Application应用

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 * InceptionV3

所有的这些模型(除了Xception)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image_dim_ordering=tf，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

应用于音乐自动标签(以Mel-spectrograms为输入)

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(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)

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(...)

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)

模型文档

Xception模型

keras.applications.xception.Xception(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, classes=1000)

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，仅当include_top=False有效，应为长为3的tuple，指明输入图片的shape，图片的宽高必须大于71，如(150,150,3)
  • classes：可选，图片分类的类别数，仅当include_top=True并且不加载预训练权重时可用。

返回值

Keras 模型对象

参考文献

License

预训练权重由我们自己训练而来，基于MIT license发布

VGG16模型

keras.applications.vgg16.VGG16(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, classes=1000)

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，仅当include_top=False有效，应为长为3的tuple，指明输入图片的shape，图片的宽高必须大于48，如(200,200,3)

返回值

  • classes：可选，图片分类的类别数，仅当include_top=True并且不加载预训练权重时可用。

Keras 模型对象

参考文献

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

VGG19模型

keras.applications.vgg19.VGG19(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, classes=1000)

VGG19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，仅当include_top=False有效，应为长为3的tuple，指明输入图片的shape，图片的宽高必须大于48，如(200,200,3)
  • classes：可选，图片分类的类别数，仅当include_top=True并且不加载预训练权重时可用。

返回值

返回值

Keras 模型对象

参考文献

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

ResNet50模型

keras.applications.resnet50.ResNet50(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, classes=1000)

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，仅当include_top=False有效，应为长为3的tuple，指明输入图片的shape，图片的宽高必须大于197，如(200,200,3)
  • classes：可选，图片分类的类别数，仅当include_top=True并且不加载预训练权重时可用。

返回值

Keras 模型对象

参考文献

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

InceptionV3模型

keras.applications.inception_v3.InceptionV3(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, classes=1000)

InceptionV3网络,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
  • classes：可选，图片分类的类别数，仅当include_top=True并且不加载预训练权重时可用。

返回值

Keras 模型对象

参考文献

License

预训练权重由我们自己训练而来，基于MIT License

MusicTaggerCRNN模型

keras.applications.music_tagger_crnn.MusicTaggerCRNN(weights='msd', input_tensor=None, include_top=True, classes=50)

该模型时一个卷积循环模型,以向量化的MelSpectrogram音乐数据为输入,能够输出音乐的风格. 你可以用keras.applications.music_tagger_crnn.preprocess_input来将一个音乐文件向量化为spectrogram.注意,使用该功能需要安装Librosa,请参考下面的使用范例.

参数

  • include_top：是否保留顶层的1层全连接网络,若设置为False,则网络输出32维的特征
  • weights：None代表随机初始化，即不加载预训练权重。'msd'代表加载预训练权重(训练自Million Song Dataset)
  • input_tensor：可填入Keras tensor作为模型的输出tensor,如使用layer.input选用一层的输入张量为模型的输入张量.

返回值

Keras 模型对象

参考文献

License

预训练权重由我们自己训练而来，基于MIT License

使用范例:音乐特征抽取与风格标定

from keras.applications.music_tagger_crnn import MusicTaggerCRNN
from keras.applications.music_tagger_crnn import preprocess_input, decode_predictions
import numpy as np

# 1. Tagging
model = MusicTaggerCRNN(weights='msd')

audio_path = 'audio_file.mp3'
melgram = preprocess_input(audio_path)
melgrams = np.expand_dims(melgram, axis=0)

preds = model.predict(melgrams)
print('Predicted:')
print(decode_predictions(preds))
# print: ('Predicted:', [[('rock', 0.097071797), ('pop', 0.042456303), ('alternative', 0.032439161), ('indie', 0.024491295), ('female vocalists', 0.016455274)]])

#. 2. Feature extraction
model = MusicTaggerCRNN(weights='msd', include_top=False)

audio_path = 'audio_file.mp3'
melgram = preprocess_input(audio_path)
melgrams = np.expand_dims(melgram, axis=0)

feats = model.predict(melgrams)
print('Features:')
print(feats[0, :10])
# print: ('Features:', [-0.19160545 0.94259131 -0.9991011 0.47644514 -0.19089699 0.99033844 0.1103896 -0.00340496 0.14823607 0.59856361])