print('')
print(' Network: %s' % NETWORK)
print(' Sample: %s' % SAMPLE)
print(' Iterations: %s' % ITERATIONS)
print('')
filter = SpeckleNoiseFilter(random_state=454)
if NETWORK == 'vgg16':
model = VGG16(weights='imagenet')
search = OptimizationSearch(
np.load('data/vgg16_%s_correct.npy' % SAMPLE, allow_pickle=True),
filter, model, vgg16_preprocess_input)
results, scores = search.perform_search(iterations=ITERATIONS)
save_filter_search_scores(
filter, results, scores,
'log/speckle_noise_filter_vgg16_%s_search.csv' % SAMPLE)
if NETWORK == 'vgg19':
model = VGG19(weights='imagenet')
search = OptimizationSearch(
np.load('data/vgg19_%s_correct.npy' % SAMPLE, allow_pickle=True),
filter, model, vgg19_preprocess_input)
results, scores = search.perform_search(iterations=ITERATIONS)
save_filter_search_scores(
filter, results, scores,
'log/speckle_noise_filter_vgg19_%s_search.csv' % SAMPLE)
if NETWORK == 'densenet':
model = DenseNet201(weights='imagenet')
search = OptimizationSearch(
print(' Sample: %s' % SAMPLE)
print(' Iterations: %s' % ITERATIONS)
print('')
filter = FourierUniformFilter()
if NETWORK == 'vgg16':
model = VGG16(weights='imagenet')
search = OptimizationSearch(
np.load('data/vgg16_%s_correct.npy' % SAMPLE, allow_pickle=True),
filter,
model,
vgg16_preprocess_input
)
results, scores = search.perform_search(iterations=ITERATIONS)
save_filter_search_scores(filter, results, scores, 'log/fourier_uniform_filter_vgg16_%s_search.csv' % SAMPLE)
if NETWORK == 'vgg19':
model = VGG19(weights='imagenet')
search = OptimizationSearch(
np.load('data/vgg19_%s_correct.npy' % SAMPLE, allow_pickle=True),
filter,
model,
vgg19_preprocess_input
)
results, scores = search.perform_search(iterations=ITERATIONS)
save_filter_search_scores(filter, results, scores, 'log/fourier_uniform_filter_vgg19_%s_search.csv' % SAMPLE)
if NETWORK == 'densenet':
model = DenseNet201(weights='imagenet')
search = OptimizationSearch(
print('')
print(' Network: %s' % NETWORK)
print(' Sample: %s' % SAMPLE)
print(' Iterations: %s' % ITERATIONS)
print('')
filter = GaussianFilter()
if NETWORK == 'vgg16':
model = VGG16(weights='imagenet')
search = OptimizationSearch(
np.load('data/vgg16_%s_correct.npy' % SAMPLE, allow_pickle=True),
filter, model, vgg16_preprocess_input)
results, scores = search.perform_search(iterations=ITERATIONS)
save_filter_search_scores(
filter, results, scores,
'log/gaussian_filter_vgg16_%s_search.csv' % SAMPLE)
if NETWORK == 'vgg19':
model = VGG19(weights='imagenet')
search = OptimizationSearch(
np.load('data/vgg19_%s_correct.npy' % SAMPLE, allow_pickle=True),
filter, model, vgg19_preprocess_input)
results, scores = search.perform_search(iterations=ITERATIONS)
save_filter_search_scores(
filter, results, scores,
'log/gaussian_filter_vgg19_%s_search.csv' % SAMPLE)
if NETWORK == 'densenet':
model = DenseNet201(weights='imagenet')
search = OptimizationSearch(