我目前正在做一个项目，需要预测一组图像中的眼部疾病。我正在使用Keras内置应用程序。在VGG16和VGG19上我获得了不错的结果，但是在Xception架构上，我一直获得每个周期正好为0.5的AUC。
我尝试了不同的优化程序和学习率，但是没有任何效果。通过从RMSProp优化器切换到Adam优化器，我解决了VGG19的相同问题，但是我无法在Xception上使用它。
def buildModel():
from keras.models import Model
from keras.layers import Dense, Flatten
from keras.optimizers import adam
input_model = applications.xception.Xception(
include_top=False,
weights='imagenet',
input_tensor=None,
input_shape=input_sizes["xception"],
pooling=None,
classes=2)
base_model = input_model
x = base_model.output
x = Flatten()(x)
predictions = Dense(2, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
for layer in base_model.layers:
layer.trainable = False
model.compile(optimizer=adam(lr=0.01), loss='binary_crossentropy', metrics=['accuracy'])
return model
class Histories(keras.callbacks.Callback):
def __init__(self, val_data):
super(Histories, self).__init__()
self.x_batch = []
self.y_batch = []
for i in range(len(val_data)):
x, y = val_data.__getitem__(i)
self.x_batch.extend(x)
self.y_batch.extend(np.ndarray.astype(y, int))
self.aucs = []
self.specificity = []
self.sensitivity = []
self.losses = []
return
def on_train_begin(self, logs={}):
initFile("results/xception_results_adam_3.txt")
return
def on_train_end(self, logs={}):
return
def on_epoch_begin(self, epoch, logs={}):
return
def on_epoch_end(self, epoch, logs={}):
self.losses.append(logs.get('loss'))
y_pred = self.model.predict(np.asarray(self.x_batch))
con_mat = confusion_matrix(np.asarray(self.y_batch).argmax(axis=-1), y_pred.argmax(axis=-1))
tn, fp, fn, tp = con_mat.ravel()
sens = tp/(tp+fn)
spec = tn/(tn+fp)
auc_score = roc_auc_score(np.asarray(self.y_batch).argmax(axis=-1), y_pred.argmax(axis=-1))
print("Specificity: %f Sensitivity: %f AUC: %f"%(spec, sens, auc_score))
print(con_mat)
self.sensitivity.append(sens)
self.specificity.append(spec)
self.aucs.append(auc_score)
writeToFile("results/xception_results_adam_3.txt", epoch, auc_score, spec, sens, self.losses[epoch])
return
# What follows is data from the Jupyter Notebook that I actually use to evaluate
#%% Initialize data
trainDirectory = 'RetinaMasks/train'
valDirectory = 'RetinaMasks/val'
testDirectory = 'RetinaMasks/test'
train_datagen = ImageDataGenerator(rescale=1. / 255)
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(
trainDirectory,
target_size=(299, 299),
batch_size=16,
class_mode='categorical')
validation_generator = test_datagen.flow_from_directory(
valDirectory,
target_size=(299, 299),
batch_size=16,
class_mode='categorical')
test_generator = test_datagen.flow_from_directory(
testDirectory,
target_size=(299, 299),
batch_size=16,
class_mode='categorical')
#%% Create model
model = buildModel("xception")
#%% Initialize metrics
from keras.callbacks import EarlyStopping
from MetricsCallback import Histories
import keras
metrics = Histories(validation_generator)
es = EarlyStopping(monitor='val_loss',
min_delta=0,
patience=20,
verbose=0,
mode='auto',
baseline=None,
restore_best_weights=False)
mcp = keras.callbacks.ModelCheckpoint("saved_models/xception.adam.lr0.1_{epoch:02d}.hdf5",
monitor='val_loss',
verbose=0,
save_best_only=False,
save_weights_only=False,
mode='auto',
period=1)
#%% Train model
from StaticDataAugmenter import superDirectorySize
history = model.fit_generator(
train_generator,
steps_per_epoch=superDirectorySize(trainDirectory) // 16,
epochs=100,
validation_data=validation_generator,
validation_steps=superDirectorySize(valDirectory) // 16,
callbacks=[metrics, es, mcp],
workers=8,
shuffle=False
)
老实说，我不知道是什么原因导致了这种行为，或者如何防止这种行为。预先谢谢您，我为冗长的代码片段表示歉意：）
答案 0 :(得分：3)
您的学习率太大。 尝试降低学习率。
我也遇到过类似的情况，这种情况发生在转学过程中。如果采用二进制分类，则在多个时期内将AUC扩展为0.5，意味着您的卷积神经网络不会学习任何东西。
使用0.0001,0.00001,0.000001的学习率。
我非常有信心，如果您降低学习速度，您的问题将得到解决：)。
答案 1 :(得分：0)
AUC为0.5表示您的网络随机猜测输出，这意味着它没有学到任何东西。例如here，已经对此进行了讨论。
如Timbus Calin所建议的那样，您可以对学习率进行“线性搜索”（从0.000001开始），然后将学习率提高10。
我建议您直接从随机搜索开始，在该搜索中，您不仅要尝试优化学习率，而且还要尝试优化其他超参数，例如批次大小。在此paper中了解有关随机搜索的更多信息。
答案 2 :(得分：0)
您没有正确计算AUC，目前您有：
auc_score = roc_auc_score(np.asarray(self.y_batch).argmax(axis=-1), y_pred.argmax(axis=-1))
auc_score = roc_auc_score(np.asarray(self.y_batch).argmax(axis=-1), y_pred[:, 1])
请注意，计算ROC所需的分数是肯定类别的概率，这是softmax输出的第二个元素。这就是为什么仅将预测的第二列用于进行AUC的原因。
答案 3 :(得分：0)
那呢？
def buildModel():
from keras.models import Model
from keras.layers import Dense, Flatten
from keras.optimizers import adam
input_model = applications.xception.Xception(
include_top=False,
weights='imagenet',
input_tensor=None,
input_shape=input_sizes["xception"],
pooling='avg', # 1
classes=2)
base_model = input_model
x = base_model.output
# x = Flatten()(x) # 2
predictions = Dense(2, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
for layer in base_model.layers:
layer.trainable = False
model.compile(optimizer=adam(lr=0.01),
loss='categorical_crossentropy', # 3
metrics=['accuracy'])
return model
class Histories(keras.callbacks.Callback):
def __init__(self, val_data):
super(Histories, self).__init__()
self.x_batch = []
self.y_batch = []
for i in range(len(val_data)):
x, y = val_data.__getitem__(i)
self.x_batch.extend(x)
self.y_batch.extend(np.ndarray.astype(y, int))
self.aucs = []
self.specificity = []
self.sensitivity = []
self.losses = []
return
def on_train_begin(self, logs={}):
initFile("results/xception_results_adam_3.txt")
return
def on_train_end(self, logs={}):
return
def on_epoch_begin(self, epoch, logs={}):
return
def on_epoch_end(self, epoch, logs={}):
self.losses.append(logs.get('loss'))
y_pred = self.model.predict(np.asarray(self.x_batch))
con_mat = confusion_matrix(np.asarray(self.y_batch).argmax(axis=-1), y_pred.argmax(axis=-1))
tn, fp, fn, tp = con_mat.ravel()
sens = tp/(tp+fn)
spec = tn/(tn+fp)
auc_score = roc_auc_score(np.asarray(self.y_batch).argmax(axis=-1), y_pred.argmax(axis=-1))
print("Specificity: %f Sensitivity: %f AUC: %f"%(spec, sens, auc_score))
print(con_mat)
self.sensitivity.append(sens)
self.specificity.append(spec)
self.aucs.append(auc_score)
writeToFile("results/xception_results_adam_3.txt", epoch, auc_score, spec, sens, self.losses[epoch])
return
# What follows is data from the Jupyter Notebook that I actually use to evaluate
#%% Initialize data
trainDirectory = 'RetinaMasks/train'
valDirectory = 'RetinaMasks/val'
testDirectory = 'RetinaMasks/test'
train_datagen = ImageDataGenerator(rescale=1. / 255)
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(
trainDirectory,
target_size=(299, 299),
batch_size=16,
class_mode='categorical')
validation_generator = test_datagen.flow_from_directory(
valDirectory,
target_size=(299, 299),
batch_size=16,
class_mode='categorical')
test_generator = test_datagen.flow_from_directory(
testDirectory,
target_size=(299, 299),
batch_size=16,
class_mode='categorical')
#%% Create model
model = buildModel("xception")
#%% Initialize metrics
from keras.callbacks import EarlyStopping
from MetricsCallback import Histories
import keras
metrics = Histories(validation_generator)
es = EarlyStopping(monitor='val_loss',
min_delta=0,
patience=20,
verbose=0,
mode='auto',
baseline=None,
restore_best_weights=False)
mcp = keras.callbacks.ModelCheckpoint("saved_models/xception.adam.lr0.1_{epoch:02d}.hdf5",
monitor='val_loss',
verbose=0,
save_best_only=False,
save_weights_only=False,
mode='auto',
period=1)
#%% Load saved model
from keras.models import load_model
# model = load_model("saved_models/vgg16.10.hdf5") # 4
#%% Train model
from StaticDataAugmenter import superDirectorySize
history = model.fit_generator(
train_generator,
steps_per_epoch=superDirectorySize(trainDirectory) // 16,
epochs=100,
validation_data=validation_generator,
validation_steps=superDirectorySize(valDirectory) // 16,
callbacks=[metrics, es, mcp],
workers=8,
shuffle=False
)
对于1和2，我认为在ReLU之后不使用池化层就使用FC层是没有意义的，请不要尝试使用它，否则可能无济于事。
对于3，为什么当生成器使用class_mode='categorical'时使用BCE？
对于4，如上所述，这意味着您正在加载VGG模型并对其进行训练，而不是使用buildModel()中的Xception。