2024年Python最全【图像分类】手撕ResNet——复现ResNet（Keras,Tensorflow 2(1)

self.conv1 = layers.Conv2D(filter_num, (3, 3), strides=stride, padding=‘same’)

self.bn1 = layers.BatchNormalization()

self.relu = layers.Activation(‘relu’)

self.conv2 = layers.Conv2D(filter_num, (3, 3), strides=1, padding=‘same’)

self.bn2 = layers.BatchNormalization()

if stride != 1:

self.downsample = Sequential()

self.downsample.add(layers.Conv2D(filter_num, (1, 1), strides=stride))

else:

self.downsample = lambda x: x

def call(self, input, training=None):

out = self.conv1(input)

out = self.bn1(out)

out = self.relu(out)

out = self.conv2(out)

out = self.bn2(out)

identity = self.downsample(input)

output = layers.add([out, identity])

output = tf.nn.relu(output)

return output

第二个残差模块

第二个残差模块

class Block(layers.Layer):

def init(self, filters, downsample=False, stride=1):

super(Block, self).init()

self.downsample = downsample

self.conv1 = layers.Conv2D(filters, (1, 1), strides=stride, padding=‘same’)

self.bn1 = layers.BatchNormalization()

self.relu = layers.Activation(‘relu’)

self.conv2 = layers.Conv2D(filters, (3, 3), strides=1, padding=‘same’)

se