Keras —— 应用模型_keras 模型应用

Keras应用是可用的具有预训练权重的深度学习模型。这些模型可用于预测，特征提取和细调。

实例化模型时权重自动下载，储存在~/.keras/models/

可用模型

在ImageNet上预训练权重的图像分类模型有：

-Xception

-VGG16

-VGG19

-ResNet50

-InceptionV3

Xception模型只有TensorFlow版，因为它依赖于SeparableConvolution层。其他模型有TensorFlow和Theano两个版本。

图像分类模型使用举例

使用ResNet50分类图像

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)]

使用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任意中间层提取特征

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)

在新类别上细调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 172 layers and unfreeze the rest:
for layer in model.layers[:172]:
   layer.trainable = False
for layer in model.layers[172:]:
   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(...)

在定制输入张量上构建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)

模型文档

Xception

keras.applications.xception.Xception(include_top=True, weights='imagenet', input_tensor=None, input_shape=None)

在ImageNet上，模型top-1验证准确率为0.790，top-5验证准确率0.945。

注意，由于依赖SeparableConvolution层，该模型只支持TensorFlow后端。此外只支持数据格式"channel_last"（高度、宽度、通道）

默认输入大小为299*299

VGG16

keras.applications.vgg16.VGG16(include_top=True, weights='imagenet', input_tensor=None, input_shape=None)

默认输入大小为224*224

VGG19

keras.applications.vgg19.VGG19(include_top=True, weights='imagenet', input_tensor=None, input_shape=None)

默认输入大小为224*224

ResNet50

keras.applications.resnet50.ResNet50(include_top=True, weights='imagenet', input_tensor=None, input_shape=None)

默认输入大小为224*224

InceptionV3

keras.applications.inception_v3.InceptionV3(include_top=True, weights='imagenet', input_tensor=None, input_shape=None)

默认输入大小为299*299