自学教程：从VGG19的任意中间层中抽取特征

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(input=base_model.input, output=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)

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利用新数据集finetune 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(input=base_model.input, output=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(...)

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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_dim_ordering() == 'tf'

model = InceptionV3(input_tensor=input_tensor, weights='imagenet', include_top=True)

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模型文档

Xception V1 模型, 权重由ImageNet训练而言

在ImageNet上,该模型取得了验证集top1 0.790和top5 0.945的正确率

注意,该模型目前仅能以TensorFlow为后端使用,由于它依赖于"SeparableConvolution"层,目前该模型只支持tf的维度顺序(width, height, channels)

默认输入图片大小为299x299

参数

• include_top：是否保留顶层的3个全连接网络
• weights：None代表随机初始化，即不加载预训练权重。'imagenet’代表加载预训练权重
• input_tensor：可填入Keras tensor作为模型的图像输出tensor
• input_shape: 可选参数，仅当include_top=False时才应指定该参数。input_shape须为长3的tuple，图片的宽和高不得小于71.

返回值

Keras 模型对象

参考文献

• Xception: Deep Learning with Depthwise Separable Convolutions

License

预训练权重由我们自己训练而来，基于MIT license发布

VGG16模型,权重由ImageNet训练而来

该模型再Theano和TensorFlow后端均可使用,并接受th和tf两种输入维度顺序

模型的默认输入尺寸时224x224

参数

• include_top：是否保留顶层的3个全连接网络
• weights：None代表随机初始化，即不加载预训练权重。'imagenet’代表加载预训练权重
• input_tensor：可填入Keras tensor作为模型的图像输出tensor
• input_shape: 可选参数，仅当include_top=False时才应指定该参数。input_shape须为长3的tuple，维度顺序依赖于image_dim_ordering，图片的宽和高不得小于48.

返回值

Keras 模型对象

参考文献

• Very Deep Convolutional Networks for Large-Scale Image Recognition：如果在研究中使用了VGG，请引用该文

License

预训练权重由牛津VGG组发布的预训练权重移植而来，基于Creative Commons Attribution License

该模型再Theano和TensorFlow后端均可使用,并接受th和tf两种输入维度顺序

模型的默认输入尺寸时224x224

参数

• include_top：是否保留顶层的3个全连接网络
• weights：None代表随机初始化，即不加载预训练权重。'imagenet’代表加载预训练权重
• input_tensor：可填入Keras tensor作为模型的图像输出tensor
• input_shape: 可选参数，仅当include_top=False时才应指定该参数。input_shape须为长3的tuple，维度顺序依赖于image_dim_ordering，图片的宽和高不得小于48.

返回值

Keras 模型对象

参考文献

• Very Deep Convolutional Networks for Large-Scale Image Recognition：如果在研究中使用了VGG，请引用该文

License

预训练权重由牛津VGG组发布的预训练权重移植而来，基于Creative Commons Attribution License

50层残差网络模型,权重训练自ImageNet

该模型再Theano和TensorFlow后端均可使用,并接受th和tf两种输入维度顺序

模型的默认输入尺寸时224x224

参数

• include_top：是否保留顶层的全连接网络
• weights：None代表随机初始化，即不加载预训练权重。'imagenet’代表加载预训练权重
• input_tensor：可填入Keras tensor作为模型的图像输出tensor
• input_shape: 可选参数，仅当include_top=False时才应指定该参数。input_shape须为长3的tuple，维度顺序依赖于image_dim_ordering，图片的宽和高不得小于197.

返回值

Keras 模型对象

参考文献

• Deep Residual Learning for Image Recognition：如果在研究中使用了ResNet50，请引用该文

License

预训练权重由Kaiming He发布的预训练权重移植而来，基于MIT License

该模型再Theano和TensorFlow后端均可使用,并接受th和tf两种输入维度顺序

模型的默认输入尺寸时299x299

参数

• include_top：是否保留顶层的全连接网络
• weights：None代表随机初始化，即不加载预训练权重。'imagenet’代表加载预训练权重
• input_tensor：可填入Keras tensor作为模型的图像输出tensor
• input_shape: 可选参数，仅当include_top=False时才应指定该参数。input_shape须为长3的tuple，其维度顺序依赖于所使用的image_dim_ordering，图片的宽和高不得小于139.

返回值

Keras 模型对象

参考文献

• Rethinking the Inception Architecture for Computer Vision：如果在研究中使用了InceptionV3，请引用该文

License

预训练权重由我们自己训练而来，基于MIT License

返回值

Keras 模型对象

参考文献

• Rethinking the Inception Architecture for Computer Vision：如果在研究中使用了InceptionV3，请引用该文

License

预训练权重由我们自己训练而来，基于MIT License

引用

https://github.com/MoyanZitto/keras-cn/blob/master/docs/other/application.md~