tensorflow - 当我使用 tensorflow2 的子类化 API 时，出现了一些奇怪的问题：有些层可以重用，有些不能，为什么

Question

在下面的两个例子中，都是使用子类化来构建相同的模型。重用层会出现一些奇怪的问题。一个不能重用所有层，另一个不能重用部分层，例如卷积， BatchNormization，但可以重用激活层。
为什么？

张量流版本：2.0.0

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1. 使用 tensorflow 中已有的层。

所有层都不能重用，例如卷积，BatchNormailzation，Activation。
在下面的代码中，当我在调用函数中将 'conv2' 更改为 'conv' 或将 'bn2' 更改为 'bn' 或 'ac2' 为 'ac' 时，会引发错误。

import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf
from tensorflow.keras.layers import Conv2D, BatchNormalization, ReLU


class Models(tf.keras.Model):
    def __init__(self):
        super().__init__()

        self.conv = Conv2D(16, (3, 3), padding='same')
        self.bn = BatchNormalization()
        self.ac = ReLU()

        self.conv2 = Conv2D(32, (3, 3), padding='same')
        self.bn2 = BatchNormalization()
        self.ac2 = ReLU()

    def call(self, x, **kwargs):
        x = self.conv(x)
        x = self.bn(x)
        x = self.ac(x)

        x = self.conv2(x)
        x = self.bn2(x)
        x = self.ac2(x)

        return x


m = Models()
m.build(input_shape=(2, 8, 8, 3))
m.summary()

---

在重新启动层时会抛出一些错误，例如：

1. 重用 BatchNormalization 层：

ValueError: Input 0 of layer batch_normalization is incompatible with the layer: expected axis 3 of input shape to have value 16 but received input with shape [2, 8, 8, 32]

2. 复用卷积层：

ValueError: Input 0 of layer conv2d is incompatible with the layer: expected axis -1 of input shape to have value 3 but received input with shape [2, 8, 8, 16]

3. 重用激活层：

ValueError: You tried to call `count_params` on re_lu_1, but the layer isn't built. You can build it manually via: `re_lu_1.build(batch_input_shape)`.

2. 使用从 tensorflow 扩展而来的自定义层。

在下面的代码中，重用卷积/BactchNorization 层的结果与之前的代码类似，但是激活层可以重用！

import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

import tensorflow as tf
from tensorflow.keras import layers


class DoubleConv(layers.Layer):

    def __init__(self, mid_kernel_numbers, out_kernel_number):
        """
        初始化含有两个卷积的卷积块

        :param mid_kernel_numbers: 中间特征图的通道数
        :param out_kernel_number: 输出特征图的通道数
        """
        super().__init__()
        self.conv1 = layers.Conv2D(mid_kernel_numbers, (3, 3), padding='same')
        self.conv2 = layers.Conv2D(out_kernel_number, (3, 3), padding='same')
        self.bn = layers.BatchNormalization()
        self.bn2 = layers.BatchNormalization()
        self.ac = layers.ReLU()
        self.ac2 = layers.ReLU()

    def call(self, input, **kwargs):
        """正向传播"""
        x = self.conv1(input)
        x = self.bn(x)
        x = self.ac(x)

        x = self.conv2(x)
        x = self.bn2(x)
        x = self.ac2(x)
        return x


class Model(tf.keras.Model):

    def __init__(self):
        """
        构建模型的类
        """
        super().__init__()
        # 初始化卷积块
        self.block = DoubleConv(16, 32)

    def call(self, x, **kwargs):
        x = self.block(x)

        return x


m = Model()
m.build(input_shape=(2, 8, 8, 3))
m.summary()

在重新启动层时会抛出一些错误，例如：

1. 重用 BatchNormalization 层：

ValueError: Input 0 of layer batch_normalization is incompatible with the layer: expected axis 3 of input shape to have value 16 but received input with shape [2, 8, 8, 32]

2. 复用卷积层：

AttributeError: 'DoubleConv' object has no attribute 'conv'

---

我的猜测有两种可能：

一个与层的名称有关。另一个与参数有关。激活层不需要参数。 但是这些并不能解释为什么会有差异。

标签： tensorflowkerasdeep-learningmodelsubclassing