[code]from tensorflow.python.keras.layers import Activation
from tensorflow.python.keras.layers import AveragePooling2D
from tensorflow.python.keras.layers import BatchNormalization
from tensorflow.python.keras.layers import Conv2D
from tensorflow.python.keras.layers import Dense
from tensorflow.python.keras.layers import Flatten
from tensorflow.python.keras.layers import GlobalAveragePooling2D
from tensorflow.python.keras.layers import GlobalMaxPooling2D
from tensorflow.python.keras.layers import Input
from tensorflow.python.keras.layers import MaxPooling2D
from tensorflow.python.keras.layers import ZeroPadding2D

from tensorflow.python.keras import backend as K
from tensorflow.python.keras import layers

class conv_block(object):
def __init__(self, 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'

self.conv1 = Conv2D(filters1, (1, 1), strides=strides, name=conv_name_base + '2a')
self.bn1 = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')
self.conv2 = Conv2D(filters2, kernel_size, padding='same', name=conv_name_base + '2b')
self.bn2 = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')
self.conv3 = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')
self.bn3 = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')
self.shortcut_conv = Conv2D(filters3, (1, 1), strides=strides, name=conv_name_base + '1')
self.shortcut_bn = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')
self.relu = Activation('relu')

def __call__(self, input_tensor):
x = self.conv1(input_tensor)
x = self.bn1(x)
x = self.relu(x)

x = self.conv2(x)
x = self.bn2(x)
x = self.relu(x)

x = self.conv3(x)
x = self.bn3(x)

shortcut = self.shortcut_conv(input_tensor)
shortcut = self.shortcut_bn(shortcut)

x = layers.add([x, shortcut])
x = self.relu(x)
return x

class identity_block(object):
def __init__(self, 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'

self.conv1 = Conv2D(filters1, (1, 1), name=conv_name_base + '2a')
self.bn1 = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')
self.conv2 = Conv2D(filters2, kernel_size, padding='same', name=conv_name_base + '2b')
self.bn2 = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')
self.conv3 = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')
self.bn3 = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')
self.relu = Activation('relu')

def __call__(self, input_tensor):
x = self.conv1(input_tensor)
x = self.bn1(x)
x = self.relu(x)

x = self.conv2(x)
x = self.bn2(x)
x = self.relu(x)

x = self.conv3(x)
x = self.bn3(x)

x = layers.add([x, input_tensor])
x = self.relu(x)
return x

class ResNet50(object):
def __init__(self, include_top=True, classes=1000, pooling=None):
if K.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1

self.include_top = include_top
self.pooling = pooling

self.conv1 = Conv2D(64, (7, 7), strides=(2, 2), padding='same', name='conv1')
self.bn1 = BatchNormalization(axis=bn_axis, name='bn_conv1')
self.relu = Activation('relu')
self.maxpool = MaxPooling2D((3, 3), strides=(2, 2))
self.layer1_list = [
conv_block(3, [64, 64, 256], stage=2, block='a', strides=(1, 1)),
identity_block(3, [64, 64, 256], stage=2, block='b'),
identity_block(3, [64, 64, 256], stage=2, block='c'),
]
self.layer2_list = [
conv_block(3, [128, 128, 512], stage=3, block='a'),
identity_block(3, [128, 128, 512], stage=3, block='b'),
identity_block(3, [128, 128, 512], stage=3, block='c'),
identity_block(3, [128, 128, 512], stage=3, block='d'),
]
self.layer3_list = [
conv_block(3, [256, 256, 1024], stage=4, block='a'),
identity_block(3, [256, 256, 1024], stage=4, block='b'),
identity_block(3, [256, 256, 1024], stage=4, block='c'),
identity_block(3, [256, 256, 1024], stage=4, block='d'),
identity_block(3, [256, 256, 1024], stage=4, block='e'),
identity_block(3, [256, 256, 1024], stage=4, block='f'),
]
self.layer4_list = [
conv_block(3, [512, 512, 2048], stage=5, block='a'),
identity_block(3, [512, 512, 2048], stage=5, block='b'),
identity_block(3, [512, 512, 2048], stage=5, block='c'),
]
self.avgpool = AveragePooling2D((7, 7), name='avg_pool')
self.flatten = Flatten()
self.fc = Dense(classes, activation='softmax', name='fc1000')

self.GAP = GlobalAveragePooling2D()
self.GMP = GlobalMaxPooling2D()

def layer1(self, x):
for i in range(len(self.layer1_list)):
x = self.layer1_list[i](x)
return x

def layer2(self, x):
for i in range(len(self.layer2_list)):
x = self.layer2_list[i](x)
return x

def layer3(self, x):
for i in range(len(self.layer3_list)):
x = self.layer3_list[i](x)
return x

def layer4(self, x):
for i in range(len(self.layer4_list)):
x = self.layer4_list[i](x)
return x

def __call__(self, img_input):
x = self.conv1(img_input)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)

x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)

x = self.avgpool(x)

if self.include_top:
x = self.flatten(x)
x = self.fc(x)
else:
if self.pooling == 'avg':
x = self.GAP(x)
elif self.pooling == 'max':
x = self.GMP(x)
return x

4.MXNet

从头到尾写了一遍发现和TensorFlow和Pytorch都很相似，感觉用起来也很不错~

https://github.com/nickhuang1996/Dogs_vs_Cats_MXNet

希望以上教程和代码可以帮助到更多学习深度学习框架的人们！！

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