from __future__ import print_function
import os
os.environ['TF_USE_LEGACY_KERAS'] = '1'
from tensorflow.python.keras.layers import Dense, Activation
from tensorflow.python.keras import backend as K, Input
from tensorflow.python.keras.optimizer_v2.adam import Adam
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.metrics import roc_curve, auc
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.python.keras.models import Model
from importance_sampling.training import ConstantTimeImportanceTraining
from example_utils import get_parser
import pennylane as qml
import tensorflow as tf
tf.compat.v1.disable_eager_execution()

def plot_history(history):
    plt.figure(figsize=(12, 5))

    # 绘制训练集和验证集的准确率
    plt.subplot(121)
    plt.plot(history.history['accuracy'])
    plt.plot(history.history['val_accuracy'])
    plt.title('Model accuracy')
    plt.ylabel('Accuracy')
    plt.xlabel('Epoch')
    plt.legend(['Train', 'Validation'], loc='upper left')

    # 绘制训练集和验证集的损失
    plt.subplot(122)
    plt.plot(history.history['loss'])
    plt.plot(history.history['val_loss'])
    plt.title('Model loss')
    plt.ylabel('Loss')
    plt.xlabel('Epoch')
    plt.legend(['Train', 'Validation'], loc='upper left')

    plt.tight_layout()
    plt.show()


def plot_roc_curve(y_test, y_pred):
    n_classes = y_test.shape[1]
    fpr = dict()
    tpr = dict()
    roc_auc = dict()

    for i in range(n_classes):
        fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_pred[:, i])
        roc_auc[i] = auc(fpr[i], tpr[i])

    # Compute micro-average ROC curve and ROC area
    fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_pred.ravel())
    roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])

    plt.figure()
    plt.plot(fpr["micro"], tpr["micro"],
             label='micro-average ROC curve (area = {0:0.2f})'
                   ''.format(roc_auc["micro"]))
    for i in range(n_classes):
        plt.plot(fpr[i], tpr[i],
                 label='ROC curve of class {0} (area = {1:0.2f})'
                       ''.format(i, roc_auc[i]))

    plt.plot([0, 1], [0, 1], 'k--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver Operating Characteristic (ROC) Curve')
    plt.legend(loc="lower right")
    plt.show()


def calculate_accuracy_fluctuation(history):
    # 获取每个 epoch 的验证集准确率
    val_accuracies = history.history['val_accuracy']

    # 计算平均准确率
    mean_accuracy = np.mean(val_accuracies)

    # 计算每个 epoch 的准确率波动（与均值的绝对差值）
    fluctuation = np.sum(np.abs(val_accuracies - mean_accuracy))

    return mean_accuracy, fluctuation


if __name__ == "__main__":
    parser = get_parser("训练模型于鸢尾花数据集")
    args = parser.parse_args()

    # 加载并预处理鸢尾花数据集
    iris = datasets.load_iris()
    x_data = iris.data
    y_data = iris.target.reshape(-1, 1)

    # 对标签进行独热编码
    encoder = OneHotEncoder(sparse_output=False)
    y_data = encoder.fit_transform(y_data)

    # 将数据集划分为训练集和测试集
    x_train, x_test, y_train, y_test = train_test_split(
        x_data, y_data, test_size=0.3, random_state=116)

    # 定义超参数
    learning_rate = 0.1
    epochs = 100
    batch_size = 8
    num_classes = 3  # 鸢尾花数据集有3类

    # 定义量子电路和量子层
    n_qubits = 4  # 量子比特数（与特征数相同）
    n_layers = 2  # 变分量子电路的层数

    dev = qml.device('default.qubit', wires=n_qubits)

    # 定义量子节点 (QNode)
    def quantum_circuit(inputs, weights):
        # 数据编码（角度编码）
        for i in range(n_qubits):
            qml.RY(inputs[i], wires=i)
        # 变分层
        qml.templates.StronglyEntanglingLayers(weights, wires=range(n_qubits))
        # 测量
        return [qml.expval(qml.PauliZ(i)) for i in range(num_classes)]  # 输出与类别数匹配

    weight_shapes = {"weights": (n_layers, n_qubits, 3)}

    # 将量子电路封装为 Keras 层
    qlayer = qml.qnn.KerasLayer(quantum_circuit, weight_shapes, output_dim=num_classes)

    # 使用 Functional API 构建模型
    inputs = Input(shape=(4,))
    x = qlayer(inputs)
    outputs = Activation('softmax')(x)
    model = Model(inputs=inputs, outputs=outputs)

    # 编译模型
    model.compile(
        optimizer=Adam(learning_rate=learning_rate),
        loss='categorical_crossentropy',
        metrics=['accuracy']
    )

    # 在构建模型前初始化 ConstantTimeImportanceTraining
    model.predict(np.zeros((1, 4)))  # 这里输入形状为 (1, 4)

    # 如果指定了重要性采样训练，则封装模型
    if args.importance_training:
        wrapped = ConstantTimeImportanceTraining(model)
    else:
        wrapped = model

    # 训练模型
    history = wrapped.fit(
        x_train, y_train,
        batch_size=batch_size,
        epochs=epochs,
        verbose=1,
        validation_data=(x_test, y_test)
    )

    # 评估模型
    score = model.evaluate(x_test, y_test, verbose=0)
    print('测试损失:', score[0])
    print('测试准确率:', score[1])

    # 计算并显示准确率波动
    mean_accuracy, fluctuation = calculate_accuracy_fluctuation(history)
    print('平均验证准确率:', mean_accuracy)
    print('准确率波动:', fluctuation)

    # 绘制训练历史
    plot_history(history)

    # 预测概率
    y_pred = model.predict(x_test)

    # 绘制 ROC 曲线
    plot_roc_curve(y_test, y_pred)
上述是我的脚本文件yuanweihua.py,这个文件记录了如何构建量子神经网络以及调用重要性采样的部分,同时还有数据预处理的部分,数据处理部分的内容与datasets.py文件中的内容有对接的部分.
import sys
import numpy as np
import logging
from collections.abc import Iterable
import tensorflow as tf
from tensorflow.python.keras.callbacks import BaseLogger, CallbackList, History, ProgbarLogger
from tensorflow.python.keras.layers import Dropout
#from tensorflow.python.keras.utils import Sequence
from blinker import signal
from .datasets import InMemoryDataset, GeneratorDataset
from .model_wrappers import OracleWrapper
from .samplers import ConditionalStartSampler, VarianceReductionCondition, \
    AdaptiveAdditiveSmoothingSampler, AdditiveSmoothingSampler, ModelSampler, \
    ConstantVarianceSampler, ConstantTimeSampler, SCSGSampler
from .reweighting import BiasedReweightingPolicy, NoReweightingPolicy
from .utils.functional import ___, compose, partial

class _BaseImportanceTraining(object):
    def __init__(self, model, score="gnorm", layer=None):
        """重要性采样训练的基类。

        参数：
            model: 要训练的 Keras 模型。
        """
        # 检查模型是否兼容，并包装模型以支持重要性采样
        self._check_model(model)
        self.original_model = model
        self.model = OracleWrapper(
            model,
            self.reweighting,
            score=score,
            layer=layer
        )
        self.original_model.optimizer = self.model.optimizer
        signal("is.sample").connect(self._on_sample)
        signal("is.score").connect(self._on_scores)

    def _on_sample(self, event):
        self._latest_sample_event = event

    def _on_scores(self, scores):
        self._latest_scores = scores

    def _check_model(self, model):
        """检查模型是否使用了 Dropout 和 BatchNorm，并警告可能影响重要性计算。"""
        if any(
                isinstance(l, (tf.keras.layers.BatchNormalization, Dropout))
                for l in model.layers
        ):
            sys.stderr.write(("[NOTICE]: 您正在使用 BatchNormalization 和/或 Dropout。\n"
                              "这些层可能会影响重要性计算，建议您用 LayerNormalization 或 "
                              "在测试模式下的 BatchNormalization 以及 L2 正则化替代它们。\n"))

    @property
    def reweighting(self):
        """控制重要性采样时偏置的重加权策略。"""
        raise NotImplementedError()

    def sampler(self, dataset, batch_size, steps_per_epoch, epochs):
        """为给定的数据集创建一个新的采样器，以使用重要性采样进行采样。"""
        raise NotImplementedError()

    def fit(self, x, y, batch_size=32, epochs=1, verbose=1, callbacks=None,
            validation_split=0.0, validation_data=None, steps_per_epoch=None,
            on_sample=None, on_scores=None):
        """使用给定的数据创建一个 InMemoryDataset 实例，并使用重要性采样训练模型。

        参数：
            x: 训练数据的 Numpy 数组或 Numpy 数组列表。
            y: 目标数据的 Numpy 数组。
            batch_size: 每个梯度更新的样本数。
            epochs: 遍历整个训练集的次数。
            verbose: {0, >0}，是否使用 Keras 的进度条回调。
            callbacks: 在训练期间调用的 Keras 回调列表。
            validation_split: [0, 1) 之间的浮点数，用于验证的数据百分比。
            validation_data: tuple，(x_val, y_val)，用于模型验证的数据。
            steps_per_epoch: int 或 None，每个 epoch 的步数。
            on_sample: 接受 sampler, idxs, w, scores 的回调函数。
            on_scores: 接受 sampler 和 scores 的回调函数。
        返回：
            包含训练期间收集的信息的 Keras `History` 对象。
        """
        # 处理验证数据集
        if validation_data is not None:
            x_train, y_train = x, y
            x_test, y_test = validation_data
        elif validation_split > 0:
            assert validation_split < 1, "验证集比例必须小于 1"
            n = int(round(validation_split * len(x)))
            idxs = np.arange(len(x))
            np.random.shuffle(idxs)
            x_train, y_train = x[idxs[n:]], y[idxs[n:]]
            x_test, y_test = x[idxs[:n]], y[idxs[:n]]
        else:
            x_train, y_train = x, y
            x_test, y_test = np.empty(shape=(0, x.shape[1])), np.empty(shape=(0, y.shape[1]))

        # 创建数据集
        dataset = InMemoryDataset(
            x_train,
            y_train,
            x_test,
            y_test,
            categorical=False  # 不需要转换标签
        )

        return self.fit_dataset(
            dataset=dataset,
            batch_size=batch_size,
            epochs=epochs,
            steps_per_epoch=steps_per_epoch,
            verbose=verbose,
            callbacks=callbacks,
            on_sample=on_sample,
            on_scores=on_scores
        )

    def fit_generator(self, train, steps_per_epoch=None, batch_size=32,
                      epochs=1, verbose=1, callbacks=None,
                      validation_data=None, validation_steps=None,
                      on_sample=None, on_scores=None):
        """创建一个 GeneratorDataset 实例，并使用重要性采样训练模型。

        参数：
            train: 返回 (inputs, targets) 的生成器或可迭代对象。
            steps_per_epoch: 每个 epoch 的梯度更新次数。
            batch_size: 每个梯度更新的样本数。
            epochs: 训练轮数。
            verbose: {0, >0}，是否使用 Keras 的进度条回调。
            validation_data: 生成器、可迭代对象或 (inputs, targets) 元组。
            validation_steps: 如果 validation_data 是生成器，则需要指定此参数。
            on_sample: 接受 sampler, idxs, w, scores 的回调函数。
            on_scores: 接受 sampler 和 scores 的回调函数。
        """

        def infinite_iterator(iterable):
            while True:
                yield from iterable

        # 处理验证数据集
        if validation_data is not None:
            if isinstance(validation_data, (tuple, list)):
                test = validation_data
                test_len = None
            elif isinstance(validation_data, Iterable):
                test = infinite_iterator(validation_data)
                try:
                    test_len = len(validation_data) * batch_size
                except TypeError:
                    test_len = validation_steps * batch_size if validation_steps else None
            else:
                test = validation_data
                test_len = validation_steps * batch_size if validation_steps else None
        else:
            test = (np.empty(shape=(0, 1)), np.empty(shape=(0, 1)))
            test_len = None
            logging.info("未提供验证数据")

        # 设置训练数据和每个 epoch 的步数
        if isinstance(train, Iterable):
            if steps_per_epoch is None:
                try:
                    steps_per_epoch = len(train)
                except TypeError:
                    raise ValueError("使用没有 __len__ 方法的生成器时必须设置 steps_per_epoch")
            train = infinite_iterator(train)
        elif steps_per_epoch is None:
            raise ValueError("steps_per_epoch 未设置")

        dataset = GeneratorDataset(train, test, test_len)

        return self.fit_dataset(
            dataset=dataset,
            batch_size=batch_size,
            epochs=epochs,
            steps_per_epoch=steps_per_epoch,
            verbose=verbose,
            callbacks=callbacks,
            on_sample=on_sample,
            on_scores=on_scores
        )

    def fit_dataset(self, dataset, steps_per_epoch=None, batch_size=32,
                    epochs=1, verbose=1, callbacks=None, on_sample=None,
                    on_scores=None):
        """在给定的数据集上训练模型。

        参数：
            dataset: `BaseDataset` 的实例，提供要训练的数据。
            steps_per_epoch: int，每个 epoch 的梯度更新次数。
            batch_size: 每个梯度更新的样本数。
            epochs: 训练轮数。
            verbose: {0, >0}，是否使用 Keras 的进度条回调。
            callbacks: Keras 回调列表。
            on_sample: 接受 sampler, idxs, w, scores 的回调函数。
            on_scores: 接受 sampler 和 scores 的回调函数。
        """
        try:
            if len(dataset.train_data) < batch_size:
                raise ValueError("batch_size 大于训练集大小")
        except RuntimeError as e:
            assert "no size" in str(e)

        # 设置 steps_per_epoch
        if steps_per_epoch is None:
            steps_per_epoch = len(dataset.train_data) // batch_size

        # 创建回调列表
        self.history = History()
        callbacks = [BaseLogger()] + (callbacks or []) + [self.history]
        if verbose > 0:
            callbacks += [ProgbarLogger(count_mode="steps")]
        callbacks = CallbackList(callbacks)
        callbacks.set_model(self.original_model)
        callbacks.set_params({
            "epochs": epochs,
            "steps": steps_per_epoch,
            "verbose": verbose,
            "do_validation": len(dataset.test_data) > 0,
            "metrics": self.metrics_names + [
                "val_" + n for n in self.metrics_names
            ]
        })

        # 创建采样器
        sampler = self.sampler(dataset, batch_size, steps_per_epoch, epochs)

        # 开始训练循环
        epoch = 0
        self.original_model.stop_training = False
        callbacks.on_train_begin()
        while epoch < epochs:
            callbacks.on_epoch_begin(epoch)

            for step in range(steps_per_epoch):
                batch_logs = {"batch": step, "size": batch_size}
                callbacks.on_batch_begin(step, batch_logs)

                # 在这里进行重要性采样
                idxs, (x_batch, y_batch), w = sampler.sample(batch_size)

                # 将 x_batch 和 y_batch 转换为 TensorFlow 张量
                x_batch = tf.convert_to_tensor(x_batch)
                y_batch = tf.convert_to_tensor(y_batch)
                w = tf.convert_to_tensor(w, dtype=tf.float32)

                # 自定义训练步骤
                loss_value, metrics_results, scores = self.train_batch(x_batch, y_batch, w)

                # 更新采样器
                sampler.update(idxs, scores)

                # 记录指标
                batch_logs['loss'] = loss_value
                for metric_name, metric_value in zip(self.metrics_names[1:], metrics_results):
                    batch_logs[metric_name] = metric_value
                callbacks.on_batch_end(step, batch_logs)

                if on_scores is not None and hasattr(self, "_latest_scores"):
                    on_scores(
                        sampler,
                        self._latest_scores
                    )

                if on_sample is not None:
                    on_sample(
                        sampler,
                        self._latest_sample_event["idxs"],
                        self._latest_sample_event["w"],
                        self._latest_sample_event["predicted_scores"]
                    )

                if self.original_model.stop_training:
                    break

            # 在每个 epoch 结束时进行评估
            epoch_logs = {}
            if len(dataset.test_data) > 0:
                val_loss, val_metrics = self.evaluate(dataset.test_data)
                epoch_logs['val_loss'] = val_loss
                for name, value in zip(self.metrics_names[1:], val_metrics):
                    epoch_logs['val_' + name] = value
            callbacks.on_epoch_end(epoch, epoch_logs)
            if self.original_model.stop_training:
                break
            epoch += 1
        callbacks.on_train_end()

        return self.history

    @property
    def metrics_names(self):
        def name(x):
            if isinstance(x, str):
                return x
            if hasattr(x, "name"):
                return x.name
            if hasattr(x, "__name__"):
                return x.__name__
            return str(x)

        metrics = self.original_model.metrics or []
        return (
            ["loss"] +
            [name(m) for m in metrics]
        )

    def train_batch(self, x, y, w):
        """使用自定义训练步骤进行训练。"""
        with tf.GradientTape() as tape:
            y_pred = self.model(x, training=True)
            loss_value = self.model.compiled_loss(
                y,
                y_pred,
                sample_weight=w,
                regularization_losses=self.model.losses
            )

        gradients = tape.gradient(loss_value, self.model.trainable_variables)
        self.model.optimizer.apply_gradients(zip(gradients, self.model.trainable_variables))

        # 更新指标
        self.model.compiled_metrics.update_state(y, y_pred, sample_weight=w)
        metrics_results = [m.result().numpy() for m in self.model.metrics]

        # 返回损失值和指标结果
        return loss_value.numpy(), metrics_results, None

    def evaluate(self, test_data):
        """评估模型在测试集上的性能。"""
        x_test, y_test = test_data[:]
        y_pred = self.model.predict(x_test, batch_size=128)
        loss_value = self.model.compiled_loss(y_test, y_pred, regularization_losses=self.model.losses)

        # 更新指标
        self.model.compiled_metrics.update_state(y_test, y_pred)
        metrics_results = [m.result().numpy() for m in self.model.metrics]

        return loss_value.numpy(), metrics_results


class _UnbiasedImportanceTraining(_BaseImportanceTraining):
    def __init__(self, model, score="gnorm", layer=None):
        self._reweighting = BiasedReweightingPolicy(1.0)  # 无偏

        super(_UnbiasedImportanceTraining, self).__init__(model, score, layer)

    @property
    def reweighting(self):
        return self._reweighting


class ConstantTimeImportanceTraining(_UnbiasedImportanceTraining):
    """使用常时间重要性采样训练模型。

    参数：
        model: 要训练的 Keras 模型。
        score: {"gnorm", "loss", "full_gnorm"}，用于重要性采样的重要性度量。
        layer: None 或 int 或 Layer，计算 gnorm 时使用的层。
    """

    def __init__(self, model, backward_pass_multiplier=2.0, extra_samples=0.1,
                 score="gnorm", layer=None):
        self._backward_pass_multiplier = backward_pass_multiplier
        self._extra_samples = extra_samples

        super(ConstantTimeImportanceTraining, self).__init__(
            model,
            score,
            layer
        )

    def sampler(self, dataset, batch_size, steps_per_epoch, epochs):
        return ConstantTimeSampler(
            dataset,
            self.reweighting,
            self.model,
            backward_pass_multiplier=self._backward_pass_multiplier,
            extra_samples=self._extra_samples
上述部分是training.py文件,这是训练的部分,yuanweihua.py调用的重要性采样也在这一部分,这一部分主要的内容就是通过调用其余文件来实现训练过程.
import sys
import numpy as np
from blinker import signal
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.layers import Layer, multiply
from tensorflow.python.keras import Model, Input
from tensorflow.python.keras.metrics import categorical_accuracy

import tensorflow as tf

from .layers import GradientNormLayer, LossLayer, MetricLayer

class ModelWrapper(object):
    """ModelWrapper 的目的是包装一个神经网络，并添加一些额外的层，以生成得分、损失和样本权重，以执行重要性采样。"""

    def _iterate_batches(self, x, y, batch_size):
        bs = batch_size
        for s in range(0, len(y), bs):
            yield [xi[s:s+bs] for xi in _tolist(x)], y[s:s+bs]

    def evaluate(self, x, y, batch_size=128):
        result = np.mean(
            np.vstack([
                self.evaluate_batch(xi, yi)
                for xi, yi in self._iterate_batches(x, y, batch_size)
            ]),
            axis=0
        )

        signal("is.evaluation").send(result)
        return result

    def score(self, x, y, batch_size=128):
        bs = batch_size
        result = np.hstack([
            self.score_batch(xi, yi).T
            for xi, yi in self._iterate_batches(x, y, batch_size)
        ]).T

        signal("is.score").send(result)
        return result

    def set_lr(self, lr):
        """设置模型的学习率。"""

        try:
            K.set_value(
                self.optimizer.lr,
                lr
            )
        except AttributeError:
            try:
                K.set_value(
                    self.model.optimizer.lr,
                    lr
                )
            except AttributeError:
                raise NotImplementedError()

        try:
            K.set_value(
                self.small.optimizer.lr,
                lr
            )
        except AttributeError:
            pass

    def evaluate_batch(self, x, y):
        raise NotImplementedError()

    def score_batch(self, x, y):
        raise NotImplementedError()

    def train_batch(self, x, y, w):
        raise NotImplementedError()


class OracleWrapper(ModelWrapper):
    AVG_LOSS = 0
    LOSS = 1
    WEIGHTED_LOSS = 2
    SCORE = 3
    METRIC0 = 4

    FUSED_ACTIVATION_WARNING = ("[WARNING]: 最后一层具有融合激活函数，例如 Dense(..., activation=\"sigmoid\")。\n"
                                "为了自动提取预激活张量，请使用单独的激活层（参见示例）。\n")

    def __init__(self, model, reweighting, score="loss", layer=None):
        self.reweighting = reweighting
        self.layer = self._gnorm_layer(model, layer)

        # 增强模型，添加重加权、得分等功能
        self._augment_model(model, score, reweighting)

    def _gnorm_layer(self, model, layer):
        # 如果提供了层，则直接使用
        if isinstance(layer, Layer):
            return layer

        # 如果提供了层的索引，则提取该层
        if isinstance(layer, int):
            return model.layers[layer]

        try:
            # 获取倒数第一层或倒数第二层，取决于最后一层是否有可训练权重
            skip_one = not bool(model.layers[-1].trainable_weights)
            last_layer = -2 if skip_one else -1

            # 如果最后一层有可训练权重，说明无法自动提取预激活张量，需要警告
            if last_layer == -1:
                config = model.layers[-1].get_config()
                if config.get("activation", "linear") != "linear":
                    sys.stderr.write(self.FUSED_ACTIVATION_WARNING)

            return model.layers[last_layer]
        except:
            # 如果出错，可能是因为我们没有使用 gnorm importance
            return None

    def _augment_model(self, model, score, reweighting):
        # 提取模型的一些信息
        loss_fn = model.loss
        optimizer = model.optimizer.__class__.from_config(model.optimizer.get_config())
        output_shape = model.output_shape[1:]
        if isinstance(loss_fn, str) and loss_fn.startswith("sparse"):
            output_shape = output_shape[:-1] + (1,)

        # 确保一些内容看起来正常
        assert not isinstance(loss_fn, list)

        # 我们需要创建两个额外的输入：
        # 1. 真实标签 y_true
        # 2. 预测的得分 pred_score
        y_true = Input(shape=output_shape)
        pred_score = Input(shape=(reweighting.weight_size,))

        # 创建损失层和得分层
        loss_tensor = LossLayer(loss_fn)([y_true, model.output])
        score_tensor = _get_scoring_layer(
            score,
            y_true,
            model.output,
            loss_fn,
            self.layer,
            model
        )

        # 创建样本权重
        weights = reweighting.weight_layer()([score_tensor, pred_score])

        # 创建加权损失
        weighted_loss = multiply([loss_tensor, weights])
        for l in model.losses:
            weighted_loss += l
        weighted_loss_mean = K.mean(weighted_loss)

        # 创建指标层
        metrics = model.metrics or []
        metrics_tensors = [
            MetricLayer(metric)([y_true, model.output])
            for metric in metrics
        ]

        # 创建一个新模型，包括额外的输入和加权损失
        inputs = _tolist(model.input) + [y_true, pred_score]
        outputs = [weighted_loss]
        self.model = Model(inputs=inputs, outputs=outputs)

        # 编译新模型
        self.model.compile(
            optimizer=optimizer,
            loss=lambda yt, yp: weighted_loss_mean,  # 自定义损失
            metrics=metrics
        )

        # 保存原始模型的优化器和损失函数
        self.optimizer = optimizer
        self.loss_fn = loss_fn

    def evaluate_batch(self, x, y):
        # 在评估时，我们不需要样本权重，因此创建一个全为 1 的权重向量
        if len(y.shape) == 1:
            y = np.expand_dims(y, axis=1)
        batch_size = y.shape[0]
        dummy_weights = np.ones((batch_size, self.reweighting.weight_size))
        inputs = _tolist(x) + [y, dummy_weights]
        outputs = self.model.evaluate(
            x=inputs,
            y=np.zeros((batch_size,)),  # 占位符，因为我们的模型输出是损失
            verbose=0,
            return_dict=True
        )

        signal("is.evaluate_batch").send(outputs)

        # 返回损失和其他指标
        return [outputs['loss']] + [outputs[m] for m in self.model.metrics_names[1:]]

    def score_batch(self, x, y):
        # 计算得分
        if len(y.shape) == 1:
            y = np.expand_dims(y, axis=1)
        batch_size = y.shape[0]
        dummy_weights = np.ones((batch_size, self.reweighting.weight_size))
        inputs = _tolist(x) + [y, dummy_weights]
        # 获取得分张量的输出
        get_score = K.function(
            inputs=self.model.inputs,
            outputs=self.model.get_layer('score_layer').output
        )
        scores = get_score(inputs)
        return scores.ravel()

    def train_batch(self, x, y, w):
        # 训练单个批次
        if len(y.shape) == 1:
            y = np.expand_dims(y, axis=1)
        batch_size = y.shape[0]
        inputs = _tolist(x) + [y, w]
        outputs = self.model.train_on_batch(
            x=inputs,
            y=np.zeros((batch_size,))  # 占位符，因为我们的模型输出是损失
        )

        # 获取损失和指标
        if isinstance(outputs, list):
            loss = outputs[0]
            metrics = outputs[1:]
        else:
            loss = outputs
            metrics = []

        # 暂时无法获取得分，因此返回 None
        scores = None

        # 返回损失、指标和得分
        result = (
            loss,
            metrics,
            scores
        )
        signal("is.training").send(result)

        return result

def _tolist(x, acceptable_iterables=(list, tuple)):
    if not isinstance(x, acceptable_iterables):
        x = [x]
    return x

def _get_scoring_layer(score, y_true, y_pred, loss_fn="categorical_crossentropy",
                       layer=None, model=None):
    """获取一个计算每个 y_true 和 y_pred 对的得分的层"""
    assert score in ["loss", "gnorm", "full_gnorm", "acc"]

    if score == "loss":
        return LossLayer(loss_fn, name='score_layer')([
            y_true,
            y_pred
        ])
    elif score == "gnorm":
        return GradientNormLayer(
            layer.output,
            loss_fn,
            fast=False,
            name='score_layer'
        )([
            y_true,
            y_pred
        ])
    elif score == "full_gnorm":
        return GradientNormLayer(
            model.trainable_weights,
            loss_fn,
            fast=False,
            name='score_layer'
        )([
            y_true,
            y_pred
        ])
    elif score == "acc":
        return MetricLayer(categorical_accuracy, name='score_layer')([
            y_true,
            y_pred
        ])

class ModelWrapperDecorator(ModelWrapper):
    def __init__(self, model_wrapper, implemented_attributes=set()):
        self.model_wrapper = model_wrapper
        self.implemented_attributes = (
            implemented_attributes | set(["model_wrapper"])
        )

    def __getattribute__(self, name):
        _getattr = object.__getattribute__
        implemented_attributes = _getattr(self, "implemented_attributes")
        if name in implemented_attributes:
            return _getattr(self, name)
        else:
            model_wrapper = _getattr(self, "model_wrapper")
            return getattr(model_wrapper, name)
上述文件是包装类文件model_wrappers.py,其主要目的是重新构建模型,重新包装一个量子神经网络
import numpy as np
import tensorflow as tf

class BaseDataset(object):
    class DataProxy(object):
        def __init__(self, dataset, subset):
            self.data = getattr(dataset, f"_{subset}_data")
            self.size = getattr(dataset, f"_{subset}_size")

        def __getitem__(self, idxs):
            return self.data(idxs)

        def __len__(self):
            return self.size()

    def _train_data(self, idxs=slice(None)):
        """返回训练数据作为 (x, y)"""
        raise NotImplementedError()

    def _train_size(self):
        """返回训练数据的大小"""
        raise NotImplementedError()

    def _test_data(self, idxs=slice(None)):
        """返回测试数据作为 (x, y)"""
        raise NotImplementedError()

    def _test_size(self):
        """返回测试数据的大小"""
        raise NotImplementedError()

    def _slice_data(self, x, y, idxs):
        if isinstance(x, (list, tuple)):
            return [xi[idxs] for xi in x], y[idxs]
        else:
            return x[idxs], y[idxs]

    def _extract_shape(self, x):
        if isinstance(x, (list, tuple)):
            return [xi.shape[1:] for xi in x]
        return x.shape[1:]

    @property
    def train_data(self):
        return self.DataProxy(self, "train")

    @property
    def test_data(self):
        return self.DataProxy(self, "test")

    @property
    def shape(self):
        """返回样本的形状"""
        raise NotImplementedError()

    @property
    def output_size(self):
        """返回输出的大小"""
        raise NotImplementedError()

    @property
    def output_shape(self):
        """返回输出的形状（序列模型可能会有所不同）"""
        return (self.output_size,)

class InMemoryDataset(BaseDataset):
    """一个包含四个numpy数组（x_train, y_train, x_test, y_test）的内存中的数据集。
    x可以是一个numpy数组列表。"""

    def __init__(self, X_train, y_train, X_test, y_test, categorical=True, preprocess_x=None):
        # 如果提供了预处理函数，则应用预处理
        if preprocess_x is not None:
            self._x_train = preprocess_x(X_train)
            self._x_test = preprocess_x(X_test)
        else:
            self._x_train = X_train
            self._x_test = X_test

        # 决定是否将目标转换为one-hot编码
        if categorical:
            if len(y_train.shape) == 1 or y_train.shape[1] == 1:
                # 如果y_train是向量或有一列，则转换为one-hot
                self._y_train = tf.keras.utils.to_categorical(y_train)
                self._y_test = tf.keras.utils.to_categorical(y_test)
            else:
                # 假设y_train已经是one-hot编码的
                self._y_train = y_train
                self._y_test = y_test
            self._output_size = self._y_train.shape[1]
        # 对于稀疏分类任务
        elif issubclass(y_train.dtype.type, np.integer):
            self._y_train = y_train
            self._y_test = y_test
            self._output_size = np.max(self._y_train) + 1  # 假设类别索引从0开始
        # 非分类任务，直接复制标签
        else:
            self._y_train = y_train
            self._y_test = y_test
            if len(self._y_train.shape) == 1:
                self._output_size = 1
            else:
                self._output_size = self._y_train.shape[1]

    def _train_data(self, idxs=slice(None)):
        return self._slice_data(self._x_train, self._y_train, idxs)

    def _train_size(self):
        return self._len(self._x_train)

    def _test_data(self, idxs=slice(None)):
        return self._slice_data(self._x_test, self._y_test, idxs)

    def _test_size(self):
        return self._len(self._x_test)

    def _len(self, x):
        if isinstance(x, (list, tuple)):
            return len(x[0])
        else:
            return len(x)

    @property
    def shape(self):
        return self._extract_shape(self._x_train)

    @property
    def output_size(self):
        return self._output_size

    @classmethod
    def from_loadable(cls, dataset, categorical=True, preprocess_x=None):
        (x_train, y_train), (x_test, y_test) = dataset.load_data()
        return cls(x_train, y_train, x_test, y_test, categorical=categorical, preprocess_x=preprocess_x)
上述代码是数据集处理代码datasets.py,主要对数据集进行预处理.
上述四个文件的内容是我使用经典神经网络的重要性采样进行修改的,修改内容是将经典神经网络修改为量子神经网络,但目前我并没有修改完成,我需要你阅读这些.py文件,告诉我后续我该如何修改,需要修改的内容在于将经典神经网络更改为量子神经网络,同时需要保证原本的逻辑不变(逻辑指的是整体思路,你可以使用不同方式完成相同的事件)
我的pennylane版本是0.15.0,tensorflow与keras版本是2.15,剩余环境如下absl-py                      2.1.0
appdirs                      1.4.4
array-record                 0.4.1
astunparse                   1.6.3
autograd                     1.7.0
blinker                      1.8.2
cached-property              1.5.2
cachetools                   5.5.0
certifi                      2024.8.30
charset-normalizer           3.3.2
click                        8.1.7
colorama                     0.4.6
contourpy                    1.3.0
cycler                       0.12.1
dill                         0.3.8
dm-tree                      0.1.8
docplex                      2.28.240
etils                        1.5.2
flatbuffers                  24.3.25
fonttools                    4.54.1
fsspec                       2024.9.0
gast                         0.4.0
google-auth                  2.35.0
google-auth-oauthlib         0.4.6
google-pasta                 0.2.0
grpcio                       1.66.1
h5py                         3.11.0
idna                         3.10
importlib_metadata           8.5.0
importlib_resources          6.4.5
joblib                       1.4.2
keras                        2.10.0
Keras-Preprocessing          1.1.2
kiwisolver                   1.4.7
libclang                     18.1.1
Markdown                     3.7
markdown-it-py               3.0.0
MarkupSafe                   2.1.5
matplotlib                   3.9.1
mdurl                        0.1.2
ml-dtypes                    0.3.2
mpmath                       1.3.0
munkres                      1.1.4
namex                        0.0.8
networkx                     3.2.1
numpy                        1.26.4
oauthlib                     3.2.2
olefile                      0.47
opt-einsum                   3.3.0
optree                       0.12.1
packaging                    24.1
pbr                          6.1.0
PennyLane                    0.15.1
Pillow                       8.4.0
pip                          24.2
promise                      2.3
protobuf                     3.20.3
psutil                       6.0.0
pyasn1                       0.6.1
pyasn1_modules               0.4.1
Pygments                     2.18.0
pyparsing                    3.1.4
PyQt5                        5.12.3
PyQt5_sip                    4.19.18
PyQtChart                    5.12
PyQtWebEngine                5.12.1
python-dateutil              2.9.0
qiskit                       1.0.2
qiskit-aer                   0.14.0.1
qiskit-algorithms            0.3.0
qiskit-optimization          0.6.1
requests                     2.32.3
requests-oauthlib            2.0.0
rich                         13.8.1
rsa                          4.9
rustworkx                    0.15.1
scikit-learn                 1.5.2
scipy                        1.13.1
semantic-version             2.6.0
setuptools                   75.1.0
six                          1.16.0
stevedore                    5.3.0
symengine                    0.11.0
sympy                        1.13.3
tensorboard                  2.10.1
tensorboard-data-server      0.6.1
tensorboard-plugin-wit       1.8.1
tensorflow                   2.10.0
tensorflow-datasets          4.9.3
tensorflow-estimator         2.10.0
tensorflow-io-gcs-filesystem 0.31.0
tensorflow-metadata          1.15.0
termcolor                    2.4.0
threadpoolctl                3.5.0
toml                         0.10.2
tornado                      6.4.1
tqdm                         4.66.5
typing_extensions            4.12.2
unicodedata2                 15.1.0
urllib3                      2.2.3
Werkzeug                     3.0.4
wheel                        0.35.1
wrapt                        1.16.0
zipp                         3.20.2
给我所有.py文件对应的修改内容,用中文与我进行交流