Skip to content
Snippets Groups Projects
Select Git revision
  • 8be20ba96796d930ea71eaba91085743178bb7c7
  • master default protected
2 results

util-format.py

Blame
  • main.py 4.66 KiB
    from read_cifar import read_cifar, split_dataset
    from knn import evaluate_knn_for_k, plot_accuracy_versus_k
    import matplotlib.pyplot as plt
    from mlp import run_mlp_training, plot_accuracy_versus_epoch
    
    
    
    if __name__=="__main__":
        # data, labels = read_cifar("data\cifar-10-batches-py")
        #split = 0.9
        #data_train, labels_train, data_test, labels_test = split_dataset(data,labels,split)
        # data_train, data_test = data_train/255.0, data_test/255.0
        # kmax = 20
        #accuracies = evaluate_knn_for_k(data_train, labels_train, data_test, labels_test,kmax)
        # accuracies = [0.351,
        #             0.31316666666666665,
        #             0.329,
        #             0.33666666666666667,
        #             0.33616666666666667,
        #             0.3413333333333333,
        #             0.343,
        #             0.3428333333333333,
        #             0.341,
        #             0.3335,
        #             0.3325,
        #             0.3328333333333333,
        #             0.33016666666666666,
        #             0.3295,
        #             0.32766666666666666,
        #             0.3285,
        #             0.327,
        #             0.32716666666666666,
        #             0.32916666666666666,
        #             0.3305]
        #plot_accuracy_versus_k(accuracies)
        ####################################
        # parameters of the MLP :
        split_factor = 0.9
        data, labels = read_cifar("data\cifar-10-batches-py")
        data_train, labels_train, data_test, labels_test = split_dataset(data, labels, split=split_factor)
        # print(len(data_test), len(data_train))
        data_train, data_test = data_train/255.0, data_test/255.0 # normalize ou data
        d_h = 64
        lr = 0.1
        num_epoch=100
        accuracies, _ = run_mlp_training(data_train, labels_train, data_test,
                                                           labels_test, d_h, lr, num_epoch)
        # accuracies = [0.08788888888888889, 0.08990740740740741, 0.09135185185185185, 0.09296296296296297, 0.09514814814814815, 0.09631481481481481, 0.09724074074074074, 0.09787037037037037, 0.09820370370370371, 0.09883333333333333, 0.09844444444444445, 0.09859259259259259, 0.09857407407407408, 0.09885185185185186, 0.09872222222222223, 0.09855555555555555, 0.09872222222222223, 0.09883333333333333, 0.0989074074074074, 0.09881481481481481, 0.0987962962962963, 0.09898148148148148, 0.09916666666666667, 0.09938888888888889, 0.09961111111111111, 0.09975925925925926, 0.09975925925925926, 0.1, 0.10003703703703704, 0.09998148148148148, 0.10007407407407408, 0.10011111111111111, 0.10001851851851852, 0.10014814814814815, 0.10012962962962962, 0.09998148148148148, 0.1000925925925926, 0.1000925925925926, 0.10007407407407408, 0.10005555555555555, 0.10014814814814815, 0.10018518518518518, 0.1002037037037037, 0.10018518518518518, 0.10016666666666667, 0.10011111111111111, 0.10016666666666667, 0.10012962962962962, 0.10007407407407408, 0.10005555555555555, 0.1, 0.1, 0.1, 0.1, 0.1, 0.09998148148148148, 0.09998148148148148, 0.09996296296296296, 0.09996296296296296, 0.09996296296296296, 0.09994444444444445, 0.09994444444444445, 0.09994444444444445, 0.0999074074074074, 0.09994444444444445, 0.09996296296296296, 0.09996296296296296, 0.09996296296296296, 0.09998148148148148, 0.09996296296296296, 0.09998148148148148, 0.1, 0.1, 0.10003703703703704, 0.10003703703703704, 0.10005555555555555, 0.10007407407407408, 0.10007407407407408, 0.10007407407407408, 0.10003703703703704, 0.10001851851851852, 0.10003703703703704, 0.10003703703703704, 0.10003703703703704, 0.10001851851851852, 0.10001851851851852, 0.10003703703703704, 0.10003703703703704, 0.10005555555555555, 0.10007407407407408, 0.10007407407407408, 0.10007407407407408, 0.10007407407407408, 0.10005555555555555, 0.10005555555555555, 0.10005555555555555, 0.10007407407407408, 0.10007407407407408, 0.10007407407407408, 0.10007407407407408]
        # print(accuracies)
        plot_accuracy_versus_epoch(accuracies)
        
        
        
        
    
    
    # Result for k = 1
    # Reading data from disk
    # [INFO] Splitting data into train/test with split=70
    # [INFO] Training set has 42000 samples and testing set has 18000 samples.
    # [INFO] Time taken 0
    # Evaluating the k-NN with k = 1
    # Computing distance matrix between train and test sets
    # finished calculating dists
    # Running the prediction using k-NN with k = 1
    # [INFO] computing accuracy of the predictions
    # accuracy = 0.3388888888888889
    
    
    # Reading data from disk
    # [INFO] Splitting data into train/test with split=70
    # [INFO] Training set has 42000 samples and testing set has 18000 samples.
    # [INFO] Time taken 0
    # Evaluating the k-NN with k = 3
    # Computing distance matrix between train and test sets
    # finished calculating dists
    # Running the prediction using k-NN with k = 3
    # [INFO] computing accuracy of the predictions
    # 0.3308333333333333