class定义class BasicBlock(layers.Layer): expansion = 1 def init(self, in_channels, out_channels, stride=1): super(BasicBlock, self).init() # 1. BasicBlock模块中的共有2个卷积;BasicBlock模块中的第1个卷积层; self.conv1 = regularized_padded_conv(out_channels, kernel_size=3, strides=stride) self.bn1 = layers.BatchNormalization() # 2. 第2个；第1个卷积如果做stride就会有一个下采样，在这个里面就不做下采样了。这一块始终保持size一致，把stride固定为1 self.conv2 = regularized_padded_conv(out_channels, kernel_size=3, strides=1) self.bn2 = layers.BatchNormalization() # 3. 判断stride是否等于1,如果为1就是没有降采样。 if stride != 1 or in_channels != self.expansion * out_channels: self.shortcut = Sequential([regularized_padded_conv(self.expansion * out_channels, kernel_size=1, strides=stride), layers.BatchNormalization()]) else: self.shortcut = lambda x, _: x self.max= layers.GlobalMaxPooling2D(kernel_size=2, stride=2) def call(self, inputs, training=False): out = self.conv1(inputs) out = self.bn1(out, training=training) out = tf.nn.relu(out) out = self.conv2(out) out = self.bn2(out, training=training) out = out + self.shortcut(inputs, training) out = self.pool(out) out = tf.nn.relu(out) return out 后如何在一个def定义的卷积网络中调用该模块

在一个def定义的卷积网络中调用该模块，可以像这样：

class ConvNet(tf.keras.Model):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.conv1 = tf.keras.layers.Conv2D(64, 3, activation='relu')
        self.bn1 = tf.keras.layers.BatchNormalization()
        self.bb1 = BasicBlock(64, 64, stride=1)
        self.bb2 = BasicBlock(64, 128, stride=2)
        self.bb3 = BasicBlock(128, 256, stride=2)
        self.avg_pool = tf.keras.layers.GlobalAveragePooling2D()
        self.fc = tf.keras.layers.Dense(10)
        
    def call(self, inputs, training=False):
        out = self.conv1(inputs)
        out = self.bn1(out, training=training)
        out = tf.nn.relu(out)
        out = self.bb1(out, training=training)
        out = self.bb2(out, training=training)
        out = self.bb3(out, training=training)
        out = self.avg_pool(out)
        out = self.fc(out)
        return out

在这个例子中，我们定义了一个ConvNet类，其中包含了一个BasicBlock模块的实例（bb1, bb2, bb3），通过调用这些实例来组成我们的整个卷积网络。在call方法中，我们可以像调用其他层一样来调用BasicBlock模块，这样就可以将其作为整个网络的一部分来使用。