary = sess.run(g.merged,
feed_dict={
g.x: training_X,
g.y: training_Y
})
writer.add_summary(summary, step)
step += 1
g.is_training = False
prediction, t_MAPE_, t_RMSE_ = sess.run([g.pred, g.MAPE, g.RMSE],
feed_dict={
g.x: testing_X,
g.y: testing_Y
})
print('Te_MAPE:' + str(t_MAPE_) + ', Te_RMSE:' + str(t_RMSE_))
# save prediction to a file
pdata.prediction_to_csv(prediction, 'C_main')
tEnd = time.time()
print("It cost %f sec" % (tEnd - tStart))
