目录
不同深度学习框架下的实现教程/github地址（好用的话记得star噢）
1.Pytorch
一个教程和项目地址，代码需要自己建立项目，或者从github上下载
PyTorch 入门实战（五）——2013kaggle比赛 猫狗大战的实现
https://github.com/nickhuang1996/Dogs_vs_Cats_Pytorch
2.TensorFlow
个人感觉写的不是很好，但是也算完成了分类任务， 可以通过tensorboard查看损失和准确率变化
https://github.com/nickhuang1996/Dogs_vs_Cats_TensorFlow_No_Keras
3.Keras
利用TensorFlow中的Keras接口，代码比较完善，博主还花了很长的时间把用到的预训练网络改成Pytorch的形式
https://github.com/nickhuang1996/Dogs_vs_Cats_TensorFlow_Keras
例如resnet50：
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！！
