y_train = data_y[:26614]#, len(low_level)+len(mid_level):
y_test = data_y[26614:33268]#, len(low_level)+len(mid_level):
"""
if load:
print("The shape of the X_train is: ", X_train.shape)
print("The shape of the y_train is: ", y_train.shape)
if load:
print("The shape of the X_test is: ", X_test.shape)
print("The shape of the y_test is: ", y_test.shape)
print(alpha)
#alpha = np.ones((1, class_num))
#googleNet默认输入32*32的图片
if args.model == "GoogLeNet":
model = GoogLeNet.build(image_height, image_width, 3, class_num)
loss_func = weighted_binary_crossentropy(alpha)
#loss_func = 'binary_crossentropy'
#loss_func = K.mean(K.binary_crossentropy(y_true, y_pred), axis=-1)
loss_weights = None
#metrics=['accuracy']
metrics = [weighted_acc]
metrics = [mA, 'accuracy']
elif args.model == "GoogLeNetGAP":
model = GoogLeNetGAP.build(image_height, image_width, 3, class_num)
loss_func = weighted_binary_crossentropy(alpha)
#loss_func = 'binary_crossentropy'
#loss_func = K.mean(K.binary_crossentropy(y_true, y_pred), axis=-1)
loss_weights = None
#metrics=['accuracy']
metrics = [weighted_acc]
metrics = [mA, 'accuracy']
#alpha = np.exp(-alpha)
print(alpha)
print(alpha.shape)
if load:
print("The shape of the X_train is: ", X_train.shape)
print("The shape of the y_train is: ", y_train.shape)
if load:
print("The shape of the X_test is: ", X_test.shape)
print("The shape of the y_test is: ", y_test.shape)
is_multi = None
#googleNet默认输入32*32的图片
if args.model == "hiarGoogLeNet":
model = hiarGoogLeNet.build(image_height, image_width, 3, [len(low_level), len(mid_level), len(high_level)])
loss_func = 'binary_crossentropy'#weighted_categorical_crossentropy(alpha)
loss_func = weighted_binary_crossentropy(alpha)
loss_weights = None
metrics=['accuracy']
metrics = [weighted_acc]
metrics = [mA, 'accuracy']
elif args.model == "hiarGoogLeNetGAP":
model = hiarGoogLeNetGAP.build(image_height, image_width, 3, [len(low_level), len(mid_level), len(high_level)])
loss_func = 'binary_crossentropy'#weighted_categorical_crossentropy(alpha)
loss_func = weighted_binary_crossentropy(alpha)
loss_weights = None
metrics=['accuracy']
metrics = [weighted_acc]
metrics = [mA, 'accuracy']
elif args.model == "hiarBayesGoogLeNet":
model = hiarBayesGoogLeNet.build(image_height, image_width, 3, [len(low_level), len(mid_level), len(high_level)])
loss_func ='binary_crossentropy'#bayes_binary_crossentropy(alpha, y_train)#weighted_categorical_crossentropy(alpha)