Tensorflow属性错误：“方法”对象没有属性“_from_serialized”

Question

我正在尝试在 Tensorflow 中训练模型并收到错误：

Attribute Error: 'method' object has no attribute '_from_serialized'

这是我复制并看到工作的代码。

看来和我的tensorflow版本和python版本的兼容性有关系。我可以运行其他模型，但当我尝试跟踪自定义指标时似乎会发生此错误。

Tensorflow-GPU 和 python 的最新兼容版本是什么，可以在跟踪自定义指标的同时运行模型？

我检查了 Tensorflow 提供的表格，这些版本应该是兼容的。

我当前的Tensorflow版本是2.10.0 Python版本是3.9.6。

是否还有其他原因可能导致此错误。我创建了多个具有不同版本的环境，但仍然收到此错误。

import os
import pickle

from tensorflow.keras import Model
from tensorflow.keras.layers import Input, Conv2D, ReLU, BatchNormalization, \
    Flatten, Dense, Reshape, Conv2DTranspose, Activation, Lambda
from tensorflow.keras import backend as K
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.losses import MeanSquaredError
import numpy as np
import tensorflow as tf

class VAE:
    """
    VAE represents a Deep Convolutional variational autoencoder architecture
    with mirrored encoder and decoder components.
    """

    def __init__(self,
                 input_shape,
                 conv_filters,
                 conv_kernels,
                 conv_strides,
                 latent_space_dim):
        self.input_shape = input_shape # [28, 28, 1]
        self.conv_filters = conv_filters # [2, 4, 8]
        self.conv_kernels = conv_kernels # [3, 5, 3]
        self.conv_strides = conv_strides # [1, 2, 2]
        self.latent_space_dim = latent_space_dim # 2
        self.reconstruction_loss_weight = 1000

        self.encoder = None
        self.decoder = None
        self.model = None

        self._num_conv_layers = len(conv_filters)
        self._shape_before_bottleneck = None
        self._model_input = None

        self._build()

    def summary(self):
        self.encoder.summary()
        self.decoder.summary()
        self.model.summary()

    def compile(self, learning_rate=0.0001):
        optimizer = Adam(learning_rate=learning_rate)
        self.model.compile(optimizer=optimizer,
                           loss=self._calculate_combined_loss,
                           metrics=[self._calculate_reconstruction_loss,
                                    self._calculate_kl_loss])

    def train(self, x_train, batch_size, num_epochs):
        self.model.fit(x_train,
                       x_train,
                       batch_size=batch_size,
                       epochs=num_epochs,
                       shuffle=True)

    def save(self, save_folder="."):
        self._create_folder_if_it_doesnt_exist(save_folder)
        self._save_parameters(save_folder)
        self._save_weights(save_folder)

    def load_weights(self, weights_path):
        self.model.load_weights(weights_path)

    def reconstruct(self, images):
        latent_representations = self.encoder.predict(images)
        reconstructed_images = self.decoder.predict(latent_representations)
        return reconstructed_images, latent_representations

    @classmethod
    def load(cls, save_folder="."):
        parameters_path = os.path.join(save_folder, "parameters.pkl")
        with open(parameters_path, "rb") as f:
            parameters = pickle.load(f)
        autoencoder = VAE(*parameters)
        weights_path = os.path.join(save_folder, "weights.h5")
        autoencoder.load_weights(weights_path)
        return autoencoder

    def _calculate_combined_loss(self, y_target, y_predicted):
        reconstruction_loss = self._calculate_reconstruction_loss(y_target, y_predicted)
        kl_loss = self._calculate_kl_loss(y_target, y_predicted)
        combined_loss = self.reconstruction_loss_weight * reconstruction_loss\
                                                         + kl_loss
        return combined_loss

    def _calculate_reconstruction_loss(self, y_target, y_predicted):
        error = y_target - y_predicted
        reconstruction_loss = K.mean(K.square(error), axis=[1, 2, 3])
        return reconstruction_loss

    def _calculate_kl_loss(self, y_target, y_predicted):
        kl_loss = -0.5 * K.sum(1 + self.log_variance - K.square(self.mu) -
                               K.exp(self.log_variance), axis=1)
        return kl_loss

    def _create_folder_if_it_doesnt_exist(self, folder):
        if not os.path.exists(folder):
            os.makedirs(folder)

    def _save_parameters(self, save_folder):
        parameters = [
            self.input_shape,
            self.conv_filters,
            self.conv_kernels,
            self.conv_strides,
            self.latent_space_dim
        ]
        save_path = os.path.join(save_folder, "parameters.pkl")
        with open(save_path, "wb") as f:
            pickle.dump(parameters, f)

    def _save_weights(self, save_folder):
        save_path = os.path.join(save_folder, "weights.h5")
        self.model.save_weights(save_path)

    def _build(self):
        self._build_encoder()
        self._build_decoder()
        self._build_autoencoder()

    def _build_autoencoder(self):
        model_input = self._model_input
        model_output = self.decoder(self.encoder(model_input))
        self.model = Model(model_input, model_output, name="autoencoder")

    def _build_decoder(self):
        decoder_input = self._add_decoder_input()
        dense_layer = self._add_dense_layer(decoder_input)
        reshape_layer = self._add_reshape_layer(dense_layer)
        conv_transpose_layers = self._add_conv_transpose_layers(reshape_layer)
        decoder_output = self._add_decoder_output(conv_transpose_layers)
        self.decoder = Model(decoder_input, decoder_output, name="decoder")

    def _add_decoder_input(self):
        return Input(shape=self.latent_space_dim, name="decoder_input")

    def _add_dense_layer(self, decoder_input):
        num_neurons = np.prod(self._shape_before_bottleneck) # [1, 2, 4] -> 8
        dense_layer = Dense(num_neurons, name="decoder_dense")(decoder_input)
        return dense_layer

    def _add_reshape_layer(self, dense_layer):
        return Reshape(self._shape_before_bottleneck)(dense_layer)

    def _add_conv_transpose_layers(self, x):
        """Add conv transpose blocks."""
        # loop through all the conv layers in reverse order and stop at the
        # first layer
        for layer_index in reversed(range(1, self._num_conv_layers)):
            x = self._add_conv_transpose_layer(layer_index, x)
        return x

    def _add_conv_transpose_layer(self, layer_index, x):
        layer_num = self._num_conv_layers - layer_index
        conv_transpose_layer = Conv2DTranspose(
            filters=self.conv_filters[layer_index],
            kernel_size=self.conv_kernels[layer_index],
            strides=self.conv_strides[layer_index],
            padding="same",
            name=f"decoder_conv_transpose_layer_{layer_num}"
        )
        x = conv_transpose_layer(x)
        x = ReLU(name=f"decoder_relu_{layer_num}")(x)
        x = BatchNormalization(name=f"decoder_bn_{layer_num}")(x)
        return x

    def _add_decoder_output(self, x):
        conv_transpose_layer = Conv2DTranspose(
            filters=1,
            kernel_size=self.conv_kernels[0],
            strides=self.conv_strides[0],
            padding="same",
            name=f"decoder_conv_transpose_layer_{self._num_conv_layers}"
        )
        x = conv_transpose_layer(x)
        output_layer = Activation("sigmoid", name="sigmoid_layer")(x)
        return output_layer

    def _build_encoder(self):
        encoder_input = self._add_encoder_input()
        conv_layers = self._add_conv_layers(encoder_input)
        bottleneck = self._add_bottleneck(conv_layers)
        self._model_input = encoder_input
        self.encoder = Model(encoder_input, bottleneck, name="encoder")

    def _add_encoder_input(self):
        return Input(shape=self.input_shape, name="encoder_input")

    def _add_conv_layers(self, encoder_input):
        """Create all convolutional blocks in encoder."""
        x = encoder_input
        for layer_index in range(self._num_conv_layers):
            x = self._add_conv_layer(layer_index, x)
        return x

    def _add_conv_layer(self, layer_index, x):
        """Add a convolutional block to a graph of layers, consisting of
        conv 2d + ReLU + batch normalization.
        """
        layer_number = layer_index + 1
        conv_layer = Conv2D(
            filters=self.conv_filters[layer_index],
            kernel_size=self.conv_kernels[layer_index],
            strides=self.conv_strides[layer_index],
            padding="same",
            name=f"encoder_conv_layer_{layer_number}"
        )
        x = conv_layer(x)
        x = ReLU(name=f"encoder_relu_{layer_number}")(x)
        x = BatchNormalization(name=f"encoder_bn_{layer_number}")(x)
        return x

    def _add_bottleneck(self, x):
        """Flatten data and add bottleneck with Guassian sampling (Dense
        layer).
        """
        self._shape_before_bottleneck = K.int_shape(x)[1:]
        x = Flatten()(x)
        self.mu = Dense(self.latent_space_dim, name="mu")(x)
        self.log_variance = Dense(self.latent_space_dim,
                                  name="log_variance")(x)

        def sample_point_from_normal_distribution(args):
            mu, log_variance = args
            epsilon = K.random_normal(shape=K.shape(self.mu), mean=0.,
                                      stddev=1.)
            sampled_point = mu + K.exp(log_variance / 2) * epsilon
            return sampled_point

        x = Lambda(sample_point_from_normal_distribution,
                   name="encoder_output")([self.mu, self.log_variance])
        return x


if __name__ == "__main__":
    autoencoder = VAE(
        input_shape=(28, 28, 1),
        conv_filters=(32, 64, 64, 64),
        conv_kernels=(3, 3, 3, 3),
        conv_strides=(1, 2, 2, 1),
        latent_space_dim=2
    )
    autoencoder.summary()
LEARNING_RATE = 0.0005
BATCH_SIZE = 32
EPOCHS = 100


def load_mnist():
    (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

    x_train = x_train.astype("float32") / 255
    x_train = x_train.reshape(x_train.shape + (1,))
    x_test = x_test.astype("float32") / 255
    x_test = x_test.reshape(x_test.shape + (1,))

    return x_train, y_train, x_test, y_test


def train(x_train, learning_rate, batch_size, epochs):
    autoencoder = VAE(
        input_shape=(28, 28, 1),
        conv_filters=(32, 64, 64, 64),
        conv_kernels=(3, 3, 3, 3),
        conv_strides=(1, 2, 2, 1),
        latent_space_dim=2
    )
    autoencoder.summary()
    autoencoder.compile(learning_rate)
    autoencoder.train(x_train, batch_size, epochs)
    return autoencoder


if __name__ == "__main__":
    x_train, _, _, _ = load_mnist()
    autoencoder = train(x_train[:10000], LEARNING_RATE, BATCH_SIZE, EPOCHS)
    autoencoder.save("model")

Answer 1

此处引发属性错误是因为您无法在方法对象上设置任何属性，即；

class Foo:
    def bar(self):
        print("bar")


if __name__ == "__main__":
    Foo().bar.baz = 1

输出：

Traceback (most recent call last): line 7, in <module>
    Foo().bar.baz = 1
AttributeError: 'method' object has no attribute 'baz'

收集 中的指标信息时training_utils_v1，将迭代模型编译时指定的指标 ( )，并为每个指标设置model.compile(..., metrics=[..])属性：_from_serialized

Traceback (most recent call last): line 7, in <module>
    Foo().bar.baz = 1
AttributeError: 'method' object has no attribute 'baz'

在提供的示例中，向 提供了两个指标model.compile，每个指标都是该类的方法VAE：

    for i, metrics in enumerate(nested_metrics):
        metrics_dict = collections.OrderedDict()
        for metric in metrics:
            metric_name = get_metric_name(metric, is_weighted)
            metric_fn = get_metric_function(
                metric, output_shape=output_shapes[i], loss_fn=loss_fns[i]
            )
            metric_fn._from_serialized = from_serialized

为了测试这一点，观察是否metrics完全省略，训练将成功开始。

提供的指标之一_calculate_reconstruction_loss是一种方法，它不需要是方法，因为它不在self正文中引用：

    def compile(self, learning_rate=0.0001):
        optimizer = Adam(learning_rate=learning_rate)
        self.model.compile(optimizer=optimizer,
                           loss=self._calculate_combined_loss,
                           metrics=[self._calculate_reconstruction_loss,
                                    self._calculate_kl_loss])

这样就可以将其移到类之外并制作成函数（某些 IDE 会以消息的形式向您推荐此操作，以达到“从方法创建函数”的效果）：

    def _calculate_reconstruction_loss(self, y_target, y_predicted):
        error = y_target - y_predicted
        reconstruction_loss = K.mean(K.square(error), axis=[1, 2, 3])
        return reconstruction_loss

然后可以修改编译语句：

    def _calculate_reconstruction_loss(y_target, y_predicted):
        error = y_target - y_predicted
        reconstruction_loss = K.mean(K.square(error), axis=[1, 2, 3])
        return reconstruction_loss

会出现相同的异常，因为我们仍然引用 call( self.calculate_kl_loss) 中的方法，但如果self.calculate_kl_loss省略，训练将成功开始：

        self.model.compile(optimizer=optimizer,
                           loss=self._calculate_combined_loss,
                           metrics=[_calculate_reconstruction_loss,
                                    self._calculate_kl_loss])

为了完整起见，回顾一下self.calculate_kl_loss正在做的事情，这有点棘手，但我们可以通过将其转换为一个采用单个参数的函数来成功地将其用作度量model，该函数返回另一个采用任意数量参数的函数（*args），这样它既可以用作度量（始终需要两个参数的函数），也可以在损失函数中使用：

        self.model.compile(optimizer=optimizer,
                           loss=self._calculate_combined_loss,
                           metrics=[_calculate_reconstruction_loss])

通过这些修订，当训练开始时，输出为：

Epoch 1/100
10000/10000 [==============================] - 43s 4ms/sample - loss: 89.4243 - _calculate_reconstruction_loss: 0.0833 - _calculate_kl_loss: 6.1707
Epoch 2/100
10000/10000 [==============================] - 46s 5ms/sample - loss: 69.0131 - _calculate_reconstruction_loss: 0.0619 - _calculate_kl_loss: 7.1129

上面详述的修订版的整个片段是：

def calculate_kl_loss(model):
    # wrap `_calculate_kl_loss` such that it takes the model as an argument,
    # returns a function which can take arbitrary number of arguments
    # (for compatibility with `metrics` and utility in the loss function)
    # and returns the kl loss
    def _calculate_kl_loss(*args):
        kl_loss = -0.5 * K.sum(1 + model.log_variance - K.square(model.mu) -
                               K.exp(model.log_variance), axis=1)
        return kl_loss
    return _calculate_kl_loss