引自：http://blog.csdn.net/sinat_26917383/article/details/72859145

中文文档：http://keras-cn.readthedocs.io/en/latest/ 
官方文档：https://keras.io/ 
文档主要是以keras2.0。 
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Keras系列：

1、keras系列︱Sequential与Model模型、keras基本结构功能（一） 
2、keras系列︱Application中五款已训练模型、VGG16框架（Sequential式、Model式）解读（二） 
3、keras系列︱图像多分类训练与利用bottleneck features进行微调（三） 
4、keras系列︱人脸表情分类与识别：opencv人脸检测+Keras情绪分类（四） 
5、keras系列︱迁移学习：利用InceptionV3进行fine-tuning及预测、完整案例（五）

一、Application的五款已训练模型 + H5py简述

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。 
后续还有对以下几个模型的参数介绍：

• 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。

模型的官方下载路径：https://github.com/fchollet/deep-learning-models/releases

其中： 
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1、th与tf的区别

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Keras提供了两套后端，Theano和Tensorflow， 
th和tf的大部分功能都被backend统一包装起来了，但二者还是存在不小的冲突，有时候你需要特别注意Keras是运行在哪种后端之上，它们的主要冲突有：

dim_ordering，也就是维度顺序。比方说一张224*224的彩色图片，theano的维度顺序是(3，224，224)，即通道维在前。而tf的维度顺序是(224，224，3)，即通道维在后。 
卷积层权重的shape：从无到有训练一个网络，不会有任何问题。但是如果你想把一个th训练出来的卷积层权重载入风格为tf的卷积层……说多了都是泪。我一直觉得这个是个bug，数据的dim_ordering有问题就罢了，为啥卷积层权重的shape还需要变换咧？我迟早要提个PR把这个bug修掉！ 
然后是卷积层kernel的翻转不翻转问题，这个我们说过很多次了，就不再多提。 
数据格式的区别，channels_last”对应原本的“tf”，“channels_first”对应原本的“th”。 
以128x128的RGB图像为例，“channels_first”应将数据组织为（3,128,128），而“channels_last”应将数据组织为（128,128,3）。 
譬如： 
vgg16_weights_th_dim_ordering_th_kernels_notop.h5 
vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5 
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2、notop模型是指什么？

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是否包含最后的3个全连接层（whether to include the 3 fully-connected layers at the top of the network）。用来做fine-tuning专用，专门开源了这类模型。 
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3、H5py简述

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keras的已训练模型是H5PY格式的，不是caffe的.caffemodel 
h5py.File类似Python的词典对象，因此我们可以查看所有的键值： 
读入

file=h5py.File('.../notop.h5','r')

f.attrs['nb_layers'],代表f的属性，其中有一个属性为'nb_layers'

>>> f.keys()
[u'block1_conv1', u'block1_conv2', u'block1_pool', u'block2_conv1', u'block2_conv2', u'block2_pool', u'block3_conv1', u'block3_conv2', u'block3_conv3', u'block3_pool', u'block4_conv1', u'block4_conv2', u'block4_conv3', u'block4_pool', u'block5_conv1', u'block5_conv2', u'block5_conv3', u'block5_pool']

可以看到f中各个层内有些什么。

for name in f:print(name)# 类似f.keys()

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4、官方案例——利用ResNet50网络进行ImageNet分类

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rom keras.applications.resnet50 import ResNet50
from keras.preprocessing import image
from keras.applications.resnet50 import preprocess_input, decode_predictions
import numpy as npmodel = 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) 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)]

还有的案例可见Keras官方文档

利用VGG16提取特征、从VGG19的任意中间层中抽取特征、在定制的输入tensor上构建InceptionV3

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5、调用参数解释

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以下几类，因为调用好像都是从网站下载权重，所以可以自己修改一下源码，让其可以读取本地H5文件。

Xception模型

ImageNet上,该模型取得了验证集top1 0.790和top5 0.945的正确率; 
,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channels_last的维度顺序(width, height, channels)

默认输入图片大小为299x299

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

VGG16模型

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channels_first和channels_last两种输入维度顺序

模型的默认输入尺寸时224x224

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

VGG19模型

VGG19模型,权重由ImageNet训练而来

该模型在Theano和TensorFlow后端均可使用,并接受channels_first和channels_last两种输入维度顺序

模型的默认输入尺寸时224x224

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

ResNet50模型

50层残差网络模型,权重训练自ImageNet

该模型在Theano和TensorFlow后端均可使用,并接受channels_first和channels_last两种输入维度顺序

模型的默认输入尺寸时224x224

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

InceptionV3模型

InceptionV3网络,权重训练自ImageNet

该模型在Theano和TensorFlow后端均可使用,并接受channels_first和channels_last两种输入维度顺序

模型的默认输入尺寸时299x299

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

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二、 keras-applications-VGG16解读——函数式

.py文件来源于：https://github.com/fchollet/deep-learning-models/blob/master/vgg16.py 
VGG16默认的输入数据格式应该是：channels_last

# -*- coding: utf-8 -*-
'''VGG16 model for Keras.
# Reference:
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556)
'''
from __future__ import print_functionimport numpy as np import warnings from keras.models import Model from keras.layers import Flatten from keras.layers import Dense from keras.layers import Input from keras.layers import Conv2D from keras.layers import MaxPooling2D from keras.layers import GlobalMaxPooling2D from keras.layers import GlobalAveragePooling2D from keras.preprocessing import image from keras.utils import layer_utils from keras.utils.data_utils import get_file from keras import backend as K from keras.applications.imagenet_utils import decode_predictions # decode_predictions 输出5个最高概率：(类名, 语义概念, 预测概率) decode_predictions(y_pred) from keras.applications.imagenet_utils import preprocess_input # 预处理 图像编码服从规定，譬如,RGB，GBR这一类的，preprocess_input(x) from keras.applications.imagenet_utils import _obtain_input_shape # 确定适当的输入形状，相当于opencv中的read.img，将图像变为数组 from keras.engine.topology import get_source_inputs WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5' WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5' def VGG16(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000): # 检查weight与分类设置是否正确 if weights not in {'imagenet', None}: raise ValueError('The `weights` argument should be either ' '`None` (random initialization) or `imagenet` ' '(pre-training on ImageNet).') if weights == 'imagenet' and include_top and classes != 1000: raise ValueError('If using `weights` as imagenet with `include_top`' ' as true, `classes` should be 1000') # 设置图像尺寸，类似caffe中的transform # Determine proper input shape input_shape = _obtain_input_shape(input_shape, default_size=224, min_size=48, # 模型所能接受的最小长宽 data_format=K.image_data_format(), # 数据的使用格式 include_top=include_top) #是否通过一个Flatten层再连接到分类器 # 数据简单处理，resize if input_tensor is None: img_input = Input(shape=input_shape) # 这里的Input是keras的格式，可以用于转换 else: if not K.is_keras_tensor(input_tensor): img_input = Input(tensor=input_tensor, shape=input_shape) else: img_input = input_tensor # 如果是tensor的数据格式，需要两步走： # 先判断是否是keras指定的数据类型，is_keras_tensor # 然后get_source_inputs(input_tensor) # 编写网络结构，prototxt # Block 1 x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input) x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x) # Block 2 x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x) x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x) # Block 3 x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x) x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x) x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x) # Block 4 x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x) x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x) x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x) # Block 5 x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x) x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x) x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x) if include_top: # Classification block x = Flatten(name='flatten')(x) x = Dense(4096, activation='relu', name='fc1')(x) x = Dense(4096, activation='relu', name='fc2')(x) x = Dense(classes, activation='softmax', name='predictions')(x) else: if pooling == 'avg': x = GlobalAveragePooling2D()(x) elif pooling == 'max': x = GlobalMaxPooling2D()(x) # 调整数据 # Ensure that the model takes into account # any potential predecessors of `input_tensor`. if input_tensor is not None: inputs = get_source_inputs(input_tensor) # get_source_inputs 返回计算需要的数据列表，List of input tensors. # 如果是tensor的数据格式，需要两步走： # 先判断是否是keras指定的数据类型，is_keras_tensor # 然后get_source_inputs(input_tensor) else: inputs = img_input # 创建模型 # Create model. model = Model(inputs, x, name='vgg16') # 加载权重 # load weights if weights == 'imagenet': if include_top: weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels.h5', WEIGHTS_PATH, cache_subdir='models') else: weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5', WEIGHTS_PATH_NO_TOP, cache_subdir='models') model.load_weights(weights_path) if K.backend() == 'theano': layer_utils.convert_all_kernels_in_model(model) if K.image_data_format() == 'channels_first': if include_top: maxpool = model.get_layer(name='block5_pool') shape = maxpool.output_shape[