我已经在小型数据集中训练并保存了一个较小的网络，我想使用转移学习。
我想在预先训练的VGG16的conv部分上使用此保存的网络，特别是我想冻结VGG的某些层，但不是全部，然后我要使用已经在较小数据集上训练过的fc，并学习结合了转移的权重的模型。
我正在跟踪很多教程：https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html和https://machinelearningmastery.com/how-to-develop-a-convolutional-neural-network-to-classify-photos-of-dogs-and-cats/我不仅想使用预先训练的功能，而且我不想只是在VGG的转换网络中添加两个新层就像我提到的，我想转移较小网络的fc层并冻结所有conv层块，但冻结其中一个VGG，然后再次训练。下面是我的代码，但是我得到一个错误（无论我如何尝试更改代码，我都会遇到类似的错误）
from keras import applications
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
from keras.models import Sequential, Model
from keras.layers import Dropout, Flatten, Dense
# path to the model weights files.
weights_path = '/home/d/Desktop/s/vgg16_weights.h5'
top_model_weights_path = '/home/d/Desktop/s/model_weights.h5'
# dimensions of our images.
img_width, img_height = 256, 256
# build the VGG16 network
base_model = applications.VGG16(weights='imagenet', include_top=False, input_shape=(256, 256, 3))
print('Model loaded.')
# set the first 25 layers (up to the last conv block)
# to non-trainable (weights will not be updated)
for layer in base_model.layers[:15]:
layer.trainable = False
# build a classifier model to put on top of the convolutional model
top_model = Sequential()
top_model.add(Flatten(input_shape=base_model.output_shape[1:]))
top_model.add(Dense(256, activation='relu'))
top_model.add(Dropout(0.5))
top_model.add(Dense(1, activation='sigmoid'))
# note that it is necessary to start with a fully-trained
# classifier, including the top classifier,
# in order to successfully do fine-tuning
top_model.load_weights(top_model_weights_path)
model= Model(inputs=base_model.input, outputs=top_model(base_model.output))
# add the model on top of the convolutional base
#model.add(top_model)
print(top_model.summary())
# compile the model with a SGD/momentum optimizer
# and a very slow learning rate.
model.compile(loss='binary_crossentropy',
optimizer=optimizers.SGD(lr=1e-4, momentum=0.9),
metrics=['accuracy'])
import matplotlib.pyplot as plt
from keras.preprocessing.image import ImageDataGenerator
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
train_dir = '/home/d/Desktop/s/data/train'
eval_dir = '/home/d/Desktop/s/data/eval'
test_dir = '/home/d/Desktop/s/data/test'
# create a data generator
train_datagen = ImageDataGenerator(rescale=1./255, #Scale the image between 0 and 1
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,)
val_datagen = ImageDataGenerator(rescale=1./255) #We do not augment validation data. we only perform rescale
test_datagen = ImageDataGenerator(rescale=1./255) #We do not augment validation data. we only perform rescale
# load and iterate training dataset
train_generator = train_datagen.flow_from_directory(train_dir, class_mode='binary', batch_size=16, shuffle='True', seed=42)
# load and iterate validation dataset
val_generator = val_datagen.flow_from_directory(eval_dir, class_mode='binary', batch_size=16, shuffle='True', seed=42)
# load and iterate test dataset
test_generator = test_datagen.flow_from_directory(test_dir, class_mode=None, batch_size=1, shuffle='False', seed=42)
#The training part
#We train for 64 epochs with about 100 steps per epoch
history = model.fit_generator(train_generator,
steps_per_epoch=train_generator.n // train_generator.batch_size,
epochs=6,
validation_data=val_generator,
validation_steps=val_generator.n // val_generator.batch_size)
我得到的错误是：
Model loaded.
Traceback (most recent call last):
File "/home/d/Desktop/s/transferLearningS.py", line 33, in <module>
top_model.load_weights(top_model_weights_path)
File "/usr/local/lib/python3.5/dist-packages/keras/engine/network.py", line 1166, in load_weights
f, self.layers, reshape=reshape)
File "/usr/local/lib/python3.5/dist-packages/keras/engine/saving.py", line 1030, in load_weights_from_hdf5_group
str(len(filtered_layers)) + ' layers.')
ValueError: You are trying to load a weight file containing 6 layers into a model with 2 layers.
我的小型网络是这样建立的：
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', input_shape=(256, 256, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(Dropout(0.2))
model.add(layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(Dropout(0.2))
model.add(layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(Dropout(0.2))
model.add(layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(Dropout(0.2))
model.add(layers.Flatten())
model.add(layers.Dropout(0.5)) #Dropout for regularization
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid')) #Sigmoid function at the end because we have just two classes
任何建议如何解决此问题？