原文链接：http://www.one2know.cn/keras3/
Application的五款已训练模型 + H5py简述
- Keras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 - th与tf的区别
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。
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） - notop模型
是否包含最后的3个全连接层，用来做微调（fine-tuning）专用，专门开源了这类模型 - H5py简述
keras的已训练模型是H5PY格式的，后缀是h5
h5py.File类似Python的词典对象，因此我们可以查看所有的键值
输入：import h5pyfile=h5py.File('.../notop.h5','r')
查看键值：f = file.attrs['nb_layers']f.key()
查看到f中各个层内有些什么：
for name in f:
print(name)
- 官方案例：利用ResNet50网络进行ImageNet分类
识别大象的品种：
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=r'..Modelresnet50_weights_tf_dim_ordering_tf_kernels.h5')
img_path = 'elephant.jpg'
img = image.load_img(img_path,target_size=(224,224))
# 现有模型输入shape为 (224, 224, 3)
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])
输出：
Predicted: [('n02504458', 'African_elephant', 0.603124), ('n02504013', 'Indian_elephant', 0.334439), ('n01871265', 'tusker', 0.062180385)]
- 五个模型
1.Xception模型：仅能以TensorFlow为后端使用，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299x299keras.applications.xception.Xception(include_top=True,weights='imagenet',input_tensor=None, input_shape=None,pooling=None, classes=1000)
2.VGG16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
默认输入图片大小为224x224keras.applications.vgg16.VGG16(include_top=True, weights='imagenet',input_tensor=None, input_shape=None,pooling=None,classes=1000)
3.VGG19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
默认输入图片大小为224x224keras.applications.vgg19.VGG19(include_top=True, weights='imagenet', input_tensor=None, input_shape=None,pooling=None,classes=1000)
4.ResNet50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
默认输入图片大小为224x224keras.applications.resnet50.ResNet50(include_top=True,weights='imagenet',input_tensor=None, input_shape=None,pooling=None,classes=1000)
5.InceptionV3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
默认输入图片大小为299x299keras.applications.inception_v3.InceptionV3(include_top=True,weights='imagenet',input_tensor=None,input_shape=None,pooling=None,classes=1000)
keras-applications-VGG16解读：函数式
- VGG16默认的输入数据格式应该是：channels_last
from __future__ import print_function
import numpy as np
import warnings
from keras.models import Model
from keras.layers import Flatten,Dense,Input,Conv2D
from keras.layers import MaxPooling2D,GlobalMaxPooling2D,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
require_flatten=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[1:]
dense = model.get_layer(name='fc1')
layer_utils.convert_dense_weights_data_format(dense, shape, 'channels_first')
if K.backend() == 'tensorflow':
warnings.warn('You are using the TensorFlow backend, yet you '
'are using the Theano '
'image data format convention '
'(`image_data_format="channels_first"`). '
'For best performance, set '
'`image_data_format="channels_last"` in '
'your Keras config '
'at ~/.keras/keras.json.')
return model
if __name__ == '__main__':
model = VGG16(include_top=True, 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)
print('Input image shape:', x.shape)
preds = model.predict(x)
print('Predicted:', decode_predictions(preds))
# decode_predictions 输出5个最高概率：(类名, 语义概念, 预测概率)
输出：
Input image shape: (1, 224, 224, 3)
Predicted: [[('n02504458', 'African_elephant', 0.62728244), ('n02504013', 'Indian_elephant', 0.19092941), ('n01871265', 'tusker', 0.18166111), ('n02437312', 'Arabian_camel', 4.5080957e-05), ('n07802026', 'hay', 1.7709652e-05)]]
- 将model下载到本地，修改下载的代码
注释掉下面两行：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'
修改下面两行：weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels.h5',WEIGHTS_PATH,cache_subdir='models')weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5',WEIGHTS_PATH_NO_TOP,cache_subdir='models') - 几个layer中的新用法
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
（1）decode_predictions用在最后输出结果上，比较好用【print(‘Predicted:’, decode_predictions(preds))】；
（2）preprocess_input，改变编码，【preprocess_input(x)】；
（3）_obtain_input_shape
相当于caffe中的transform，在预测的时候，需要对预测的图片进行一定的预处理。input_shape = _obtain_input_shape(input_shape,default_size=224,min_size=48,data_format=K.image_data_format(),include_top=include_top)
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 - 当include_top=True时
fc_model = VGG16(include_top=True)notop_model = VGG16(include_top=False)
用VGG16做fine-tuning的时候，得到的notop_model就是没有全连接层的模型，然后再去添加自己的层。
当是健全的网络结构的时候，fc_model需要添加以下的内容以补全网络结构：
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)
pool层之后接一个flatten层，修改数据格式，然后接两个dense层，最后有softmax的Dense层
- channels_first转成channels_last格式
maxpool = model.get_layer(name='block5_pool')
# model.get_layer()依据层名或下标获得层对象
shape = maxpool.output_shape[1:]
# 获取block5_pool层输出的数据格式
dense = model.get_layer(name='fc1')
layer_utils.convert_dense_weights_data_format(dense, shape, 'channels_first')
convert_dense_weights_data_format将convnet的权重从一种数据格式移植到另一种数据格式时，如果convnet包含一个平坦层（应用于最后一个卷积特征映射），然后是一个密集层，则应更新该密集层的权重，以反映新的维度顺序。
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