【图像分类】手撕ResNet——复现ResNet（Keras,Tensorflow 2.x）
【摘要】
目录
摘要
实现残差模块
ResNet18， ResNet34
ResNet50、ResNet101、ResNet152
摘要
ResNet（Residual Neural Network）由微软研究院的Kaiming He等四名华人提出，通过使用ResNet Unit成功训练出了152层的神经网络，并在ILS...
目录
摘要
ResNet（Residual Neural Network）由微软研究院的Kaiming He等四名华人提出，通过使用ResNet Unit成功训练出了152层的神经网络，并在ILSVRC2015比赛中取得冠军，在top5上的错误率为3.57%，同时参数量比VGGNet低，效果非常明显。
模型的创新点在于提出残差学习的思想，在网络中增加了直连通道，将原始输入信息直接传到后面的层中，如下图所示：
传统的卷积网络或者全连接网络在信息传递的时候或多或少会存在信息丢失，损耗等问题，同时还有导致梯度消失或者梯度爆炸，导致很深的网络无法训练。ResNet在一定程度上解决了这个问题，通过直接将输入信息绕道传到输出，保护信息的完整性，整个网络只需要学习输入、输出差别的那一部分，简化学习目标和难度。VGGNet和ResNet的对比如下图所示。ResNet最大的区别在于有很多的旁路将输入直接连接到后面的层，这种结构也被称为shortcut或者skip connections。
在ResNet网络结构中会用到两种残差模块，一种是以两个3*3的卷积网络串接在一起作为一个残差模块，另外一种是1*1、3*3、1*1的3个卷积网络串接在一起作为一个残差模块。如下图所示：
ResNet有不同的网络层数，比较常用的是18-layer，34-layer，50-layer，101-layer，152-layer。他们都是由上述的残差模块堆叠在一起实现的。 下图展示了不同的ResNet模型。
实现残差模块
第一个残差模块
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# 第一个残差模块
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class BasicBlock(layers.Layer):
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def __init__(self, filter_num, stride=1):
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super(BasicBlock, self).__init__()
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self.conv1 = layers.Conv2D(filter_num, (3, 3), strides=stride, padding='same')
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self.bn1 = layers.BatchNormalization()
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self.relu = layers.Activation('relu')
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self.conv2 = layers.Conv2D(filter_num, (3, 3), strides=1, padding='same')
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self.bn2 = layers.BatchNormalization()
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if stride != 1:
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self.downsample = Sequential()
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self.downsample.add(layers.Conv2D(filter_num, (1, 1), strides=stride))
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else:
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self.downsample = lambda x: x
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def call(self, input, training=None):
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out = self.conv1(input)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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identity = self.downsample(input)
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output = layers.add([out, identity])
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output = tf.nn.relu(output)
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return output
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第二个残差模块
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# 第二个残差模块
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class Block(layers.Layer):
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def __init__(self, filters, downsample=False, stride=1):
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super(Block, self).__init__()
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self.downsample = downsample
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self.conv1 = layers.Conv2D(filters, (1, 1), strides=stride, padding='same')
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self.bn1 = layers.BatchNormalization()
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self.relu = layers.Activation('relu')
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self.conv2 = layers.Conv2D(filters, (3, 3), strides=1, padding='same')
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self.bn2 = layers.BatchNormalization()
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self.conv3 = layers.Conv2D(4 * filters, (1, 1), strides=1, padding='same')
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self.bn3 = layers.BatchNormalization()
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if self.downsample:
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self.shortcut = Sequential()
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self.shortcut.add(layers.Conv2D(4 * filters, (1, 1), strides=stride))
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self.shortcut.add(layers.BatchNormalization(axis=3))
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def call(self, input, training=None):
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out = self.conv1(input)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.relu(out)
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out = self.conv3(out)
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out = self.bn3(out)
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if self.downsample:
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shortcut = self.shortcut(input)
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else:
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shortcut = input
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output = layers.add([out, shortcut])
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output = tf.nn.relu(output)
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return output
ResNet18， ResNet34
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import tensorflow as tf
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from tensorflow import keras
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from tensorflow.keras import layers, Sequential
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-
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# 第一个残差模块
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class BasicBlock(layers.Layer):
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def __init__(self, filter_num, stride=1):
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super(BasicBlock, self).__init__()
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self.conv1 = layers.Conv2D(filter_num, (3, 3), strides=stride, padding='same')
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self.bn1 = layers.BatchNormalization()
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self.relu = layers.Activation('relu')
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self.conv2 = layers.Conv2D(filter_num, (3, 3), strides=1, padding='same')
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self.bn2 = layers.BatchNormalization()
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if stride != 1:
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self.downsample = Sequential()
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self.downsample.add(layers.Conv2D(filter_num, (1, 1), strides=stride))
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else:
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self.downsample = lambda x: x
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def call(self, input, training=None):
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out = self.conv1(input)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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identity = self.downsample(input)
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output = layers.add([out, identity])
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output = tf.nn.relu(output)
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return output
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-
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class ResNet(keras.Model):
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def __init__(self, layer_dims, num_classes=10):
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super(ResNet, self).__init__()
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# 预处理层
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self.padding = keras.layers.ZeroPadding2D((3, 3))
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self.stem = Sequential([
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layers.Conv2D(64, (7, 7), strides=(2, 2)),
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layers.BatchNormalization(),
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layers.Activation('relu'),
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layers.MaxPool2D(pool_size=(3, 3), strides=(2, 2), padding='same')
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])
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# resblock
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self.layer1 = self.build_resblock(64, layer_dims[0])
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self.layer2 = self.build_resblock(128, layer_dims[1], stride=2)
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self.layer3 = self.build_resblock(256, layer_dims[2], stride=2)
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self.layer4 = self.build_resblock(512, layer_dims[3], stride=2)
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# 全局池化
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self.avgpool = layers.GlobalAveragePooling2D()
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# 全连接层
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self.fc = layers.Dense(num_classes, activation=tf.keras.activations.softmax)
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def call(self, input, training=None):
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x=self.padding(input)
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x = self.stem(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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# [b,c]
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x = self.avgpool(x)
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x = self.fc(x)
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return x
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def build_resblock(self, filter_num, blocks, stride=1):
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res_blocks = Sequential()
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res_blocks.add(BasicBlock(filter_num, stride))
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for pre in range(1, blocks):
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res_blocks.add(BasicBlock(filter_num, stride=1))
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return res_blocks
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-
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def ResNet34(num_classes=10):
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return ResNet([2, 2, 2, 2], num_classes=num_classes)
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-
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def ResNet34(num_classes=10):
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return ResNet([3, 4, 6, 3], num_classes=num_classes)
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-
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model = ResNet34(num_classes=1000)
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model.build(input_shape=(1, 224, 224, 3))
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print(model.summary()) # 统计网络参数
ResNet50、ResNet101、ResNet152
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import tensorflow as tf
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from tensorflow import keras
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from tensorflow.keras import layers, Sequential
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-
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# 第一个残差模块
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class Block(layers.Layer):
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def __init__(self, filters, downsample=False, stride=1):
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super(Block, self).__init__()
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self.downsample = downsample
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self.conv1 = layers.Conv2D(filters, (1, 1), strides=stride, padding='same')
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self.bn1 = layers.BatchNormalization()
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self.relu = layers.Activation('relu')
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self.conv2 = layers.Conv2D(filters, (3, 3), strides=1, padding='same')
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self.bn2 = layers.BatchNormalization()
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self.conv3 = layers.Conv2D(4 * filters, (1, 1), strides=1, padding='same')
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self.bn3 = layers.BatchNormalization()
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if self.downsample:
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self.shortcut = Sequential()
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self.shortcut.add(layers.Conv2D(4 * filters, (1, 1), strides=stride))
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self.shortcut.add(layers.BatchNormalization(axis=3))
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def call(self, input, training=None):
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out = self.conv1(input)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.relu(out)
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out = self.conv3(out)
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out = self.bn3(out)
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if self.downsample:
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shortcut = self.shortcut(input)
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else:
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shortcut = input
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output = layers.add([out, shortcut])
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output = tf.nn.relu(output)
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return output
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class ResNet(keras.Model):
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def __init__(self, layer_dims, num_classes=10):
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super(ResNet, self).__init__()
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# 预处理层
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self.padding = keras.layers.ZeroPadding2D((3, 3))
-
self.stem = Sequential([
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layers.Conv2D(64, (7, 7), strides=(2, 2)),
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layers.BatchNormalization(),
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layers.Activation('relu'),
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layers.MaxPool2D(pool_size=(3, 3), strides=(2, 2), padding='same')
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])
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# resblock
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self.layer1 = self.build_resblock(64, layer_dims[0],stride=1)
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self.layer2 = self.build_resblock(128, layer_dims[1], stride=2)
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self.layer3 = self.build_resblock(256, layer_dims[2], stride=2)
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self.layer4 = self.build_resblock(512, layer_dims[3], stride=2)
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# 全局池化
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self.avgpool = layers.GlobalAveragePooling2D()
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# 全连接层
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self.fc = layers.Dense(num_classes, activation=tf.keras.activations.softmax)
-
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def call(self, input, training=None):
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x = self.padding(input)
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x = self.stem(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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# [b,c]
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x = self.avgpool(x)
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x = self.fc(x)
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return x
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def build_resblock(self, filter_num, blocks, stride=1):
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res_blocks = Sequential()
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if stride != 1 or filter_num * 4 != 64:
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res_blocks.add(Block(filter_num, downsample=True,stride=stride))
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for pre in range(1, blocks):
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res_blocks.add(Block(filter_num, stride=1))
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return res_blocks
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-
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def ResNet50(num_classes=10):
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return ResNet([3, 4, 6, 3], num_classes=num_classes)
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-
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def ResNet101(num_classes=10):
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return ResNet([3, 4, 23, 3], num_classes=num_classes)
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def ResNet152(num_classes=10):
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return ResNet([3, 8, 36, 3], num_classes=num_classes)
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model = ResNet50(num_classes=1000)
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model.build(input_shape=(1, 224, 224, 3))
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print(model.summary()) # 统计网络参数
运行结果：
文章来源: wanghao.blog.csdn.net，作者：AI浩，版权归原作者所有，如需转载，请联系作者。
原文链接：wanghao.blog.csdn.net/article/details/117420186
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