我正面临着一个问题，我很难理解为什么我会有这种行为。
我正在尝试使用预先训练的resnet 50（keras）模型进行二进制图像分类，我还构建了一个简单的cnn。我有大约8k大小为200x200的平衡RGB图像，并将该集合分为三个子集（训练70％，验证15％，测试15％）。
我构建了一个生成器，用于根据keras.utils.Sequence向模型提供数据。
我的问题是我的模型倾向于在训练集上学习，但在验证集上，我在预训练的resnet50和简单的cnn上的结果不佳。 我尝试了几种方法来解决此问题，但根本没有改善。
这是获得的结果的示例：
Epoch 1/200
716/716 [==============================] - 320s 447ms/step - loss: 8.6096 - acc: 0.4728 - val_loss: 8.6140 - val_acc: 0.5335
Epoch 00001: val_loss improved from inf to 8.61396, saving model to ../models_saved/resnet_adam_best.h5
Epoch 2/200
716/716 [==============================] - 287s 401ms/step - loss: 8.1217 - acc: 0.5906 - val_loss: 10.9314 - val_acc: 0.4632
Epoch 00002: val_loss did not improve from 8.61396
Epoch 3/200
716/716 [==============================] - 249s 348ms/step - loss: 7.5357 - acc: 0.6695 - val_loss: 11.1432 - val_acc: 0.4657
Epoch 00003: val_loss did not improve from 8.61396
Epoch 4/200
716/716 [==============================] - 284s 397ms/step - loss: 7.5092 - acc: 0.6828 - val_loss: 10.0665 - val_acc: 0.5351
Epoch 00004: val_loss did not improve from 8.61396
Epoch 5/200
716/716 [==============================] - 261s 365ms/step - loss: 7.0679 - acc: 0.7102 - val_loss: 4.2205 - val_acc: 0.5351
Epoch 00005: val_loss improved from 8.61396 to 4.22050, saving model to ../models_saved/resnet_adam_best.h5
Epoch 6/200
716/716 [==============================] - 285s 398ms/step - loss: 6.9945 - acc: 0.7161 - val_loss: 10.2276 - val_acc: 0.5335
....
这是用于将数据加载到我的模型中的类。
class DataGenerator(keras.utils.Sequence):
def __init__(self, inputs,
labels, img_size,
input_shape,
batch_size, num_classes,
validation=False):
self.inputs = inputs
self.labels = labels
self.img_size = img_size
self.input_shape = input_shape
self.batch_size = batch_size
self.num_classes = num_classes
self.validation = validation
self.indexes = np.arange(len(self.inputs))
self.inc = 0
def __getitem__(self, index):
"""Generate one batch of data
Parameters
----------
index :the index from which batch will be taken
Returns
-------
out : a tuple that contains (inputs and labels associated)
"""
batch_inputs = np.zeros((self.batch_size, *self.input_shape))
batch_labels = np.zeros((self.batch_size, self.num_classes))
# Generate data
for i in range(self.batch_size):
# choose random index in features
if self.validation:
index = self.indexes[self.inc]
self.inc += 1
if self.inc == len(self.inputs):
self.inc = 0
else:
index = random.randint(0, len(self.inputs) - 1)
batch_inputs[i] = self.rgb_processing(self.inputs[index])
batch_labels[i] = to_categorical(self.labels[index], num_classes=self.num_classes)
return batch_inputs, batch_labels
def __len__(self):
"""Denotes the number of batches per epoch
Returns
-------
out : number of batches per epochs
"""
return int(np.floor(len(self.inputs) / self.batch_size))
def rgb_processing(self, path):
img = load_img(path)
rgb = img.get_rgb_array()
if not self.validation:
if random.choice([True, False]):
rgb = random_rotation(rgb)
return rgb/np.max(rgb)
class Models:
def __init__(self, input_shape, classes):
self.input_shape = input_shape
self.classes = classes
pass
def simpleCNN(self, optimizer):
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
activation='relu',
input_shape=self.input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(len(self.classes), activation='softmax'))
model.compile(loss=keras.losses.binary_crossentropy,
optimizer=optimizer,
metrics=['accuracy'])
return model
def resnet50(self, optimizer):
model = keras.applications.resnet50.ResNet50(include_top=False,
input_shape=self.input_shape,
weights='imagenet')
model.summary()
model.layers.pop()
model.summary()
for layer in model.layers:
layer.trainable = False
output = Flatten()(model.output)
#I also tried to add dropout layers here with batch normalization but it does not change results
output = Dense(len(self.classes), activation='softmax')(output)
finetuned_model = Model(inputs=model.input,
outputs=output)
finetuned_model.compile(optimizer=optimizer,
loss=keras.losses.binary_crossentropy,
metrics=['accuracy'])
return finetuned_model
这是这些函数的调用方式：
train_batches = DataGenerator(inputs=train.X.values,
labels=train.y.values,
img_size=img_size,
input_shape=input_shape,
batch_size=batch_size,
num_classes=len(CLASSES))
validate_batches = DataGenerator(inputs=validate.X.values,
labels=validate.y.values,
img_size=img_size,
input_shape=input_shape,
batch_size=batch_size,
num_classes=len(CLASSES),
validation=True)
if model_name == "cnn":
model = models.simpleCNN(optimizer=Adam(lr=0.0001))
elif model_name == "resnet":
model = models.resnet50(optimizer=Adam(lr=0.0001))
early_stopping = EarlyStopping(patience=15)
checkpointer = ModelCheckpoint(output_name + '_best.h5', verbose=1, save_best_only=True)
history = model.fit_generator(train_batches, steps_per_epoch=num_train_steps, epochs=epochs,
callbacks=[early_stopping, checkpointer], validation_data=validate_batches,
validation_steps=num_valid_steps)
答案 0 :(得分：0)
我终于找到了导致这种过度拟合的主要因素。由于我使用了预先训练的模型。我将图层设置为不可训练。因此，我试图将它们设置为可训练的，并且似乎可以解决问题。
for layer in model.layers:
layer.trainable = False
我的假设是我的图像与用于训练模型的数据相距太远。
我还在resnet模型的末尾添加了一些辍学和批处理规范化功能。