【图像分类】用最简短的代码复现SeNet，小白一定要收藏（keras，Tensorflow2(1)

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()

se-block

self.se_globalpool = keras.layers.GlobalAveragePooling2D()

self.se_resize = keras.layers.Reshape((1, 1, filter_num))

self.se_fc1 = keras.layers.Dense(units=filter_num // 16, activation=‘relu’,

use_bias=False)

self.se_fc2 = keras.layers.Dense(units=filter_num, activation=‘sigmoid’,

use_bias=False)

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)

se_block

b = out

out = self.se_globalpool(out)

out = self.se_resize(out)

out = self.se_fc1(out)

out = self.se_fc2(out)

out = keras.layers.Multiply()([b, out])

identity = self.downsample(input)

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

output = tf.nn.relu(output)

return output

第二个残差模块

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第二个残差模块用于实现ResNet50、ResNet101、ResNet152模型，SENet模块嵌入到第三个卷积后面。

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’)

self.bn2 = layers.BatchNormalization()

self.conv3 = layers.Conv2D(4 * filters, (1, 1), strides=1, padding=‘same’)

self.bn3 = layers.BatchNormalization()

se-block

self.se_globalpool = keras.layers.GlobalAveragePooling2D()

self.se_resize = keras.layers.Reshape((1, 1, 4 * filters))

self.se_fc1 = keras.layers.Dense(units=4 * filters // 16, activation=‘re