AntoineH/smart_augmentation
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Resultats avec early stop sur data test

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Harle, Antoine (Contracteur) 2019-11-14 13:13:03 -05:00
parent 93d91815f5
commit 67f99f3354
85 changed files with 42780 additions and 6607 deletions
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26
higher/test_dataug.py
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@ -64,12 +64,12 @@ if __name__ == "__main__":
print('-'*9)
'''
#### Augmented Model ####
#'''
'''
t0 = time.process_time()
tf_dict = {k: TF.TF_dict[k] for k in tf_names}
#tf_dict = TF.TF_dict
aug_model = Augmented_model(Data_augV4(TF_dict=tf_dict, N_TF=2, mix_dist=0.0), LeNet(3,10)).to(device)
#aug_model = Augmented_model(Data_augV4(TF_dict=tf_dict, N_TF=2, mix_dist=0.0), WideResNet(num_classes=10, wrn_size=160)).to(device)
#aug_model = Augmented_model(Data_augV4(TF_dict=tf_dict, N_TF=2, mix_dist=0.0), LeNet(3,10)).to(device)
aug_model = Augmented_model(Data_augV4(TF_dict=tf_dict, N_TF=2, mix_dist=0.0), WideResNet(num_classes=10, wrn_size=160)).to(device)
print(str(aug_model), 'on', device_name)
#run_simple_dataug(inner_it=n_inner_iter, epochs=epochs)
log= run_dist_dataugV2(model=aug_model, epochs=epochs, inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, print_freq=1, loss_patience=10)
@ -86,15 +86,15 @@ if __name__ == "__main__":
print('Execution Time : %.00f (s?)'%(time.process_time() - t0))
print('-'*9)
#'''
#### TF number tests ####
'''
#### TF number tests ####
#'''
res_folder="res/TF_nb_tests/"
epochs= 200
inner_its = [10]
epochs= 100
inner_its = [0, 10]
dataug_epoch_starts= [0]
TF_nb = range(1,len(TF.TF_dict)+1) #[len(TF.TF_dict)]
N_seq_TF= [1] #[1, 2, 3, 4]
TF_nb = [len(TF.TF_dict)] #range(10,len(TF.TF_dict)+1) #[len(TF.TF_dict)]
N_seq_TF= [2, 3, 4, 6]
try:
os.mkdir(res_folder)
@ -114,17 +114,17 @@ if __name__ == "__main__":
aug_model = Augmented_model(Data_augV4(TF_dict=ntf_dict, N_TF=n_tf, mix_dist=0.0), LeNet(3,10)).to(device)
print(str(aug_model), 'on', device_name)
#run_simple_dataug(inner_it=n_inner_iter, epochs=epochs)
log= run_dist_dataugV2(model=aug_model, epochs=epochs, inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, print_freq=10, loss_patience=10)
log= run_dist_dataugV2(model=aug_model, epochs=epochs, inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, print_freq=10, loss_patience=None)
####
plot_res(log, fig_name=res_folder+"{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter))
plot_res(log, fig_name=res_folder+"{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter), param_names=keys)
print('-'*9)
times = [x["time"] for x in log]
out = {"Accuracy": max([x["acc"] for x in log]), "Time": (np.mean(times),np.std(times)), "Device": device_name, "Param_names": aug_model.TF_names(), "Log": log}
print(str(aug_model),": acc", out["Accuracy"], "in (ms):", out["Time"][0], "+/-", out["Time"][1])
print(str(aug_model),": acc", out["Accuracy"], "in (s?):", out["Time"][0], "+/-", out["Time"][1])
with open(res_folder+"log/%s.json" % "{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter), "w+") as f:
json.dump(out, f, indent=True)
print('Log :\"',f.name, '\" saved !')
print('-'*9)
'''
#'''