def trainCnnPointEstimator(modelType): hidden,measure = gm.loadData('Generated.Data/' + modelType + '.Train') testHidden, testMeasure = gm.loadData('Generated.Data/' + modelType + '.Test') cnnPointEstimator = nn.CnnPointEstimator(hidden.shape[1]) cnnPointEstimator.train(2e-4,100,200,measure,hidden, 'Trained.Models/CNN.Point.Estimator.' + modelType + '.ckpt',testMeasure, testHidden,1213,showKalman=(modelType=='LG'))
def testCnnPointEstimator(modelType,sampleNo): sampleNo -= 1 testHidden,testMeasure = gm.loadData('Generated.Data/' + modelType + '.Test') cnnPointEstimator = nn.CnnPointEstimator(testHidden.shape[1]) estimated = cnnPointEstimator.infer(testMeasure[sampleNo], 'Trained.Models/CNN.Point.Estimator.' + modelType + '/CNN.Point.Estimator.' + modelType + '.ckpt') loss = cnnPointEstimator.computeLoss(estimated,testHidden[sampleNo], 'Trained.Models/CNN.Point.Estimator.' + modelType + '/CNN.Point.Estimator.' + modelType + '.ckpt') if modelType=='LG': testKalmanZ,dump = ks.loadResults('Results.Data/' + modelType + '.Kalman.Results') plt.figure(figsize=(10,5)) plt.scatter(np.arange(testHidden.shape[1]),testHidden[sampleNo], marker='o',color='blue',s=4) if modelType=='LG': plt.plot(testKalmanZ[sampleNo],color='green') plt.plot(estimated.flatten(),color='red') plt.show()