如何在加载 vgg16_weights.h5 时使用 vgg-net？

Question

我使用 keras 的 VGG-16 网络。这是 detail

我的问题是如何使用这个网进行微调，这个网必须使用224*224的图像尺寸吗？而且我用这个网一定要用1000类？如果我不使用 1000 类，它会导致错误

Exception: Layer shape (4096L, 10L) not compatible with weight shape (4096, 1000).

求助，谢谢！

Answer 1

我发了一个详细的回答in this issue如果你想看的话。以下代码段将帮助您确定最后一层的尺寸：

from keras.models import Sequential, Graph
from keras.layers import Convolution2D, ZeroPadding2D, MaxPooling2D
import keras.backend as K

img_width, img_height = 128, 128

# build the VGG16 network with our input_img as input
first_layer = ZeroPadding2D((1, 1), input_shape=(3, img_width, img_height))

model = Sequential()
model.add(first_layer)
model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_2'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))

model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_2'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))

model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_2'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))

model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_2'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))

model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_2'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))

# get the symbolic outputs of each "key" layer (we gave them unique names).
layer_dict = dict([(layer.name, layer) for layer in model.layers])

# load the weights

import h5py

weights_path = 'vgg16_weights.h5'

f = h5py.File(weights_path)
for k in range(f.attrs['nb_layers']):
    if k >= len(model.layers):
        # we don't look at the last (fully-connected) layers in the savefile
        break
    g = f['layer_{}'.format(k)]
    weights = [g['param_{}'.format(p)] for p in range(g.attrs['nb_params'])]
    model.layers[k].set_weights(weights)
f.close()
print('Model loaded.')

# Here is what you want:

graph_m = Graph()
graph_m.add_input('my_inp', input_shape=(3, img_width, img_height))
graph_m.add_node(model, name='your_model', input='my_inp')
graph_m.add_node(Flatten(), name='Flatten', input='your_model')
graph_m.add_node(Dense(4096, activation='relu'), name='Dense1',      input='Flatten')
graph_m.add_node(Dropout(0.5), name='Dropout1', input='Dense1')
graph_m.add_node(Dense(4096, activation='relu'), name='Dense2',  input='Dropout1')
graph_m.add_node(Dropout(0.5), name='Dropout2', input='Dense2')
graph_m.add_node(Dense(10, activation='softmax'), name='Final', input='Dropout2')
graph_m.add_output(name='out1', input='Final')
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
graph_m.compile(optimizer=sgd, loss={'out1': 'categorical_crossentropy'})

请注意，您可以冻结特征提取层的训练，只微调最后的全连接层。从 doc 开始，您只需添加 trainable = False 即可冻结一层的训练。 Ex 冻结：

...
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_1', trainable=False))
...

Ex 可训练：

...
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_1',     trainable=True))
...

trainable 默认为 True，因此如果您不了解该功能，就会发生一些事情...

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