Question
gaoyacui opened this issue · 6 comments
When I run experiment AB, I always report the following error:
AB:
is_training = False # enable or disable train models. if enable training, save best models will be update.
def EXPAB_train_and_test(exp_name,exps,is_training):
train_classes = sorted(list(set(data.y_train)))
train_indices = [np.where(data.y_train == i)[0] for i in train_classes]
for exp in exps:
scores_1_shot = []
scores_5_shot = []
scores_5_shot_prod = []
scores_wdcnn = []
num = int(exp/len(train_classes))
settings['evaluate_every'] = 300 if exp<1000 else 600
print(settings['evaluate_every'])
for time_idx in range(times):
seed = int(time_idx/4)10
np.random.seed(seed)
print('random seed:',seed)
print("\n%s-%s"%(exp,time_idx) + ''*80)
settings["save_path"] = "tmp/%s/size_%s/time_%s/" % (exp_name,exp,time_idx)
data._mkdir(settings["save_path"])
train_idxs = []
val_idxs = []
for i, c in enumerate(train_classes):
select_idx = train_indices[i][np.random.choice(len(train_indices[i]), num, replace=False)]
split = int(0.6*num)
train_idxs.extend(select_idx[:split])
val_idxs.extend(select_idx[split:])
X_train, y_train = data.X_train[train_idxs],data.y_train[train_idxs],
X_val, y_val = data.X_train[val_idxs],data.y_train[val_idxs],
print(train_idxs[0:10])
print(val_idxs[0:10])
# load one-shot model and training
siamese_net = models.load_siamese_net()
siamese_loader = siamese.Siamese_Loader(X_train,
y_train,
X_val,
y_val)
if(is_training):
print(siamese.train_and_test_oneshot(settings,siamese_net,siamese_loader))
# load wdcnn model and training
y_train = keras.utils.to_categorical(y_train, data.nclasses)
y_val = keras.utils.to_categorical(y_val, data.nclasses)
y_test = keras.utils.to_categorical(data.y_test, data.nclasses)
earlyStopping = EarlyStopping(monitor='val_loss', patience=20, verbose=0, mode='min')
# checkpoint
# filepath="tmp/weights-best-cnn-{epoch:02d}-{val_acc:.2f}.hdf5"
filepath="%sweights-best-10-cnn-low-data.hdf5" % (settings["save_path"])
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=0, save_best_only=True, mode='max')
callbacks_list = [earlyStopping,checkpoint]
wdcnn_net = models.load_wdcnn_net()
if(is_training):
wdcnn_net.fit(X_train, y_train,
batch_size=32,
epochs=300,
verbose=0,
callbacks=callbacks_list,
validation_data=(X_val, y_val))
# loading best weights and testing
print("load best weights",settings["save_path"] + settings['save_weights_file'])
siamese_net.load_weights(settings["save_path"] + settings['save_weights_file'])
print("load best weights",filepath)
wdcnn_net.load_weights(filepath)
for snr in snrs:
print("\n%s_%s_%s"%(exp,time_idx,snr) + '*'*80)
X_test_noise = []
if snr != None:
for x in data.X_test:
X_test_noise.append(utils.noise_rw(x,snr))
X_test_noise = np.array(X_test_noise)
else:
X_test_noise = data.X_test
# test 1_shot and 5_shot
siamese_loader.set_val(X_test_noise,data.y_test)
s = 'val'
preds_5_shot = []
prods_5_shot = []
scores = []
for k in range(5):
val_acc,preds, prods = siamese_loader.test_oneshot2(siamese_net,len(siamese_loader.classes[s]),
len(siamese_loader.data[s]),verbose=False)
# utils.confusion_plot(preds[:,1],preds[:,0])
print(val_acc,preds.shape, prods.shape)
scores.append(val_acc)
preds_5_shot.append(preds[:,1])
prods_5_shot.append(prods)
preds = []
for line in np.array(preds_5_shot).T:
pass
preds.append(np.argmax(np.bincount(line)))
# utils.confusion_plot(np.array(preds),data.y_test)
prod_preds = np.argmax(np.sum(prods_5_shot,axis=0),axis=1).reshape(-1)
score_5_shot = accuracy_score(data.y_test,np.array(preds))*100
print('5_shot:',score_5_shot)
score_5_shot_prod = accuracy_score(data.y_test,prod_preds)*100
print('5_shot_prod:',score_5_shot_prod)
scores_1_shot.append(scores[0])
scores_5_shot.append(score_5_shot)
scores_5_shot_prod.append(score_5_shot_prod)
# test wdcnn
score = wdcnn_net.evaluate(X_test_noise, y_test, verbose=0)[1]*100
print('wdcnn:', score)
scores_wdcnn.append(score)
a =pd.DataFrame(np.array(scores_1_shot).reshape(-1,len(snrs)))
a.columns = snrs
a.to_csv("tmp/%s/size_%s/scores_1_shot.csv" % (exp_name,exp),index=True)
a =pd.DataFrame(np.array(scores_5_shot).reshape(-1,len(snrs)))
a.columns = snrs
a.to_csv("tmp/%s/size_%s/scores_5_shot.csv" % (exp_name,exp),index=True)
a =pd.DataFrame(np.array(scores_5_shot_prod).reshape(-1,len(snrs)))
a.columns = snrs
a.to_csv("tmp/%s/size_%s/scores_5_shot_prod.csv" % (exp_name,exp),index=True)
a =pd.DataFrame(np.array(scores_wdcnn).reshape(-1,len(snrs)))
a.columns = snrs
a.to_csv("tmp/%s/size_%s/scores_wdcnn.csv" % (exp_name,exp),index=True)
EXPAB_train_and_test(exp_name,exps,is_training)
error:
AttributeError Traceback (most recent call last)
in
135
136
--> 137 EXPAB_train_and_test(exp_name,exps,is_training)
138
in EXPAB_train_and_test(exp_name, exps, is_training)
67 # loading best weights and testing
68 print("load best weights",settings["save_path"] + settings['save_weights_file'])
---> 69 siamese_net.load_weights(settings["save_path"] + settings['save_weights_file'])
70 print("load best weights",filepath)
71 wdcnn_net.load_weights(filepath)
D:\anaconda\envs\tensorflow-gpu\lib\site-packages\keras\engine\network.py in load_weights(self, filepath, by_name, skip_mismatch, reshape)
1164 else:
1165 saving.load_weights_from_hdf5_group(
-> 1166 f, self.layers, reshape=reshape)
1167
1168 def _updated_config(self):
D:\anaconda\envs\tensorflow-gpu\lib\site-packages\keras\engine\saving.py in load_weights_from_hdf5_group(f, layers, reshape)
1007 """
1008 if 'keras_version' in f.attrs:
-> 1009 original_keras_version = f.attrs['keras_version'].decode('utf8')
1010 else:
1011 original_keras_version = '1'
AttributeError: 'str' object has no attribute 'decode'
Please advise, thank you
It would be great if there is a software that converts keras to pytorch.
Hello, do you have the TMP and DataSets files?
I can't open the link the author gave me.
If you have , please email me at 1040101322@qq.com
When I run experiment AB, I always report the following error:
AB:
is_training = False # enable or disable train models. if enable training, save best models will be update.def EXPAB_train_and_test(exp_name,exps,is_training):
train_classes = sorted(list(set(data.y_train)))
train_indices = [np.where(data.y_train == i)[0] for i in train_classes]
for exp in exps:
scores_1_shot = []
scores_5_shot = []
scores_5_shot_prod = []
scores_wdcnn = []
num = int(exp/len(train_classes))
settings['evaluate_every'] = 300 if exp<1000 else 600
print(settings['evaluate_every'])
for time_idx in range(times):
seed = int(time_idx/4)10 np.random.seed(seed) print('random seed:',seed) print("\n%s-%s"%(exp,time_idx) + ''*80)
settings["save_path"] = "tmp/%s/size_%s/time_%s/" % (exp_name,exp,time_idx)
data._mkdir(settings["save_path"])train_idxs = [] val_idxs = [] for i, c in enumerate(train_classes): select_idx = train_indices[i][np.random.choice(len(train_indices[i]), num, replace=False)] split = int(0.6*num) train_idxs.extend(select_idx[:split]) val_idxs.extend(select_idx[split:]) X_train, y_train = data.X_train[train_idxs],data.y_train[train_idxs], X_val, y_val = data.X_train[val_idxs],data.y_train[val_idxs], print(train_idxs[0:10]) print(val_idxs[0:10]) # load one-shot model and training siamese_net = models.load_siamese_net() siamese_loader = siamese.Siamese_Loader(X_train, y_train, X_val, y_val) if(is_training): print(siamese.train_and_test_oneshot(settings,siamese_net,siamese_loader)) # load wdcnn model and training y_train = keras.utils.to_categorical(y_train, data.nclasses) y_val = keras.utils.to_categorical(y_val, data.nclasses) y_test = keras.utils.to_categorical(data.y_test, data.nclasses) earlyStopping = EarlyStopping(monitor='val_loss', patience=20, verbose=0, mode='min') # checkpoint # filepath="tmp/weights-best-cnn-{epoch:02d}-{val_acc:.2f}.hdf5" filepath="%sweights-best-10-cnn-low-data.hdf5" % (settings["save_path"]) checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=0, save_best_only=True, mode='max') callbacks_list = [earlyStopping,checkpoint] wdcnn_net = models.load_wdcnn_net() if(is_training): wdcnn_net.fit(X_train, y_train, batch_size=32, epochs=300, verbose=0, callbacks=callbacks_list, validation_data=(X_val, y_val)) # loading best weights and testing print("load best weights",settings["save_path"] + settings['save_weights_file']) siamese_net.load_weights(settings["save_path"] + settings['save_weights_file']) print("load best weights",filepath) wdcnn_net.load_weights(filepath) for snr in snrs: print("\n%s_%s_%s"%(exp,time_idx,snr) + '*'*80) X_test_noise = [] if snr != None: for x in data.X_test: X_test_noise.append(utils.noise_rw(x,snr)) X_test_noise = np.array(X_test_noise) else: X_test_noise = data.X_test # test 1_shot and 5_shot siamese_loader.set_val(X_test_noise,data.y_test) s = 'val' preds_5_shot = [] prods_5_shot = [] scores = [] for k in range(5): val_acc,preds, prods = siamese_loader.test_oneshot2(siamese_net,len(siamese_loader.classes[s]), len(siamese_loader.data[s]),verbose=False) # utils.confusion_plot(preds[:,1],preds[:,0]) print(val_acc,preds.shape, prods.shape) scores.append(val_acc) preds_5_shot.append(preds[:,1]) prods_5_shot.append(prods) preds = [] for line in np.array(preds_5_shot).T: pass preds.append(np.argmax(np.bincount(line))) # utils.confusion_plot(np.array(preds),data.y_test) prod_preds = np.argmax(np.sum(prods_5_shot,axis=0),axis=1).reshape(-1) score_5_shot = accuracy_score(data.y_test,np.array(preds))*100 print('5_shot:',score_5_shot) score_5_shot_prod = accuracy_score(data.y_test,prod_preds)*100 print('5_shot_prod:',score_5_shot_prod) scores_1_shot.append(scores[0]) scores_5_shot.append(score_5_shot) scores_5_shot_prod.append(score_5_shot_prod) # test wdcnn score = wdcnn_net.evaluate(X_test_noise, y_test, verbose=0)[1]*100 print('wdcnn:', score) scores_wdcnn.append(score) a =pd.DataFrame(np.array(scores_1_shot).reshape(-1,len(snrs))) a.columns = snrs a.to_csv("tmp/%s/size_%s/scores_1_shot.csv" % (exp_name,exp),index=True) a =pd.DataFrame(np.array(scores_5_shot).reshape(-1,len(snrs))) a.columns = snrs a.to_csv("tmp/%s/size_%s/scores_5_shot.csv" % (exp_name,exp),index=True) a =pd.DataFrame(np.array(scores_5_shot_prod).reshape(-1,len(snrs))) a.columns = snrs a.to_csv("tmp/%s/size_%s/scores_5_shot_prod.csv" % (exp_name,exp),index=True) a =pd.DataFrame(np.array(scores_wdcnn).reshape(-1,len(snrs))) a.columns = snrs a.to_csv("tmp/%s/size_%s/scores_wdcnn.csv" % (exp_name,exp),index=True)EXPAB_train_and_test(exp_name,exps,is_training)
error:
AttributeError Traceback (most recent call last)in
135
136
--> 137 EXPAB_train_and_test(exp_name,exps,is_training)
138in EXPAB_train_and_test(exp_name, exps, is_training)
67 # loading best weights and testing
68 print("load best weights",settings["save_path"] + settings['save_weights_file'])
---> 69 siamese_net.load_weights(settings["save_path"] + settings['save_weights_file'])
70 print("load best weights",filepath)
71 wdcnn_net.load_weights(filepath)D:\anaconda\envs\tensorflow-gpu\lib\site-packages\keras\engine\network.py in load_weights(self, filepath, by_name, skip_mismatch, reshape)
1164 else:
1165 saving.load_weights_from_hdf5_group(
-> 1166 f, self.layers, reshape=reshape)
1167
1168 def _updated_config(self):D:\anaconda\envs\tensorflow-gpu\lib\site-packages\keras\engine\saving.py in load_weights_from_hdf5_group(f, layers, reshape)
1007 """
1008 if 'keras_version' in f.attrs:
-> 1009 original_keras_version = f.attrs['keras_version'].decode('utf8')
1010 else:
1011 original_keras_version = '1'AttributeError: 'str' object has no attribute 'decode'
Please advise, thank you
This is keras package error. Please check the information below:
D:\anaconda\envs\tensorflow-gpu\lib\site-packages\keras\engine\saving.py in load_weights_from_hdf5_group(f, layers, reshape)
1007 """
1008 if 'keras_version' in f.attrs:
-> 1009 original_keras_version = f.attrs['keras_version'].decode('utf8')
1010 else:
1011 original_keras_version = '1'
AttributeError: 'str' object has no attribute 'decode'
It would be great if there is a software that converts keras to pytorch.
You can try it in models.py and siamese.py. But it would be hard.
Hello, do you have the TMP and DataSets files?
I can't open the link the author gave me.
If you have , please email me at 1040101322@qq.com
DataSets: https://pan.baidu.com/s/1WgJMPSDcipugR1Bh4KRadg 提取码: 7uu9
TMP: https://pan.baidu.com/s/1k9xkejB-3YRqDunKA9AUsw 提取码: htgw