概述
1.vgg16
# coding=utf-8
"""
__project_ = 'Backbone'
__file_name__ = 'VGG16'
__author__ = 'WIN10'
__time__ = '2020/5/26 11:44'
__product_name = PyCharm
"""
import numpy as np
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 import backend as K
from keras.engine.topology import get_source_inputs
from keras.utils.data_utils import get_file
from keras.applications.imagenet_utils import decode_predictions
from keras.applications.imagenet_utils import preprocess_input
from keras_applications.imagenet_utils import _obtain_input_shape
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):
# Determine proper input shape
input_shape = _obtain_input_shape(input_shape,
default_size=224,
min_size=48,
data_format=K.image_data_format(),
require_flatten=include_top)
if input_tensor is None:
img_input=Input(shape=input_shape)
else:
if not K.is_keras_tensor(input_tensor):
img_input=Input(tensor=input_tensor,shape=input_shape)
else:
img_input=input_tensor
#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)
#block2
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)
#block3
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)
#block4
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)
#block5
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:
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)
if input_tensor is not None:
inputs=get_source_inputs(input_tensor)
else:
inputs=img_input
model=Model(inputs,x,name='vgg16')
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)
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))
2.resnet50
(残差模块)
# coding=utf-8
"""
__project_ = 'Backbone'
__file_name__ = 'resnet50'
__author__ = 'WIN10'
__time__ = '2020/5/26 14:06'
__product_name = PyCharm
"""
import keras.backend as K
from keras.layers import Input
from keras import layers
from keras.layers import Dense
from keras.layers import Activation
from keras.layers import Flatten
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import GlobalMaxPooling2D
from keras.layers import ZeroPadding2D
from keras.layers import AveragePooling2D
from keras.layers import GlobalAveragePooling2D
from keras.layers import BatchNormalization
from keras.models import Model
from keras.preprocessing import image
from keras.engine.topology import get_source_inputs
from keras.utils.data_utils import get_file
from keras.utils import layer_utils
from keras.applications.imagenet_utils import decode_predictions
from keras.applications.imagenet_utils import preprocess_input
from keras_applications.imagenet_utils import _obtain_input_shape
import numpy as np
WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5'
WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'
def identity_block(input_tensor,kernel_size,filters,stage,block):
filters1,filters2,filters3=filters
if K.image_data_format()=='channels_last':
bn_axis=3
else:
bn_axis=1
conv_name_base='res'+str(stage)+block+'_branch'
bn_name_base='bn'+str(stage)+block+'_branch'
x=Conv2D(filters1,(1,1),name=conv_name_base+'2a')(input_tensor)
x=BatchNormalization(axis=bn_axis,name=bn_name_base+'2a')(x)
x=Activation('relu')(x)
x=Conv2D(filters2,kernel_size,padding='same',name=conv_name_base+'2b')(x)
x=BatchNormalization(axis=bn_axis,name=bn_name_base+'2b')(x)
x=Activation('relu')(x)
x=Conv2D(filters3,(1,1),name=conv_name_base+'2c')(x)
x=BatchNormalization(axis=bn_axis,name=bn_name_base+'2c')(x)
x=layers.add([x,input_tensor])
x=Activation('relu')(x)
return x
def conv_block(input_tensor,kernel_size,filters,stage,block,strides=(2,2)):
filters1,filters2,filters3=filters
if K.image_data_format()=='channels_last':
bn_axis=3
else:
bn_axis=1
conv_name_base='res'+str(stage)+block+'_branch'
bn_name_base='bn'+str(stage)+block+'_branch'
x=Conv2D(filters1,(1,1),strides=strides,name=conv_name_base+'2a')(input_tensor)
x=BatchNormalization(axis=bn_axis,name=bn_name_base+'2a')(x)
x=Activation('relu')(x)
x=Conv2D(filters2,kernel_size,padding='same',name=conv_name_base+'2b')(x)
x=BatchNormalization(axis=bn_axis,name=bn_name_base+'2b')(x)
x=Activation('relu')(x)
x=Conv2D(filters3,(1,1),name=conv_name_base+'2c')(x)
x=BatchNormalization(axis=bn_axis,name=bn_name_base+'2c')(x)
shortcut=Conv2D(filters3,(1,1),strides=strides,name=conv_name_base+'1')(input_tensor)
shortcut=BatchNormalization(axis=bn_axis,name=bn_name_base+'1')(shortcut)
x=layers.add([x,shortcut])
x=Activation('relu')(x)
return x
def ResNet50(include_top=True,weights='imagenet',input_tensor=None,input_shape=None,pooling=None,classes=1000):
input_shape = _obtain_input_shape(input_shape,
default_size=224,
min_size=197,
data_format=K.image_data_format(),
require_flatten=include_top)
if input_tensor is None:
img_input = Input(shape=input_shape)
else:
if not K.is_keras_tensor(input_tensor):
img_input = Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
if K.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
x=ZeroPadding2D((3,3))(img_input)
x=Conv2D(64,(7,7),strides=(2,2),name='conv1')(x)
x=BatchNormalization(axis=bn_axis,name='bn_conv1')(x)
x=Activation('relu')(x)
x=MaxPooling2D((3,3),strides=(2,2))(x)
x=conv_block(x,3,[64,64,256],stage=2,block='a',strides=(1,1))
x=identity_block(x,3,[64,64,256],stage=2,block='b')
x=identity_block(x,3,[64,64,256],stage=2,block='c')
x=conv_block(x,3,[128,128,512],stage=3,block='a')
x=identity_block(x,3,[128,128,512],stage=3,block='b')
x=identity_block(x,3,[128,128,512],stage=3,block='c')
x=identity_block(x,3,[128,128,512],stage=3,block='d')
x=conv_block(x,3,[256,256,1024],stage=4,block='a')
x=identity_block(x,3,[256,256,1024],stage=4,block='b')
x=identity_block(x,3,[256,256,1024],stage=4,block='c')
x=identity_block(x,3,[256,256,1024],stage=4,block='d')
x=identity_block(x,3,[256,256,1024],stage=4,block='e')
x=identity_block(x,3,[256,256,1024],stage=4,block='f')
x=conv_block(x,3,[512,512,2048],stage=5,block='a')
x=identity_block(x,3,[512,512,2048],stage=5,block='b')
x=identity_block(x,3,[512,512,2048],stage=5,block='c')
x=AveragePooling2D((7,7),name='avg_pool')(x)
if include_top:
x=Flatten()(x)
x=Dense(classes,activation='softmax',name='fc1000')(x)
else:
if pooling=='avg':
x=GlobalAveragePooling2D()(x)
elif pooling=='max':
x=GlobalMaxPooling2D()(x)
if input_tensor is not None:
inputs = get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
model = Model(inputs, x, name='resnet50')
# load weights
if weights == 'imagenet':
if include_top:
weights_path = get_file('resnet50_weights_tf_dim_ordering_tf_kernels.h5',
WEIGHTS_PATH,
cache_subdir='models',
md5_hash='a7b3fe01876f51b976af0dea6bc144eb')
else:
weights_path = get_file('resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5',
WEIGHTS_PATH_NO_TOP,
cache_subdir='models',
md5_hash='a268eb855778b3df3c7506639542a6af')
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='avg_pool')
shape = maxpool.output_shape[1:]
dense = model.get_layer(name='fc1000')
layer_utils.convert_dense_weights_data_format(dense, shape, 'channels_first')
return model
if __name__ == '__main__':
model = ResNet50(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))3.densenet
（全部链接的残差模块）
# coding=utf-8
"""
__project_ = 'Backbone'
__file_name__ = 'DenseNet'
__author__ = 'WIN10'
__time__ = '2020/5/27 10:56'
__product_name = PyCharm
"""
import keras.backend as K
from keras.layers import Input
from keras.layers import Dense
from keras.layers import Activation
from keras.utils.data_utils import get_file
from keras.engine.topology import get_source_inputs
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import GlobalMaxPooling2D
from keras.layers import ZeroPadding2D
from keras.layers import AveragePooling2D
from keras.layers import GlobalAveragePooling2D
from keras.layers import BatchNormalization
from keras.layers import Concatenate
from keras.models import Model
from keras_applications.imagenet_utils import _obtain_input_shape
from keras.preprocessing import image
import numpy as np
from keras.applications.imagenet_utils import decode_predictions
from keras.applications.imagenet_utils import preprocess_input
BASE_WEIGTHS_PATH = (
'https://github.com/keras-team/keras-applications/'
'releases/download/densenet/')
DENSENET121_WEIGHT_PATH = (
BASE_WEIGTHS_PATH +
'densenet121_weights_tf_dim_ordering_tf_kernels.h5')
DENSENET121_WEIGHT_PATH_NO_TOP = (
BASE_WEIGTHS_PATH +
'densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5')
DENSENET169_WEIGHT_PATH = (
BASE_WEIGTHS_PATH +
'densenet169_weights_tf_dim_ordering_tf_kernels.h5')
DENSENET169_WEIGHT_PATH_NO_TOP = (
BASE_WEIGTHS_PATH +
'densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5')
DENSENET201_WEIGHT_PATH = (
BASE_WEIGTHS_PATH +
'densenet201_weights_tf_dim_ordering_tf_kernels.h5')
DENSENET201_WEIGHT_PATH_NO_TOP = (
BASE_WEIGTHS_PATH +
'densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5')
def conv_block(x, growth_rate, name):
if K.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
x1=BatchNormalization(axis=bn_axis,epsilon=1.001e-5,name=name+'_0_bn')(x)
x1=Activation('relu',name=name+'_0_relu')(x1)
x1=Conv2D(4*growth_rate,1,use_bias=False,name=name+'_1_conv')(x1)
x1=BatchNormalization(axis=bn_axis,epsilon=1.001e-5,name=name+'_1_bn')(x1)
x1=Activation('relu',name=name+'_1_relu')(x1)
x1=Conv2D(growth_rate,3,padding='same',use_bias=False,name=name+'_2_conv')(x1)
x=Concatenate(axis=bn_axis,name=name+'_concat')([x,x1])
return x
def dense_block(x,blocks,name):
for i in range(blocks):
x=conv_block(x,32,name=name+'_block'+str(i+1))
return x
def transition_block(x,reduction,name):
if K.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
x=BatchNormalization(axis=bn_axis,epsilon=1.001e-5,name=name+'_bn')(x)
x=Activation('relu',name=name+'_relu')(x)
x=Conv2D(int(K.int_shape(x)[bn_axis]*reduction),1,use_bias=False,name=name+'_conv')(x)
x=AveragePooling2D(2,strides=2,name=name+'_pool')(x)
return x
def DenseNet(blocks,include_top=True,weights='imagenet',input_tensor=None,input_shape=None,pooling=None,classes=1000,**kwargs):
# Determine proper input shape
input_shape = _obtain_input_shape(input_shape,
default_size=224,
min_size=32,
data_format=K.image_data_format(),
require_flatten=include_top,
weights=weights)
if input_tensor is None:
img_input = Input(shape=input_shape)
else:
if not K.is_keras_tensor(input_tensor):
img_input = Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
if K.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
x=ZeroPadding2D(padding=((3,3),(3,3)))(img_input)
x=Conv2D(64,7,strides=2,use_bias=False,name='conv1/conv')(x)
x=BatchNormalization(axis=bn_axis,epsilon=1.001e-5,name='conv1/bn')(x)
x=Activation('relu',name='conv1/relu')(x)
x=ZeroPadding2D(padding=((1,1),(1,1)))(x)
x=MaxPooling2D(3,strides=2,name='pool1')(x)
x=dense_block(x,blocks[0],name='conv2')
x=transition_block(x,0.5,name='pool2')
x = dense_block(x, blocks[1], name='conv3')
x = transition_block(x, 0.5, name='pool3')
x=dense_block(x,blocks[2],name='conv4')
x=transition_block(x,0.5,name='pool4')
x=dense_block(x,blocks[3],name='conv5')
x=BatchNormalization(axis=bn_axis,epsilon=1.001e-5,name='bn')(x)
x=Activation('relu',name='relu')(x)
if include_top:
x=GlobalAveragePooling2D(name='avg_pool')(x)
x=Dense(classes,activation='softmax',name='fc1000')(x)
else:
if pooling=='avg':
x=GlobalAveragePooling2D(name='avg_pool')(x)
elif pooling=='max':
x=GlobalMaxPooling2D(name='max_pool')(x)
if input_tensor is not None:
inputs = get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
if blocks == [6, 12, 24, 16]:
model = Model(inputs, x, name='densenet121')
elif blocks == [6, 12, 32, 32]:
model = Model(inputs, x, name='densenet169')
elif blocks == [6, 12, 48, 32]:
model = Model(inputs, x, name='densenet201')
else:
model = Model(inputs, x, name='densenet')
# Load weights.
if weights == 'imagenet':
if include_top:
if blocks == [6, 12, 24, 16]:
weights_path = get_file(
'densenet121_weights_tf_dim_ordering_tf_kernels.h5',
DENSENET121_WEIGHT_PATH,
cache_subdir='models',
file_hash='9d60b8095a5708f2dcce2bca79d332c7')
elif blocks == [6, 12, 32, 32]:
weights_path = get_file(
'densenet169_weights_tf_dim_ordering_tf_kernels.h5',
DENSENET169_WEIGHT_PATH,
cache_subdir='models',
file_hash='d699b8f76981ab1b30698df4c175e90b')
elif blocks == [6, 12, 48, 32]:
weights_path = get_file(
'densenet201_weights_tf_dim_ordering_tf_kernels.h5',
DENSENET201_WEIGHT_PATH,
cache_subdir='models',
file_hash='1ceb130c1ea1b78c3bf6114dbdfd8807')
else:
if blocks == [6, 12, 24, 16]:
weights_path = get_file(
'densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5',
DENSENET121_WEIGHT_PATH_NO_TOP,
cache_subdir='models',
file_hash='30ee3e1110167f948a6b9946edeeb738')
elif blocks == [6, 12, 32, 32]:
weights_path = get_file(
'densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5',
DENSENET169_WEIGHT_PATH_NO_TOP,
cache_subdir='models',
file_hash='b8c4d4c20dd625c148057b9ff1c1176b')
elif blocks == [6, 12, 48, 32]:
weights_path = get_file(
'densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5',
DENSENET201_WEIGHT_PATH_NO_TOP,
cache_subdir='models',
file_hash='c13680b51ded0fb44dff2d8f86ac8bb1')
model.load_weights(weights_path)
elif weights is not None:
model.load_weights(weights)
return model
def DenseNet121(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
**kwargs):
return DenseNet([6, 12, 24, 16],
include_top, weights,
input_tensor, input_shape,
pooling, classes,
**kwargs)
def DenseNet169(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
**kwargs):
return DenseNet([6, 12, 32, 32],
include_top, weights,
input_tensor, input_shape,
pooling, classes,
**kwargs)
def DenseNet201(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
**kwargs):
return DenseNet([6, 12, 48, 32],
include_top, weights,
input_tensor, input_shape,
pooling, classes,
**kwargs)
if __name__ == '__main__':
model = DenseNet121(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))4.inceptionv3
（空间分离卷积）
# coding=utf-8
"""
__project_ = 'Backbone'
__file_name__ = 'inception_v3'
__author__ = 'WIN10'
__time__ = '2020/5/27 12:03'
__product_name = PyCharm
"""
from keras.utils.data_utils import get_file
from keras.engine.topology import get_source_inputs
from keras.models import Model
from keras.layers import *
from keras_applications.imagenet_utils import _obtain_input_shape
from keras.preprocessing import image
import numpy as np
from keras.applications.imagenet_utils import decode_predictions
from keras.applications.imagenet_utils import preprocess_input
WEIGHTS_PATH = (
'https://github.com/fchollet/deep-learning-models/'
'releases/download/v0.5/'
'inception_v3_weights_tf_dim_ordering_tf_kernels.h5')
WEIGHTS_PATH_NO_TOP = (
'https://github.com/fchollet/deep-learning-models/'
'releases/download/v0.5/'
'inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5')
def conv2d_bn(x, filters, num_row, num_col, padding='same', strides=(1, 1), name=None):
if name is not None:
bn_name = name + '_bn'
conv_name = name + '_conv'
else:
bn_name = None
conv_name = None
if K.image_data_format() == 'channels_first':
bn_axis = 1
else:
bn_axis = 3
x = Conv2D(filters, (num_row, num_col), strides=strides, padding=padding, use_bias=False, name=conv_name)(x)
x = BatchNormalization(axis=bn_axis, scale=False, name=bn_name)(x)
x = Activation('relu', name=name)(x)
return x
def InceptionV3(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000,
**kwargs):
# Determine proper input shape
input_shape = _obtain_input_shape(
input_shape,
default_size=299,
min_size=75,
data_format=K.image_data_format(),
require_flatten=include_top,
weights=weights)
if input_tensor is None:
img_input = Input(shape=input_shape)
else:
if not K.is_keras_tensor(input_tensor):
img_input = Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
if K.image_data_format() == 'channels_first':
bn_axis = 1
else:
bn_axis = 3
x = conv2d_bn(img_input, 32, 3, 3, strides=(2, 2), padding='valid')
x = conv2d_bn(x, 32, 3, 3, padding='valid')
x = conv2d_bn(x, 64, 3, 3)
x = MaxPooling2D((3, 3), strides=(2, 2))(x)
x = conv2d_bn(x, 80, 1, 1, padding='valid')
x = conv2d_bn(x, 192, 3, 3, padding='valid')
x = MaxPooling2D((3, 3), strides=(2, 2))(x)
# mixed 0:35x35x256
branch1x1 = conv2d_bn(x, 64, 1, 1)
branch5x5 = conv2d_bn(x, 48, 1, 1)
branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)
branch3x3db1 = conv2d_bn(x, 64, 1, 1)
branch3x3db1 = conv2d_bn(branch3x3db1, 96, 3, 3)
branch3x3db1 = conv2d_bn(branch3x3db1, 96, 3, 3)
branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)
branch_pool = conv2d_bn(branch_pool, 32, 1, 1)
x = concatenate([branch1x1, branch5x5, branch3x3db1, branch_pool], axis=bn_axis, name='mixed0')
# mixed 1: 35x35x288
branch1x1 = conv2d_bn(x, 64, 1, 1)
branch5x5 = conv2d_bn(x, 48, 1, 1)
branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)
branch3x3db1 = conv2d_bn(x, 64, 1, 1)
branch3x3db1 = conv2d_bn(branch3x3db1, 96, 3, 3)
branch3x3db1 = conv2d_bn(branch3x3db1, 96, 3, 3)
branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)
branch_pool = conv2d_bn(branch_pool, 64, 1, 1)
x = concatenate([branch1x1, branch5x5, branch3x3db1, branch_pool], axis=bn_axis, name='mixed1')
# mixed 2: 35x35x288
branch1x1 = conv2d_bn(x, 64, 1, 1)
branch5x5 = conv2d_bn(x, 48, 1, 1)
branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)
branch3x3db1 = conv2d_bn(x, 64, 1, 1)
branch3x3db1 = conv2d_bn(branch3x3db1, 96, 3, 3)
branch3x3db1 = conv2d_bn(branch3x3db1, 96, 3, 3)
branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)
branch_pool = conv2d_bn(branch_pool, 64, 1, 1)
x = concatenate([branch1x1, branch5x5, branch3x3db1, branch_pool], axis=bn_axis, name='mixed2')
# mixed 3: 17x17x768
branch3x3 = conv2d_bn(x, 384, 3, 3, strides=(2, 2), padding='valid')
branch3x3db1 = conv2d_bn(x, 64, 1, 1)
branch3x3db1 = conv2d_bn(branch3x3db1, 96, 3, 3)
branch3x3db1 = conv2d_bn(branch3x3db1, 96, 3, 3, strides=(2, 2), padding='valid')
branch_pool = MaxPooling2D((3, 3), strides=(2, 2))(x)
x = concatenate([branch3x3, branch3x3db1, branch_pool], axis=bn_axis, name='mixed3')
# mixed 4: 17 x 17 x 768
branch1x1 = conv2d_bn(x, 192, 1, 1)
branch7x7 = conv2d_bn(x, 128, 1, 1)
branch7x7 = conv2d_bn(branch7x7, 128, 1, 7)
branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)
branch7x7db1 = conv2d_bn(x, 128, 1, 1)
branch7x7db1 = conv2d_bn(branch7x7db1, 128, 7, 1)
branch7x7db1 = conv2d_bn(branch7x7db1, 128, 1, 7)
branch7x7db1 = conv2d_bn(branch7x7db1, 128, 7, 1)
branch7x7db1 = conv2d_bn(branch7x7db1, 192, 1, 7)
branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)
branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
x = concatenate([branch1x1, branch7x7, branch7x7db1, branch_pool], axis=bn_axis, name='mixed4')
# mixed 5, 6: 17 x 17 x 768
for i in range(2):
branch1x1 = conv2d_bn(x, 192, 1, 1)
branch7x7 = conv2d_bn(x, 160, 1, 1)
branch7x7 = conv2d_bn(branch7x7, 160, 1, 7)
branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)
branch7x7db1 = conv2d_bn(x, 160, 1, 1)
branch7x7db1 = conv2d_bn(branch7x7db1, 160, 7, 1)
branch7x7db1 = conv2d_bn(branch7x7db1, 160, 1, 7)
branch7x7db1 = conv2d_bn(branch7x7db1, 160, 7, 1)
branch7x7db1 = conv2d_bn(branch7x7db1, 192, 1, 7)
branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x)
branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
x = concatenate([branch1x1, branch7x7, branch7x7db1, branch_pool], axis=bn_axis, name='mixed' + str(5 + i))
# mixed 7: 17 x 17 x 768
branch1x1 = conv2d_bn(x, 192, 1, 1)
branch7x7 = conv2d_bn(x, 192, 1, 1)
branch7x7 = conv2d_bn(branch7x7, 192, 1, 7)
branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)
branch7x7dbl = conv2d_bn(x, 192, 1, 1)
branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1)
branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1)
branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
branch_pool = AveragePooling2D((3, 3),
strides=(1, 1),
padding='same')(x)
branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
x = concatenate(
[branch1x1, branch7x7, branch7x7dbl, branch_pool],
axis=bn_axis,
name='mixed7')
# mixed 8: 8 x 8 x 1280
branch3x3 = conv2d_bn(x, 192, 1, 1)
branch3x3 = conv2d_bn(branch3x3, 320, 3, 3,
strides=(2, 2), padding='valid')
branch7x7x3 = conv2d_bn(x, 192, 1, 1)
branch7x7x3 = conv2d_bn(branch7x7x3, 192, 1, 7)
branch7x7x3 = conv2d_bn(branch7x7x3, 192, 7, 1)
branch7x7x3 = conv2d_bn(
branch7x7x3, 192, 3, 3, strides=(2, 2), padding='valid')
branch_pool = MaxPooling2D((3, 3), strides=(2, 2))(x)
x = concatenate(
[branch3x3, branch7x7x3, branch_pool],
axis=bn_axis,
name='mixed8')
# mixed 9,10: 8 x 8 x 2048
for i in range(2):
branch1x1 = conv2d_bn(x, 320, 1, 1)
branch3x3 = conv2d_bn(x, 384, 1, 1)
branch3x3_1 = conv2d_bn(branch3x3, 384, 1, 3)
branch3x3_2 = conv2d_bn(branch3x3, 384, 3, 1)
branch3x3 = concatenate(
[branch3x3_1, branch3x3_2],
axis=bn_axis,
name='mixed9_' + str(i))
branch3x3dbl = conv2d_bn(x, 448, 1, 1)
branch3x3dbl = conv2d_bn(branch3x3dbl, 384, 3, 3)
branch3x3dbl_1 = conv2d_bn(branch3x3dbl, 384, 1, 3)
branch3x3dbl_2 = conv2d_bn(branch3x3dbl, 384, 3, 1)
branch3x3dbl = concatenate(
[branch3x3dbl_1, branch3x3dbl_2], axis=bn_axis)
branch_pool = AveragePooling2D(
(3, 3), strides=(1, 1), padding='same')(x)
branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
x = concatenate(
[branch1x1, branch3x3, branch3x3dbl, branch_pool],
axis=bn_axis,
name='mixed' + str(9 + i))
if include_top:
# Classification block
x = GlobalAveragePooling2D(name='avg_pool')(x)
x = Dense(classes, activation='softmax', name='predictions')(x)
else:
if pooling == 'avg':
x = GlobalAveragePooling2D()(x)
elif pooling == 'max':
x = GlobalMaxPooling2D()(x)
if input_tensor is not None:
inputs = get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
model = Model(inputs, x, name='inception_v3')
# Load weights.
if weights == 'imagenet':
if include_top:
weights_path = get_file(
'inception_v3_weights_tf_dim_ordering_tf_kernels.h5',
WEIGHTS_PATH,
cache_subdir='models',
file_hash='9a0d58056eeedaa3f26cb7ebd46da564')
else:
weights_path = get_file(
'inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5',
WEIGHTS_PATH_NO_TOP,
cache_subdir='models',
file_hash='bcbd6486424b2319ff4ef7d526e38f63')
model.load_weights(weights_path)
elif weights is not None:
model.load_weights(weights)
return model
if __name__ == '__main__':
model = InceptionV3(include_top=True, weights='imagenet')
print(model.summary())
img_path = 'elephant.jpg'
img = image.load_img(img_path, target_size=(299, 299))
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))
5.mobilenet_v2
深度分类加上残差模块
# coding=utf-8
"""
__project_ = 'Backbone'
__file_name__ = 'mobilenet'
__author__ = 'WIN10'
__time__ = '2020/5/27 18:08'
__product_name = PyCharm
"""
from keras.utils.data_utils import get_file
from keras.engine.topology import get_source_inputs
from keras.models import Model
from keras.layers import *
from keras_applications.imagenet_utils import _obtain_input_shape
from keras.preprocessing import image
import numpy as np
from keras.applications.imagenet_utils import decode_predictions
from keras.applications.imagenet_utils import preprocess_input
from keras_applications import correct_pad
# TODO Change path to v1.1
BASE_WEIGHT_PATH = ('https://github.com/JonathanCMitchell/mobilenet_v2_keras/'
'releases/download/v1.1/')
def _make_divisible(v, divisor, min_value=None):
if min_value is None:
min_value = divisor
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
if new_v < 0.9 * v:
new_v += divisor
return new_v
# 深度分离卷积
def _inverted_res_block(inputs, expansion, stride, alpha, filters, block_id):
channel_axis = 1 if K.image_data_format() == 'channels_first' else -1
in_channels = K.int_shape(inputs)[channel_axis]
pointwise_conv_filters = int(filters * alpha)
pointwise_filters = _make_divisible(pointwise_conv_filters, 8)
x = inputs
prefix = 'block_{}_'.format(block_id)
if block_id:
x = Conv2D(expansion * in_channels,
kernel_size=1,
padding='same',
use_bias=False,
activation=None,
name=prefix + 'expand')(x)
x = BatchNormalization(axis=channel_axis, epsilon=1e-3, momentum=0.999, name=prefix + '_expand_BN')(x)
x = ReLU(6., name=prefix + 'expand_relu')(x)
else:
prefix = 'expanded_conv_'
# depthwise
if stride == 2:
x = ZeroPadding2D(padding=correct_pad(K, x, 3), name=prefix + 'pad')(x)
x = DepthwiseConv2D(kernel_size=3, strides=stride, activation=None, use_bias=False,
padding='same' if stride == 1 else 'valid', name=prefix + 'depthwise')(x)
x = BatchNormalization(axis=channel_axis, epsilon=1e-3, momentum=0.999, name=prefix + 'depthwise_BN')(x)
x = ReLU(6., name=prefix + 'depthwise_relu')(x)
# project
x = Conv2D(pointwise_filters, kernel_size=1, padding='same', use_bias=False, activation=None,
name=prefix + 'project')(x)
x = BatchNormalization(axis=channel_axis, epsilon=1e-3, momentum=0.999, name=prefix + 'project_BN')(x)
if in_channels == pointwise_filters and stride == 1:
return Add(name=prefix + 'add')([inputs, x])
return x
def MobileNetV2(input_shape=None, alpha=1.0, include_top=True, weights='imagenet', input_tensor=None, pooling=None,
classes=1000, **kwargs):
if input_shape is not None and input_tensor is not None:
try:
is_input_t_tensor = K.is_keras_tensor(input_tensor)
except ValueError:
try:
is_input_t_tensor = K.is_keras_tensor(
get_source_inputs(input_tensor))
except ValueError:
raise ValueError('input_tensor: ', input_tensor,
'is not type input_tensor')
if is_input_t_tensor:
if K.image_data_format == 'channels_first':
if K.int_shape(input_tensor)[1] != input_shape[1]:
raise ValueError('input_shape: ', input_shape,
'and input_tensor: ', input_tensor,
'do not meet the same shape requirements')
else:
if K.int_shape(input_tensor)[2] != input_shape[1]:
raise ValueError('input_shape: ', input_shape,
'and input_tensor: ', input_tensor,
'do not meet the same shape requirements')
else:
raise ValueError('input_tensor specified: ', input_tensor,
'is not a keras tensor')
# If input_shape is None, infer shape from input_tensor
if input_shape is None and input_tensor is not None:
try:
K.is_keras_tensor(input_tensor)
except ValueError:
raise ValueError('input_tensor: ', input_tensor,
'is type: ', type(input_tensor),
'which is not a valid type')
if input_shape is None and not K.is_keras_tensor(input_tensor):
default_size = 224
elif input_shape is None and K.is_keras_tensor(input_tensor):
if K.image_data_format() == 'channels_first':
rows = K.int_shape(input_tensor)[2]
cols = K.int_shape(input_tensor)[3]
else:
rows = K.int_shape(input_tensor)[1]
cols = K.int_shape(input_tensor)[2]
if rows == cols and rows in [96, 128, 160, 192, 224]:
default_size = rows
else:
default_size = 224
# If input_shape is None and no input_tensor
elif input_shape is None:
default_size = 224
# If input_shape is not None, assume default size
else:
if K.image_data_format() == 'channels_first':
rows = input_shape[1]
cols = input_shape[2]
else:
rows = input_shape[0]
cols = input_shape[1]
if rows == cols and rows in [96, 128, 160, 192, 224]:
default_size = rows
else:
default_size = 224
input_shape = _obtain_input_shape(input_shape,
default_size=default_size,
min_size=32,
data_format=K.image_data_format(),
require_flatten=include_top,
weights=weights)
if K.image_data_format() == 'channels_last':
row_axis, col_axis = (0, 1)
else:
row_axis, col_axis = (1, 2)
rows = input_shape[row_axis]
cols = input_shape[col_axis]
if weights == 'imagenet':
if alpha not in [0.35, 0.50, 0.75, 1.0, 1.3, 1.4]:
raise ValueError('If imagenet weights are being loaded, '
'alpha can be one of `0.35`, `0.50`, `0.75`, '
'`1.0`, `1.3` or `1.4` only.')
if rows != cols or rows not in [96, 128, 160, 192, 224]:
rows = 224
warnings.warn('`input_shape` is undefined or non-square, '
'or `rows` is not in [96, 128, 160, 192, 224].'
' Weights for input shape (224, 224) will be'
' loaded as the default.')
if input_tensor is None:
img_input = Input(shape=input_shape)
else:
if not K.is_keras_tensor(input_tensor):
img_input = Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
channel_axis = 1 if K.image_data_format() == 'channels_first' else -1
first_block_filters = _make_divisible(32 * alpha, 8)
x = ZeroPadding2D(padding=correct_pad(K, img_input, 3), name='Conv1_pad')(img_input)
x = Conv2D(first_block_filters, kernel_size=3, strides=(2, 2), padding='valid', use_bias=False, name='Conv1')(x)
x = BatchNormalization(axis=channel_axis, epsilon=1e-3, momentum=0.999, name='bn_Conv1')(x)
x = ReLU(6., name='Conv1_relu')(x)
x = _inverted_res_block(x, filters=16, alpha=alpha, stride=1,
expansion=1, block_id=0)
x = _inverted_res_block(x, filters=24, alpha=alpha, stride=2,
expansion=6, block_id=1)
x = _inverted_res_block(x, filters=24, alpha=alpha, stride=1,
expansion=6, block_id=2)
x = _inverted_res_block(x, filters=32, alpha=alpha, stride=2,
expansion=6, block_id=3)
x = _inverted_res_block(x, filters=32, alpha=alpha, stride=1,
expansion=6, block_id=4)
x = _inverted_res_block(x, filters=32, alpha=alpha, stride=1,
expansion=6, block_id=5)
x = _inverted_res_block(x, filters=64, alpha=alpha, stride=2,
expansion=6, block_id=6)
x = _inverted_res_block(x, filters=64, alpha=alpha, stride=1,
expansion=6, block_id=7)
x = _inverted_res_block(x, filters=64, alpha=alpha, stride=1,
expansion=6, block_id=8)
x = _inverted_res_block(x, filters=64, alpha=alpha, stride=1,
expansion=6, block_id=9)
x = _inverted_res_block(x, filters=96, alpha=alpha, stride=1,
expansion=6, block_id=10)
x = _inverted_res_block(x, filters=96, alpha=alpha, stride=1,
expansion=6, block_id=11)
x = _inverted_res_block(x, filters=96, alpha=alpha, stride=1,
expansion=6, block_id=12)
x = _inverted_res_block(x, filters=160, alpha=alpha, stride=2,
expansion=6, block_id=13)
x = _inverted_res_block(x, filters=160, alpha=alpha, stride=1,
expansion=6, block_id=14)
x = _inverted_res_block(x, filters=160, alpha=alpha, stride=1,
expansion=6, block_id=15)
x = _inverted_res_block(x, filters=320, alpha=alpha, stride=1,
expansion=6, block_id=16)
if alpha > 1.0:
last_block_filters = _make_divisible(1280 * alpha, 8)
else:
last_block_filters = 1280
x = Conv2D(last_block_filters, kernel_size=1, use_bias=False, name='Conv_1')(x)
x = BatchNormalization(axis=channel_axis, epsilon=1e-3, momentum=0.999, name='Conv1_bn')(x)
x = ReLU(6., name='out_relu')(x)
if include_top:
x = GlobalAveragePooling2D()(x)
x = Dense(classes, activation='softmax',
use_bias=True, name='Logits')(x)
else:
if pooling == 'avg':
x = GlobalAveragePooling2D()(x)
elif pooling == 'max':
x = GlobalMaxPooling2D()(x)
if input_tensor is not None:
inputs = get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
model = Model(inputs, x, name='mobilenetv2_%0.2f_%s' % (alpha, rows))
# Load weights.
if weights == 'imagenet':
if include_top:
model_name = ('mobilenet_v2_weights_tf_dim_ordering_tf_kernels_' +
str(alpha) + '_' + str(rows) + '.h5')
weight_path = BASE_WEIGHT_PATH + model_name
weights_path = get_file(
model_name, weight_path, cache_subdir='models')
else:
model_name = ('mobilenet_v2_weights_tf_dim_ordering_tf_kernels_' +
str(alpha) + '_' + str(rows) + '_no_top' + '.h5')
weight_path = BASE_WEIGHT_PATH + model_name
weights_path = get_file(
model_name, weight_path, cache_subdir='models')
model.load_weights(weights_path)
elif weights is not None:
model.load_weights(weights)
return model
if __name__ == '__main__':
model = MobileNetV2(include_top=True, weights='imagenet')
print(model.summary())
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))
6.xception
（深度和空间分离加上残差模块）
# coding=utf-8
"""
__project_ = 'Backbone'
__file_name__ = 'xception'
__author__ = 'WIN10'
__time__ = '2020/5/27 12:01'
__product_name = PyCharm
"""
from keras.utils.data_utils import get_file
from keras.engine.topology import get_source_inputs
from keras.models import Model
from keras.layers import *
from keras_applications.imagenet_utils import _obtain_input_shape
from keras.preprocessing import image
import numpy as np
from keras.applications.imagenet_utils import decode_predictions
from keras.applications.imagenet_utils import preprocess_input
TF_WEIGHTS_PATH = (
'https://github.com/fchollet/deep-learning-models/'
'releases/download/v0.4/'
'xception_weights_tf_dim_ordering_tf_kernels.h5')
TF_WEIGHTS_PATH_NO_TOP = (
'https://github.com/fchollet/deep-learning-models/'
'releases/download/v0.4/'
'xception_weights_tf_dim_ordering_tf_kernels_notop.h5')
def Xception(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000,
**kwargs):
# Determine proper input shape
input_shape = _obtain_input_shape(
input_shape,
default_size=299,
min_size=71,
data_format=K.image_data_format(),
require_flatten=include_top,
weights=weights)
if input_tensor is None:
img_input = Input(shape=input_shape)
else:
if not K.is_keras_tensor(input_tensor):
img_input = Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
if K.image_data_format() == 'channels_first':
bn_axis = 1
else:
bn_axis = 3
x = Conv2D(32, (3, 3), strides=(2, 2), use_bias=False, name='block1_conv1')(img_input)
x = BatchNormalization(axis=bn_axis, name='block1_conv1_bn')(x)
x = Activation('relu', name='block1_conv1_act')(x)
x = Conv2D(64, (3, 3), use_bias=False, name='block1_conv2')(x)
x = BatchNormalization(axis=bn_axis, name='block1_conv2_bn')(x)
x = Activation('relu', name='block1_conv2_act')(x)
residual = Conv2D(128, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x)
residual = BatchNormalization(axis=bn_axis)(residual)
x = SeparableConv2D(128, (3, 3), padding='same', use_bias=False, name='block2_sepconv1')(x)
x = BatchNormalization(axis=bn_axis, name='block2_sepconv1_bn')(x)
x = Activation('relu', name='block2_sepconv2_act')(x)
x = SeparableConv2D(128, (3, 3), padding='same', use_bias=False, name='block2_sepconv2')(x)
x = BatchNormalization(axis=bn_axis, name='block2_sepconv2_bn')(x)
x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block2_pool')(x)
x = add([x, residual])
residual = Conv2D(256, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x)
residual = BatchNormalization(axis=bn_axis)(residual)
x = Activation('relu', name='block3_sepconv1_act')(x)
x = SeparableConv2D(256, (3, 3), padding='same', use_bias=False, name='block3_sepconv1')(x)
x = BatchNormalization(axis=bn_axis, name='block3_sepconv1_bn')(x)
x = Activation('relu', name='block3_sepconv2_act')(x)
x = SeparableConv2D(256, (3, 3), padding='same', use_bias=False, name='block3_sepconv2')(x)
x = BatchNormalization(axis=bn_axis, name='block3_sepconv2_bn')(x)
x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block3_pool')(x)
x = add([x, residual])
residual = Conv2D(728, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x)
residual = BatchNormalization(axis=bn_axis)(residual)
x = Activation('relu', name='block4_sepconv1_act')(x)
x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name='block4_sepconv1')(x)
x = BatchNormalization(axis=bn_axis, name='block4_sepconv1_bn')(x)
x = Activation('relu', name='block4_sepconv2_act')(x)
x = SeparableConv2D(728, (3, 3),
padding='same',
use_bias=False,
name='block4_sepconv2')(x)
x = BatchNormalization(axis=bn_axis, name='block4_sepconv2_bn')(x)
x = MaxPooling2D((3, 3), strides=(2, 2),
padding='same',
name='block4_pool')(x)
x = add([x, residual])
for i in range(8):
residual = x
prefix = 'block' + str(i + 5)
x = Activation('relu', name=prefix + '_sepconv1_act')(x)
x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv1')(x)
x = BatchNormalization(axis=bn_axis, name=prefix + '_sepconv1_bn')(x)
x = Activation('relu', name=prefix + '_sepconv2_act')(x)
x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv2')(x)
x = BatchNormalization(axis=bn_axis, name=prefix + '_sepconv2_bn')(x)
x = Activation('relu', name=prefix + '_sepconv3_act')(x)
x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv3')(x)
x = BatchNormalization(axis=bn_axis, name=prefix + '_sepconv3_bn')(x)
x = add([x, residual])
residual=Conv2D(1024,(1,1),strides=(2,2),padding='same',use_bias=False)(x)
residual=BatchNormalization(axis=bn_axis)(residual)
x = Activation('relu', name='block13_sepconv1_act')(x)
x = SeparableConv2D(728, (3, 3),
padding='same',
use_bias=False,
name='block13_sepconv1')(x)
x = BatchNormalization(axis=bn_axis, name='block13_sepconv1_bn')(x)
x = Activation('relu', name='block13_sepconv2_act')(x)
x = SeparableConv2D(1024, (3, 3),
padding='same',
use_bias=False,
name='block13_sepconv2')(x)
x = BatchNormalization(axis=bn_axis, name='block13_sepconv2_bn')(x)
x = MaxPooling2D((3, 3),
strides=(2, 2),
padding='same',
name='block13_pool')(x)
x = add([x, residual])
x = SeparableConv2D(1536, (3, 3),
padding='same',
use_bias=False,
name='block14_sepconv1')(x)
x = BatchNormalization(axis=bn_axis, name='block14_sepconv1_bn')(x)
x = Activation('relu', name='block14_sepconv1_act')(x)
x = SeparableConv2D(2048, (3, 3),
padding='same',
use_bias=False,
name='block14_sepconv2')(x)
x = BatchNormalization(axis=bn_axis, name='block14_sepconv2_bn')(x)
x = Activation('relu', name='block14_sepconv2_act')(x)
if include_top:
x = GlobalAveragePooling2D(name='avg_pool')(x)
x = Dense(classes, activation='softmax', name='predictions')(x)
else:
if pooling == 'avg':
x = GlobalAveragePooling2D()(x)
elif pooling == 'max':
x = GlobalMaxPooling2D()(x)
# 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)
else:
inputs = img_input
# Create model.
model = Model(inputs, x, name='xception')
# Load weights.
if weights == 'imagenet':
if include_top:
weights_path = get_file(
'xception_weights_tf_dim_ordering_tf_kernels.h5',
TF_WEIGHTS_PATH,
cache_subdir='models',
file_hash='0a58e3b7378bc2990ea3b43d5981f1f6')
else:
weights_path = get_file(
'xception_weights_tf_dim_ordering_tf_kernels_notop.h5',
TF_WEIGHTS_PATH_NO_TOP,
cache_subdir='models',
file_hash='b0042744bf5b25fce3cb969f33bebb97')
model.load_weights(weights_path)
elif weights is not None:
model.load_weights(weights)
return model
if __name__ == '__main__':
model = Xception(include_top=True, weights='imagenet')
print(model.summary())
img_path = 'elephant.jpg'
img = image.load_img(img_path, target_size=(299, 299))
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))
6.mobilnet
最后
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