From dd681b776f0e7b321221df960801d4126dd1bd36 Mon Sep 17 00:00:00 2001 From: m35988ys Date: Sun, 12 Nov 2023 19:14:17 +0000 Subject: [PATCH] update keras verbose level --- src/spectra_gen.py | 6 +++--- src/to_explain.py | 2 +- src/utils.py | 2 +- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/src/spectra_gen.py b/src/spectra_gen.py index d286d6a..1501119 100644 --- a/src/spectra_gen.py +++ b/src/spectra_gen.py @@ -34,7 +34,7 @@ def spectra_sym_gen(eobj, x, y, adv_value=1, testgen_factor=.2, testgen_size=0): else: np.put(t, L, adv_value) x_flag.flat[L]=True #np.put(x, L, True) - new_y=np.argsort(model.predict(sbfl_preprocess(eobj, np.array([t]))))[0][-eobj.top_classes:] + new_y=np.argsort(model.predict(sbfl_preprocess(eobj, np.array([t])), verbose=10))[0][-eobj.top_classes:] is_adv=(len(np.intersect1d(y, new_y))==0) if is_adv: @@ -54,7 +54,7 @@ def spectra_sym_gen(eobj, x, y, adv_value=1, testgen_factor=.2, testgen_size=0): else: np.put(t, L, adv_value) x_flag.flat[L]=True #np.put(x, L, True) - new_y=np.argsort(model.predict(sbfl_preprocess(eobj, np.array([t]))))[0][-eobj.top_classes:] + new_y=np.argsort(model.predict(sbfl_preprocess(eobj, np.array([t])), verbose=10))[0][-eobj.top_classes:] #is_adv=(len(np.intersect1d(y, new_y))==0) #ite-=0.01 #L2=L0[0:int(ite/testgen_factor*portion)] @@ -85,7 +85,7 @@ def spectra_sym_gen(eobj, x, y, adv_value=1, testgen_factor=.2, testgen_size=0): else: np.put(t, L, adv_value) x_flag.flat[L]=True #np.put(x, L, True) - new_y=np.argsort(model.predict(sbfl_preprocess(eobj, np.array([t]))))[0][-eobj.top_classes:] + new_y=np.argsort(model.predict(sbfl_preprocess(eobj, np.array([t])), verbose=10))[0][-eobj.top_classes:] #t2=x.copy() #ite=(ite+1)/2 ##ite+=0.01 diff --git a/src/to_explain.py b/src/to_explain.py index 50a758b..477e58f 100644 --- a/src/to_explain.py +++ b/src/to_explain.py @@ -24,7 +24,7 @@ def to_explain(eobj): for i in range(0, len(eobj.inputs)): x=eobj.inputs[i] - res=model.predict(sbfl_preprocess(eobj, np.array([x]))) + res=model.predict(sbfl_preprocess(eobj, np.array([x])), verbose=10) y=np.argsort(res)[0][-eobj.top_classes:] print ('\n[Input {2}: {0} / Output Label (to Explain): {1}]'.format(eobj.fnames[i], y, i)) diff --git a/src/utils.py b/src/utils.py index 87f87cc..cc63f4f 100644 --- a/src/utils.py +++ b/src/utils.py @@ -114,7 +114,7 @@ def top_plot(sbfl_element, ind, di, metric='', eobj=None, bg=128, online=False, count+=1 if count%base==0: save_an_image(im_o, '{1}-{0}'.format(int(count/base), metric), di) - res=sbfl_element.model.predict(sbfl_preprocess(eobj, np.array([im_o]))) + res=sbfl_element.model.predict(sbfl_preprocess(eobj, np.array([im_o])), verbose=10) y=np.argsort(res)[0][-eobj.top_classes:] #print (int(count/base), '>>>', y, sbfl_element.y, y==sbfl_element.y) if y==sbfl_element.y and not found_exp: