def main():
train_data_path = '/data/fmri/Folder/AD_classification/Data/input_data/preprocessed_data/CV_OULU_Con_AD_preprocessed.npz'
train_data_classifer = load(train_data_path)['masked_voxels']
train_data_path = '/data/fmri/Folder/AD_classification/Data/input_data/Augmented_data/CV_OULU_Con_AD_aug.npz'
train_data_CNN = load(train_data_path)['masked_voxels']
test_data_path = '/data/fmri/Folder/AD_classification/Data/input_data/CV_ADNI_Con_AD.npz'
test_data_CNN = load(test_data_path)['masked_voxels']
test_data_path = '/data/fmri/Folder/AD_classification/Data/input_data/preprocessed_data/CV_ADNI_Con_AD_preprocessed.npz'
test_data_classifer = load(test_data_path)['masked_voxels']
transposing_order = [3, 0, 2, 1]
train_data_CNN = data_preprocessing.transposnig(train_data_CNN,
transposing_order)
test_data_CNN = data_preprocessing.transposnig(test_data_CNN,
transposing_order)
train_labels_path = '/data/fmri/Folder/AD_classification/Data/input_data/labels/train_labels_aug_data.npz'
train_labels_CNN = load(train_labels_path)['labels']
shuffling_indicies = np.random.permutation(len(train_labels_CNN))
temp = train_data_CNN[shuffling_indicies, :, :, :]
train_data_CNN = temp
train_labels_CNN = train_labels_CNN[shuffling_indicies]
train_labels_path = '/data/fmri/Folder/AD_classification/Data/input_data/labels/train_labels.npz'
train_labels_classifer = load(train_labels_path)['labels']
shuffling_indicies = np.random.permutation(len(train_labels_classifer))
temp = train_data_classifer[shuffling_indicies, :, :, :]
train_data_classifer = temp
train_labels_classifer = train_labels_classifer[shuffling_indicies]
#test_data_path = load_data.find_path(test_data_file_name)
#test_data_path='/data/fmri/Folder/AD_classification/Data/input_data/CV_ADNI_Con_AD.npz'
#test_data = load(test_data_path)['masked_voxels']
#test_labels_path=load_data.find_path(test_labels_file_name)
test_labels_path = '/data/fmri/Folder/AD_classification/Data/input_data/labels/test_labels.npz'
test_labels = load(test_labels_path)['labels']
shuffling_indicies = np.random.permutation(len(test_labels))
test_data_CNN = test_data_CNN[shuffling_indicies, :, :, :]
test_data_classifer = test_data_classifer[shuffling_indicies, :, :, :]
test_labels = test_labels[shuffling_indicies]
train_data_CNN, test_data_CNN, train_labels_CNN, test_labels = preprocessing_methods.preprocessing(
train_data_CNN, test_data_CNN, train_labels_CNN, test_labels, 4, 0,
None, None)
factors = [(224, 45), (224, 45), (3, 54)]
train_data_CNN = resample_poly(train_data_CNN,
factors[0][0],
factors[0][1],
axis=1)
train_data_CNN = resample_poly(train_data_CNN,
factors[1][0],
factors[1][1],
axis=2)
#train_data_CNN=resample_poly(train_data_CNN, factors[2][0], factors[2][1], axis=3)
test_data_CNN = resample_poly(test_data_CNN,
factors[0][0],
factors[0][1],
axis=1)
test_data_CNN = resample_poly(test_data_CNN,
factors[1][0],
factors[1][1],
axis=2)
#test_data_CNN=resample_poly(test_data_CNN, factors[2][0], factors[2][1], axis=3)
train_CNN = 0
feature_extraction = 1
if train_CNN == 1 and feature_extraction == 1:
line1 = 'CNN model is trained and saved and then used as feature extractor'
line2 = 'CNN model used for feature extraction is :'
elif train_CNN == 1 and feature_extraction == 0:
line1 = 'CNN model is trained and used to test the test data'
line2 = 'CNN model used is :'
elif train_CNN == 0 and feature_extraction == 1:
line1 = 'using a saved model to extract fetaures'
line2 = 'The model used used is a saved model'
else:
print(
'Value Error: train_CNN and feature_extraction cannnot have these values'
)
results_directory = 'Results'
num_classes = 2
epoch = 1000
batch_size_factor = 1
optimizer = 'adam'
CNN_models = ['VGG16', 'VGG19']
#intermedidate_layer=[7,7,7,16]
hyperparameters = {
'dropouts': [0.25, 0.5, 0.5],
'activation_function': ['relu', 'relu', 'relu', 'sigmoid'],
'epoch': 10,
'opt': 'adam',
'penalty': 'l1',
'C': 100,
'neighbors': 50
}
data = {
'train_data': train_data_CNN,
'test_data': test_data_CNN,
'train_labels': train_labels_CNN,
'test_labels': test_labels
}
preprocessing_method = 'method 4'
i = 0
for CNN_model in CNN_models:
result_path = generate_result_.create_results_dir(results_directory)
print(CNN_model)
feature_extractor_parameters = {
'data': data,
'hyperparameters': hyperparameters,
'model_type': 'pretrained',
'CNN_model': CNN_model,
'intermediate_layer': 7,
'classifer_name': 'all'
}
CNN.CNN_main(train_data_CNN, test_data_CNN, result_path,
train_labels_CNN, test_labels, num_classes, epoch,
batch_size_factor, optimizer, CNN_model, train_CNN,
feature_extraction, feature_extractor_parameters)
f = open(os.path.join(result_path, 'README'), "w+")
line3 = CNN_model
line4 = 'The preprocessing methods used is ' + ' ' + preprocessing_method
line5 = 'The number of epochs used to train the CNN_model is ' + str(
epoch)
line6 = 'the oprimizer used is ' + optimizer
f.write("{}"
"\n"
"{}"
"\n"
"{}"
"\n"
"{}"
"\n"
"{}"
"\n"
"{}"
"\n".format(line1, line2, line3, line4, line5, line6))
i = i + 1
