我有一个CNN，它使用VGG16架构保存了训练和测试数据的瓶颈特征，然后将这些特征上载到我的自定义完全连接层以对图像进行分类。
#create data augmentations for training set; helps reduce overfitting and find more features
train_datagen = ImageDataGenerator(rescale=1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip=True)
#use ImageDataGenerator to upload validation images; data augmentation not necessary for
validating process
val_datagen = ImageDataGenerator(rescale=1./255)
#load VGG16 model, pretrained on imagenet database
model = applications.VGG16(include_top=False, weights='imagenet')
#generator to load images into NN
train_generator = train_datagen.flow_from_directory(
train_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode=None,
shuffle=False)
#total number of images used for training data
num_train = len(train_generator.filenames)
#save features to numpy array file so features do not overload memory
bottleneck_features_train = model.predict_generator(train_generator, num_train // batch_size)
val_generator = val_datagen.flow_from_directory(
val_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode=None,
shuffle=False)
num_val = len(val_generator.filenames)
bottleneck_features_validation = model.predict_generator(val_generator, num_val // batch_size)`
#used to retrieve the labels of the images
label_datagen = ImageDataGenerator(rescale=1./255)
#generators can create class labels for each image in either
train_label_generator = label_datagen.flow_from_directory(
train_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode=None,
shuffle=False)
#total number of images used for training data
num_train = len(train_label_generator.filenames)
#load features from VGG16 and pair each image with corresponding label (0 for normal, 1 for pneumonia)
#train_data = np.load('xray/bottleneck_features_train.npy')
#get the class labels generated by train_label_generator
train_labels = train_label_generator.classes
val_label_generator = label_datagen.flow_from_directory(
val_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode=None,
shuffle=False)
num_val = len(val_label_generator.filenames)
#val_data = np.load('xray/bottleneck_features_validation.npy')
val_labels = val_label_generator.classes
#create fully connected layers, replacing the ones cut off from the VGG16 model
model = Sequential()
#converts model's expected input dimensions to same shape as bottleneck feature arrays
model.add(Flatten(input_shape=bottleneck_features_train.shape[1:]))
#ignores a fraction of input neurons so they do not become co-dependent on each other; helps prevent overfitting
model.add(Dropout(0.7))
#normal fully-connected layer with relu activation. Replaces all negative inputs with 0 and does not fire neuron,
#creating a lighetr network
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.7))
#output layer to classify 0 or 1
model.add(Dense(1, activation='sigmoid'))
#compile model and specify which optimizer and loss function to use
#optimizer used to update the weights to optimal values; adam optimizer maintains seperate learning rates
#for each weight and updates accordingly
#cross-entropy function measures the ability of model to correctly classify 0 or 1
model.compile(optimizer=optimizers.Adam(lr=0.0007), loss='binary_crossentropy', metrics=['accuracy'])
#used to stop training if NN shows no improvement for 5 epochs
early_stop = EarlyStopping(monitor='val_loss', min_delta=0.01, patience=5, verbose=1)
#checks each epoch as it runs and saves the weight file from the model with the lowest validation loss
checkpointer = ModelCheckpoint(filepath=top_model_weights_dir, verbose=1, save_best_only=True)
#fit the model to the data
history = model.fit(bottleneck_features_train, train_labels,
epochs=epochs,
batch_size=batch_size,
callbacks = [early_stop, checkpointer],
verbose=2,
validation_data=(bottleneck_features_validation, val_labels))`
调用train_top_model（）后，CNN在大约10个纪元后获得86％的准确性。
但是，当我尝试通过直接在VGG16层之上构建完全连接的层来实现此体系结构时，网络陷入val_acc为0.5000的状态，并且基本上无法进行训练。代码有任何问题吗？
epochs = 10
batch_size = 20
train_datagen = ImageDataGenerator(rescale=1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip=True)
val_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
train_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary',
shuffle=False)
num_train = len(train_generator.filenames)
val_generator = val_datagen.flow_from_directory(
val_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary',
shuffle=False)
num_val = len(val_generator.filenames)`
base_model = applications.VGG16(weights='imagenet', include_top=False, input_shape=(img_width,
img_height, 3))
x = base_model.output
x = Flatten()(x)
x = Dropout(0.7)(x)
x = Dense(128, activation='relu')(x)
x = Dropout(0.7)(x)
predictions = Dense(1, activation='sigmoid')(x)
model = Model(inputs=base_model.input, outputs=predictions)
for layer in model.layers[:19]:
layer.trainable = False
checkpointer = ModelCheckpoint(filepath=top_model_weights_dir, verbose=1, save_best_only=True)
model.compile(optimizer=optimizers.Adam(lr=0.0007), loss='binary_crossentropy', metrics=
['accuracy'])
history = model.fit_generator(train_generator,
steps_per_epoch=(num_train//batch_size),
validation_data=val_generator,
validation_steps=(num_val//batch_size),
callbacks=[checkpointer],
verbose=1,
epochs=epochs)
答案 0 :(得分：0)
原因是在第二种方法中，您没有冻结VGG16层。换句话说，您正在训练整个网络。而在第一种方法中，您只是在训练完全连接的层的权重。 使用类似这样的内容：
for layer in base_model.layers[:end_layer]:
layer.trainable = False
其中end_layer是您要导入的最后一层。