各种深度学习框架实现猫狗大战
【摘要】
目录
不同深度学习框架下的实现教程/github地址
1.Pytorch
2.TensorFlow
3.Keras
4.MXNet
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...
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：
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from tensorflow.python.keras.layers import Activation
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from tensorflow.python.keras.layers import AveragePooling2D
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from tensorflow.python.keras.layers import BatchNormalization
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from tensorflow.python.keras.layers import Conv2D
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from tensorflow.python.keras.layers import Dense
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from tensorflow.python.keras.layers import Flatten
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from tensorflow.python.keras.layers import GlobalAveragePooling2D
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from tensorflow.python.keras.layers import GlobalMaxPooling2D
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from tensorflow.python.keras.layers import Input
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from tensorflow.python.keras.layers import MaxPooling2D
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from tensorflow.python.keras.layers import ZeroPadding2D
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from tensorflow.python.keras import backend as K
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from tensorflow.python.keras import layers
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class conv_block(object):
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def __init__(self, kernel_size, filters, stage, block, strides=(2, 2)):
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filters1, filters2, filters3 = filters
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if K.image_data_format() == 'channels_last':
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bn_axis = 3
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else:
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bn_axis = 1
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conv_name_base = 'res' + str(stage) + block + '_branch'
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bn_name_base = 'bn' + str(stage) + block + '_branch'
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self.conv1 = Conv2D(filters1, (1, 1), strides=strides, name=conv_name_base + '2a')
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self.bn1 = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')
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self.conv2 = Conv2D(filters2, kernel_size, padding='same', name=conv_name_base + '2b')
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self.bn2 = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')
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self.conv3 = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')
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self.bn3 = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')
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self.shortcut_conv = Conv2D(filters3, (1, 1), strides=strides, name=conv_name_base + '1')
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self.shortcut_bn = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')
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self.relu = Activation('relu')
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def __call__(self, input_tensor):
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x = self.conv1(input_tensor)
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x = self.bn1(x)
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x = self.relu(x)
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x = self.conv2(x)
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x = self.bn2(x)
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x = self.relu(x)
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x = self.conv3(x)
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x = self.bn3(x)
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shortcut = self.shortcut_conv(input_tensor)
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shortcut = self.shortcut_bn(shortcut)
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x = layers.add([x, shortcut])
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x = self.relu(x)
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return x
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class identity_block(object):
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def __init__(self, kernel_size, filters, stage, block):
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filters1, filters2, filters3 = filters
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if K.image_data_format() == 'channels_last':
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bn_axis = 3
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else:
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bn_axis = 1
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conv_name_base = 'res' + str(stage) + block + '_branch'
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bn_name_base = 'bn' + str(stage) + block + '_branch'
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self.conv1 = Conv2D(filters1, (1, 1), name=conv_name_base + '2a')
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self.bn1 = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')
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self.conv2 = Conv2D(filters2, kernel_size, padding='same', name=conv_name_base + '2b')
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self.bn2 = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')
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self.conv3 = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')
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self.bn3 = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')
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self.relu = Activation('relu')
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def __call__(self, input_tensor):
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x = self.conv1(input_tensor)
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x = self.bn1(x)
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x = self.relu(x)
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x = self.conv2(x)
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x = self.bn2(x)
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x = self.relu(x)
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x = self.conv3(x)
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x = self.bn3(x)
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x = layers.add([x, input_tensor])
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x = self.relu(x)
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return x
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class ResNet50(object):
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def __init__(self, include_top=True, classes=1000, pooling=None):
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if K.image_data_format() == 'channels_last':
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bn_axis = 3
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else:
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bn_axis = 1
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self.include_top = include_top
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self.pooling = pooling
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self.conv1 = Conv2D(64, (7, 7), strides=(2, 2), padding='same', name='conv1')
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self.bn1 = BatchNormalization(axis=bn_axis, name='bn_conv1')
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self.relu = Activation('relu')
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self.maxpool = MaxPooling2D((3, 3), strides=(2, 2))
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self.layer1_list = [
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conv_block(3, [64, 64, 256], stage=2, block='a', strides=(1, 1)),
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identity_block(3, [64, 64, 256], stage=2, block='b'),
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identity_block(3, [64, 64, 256], stage=2, block='c'),
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]
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self.layer2_list = [
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conv_block(3, [128, 128, 512], stage=3, block='a'),
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identity_block(3, [128, 128, 512], stage=3, block='b'),
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identity_block(3, [128, 128, 512], stage=3, block='c'),
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identity_block(3, [128, 128, 512], stage=3, block='d'),
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]
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self.layer3_list = [
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conv_block(3, [256, 256, 1024], stage=4, block='a'),
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identity_block(3, [256, 256, 1024], stage=4, block='b'),
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identity_block(3, [256, 256, 1024], stage=4, block='c'),
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identity_block(3, [256, 256, 1024], stage=4, block='d'),
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identity_block(3, [256, 256, 1024], stage=4, block='e'),
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identity_block(3, [256, 256, 1024], stage=4, block='f'),
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]
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self.layer4_list = [
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conv_block(3, [512, 512, 2048], stage=5, block='a'),
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identity_block(3, [512, 512, 2048], stage=5, block='b'),
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identity_block(3, [512, 512, 2048], stage=5, block='c'),
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]
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self.avgpool = AveragePooling2D((7, 7), name='avg_pool')
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self.flatten = Flatten()
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self.fc = Dense(classes, activation='softmax', name='fc1000')
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self.GAP = GlobalAveragePooling2D()
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self.GMP = GlobalMaxPooling2D()
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def layer1(self, x):
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for i in range(len(self.layer1_list)):
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x = self.layer1_list[i](x)
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return x
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def layer2(self, x):
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for i in range(len(self.layer2_list)):
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x = self.layer2_list[i](x)
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return x
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def layer3(self, x):
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for i in range(len(self.layer3_list)):
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x = self.layer3_list[i](x)
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return x
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def layer4(self, x):
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for i in range(len(self.layer4_list)):
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x = self.layer4_list[i](x)
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return x
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def __call__(self, img_input):
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x = self.conv1(img_input)
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x = self.bn1(x)
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x = self.relu(x)
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x = self.maxpool(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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x = self.avgpool(x)
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if self.include_top:
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x = self.flatten(x)
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x = self.fc(x)
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else:
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if self.pooling == 'avg':
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x = self.GAP(x)
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elif self.pooling == 'max':
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x = self.GMP(x)
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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！！
文章来源: nickhuang1996.blog.csdn.net，作者：悲恋花丶无心之人，版权归原作者所有，如需转载，请联系作者。
原文链接：nickhuang1996.blog.csdn.net/article/details/103644917
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