Kera的应用模块Application提供了带有预训练权重的Keras模型，这些模型可以用来进行预测、特征提取和finetune
模型的预训练权重将下载到~/.keras/models/并在载入模型时自动载入
可用的模型
应用于图像分类的模型,权重训练自ImageNet： Xception VGG16 VGG19 ResNet50 InceptionV3InceptionResNetV2 MobileNet DenseNet NasNet MobileNetV2
所有的这些模型(除了Xception和MobileNet)都兼容Theano和Tensorflow，并会自动基于~/.keras/keras.json的Keras的图像维度进行自动设置。例如，如果你设置data_format="channel_last"，则加载的模型将按照TensorFlow的维度顺序来构造，即“Width-Height-Depth”的顺序
Xception模型仅在TensorFlow下可用，因为它依赖的SeparableConvolution层仅在TensorFlow可用。MobileNet仅在TensorFlow下可用，因为它依赖的DepethwiseConvolution层仅在TF下可用。
以上模型（暂时除了MobileNet）的预训练权重可以在我的百度网盘下载，如果有更新的话会在这里报告
图片分类模型的示例
利用ResNet50网络进行ImageNet分类
from keras.applications.resnet50 import ResNet50
from keras.preprocessing import image
from keras.applications.resnet50 import preprocess_input, decode_predictions
import numpy as np
model = ResNet50(weights='imagenet')
img_path = 'elephant.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
preds = model.predict(x)
# decode the results into a list of tuples (class, description, probability)
# (one such list for each sample in the batch)
print('Predicted:', decode_predictions(preds, top=3)[0])
# Predicted: [(u'n02504013', u'Indian_elephant', 0.82658225), (u'n01871265', u'tusker', 0.1122357), (u'n02504458', u'African_elephant', 0.061040461)]
利用VGG16提取特征
from keras.applications.vgg16 import VGG16
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input
import numpy as np
model = VGG16(weights='imagenet', include_top=False)
img_path = 'elephant.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
features = model.predict(x)
从VGG19的任意中间层中抽取特征
from keras.applications.vgg19 import VGG19
from keras.preprocessing import image
from keras.applications.vgg19 import preprocess_input
from keras.models import Model
import numpy as np
base_model = VGG19(weights='imagenet')
model = Model(inputs=base_model.input, outputs=base_model.get_layer('block4_pool').output)
img_path = 'elephant.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
block4_pool_features = model.predict(x)
在新类别上fine-tune inceptionV3
from keras.applications.inception_v3 import InceptionV3
from keras.preprocessing import image
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras import backend as K
# create the base pre-trained model
base_model = InceptionV3(weights='imagenet', include_top=False)
# add a global spatial average pooling layer
x = base_model.output
x = GlobalAveragePooling2D()(x)
# let's add a fully-connected layer
x = Dense(1024, activation='relu')(x)
# and a logistic layer -- let's say we have 200 classes
predictions = Dense(200, activation='softmax')(x)
# this is the model we will train
model = Model(inputs=base_model.input, outputs=predictions)
# first: train only the top layers (which were randomly initialized)
# i.e. freeze all convolutional InceptionV3 layers
for layer in base_model.layers:
layer.trainable = False
# compile the model (should be done *after* setting layers to non-trainable)
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
# train the model on the new data for a few epochs
model.fit_generator(...)
# at this point, the top layers are well trained and we can start fine-tuning
# convolutional layers from inception V3. We will freeze the bottom N layers
# and train the remaining top layers.
# let's visualize layer names and layer indices to see how many layers
# we should freeze:
for i, layer in enumerate(base_model.layers):
print(i, layer.name)
# we chose to train the top 2 inception blocks, i.e. we will freeze
# the first 249 layers and unfreeze the rest:
for layer in model.layers[:249]:
layer.trainable = False
for layer in model.layers[249:]:
layer.trainable = True
# we need to recompile the model for these modifications to take effect
# we use SGD with a low learning rate
from keras.optimizers import SGD
model.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy')
# we train our model again (this time fine-tuning the top 2 inception blocks
# alongside the top Dense layers
model.fit_generator(...)
在定制的输入tensor上构建InceptionV3
from keras.applications.inception_v3 import InceptionV3
from keras.layers import Input
# this could also be the output a different Keras model or layer
input_tensor = Input(shape=(224, 224, 3)) # this assumes K.image_data_format() == 'channels_last'
model = InceptionV3(input_tensor=input_tensor, weights='imagenet', include_top=True)
模型信息
| 模型 | 大小 | Top1准确率 | Top5准确率 | 参数数目 | 深度 |
|---|---|---|---|---|---|
| Xception | 88MB | 0.790 | 0.945 | 22,910,480 | 126 |
| VGG16 | 528MB | 0.715 | 0.901 | 138,357,544 | 23 |
| VGG19 | 549MB | 0.727 | 0.910 | 143,667,240 | 26 |
| ResNet50 | 99MB | 0.759 | 0.929 | 25,636,712 | 168 |
| InceptionV3 | 92MB | 0.788 | 0.944 | 23,851,784 | 159 |
| IncetionResNetV2 | 215MB | 0.804 | 0.953 | 55,873,736 | 572 |
| MobileNet | 17MB | 0.665 | 0.871 | 4,253,864 | 88 |
| MobileNetV2 | 14MB | 0.713 | 0.901 | 3,538,984 | 88 |
| DenseNet121 | 33MB | 0.750 | 0.923 | 8,062,504 | 121 |
| DenseNet169 | 57MB | 0.762 | 0.932 | 14,307,880 | 169 |
| DenseNet201 | 80MB | 0.773 | 0.936 | 20,242,984 | 201 |
| NASNetMobile | 23MB | 0.744 | 0.919 | 5,326,716 | - |
| NASNetLarge | 343MB | 0.825 | 0.960 | 88,949,818 | - |
引：https://keras-cn.readthedocs.io/en/latest/other/application/