Question

我正在尝试按照教程Fine-tuning the top layers of a pre-trained network。

为此，我想使用预训练的keras-facenet并在顶部添加我的分类器。我使用vggface作为基本模型。 Facenet基于VGGFace。所以这是运行我的代码后得到的错误：

ValueError                                Traceback (most recent call last)
<ipython-input-9-261fed5d7ddc> in <module>()
     20 model.add(layers.Dense(12, activation='sigmoid'))
     21 
---> 22 model.load_weights(top_model_weights_path)
     23 
     24 

/usr/local/lib/python3.6/dist-packages/keras/models.py in load_weights(self, filepath, by_name, skip_mismatch, reshape)
    766                                                               reshape=reshape)
    767             else:
--> 768                 topology.load_weights_from_hdf5_group(f, layers, reshape=reshape)
    769 
    770     def save_weights(self, filepath, overwrite=True):

/usr/local/lib/python3.6/dist-packages/keras/engine/topology.py in load_weights_from_hdf5_group(f, layers, reshape)
   3363                          'containing ' + str(len(layer_names)) +
   3364                          ' layers into a model with ' +
-> 3365                          str(len(filtered_layers)) + ' layers.')
   3366 
   3367     # We batch weight value assignments in a single backend call

ValueError: You are trying to load a weight file containing 245 layers into a model with 2 layers.

以下是代码：

# path to the model weights files.
weights_path = 'keras-facenet/weights/facenet_keras_weights.h5'
top_model_weights_path = 'keras-facenet/model/facenet_keras.h5'
# dimensions of our images.
img_width, img_height = 224, 224

train_data_dir = 'dataset_cfps/train'
validation_data_dir = 'dataset_cfps/validation'
nb_train_samples = 1774
nb_validation_samples = 313
epochs = 50
batch_size = 16

vggface = VGGFace(model='resnet50', include_top=False, input_shape=(img_width, img_height, 3))

# Create the model
model = models.Sequential()
model.add(layers.Flatten( input_shape=vggface.output_shape[1:]))
model.add(layers.Dense(256, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(12, activation='sigmoid'))

model.load_weights(top_model_weights_path)

custom_vgg_model = Model(vggface.input, model(vggface.output))

for layer in custom_vgg_model.layers[:-3]:
    layer.trainable = False

custom_vgg_model.compile(loss='categorical_crossentropy',
              optimizer=optimizers.SGD(lr=1e-4, momentum=0.9),
              metrics=['accuracy'])

# prepare data augmentation configuration
train_datagen = ImageDataGenerator(
    rescale=1. / 255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

test_datagen = ImageDataGenerator(rescale=1. / 255)

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode='categorical')

validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode='categorical')

custom_vgg_model.summary()

# fine-tune the model
custom_vgg_model.fit_generator(
    train_generator,
    steps_per_epoch=nb_train_samples // batch_size,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=nb_validation_samples // batch_size,
    verbose=2)

# Save the model
custom_vgg_model.save('facenet_latest_lr4.h5')

错误可能是什么问题？是否预训练和分类模型之间的层面尺寸不同？

Answer 1

我之前曾犯过此错误，尽管存在另一个数据集和体系结构。问题在于拓扑是不同的。您可以尝试以下代码：

model.load_weights('filename.h5' , by_name = True, skip_mismatch = True)

它只会加载权重对应的图层。

文档位于topology.py

中

Answer 2

首先，您尝试将权重加载到分类器模型中，而不是VGGFace模型中。在您的代码中，您将必须编写vggface.load_weights(top_model_weights_path)而不是model.load_weights(top_model_weights_path)。假设两个模型在架构上都相同，那么这应该起作用。但是，为什么您还要这么复杂呢？通过创建weights='vggface'时传递VGGFace，VGGFace似乎提供了一种简单而便捷的方法来（减少）加载预先训练的权重。除了您确实要使用keras-facenet的权重之外，您还应该采用上述方法。

此外，VGGFace模型似乎还没有与您的自定义分类器关联。我不确定是否可以使用顺序模型来实现。可能您需要使用functional API并使用vggface来调用分类器的第一层。

还请注意，使用model(vggface.output)调用VGGFace模型，将您的自定义模型作为输入，而不是相反。

从keras中的预训练模型加载权重进行微调时，图层出错

2 个答案: