import sys
import os
import pennylane as qml
from pennylane import numpy as np
import tensorflow as tf
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder, StandardScaler, MinMaxScaler
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
from tensorflow.keras.layers import Dense
from example_utils import get_parser
from importance_sampling.training import ConstantTimeImportanceTraining
from tensorflow.keras.callbacks import EarlyStopping

# 重定向标准输出以捕获打印输出
class Tee(object):
    def __init__(self, *files):
        self.files = files
    def write(self, obj):
        for f in self.files:
            f.write(obj)
            f.flush()
    def flush(self):
        for f in self.files:
            f.flush()

output_file = open('output_modified.log', 'w', encoding='utf-8')
original_stdout = sys.stdout
sys.stdout = Tee(sys.stdout, output_file)

def save_plot(fig, filename):
    fig.savefig(filename)
    plt.close(fig)

def plot_history(history):
    # 绘制训练和验证准确率
    fig, ax = plt.subplots(figsize=(10, 5))
    ax.plot(history.history['accuracy'], label='Train Accuracy')
    ax.plot(history.history['val_accuracy'], label='Validation Accuracy')
    ax.set_title('Model Accuracy')
    ax.set_ylabel('Accuracy')
    ax.set_xlabel('Epoch')
    ax.legend(loc='upper left')
    save_plot(fig, 'accuracy_plot.png')

    # 绘制训练和验证损失
    fig, ax = plt.subplots(figsize=(10, 5))
    ax.plot(history.history['loss'], label='Train Loss')
    ax.plot(history.history['val_loss'], label='Validation Loss')
    ax.set_title('Model Loss')
    ax.set_ylabel('Loss')
    ax.set_xlabel('Epoch')
    ax.legend(loc='upper left')
    save_plot(fig, 'loss_plot.png')

def calculate_accuracy_fluctuation(history):
    val_accuracies = history.history['val_accuracy']
    mean_accuracy = np.mean(val_accuracies)
    fluctuation = np.sum(np.abs(val_accuracies - mean_accuracy))
    return mean_accuracy, fluctuation

# 定义具有14个量子比特的量子设备
dev = qml.device("default.qubit", wires=14)

def encode_group(control_qubits, data_qubits, angle_q2, angle_q3):
    control_values = [[0, 0], [0, 1], [1, 0], [1, 1]]

    for i in range(4):
        cv = control_values[i]
        angle_q3_val = angle_q3[i]

        # 翻转为0的控制量子比特以进行受控操作
        for qubit, val in zip(control_qubits, cv):
            if val == 0:
                qml.PauliX(wires=qubit)

        # 应用受控RY旋转
        qml.ctrl(qml.RY, control=control_qubits)(angle_q2, wires=data_qubits['q2'])
        qml.ctrl(qml.RY, control=control_qubits)(angle_q3_val, wires=data_qubits['q3'])

        # 恢复控制量子比特
        for qubit, val in zip(control_qubits, cv):
            if val == 0:
                qml.PauliX(wires=qubit)

def apply_controlled_swap(swap_qubit, q_swap1, q_swap2):
    """
    应用受控SWAP门的模块化方法。

    参数：
    - swap_qubit (int): SWAP门的控制量子比特。
    - q_swap1 (int): 第一个交换的量子比特。
    - q_swap2 (int): 第二个交换的量子比特。
    """
    qml.Hadamard(wires=swap_qubit)
    qml.CSWAP(wires=[swap_qubit, q_swap1, q_swap2])
    qml.Hadamard(wires=swap_qubit)

@qml.qnode(dev, interface='tf', diff_method='backprop')
def quantum_neural_network(inputs, encoding_weights, rot_weights, entangle_weights):
    control_qubits = [0, 1]

    encoding_data = [
        {'data_qubits': {'q2': 2, 'q3': 3}, 'swap_qubit': 4},
        {'data_qubits': {'q2': 5, 'q3': 6}, 'swap_qubit': 7},
        {'data_qubits': {'q2': 8, 'q3': 9}, 'swap_qubit': 10},
        {'data_qubits': {'q2': 11, 'q3': 12}, 'swap_qubit': 13}
    ]

    # 角度编码：将输入数据映射到 [0, pi/2] 范围
    angles_q2_list = inputs  # inputs的形状为 (4,)，表示一个样本的四个特征

    # 编码权重映射到 angles_q3
    angles_q3_list = encoding_weights  # encoding_weights的形状为 (4, n_layers, 4)

    # 编码过程
    for idx, data_set in enumerate(encoding_data):
        data_qubits = data_set['data_qubits']
        swap_qubit = data_set['swap_qubit']

        angle_q2 = angles_q2_list[idx]  # 标量张量，对应一个特征

        for layer in range(rot_weights.shape[0]):
            angle_q3_values = angles_q3_list[idx, layer, :]  # 形状: (4,)
            encode_group(control_qubits, data_qubits, angle_q2, angle_q3_values)

    # 应用交换操作
    for data_set in encoding_data:
        swap_qubit = data_set['swap_qubit']
        data_qubits = data_set['data_qubits']
        apply_controlled_swap(swap_qubit, data_qubits['q3'], data_qubits['q2'])

    # 应用旋转层和参数化纠缠门
    for layer in range(rot_weights.shape[0]):
        for qubit in range(rot_weights.shape[1]):
            rot_angles = rot_weights[layer, qubit, :]  # 形状: (3,)
            qml.Rot(rot_angles[0], rot_angles[1], rot_angles[2], wires=qubit)
        # 应用参数化纠缠门
        for qubit in range(rot_weights.shape[1] - 1):
            qml.CRZ(entangle_weights[layer, qubit], wires=[qubit, qubit + 1])

    # 在多个量子比特上测量 PauliZ 期望值
    measured_qubits = [4, 7, 10, 13]
    results = [qml.expval(qml.PauliZ(qubit)) for qubit in measured_qubits]

    return results

# 定义用于绘制电路的 QNode
@qml.qnode(dev)
def quantum_neural_network_draw(inputs, encoding_weights, rot_weights, entangle_weights):
    control_qubits = [0, 1]

    encoding_data = [
        {'data_qubits': {'q2': 2, 'q3': 3}, 'swap_qubit': 4},
        {'data_qubits': {'q2': 5, 'q3': 6}, 'swap_qubit': 7},
        {'data_qubits': {'q2': 8, 'q3': 9}, 'swap_qubit': 10},
        {'data_qubits': {'q2': 11, 'q3': 12}, 'swap_qubit': 13}
    ]

    # 角度编码：将输入数据映射到 [0, pi/2] 范围
    angles_q2_list = inputs  # inputs的形状为 (4,)，表示一个样本的四个特征

    # 编码权重映射到 angles_q3
    angles_q3_list = encoding_weights  # encoding_weights的形状为 (4, n_layers, 4)

    # 编码过程
    for idx, data_set in enumerate(encoding_data):
        data_qubits = data_set['data_qubits']
        swap_qubit = data_set['swap_qubit']

        angle_q2 = angles_q2_list[idx]  # 标量张量，对应一个特征

        for layer in range(rot_weights.shape[0]):
            angle_q3_values = angles_q3_list[idx, layer, :]  # 形状: (4,)
            encode_group(control_qubits, data_qubits, angle_q2, angle_q3_values)

    # 应用交换操作
    for data_set in encoding_data:
        swap_qubit = data_set['swap_qubit']
        data_qubits = data_set['data_qubits']
        apply_controlled_swap(swap_qubit, data_set['data_qubits']['q3'], data_set['data_qubits']['q2'])

    # 应用旋转层和参数化纠缠门
    for layer in range(rot_weights.shape[0]):
        for qubit in range(rot_weights.shape[1]):
            rot_angles = rot_weights[layer, qubit, :]  # 形状: (3,)
            qml.Rot(rot_angles[0], rot_angles[1], rot_angles[2], wires=qubit)
        # 应用参数化纠缠门
        for qubit in range(rot_weights.shape[1] - 1):
            qml.CRZ(entangle_weights[layer, qubit], wires=[qubit, qubit + 1])

    # 在多个量子比特上测量 PauliZ 期望值
    measured_qubits = [4, 7, 10, 13]
    results = [qml.expval(qml.PauliZ(qubit)) for qubit in measured_qubits]

    return results

# 定义量子神经网络层
class QuantumNeuralNetworkLayer(tf.keras.layers.Layer):
    def __init__(self, n_qubits, n_layers, num_classes, **kwargs):
        super(QuantumNeuralNetworkLayer, self).__init__(**kwargs)
        self.n_qubits = n_qubits
        self.n_layers = n_layers
        self.num_classes = num_classes

        # 定义编码权重，具有 L2 正则化
        self.encoding_weights = self.add_weight(
            shape=(4, n_layers, 4),  # 4 个编码组，每层有 4 个角度
            initializer=tf.keras.initializers.GlorotUniform(),
            regularizer=tf.keras.regularizers.l2(0.01),
            trainable=True,
            name='encoding_weights',
            dtype=tf.float64
        )

        # 定义旋转层权重，具有 L2 正则化
        self.rot_weights = self.add_weight(
            shape=(n_layers, n_qubits, 3),
            initializer=tf.keras.initializers.GlorotUniform(),
            regularizer=tf.keras.regularizers.l2(0.01),
            trainable=True,
            name='rot_weights',
            dtype=tf.float64
        )

        # 定义纠缠权重，具有 L2 正则化
        self.entangle_weights = self.add_weight(
            shape=(n_layers, n_qubits - 1),
            initializer=tf.keras.initializers.RandomUniform(0, 2 * np.pi),
            regularizer=tf.keras.regularizers.l2(0.01),
            trainable=True,
            name='entangle_weights',
            dtype=tf.float64
        )

    def call(self, inputs):
        def apply_qnode(x):
            x = tf.cast(x, dtype=tf.float64)
            result = quantum_neural_network(x, self.encoding_weights, self.rot_weights, self.entangle_weights)
            return tf.cast(result, dtype=tf.float32)

        outputs = tf.map_fn(
            apply_qnode,
            inputs,
            dtype=tf.float32
        )

        return outputs

# 定义量子神经网络模型
class QuantumNeuralNetwork(tf.keras.Model):
    def __init__(self, n_qubits, n_layers, num_classes, **kwargs):
        super(QuantumNeuralNetwork, self).__init__(**kwargs)
        self.n_qubits = n_qubits
        self.n_layers = n_layers
        self.num_classes = num_classes

        # 量子层
        self.quantum_layer = QuantumNeuralNetworkLayer(n_qubits, n_layers, num_classes)

        # 输出层，直接连接到量子输出
        self.output_layer = Dense(num_classes, activation='softmax',
                                  kernel_regularizer=tf.keras.regularizers.l2(0.01))

    def call(self, inputs):
        q_output = self.quantum_layer(inputs)
        return self.output_layer(q_output)

if __name__ == "__main__":
    parser = get_parser("Train model on Iris dataset")
    args = parser.parse_args()

    iris = load_iris()
    X = iris['data'].astype(np.float32)
    y = iris['target'].reshape(-1, 1)

    # 数据标准化
    scaler = StandardScaler()
    X_standardized = scaler.fit_transform(X)

    # 将标准化后的数据映射到 [0, 1] 范围
    min_max_scaler = MinMaxScaler(feature_range=(0, 1))
    X_normalized = min_max_scaler.fit_transform(X_standardized)

    # 将归一化后的数据映射到 [0, pi/2] 范围
    X_mapped = X_normalized * (np.pi / 2)

    # 对标签进行 One-Hot 编码
    encoder = OneHotEncoder(sparse_output=False)
    y_encoded = encoder.fit_transform(y).astype(np.float32)

    # 修改后的数据拆分
    # 首先拆分成临时训练集和最终测试集
    x_temp, x_test, y_temp, y_test = train_test_split(
        X_mapped, y_encoded, test_size=0.2, random_state=116, stratify=y_encoded
    )

    # 然后将临时训练集拆分为训练集和验证集
    x_train, x_val, y_train, y_val = train_test_split(
        x_temp, y_temp, test_size=0.2, random_state=116, stratify=y_temp
    )

    # 超参数
    learning_rate = 0.02  # 学习率
    epochs = 200          # 训练轮数
    batch_size = 16       # 批量大小
    num_classes = 3
    n_qubits = 14         # 14 个量子比特（0 到 13）
    n_layers = 3          # 层数

    model = QuantumNeuralNetwork(
        n_qubits=n_qubits,
        n_layers=n_layers,
        num_classes=num_classes
    )

    model.compile(
        optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate),
        loss='categorical_crossentropy',
        metrics=['accuracy']
    )

    def is_quantum_model(model):
        for layer in model.layers:
            if isinstance(layer, QuantumNeuralNetworkLayer):
                return True
        return False

    if is_quantum_model(model):
        print("该模型包含量子神经网络（QNN）层。")
    else:
        print("该模型不包含量子神经网络（QNN）层。")

    print("\n量子电路图：")
    sample_inputs = np.ones(4, dtype=np.float64) * (np.pi / 2)  # 使用 4 个输入特征的 float64 类型
    sample_encoding_weights = np.zeros((4, n_layers, 4), dtype=np.float64)  # 编码权重
    sample_rot_weights = np.zeros((n_layers, n_qubits, 3), dtype=np.float64)  # 旋转权重
    sample_entangle_weights = np.zeros((n_layers, n_qubits - 1), dtype=np.float64)  # 纠缠权重

    print(qml.draw(quantum_neural_network_draw)(sample_inputs, sample_encoding_weights, sample_rot_weights, sample_entangle_weights))  # 使用 QNode 绘制

    # 提前停止回调，增加耐心值
    early_stopping = EarlyStopping(monitor='val_loss', patience=20, restore_best_weights=True)

    if args.importance_training:
        wrapped_model = ConstantTimeImportanceTraining(model, score="loss")
        history = wrapped_model.fit(
            x_train, y_train,
            batch_size=batch_size,
            epochs=epochs,
            verbose=1,
            validation_data=(x_val, y_val),
            callbacks=[early_stopping]
        )
    else:
        history = model.fit(
            x_train, y_train,
            batch_size=batch_size,
            epochs=epochs,
            verbose=1,
            validation_data=(x_val, y_val),
            callbacks=[early_stopping]
        )

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

    mean_accuracy, fluctuation = calculate_accuracy_fluctuation(history)
    print('验证集平均准确率:', mean_accuracy)
    print('准确率波动:', fluctuation)

    plot_history(history)

    # 关闭输出文件并恢复标准输出
    sys.stdout = original_stdout
    output_file.close()

print("脚本执行完毕。请检查 'output_modified.log' 以获取打印输出，并在当前目录下查看保存的图表。")
上述是实验运行的脚本文件
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.model.model.metrics or []
        return (
                ["loss"] +
                [name(m) for m in metrics]
        )

    def train_batch(self, x, y, w):
        with tf.GradientTape() as tape:
            y_pred = self.model.model(x, training=True)

            # 获取损失函数
            loss_fn = self.model.model.loss

            # 处理字符串形式的损失函数
            if isinstance(loss_fn, str):
                loss_fn = tf.keras.losses.get(loss_fn)
            elif isinstance(loss_fn, dict):
                loss_fn = tf.keras.losses.get(loss_fn)
            elif not callable(loss_fn):
                raise ValueError(f"Unsupported loss function type: {type(loss_fn)}")

            # 确保损失函数的 reduction 设置为 NONE
            if isinstance(loss_fn, tf.keras.losses.Loss):
                loss_config = loss_fn.get_config()
                per_example_loss_fn = loss_fn.__class__(reduction=tf.keras.losses.Reduction.NONE, **loss_config)
                per_sample_losses = per_example_loss_fn(y, y_pred)
            else:
                per_sample_losses = loss_fn(y, y_pred)

            # 使用正确的评分方法
            if self.model.score_method == "loss":
                scores = per_sample_losses
            else:
                scores = tf.ones_like(per_sample_losses)

            # 计算样本权重和加权损失
            weights = self.reweighting.weight_layer()([scores, w])

            # **确保两个张量都是 float32**
            per_sample_losses = tf.cast(per_sample_losses, tf.float32)
            weights = tf.cast(weights, tf.float32)

            weighted_losses = per_sample_losses * weights
            loss = tf.reduce_mean(weighted_losses)

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

        self.model.model.compiled_metrics.update_state(y, y_pred, sample_weight=weights)
        metrics = [metric.result().numpy() for metric in self.model.model.metrics]

        return loss.numpy(), metrics, scores.numpy()

    def evaluate(self, test_data):
        print(f"test_data type: {type(test_data)}, length: {len(test_data)}")
        print(f"test_data contents: {test_data}")

        x_test, y_test = test_data[:2]
        # 只获取前两个元素
        x_test, y_test = test_data[:2]

        # 如果 x_test 或 y_test 是元组，提取其中的数组
        if isinstance(x_test, tuple) or isinstance(x_test, list):
            x_test = x_test[0]
        if isinstance(y_test, tuple) or isinstance(y_test, list):
            y_test = y_test[0]

        # 确保 x_test 和 y_test 是 NumPy 数组或张量
        x_test = np.asarray(x_test)
        y_test = np.asarray(y_test)

        y_pred = self.model.model.predict(x_test, batch_size=128)

        # 确保 y_test 和 y_pred 是张量
        y_test = tf.convert_to_tensor(y_test)
        y_pred = tf.convert_to_tensor(y_pred)

        loss_value = self.model.model.compiled_loss(y_test, y_pred, regularization_losses=self.model.model.losses)

        # 更新指标
        self.model.model.compiled_metrics.update_state(y_test, y_pred)
        metrics_results = [m.result().numpy() for m in self.model.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_time=self._backward_pass_multiplier,
        )
上述是training.py文件
import numpy as np
from blinker import signal

def _get_dataset_length(dset, default=1):
    """Return the dataset's training data length and in case the dataset is uncountable return a default value."""
    try:
        return len(dset.train_data)
    except RuntimeError:
        return default

class VarianceReductionCondition:
    def __init__(self, tau_th):
        self.tau_th = tau_th
        self.satisfied = False
        self.tau = 0

    def update(self, scores):
        # 计算当前批次得分的方差与均值
        variance = np.var(scores)
        mean = np.mean(scores)
        if mean == 0:
            self.tau = 0
        else:
            self.tau = variance / mean
        # 更新条件状态
        if self.tau >= self.tau_th:
            self.satisfied = True
        else:
            self.satisfied = False

class BaseSampler(object):
    """BaseSampler denotes the interface for all the samplers.

    Samplers should provide the rest of the program with data points to train on and corresponding relative weights.
    """
    def __init__(self, dataset, reweighting):
        self.dataset = dataset
        self.reweighting = reweighting

    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 _send_messages(self, idxs, xy, w, predicted_scores):
        signal("is.sample").send({
            "idxs": idxs,
            "xy": xy,
            "w": w,
            "predicted_scores": predicted_scores
        })

    def _get_samples_with_scores(self, batch_size):
        """Child classes should implement this method.

        Arguments
        ---------
        batch_size: int
            Return at least that many samples

        Return
        ------
        idxs: array
            The indices of some samples in the dataset
        scores: array or None
            The predicted importance scores for the corresponding idxs or None for uniform sampling
        xy: tuple or None
            Optionally return the data for the corresponding idxs
        """
        raise NotImplementedError()

    def sample(self, batch_size):
        # Get the importance scores of some samples
        idxs1, scores, xy = self._get_samples_with_scores(batch_size)

        # Sample from the available ones
        if scores is not None:
            # Use argsort to sort scores
            idxs_sorted = np.argsort(scores)
            idxs2 = self._select_samples(idxs_sorted, scores, batch_size)
        else:
            # If scores is None, randomly select batch_size sample indices
            idxs2 = np.random.choice(len(idxs1), batch_size, p=None)

        w = self.reweighting.sample_weights(idxs2, scores)

        # Make sure we have the data
        if xy is None:
            xy = self.dataset.train_data[idxs1[idxs2]]
        else:
            x, y = xy
            xy = self._slice_data(x, y, idxs2)

        scores = scores[idxs2] if scores is not None else np.ones(batch_size)
        self._send_messages(idxs1[idxs2], xy, w, scores)
        return idxs1[idxs2], xy, w

    def update(self, idxs, results):
        pass

    def _select_samples(self, idxs_sorted, scores, batch_size):
        """Child classes should implement this method to select sample indices."""
        raise NotImplementedError()

class ConstantTimeSampler(BaseSampler):
    def __init__(self, dataset, reweighting, model, batch_size=16,
                 backward_time=2.0, tau_th=0.9, ratio=0.5, min_a=0.2):
        self.condition = VarianceReductionCondition(tau_th)
        self.backward_time = backward_time
        self.ratio = ratio
        self.min_a = min_a
        self.model = model
        self.batch_size = batch_size
        self.N = len(dataset.train_data) if hasattr(dataset, 'train_data') else _get_dataset_length(dataset, default=1)
        print(f"Dataset size (self.N): {self.N}")
        self.usage_counts = np.zeros(self.N, dtype=int)

        super(ConstantTimeSampler, self).__init__(dataset, reweighting)

    def _select_top_scores(self, scores, num_high, B):
        """
        选择分数最高的样本

        Parameters:
            scores: np.array, 样本分数
            num_high: int, 需要选择的高分样本数量
            B: int, 总样本数量

        Returns:
            np.array: 选中的样本索引
        """
        # 获取排序后的索引
        sorted_indices = np.argsort(scores)[::-1]

        # 选择前num_high个索引
        high_score_indices = sorted_indices[:num_high]

        return high_score_indices

    def sample(self, batch_size):
        print(f"Entering sample method")
        print(f"batch_size: {batch_size}, self.N: {self.N}")
        print(f"Condition satisfied: {self.condition.satisfied}")
        print(f"Requested batch_size: {batch_size}")

        if batch_size > self.N:
            batch_size = min(batch_size, self.N)

        scores_selected = None

        if self.condition.satisfied:
            print("Entering satisfied condition block")
            t = (1.0 + self.backward_time) * batch_size
            B = 32  # Number of samples to evaluate
            idxs = np.random.choice(self.N, B, replace=False)
            x, y = self.dataset.train_data[idxs]

            # 获取并处理分数
            scores = self.model.score(x, y, batch_size=B)
            scores = np.nan_to_num(scores, nan=0.0, posinf=1.0, neginf=0.0)
            scores = scores + 1e-8

            # 归一化分数
            if np.max(scores) != np.min(scores):
                scores = (scores - np.min(scores)) / (np.max(scores) - np.min(scores))
            else:
                scores = np.full_like(scores, 0.5)

            # 缩放到0-7范围
            scores = scores * 7 + 1e-8
            scores = np.clip(scores, 1e-8, 7)
            scores = np.ceil(scores)

            print("Normalized and adjusted scores:", scores)

            # 计算标准差
            std_scores = np.std(scores)
            print("Standard deviation of scores:", std_scores)

            # 确定devide值
            devide = self._determine_devide(std_scores, batch_size)
            print(f"Calculated devide value: {devide}")

            num_high = devide
            num_other = batch_size - devide

            # 选择高分样本
            if num_high > 0:
                idxs_high = self._select_top_scores(scores, num_high, B)
            else:
                idxs_high = np.array([], dtype=int)

            # 处理剩余样本
            if num_other > 0:
                remaining_indices = np.setdiff1d(np.arange(B), idxs_high)
                if len(remaining_indices) >= num_other:
                    idxs_other = np.random.choice(remaining_indices, num_other, replace=False)
                else:
                    idxs_other = np.random.choice(remaining_indices, num_other, replace=True)
            else:
                idxs_other = np.array([], dtype=int)

            # 合并索引
            idxs2 = np.concatenate([idxs_high, idxs_other])

            if len(idxs2) == 0:
                print("Warning: Failed to get valid sample indices, falling back to random sampling.")
                return self._random_sample(batch_size)

            # 获取选中的样本和分数
            x, y = self._slice_data(x, y, idxs2)
            scores_selected = scores[idxs2]

            scores_selected = scores_selected + 1e-8
            w = self.reweighting.sample_weights(np.arange(len(scores_selected)), scores_selected)
        else:
            # 随机采样逻辑保持不变
            idxs = np.random.choice(self.N, batch_size, replace=False)
            idxs2 = np.arange(len(idxs))
            x, y = self.dataset.train_data[idxs]
            scores_selected = np.ones(len(idxs))
            w = np.ones((len(idxs), self.reweighting.weight_size))

        # 更新条件状态
        self.condition.update(scores_selected)

        self._send_messages(idxs[idxs2], (x, y), w, scores_selected)
        print(f"Returned batch shapes: x: {x.shape}, y: {y.shape}, w: {w.shape}")
        return (idxs[idxs2], (x, y), w)

    def _determine_devide(self, std_scores, batch_size):
        # 您可以根据标准差调整阈值
        # 这里简单地根据标准差大小决定 devide 值
        if std_scores > 1.0:
            devide = int(batch_size * 0.7)
        elif std_scores > 0.5:
            devide = int(batch_size * 0.5)
        else:
            devide = int(batch_size * 0.3)
        return devide
上述是samplers.py文件
import numpy as np
from blinker import signal
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.layers import Layer
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):
            if isinstance(x, (list, tuple)):
                xs = [xi[s:s+bs] for xi in x]
            else:
                xs = x[s:s+bs]
            yield xs, 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)
        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]

        # 对于量子神经网络，可能无法自动获取梯度范数层
        # 这里暂时返回 None，表示不使用特定的层
        return None

    def _augment_model(self, model, score, reweighting):
        # 保存原始模型
        self.original_model = model

        # 创建包装后的模型，不修改原始模型的结构
        self.model = model  # 使用原始模型

        # 保存必要的信息
        self.score_method = score
        self.reweighting = reweighting

        # 提取模型的一些信息
        self.loss_fn = model.loss
        self.optimizer = model.optimizer.__class__.from_config(model.optimizer.get_config())
        self.metrics = model.metrics or []

    def evaluate_batch(self, x, y):
        # 使用 model.evaluate
        outputs = self.model.evaluate(
            x=x,
            y=y,
            verbose=0,
            return_dict=True
        )
        loss = outputs['loss']
        metrics = [outputs[m] for m in self.model.metrics_names[1:]]

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

        # 返回损失和其他指标
        return [loss] + metrics

    # 在 model_wrappers.py 文件中
    def score_batch(self, x, y):
        y_pred = self.model(x, training=False)
        loss_fn = self.model.loss

        # 如果 loss_fn 是字符串，使用 tf.keras.losses.get 将其转换为损失函数对象
        if isinstance(loss_fn, str):
            loss_fn = tf.keras.losses.get(loss_fn)
        elif isinstance(loss_fn, dict):
            loss_fn = tf.keras.losses.get(loss_fn)
        elif not callable(loss_fn):
            raise ValueError(f"Unsupported loss function type: {type(loss_fn)}")

        # 确保损失函数具有 reduction 参数，并设置为 NONE
        if isinstance(loss_fn, tf.keras.losses.Loss):
            loss_config = loss_fn.get_config()
            per_example_loss_fn = loss_fn.__class__(reduction=tf.keras.losses.Reduction.NONE, **loss_config)
            per_sample_losses = per_example_loss_fn(y, y_pred)
        else:
            per_sample_losses = loss_fn(y, y_pred)

        if self.score_method == "loss":
            scores = per_sample_losses
        else:
            scores = tf.ones_like(per_sample_losses)

        return scores.numpy()

    def train_batch(self, x, y, w):
        with tf.GradientTape() as tape:
            y_pred = self.model(x, training=True)
            # 计算每个样本的损失（不进行 reduction）
            per_sample_losses = self.model.compiled_loss(
                y,
                y_pred,
                sample_weight=None,
                regularization_losses=self.model.losses,
                reduction=tf.keras.losses.Reduction.NONE
            )
            # 计算得分
            if self.score_method == "loss":
                scores = per_sample_losses
            else:
                # 实现其他得分方式
                scores = tf.ones_like(per_sample_losses)

            # 计算样本权重
            weights = self.reweighting.weight_layer()([scores, w])

            # 计算加权损失
            weighted_losses = per_sample_losses * weights
            loss = tf.reduce_mean(weighted_losses)

        # 获取模型的可训练参数
        trainable_vars = self.model.trainable_variables

        # 计算梯度
        gradients = tape.gradient(loss, trainable_vars)

        # 更新模型参数
        self.model.optimizer.apply_gradients(zip(gradients, trainable_vars))

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

        # 返回损失、指标和得分
        return loss.numpy(), metrics, scores.numpy()

    def __call__(self, x, training=False):
        return self.model(x, training=training)
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)
上述是model_wrappers.py文件
#
# Copyright (c) 2017 Idiap Research Institute, http://www.idiap.ch/
# Written by Angelos Katharopoulos <<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>>
#

from tensorflow.python.keras import backend as K
from tensorflow.python.keras.layers import Layer
import numpy as np


class ReweightingPolicy(object):
    """ReweightingPolicy defines how we weigh the samples given their
    importance.
    
    Each policy should provide
        1. A layer implementation for use with Keras models
        2. A python implementation for use with the samplers
    """
    def weight_layer(self):
        """Return a layer that accepts the scores and the return value of the
        sample_weights() method and produces new sample weights."""
        raise NotImplementedError()

    def sample_weights(self, idxs, scores):
        """Given the scores and the chosen indices return whatever is needed by
        the weight_layer to produce the sample weights."""
        raise NotImplementedError()

    @property
    def weight_size(self):
        """Return how many numbers per sample make up the sample weights"""
        raise NotImplementedError()


class AdjustedBiasedReweightingPolicy(ReweightingPolicy):
    """AdjustedBiasedReweightingPolicy adjusts the biased sample weights with
    the importance that has just been computed in the forward-backward pass.

    See AdjustedBiasedReweighting for details.
    """
    def __init__(self, k=1.0):
        self.k = k

    def weight_layer(self):
        return AdjustedBiasedReweighting(self.k)

    def sample_weights(self, idxs, scores):
        N = len(scores)
        S1 = scores[np.setdiff1d(np.arange(N), idxs)].sum()

        return np.tile([float(N), float(S1)], (len(idxs), 1))

    @property
    def weight_size(self):
        return 2


class BiasedReweightingPolicy(ReweightingPolicy):
    """BiasedReweightingPolicy computes the sample weights before  the
    forward-backward pass based on the sampling probabilities. It can introduce
    a bias that focuses on the hard examples when combined with the loss as an
    importance metric."""
    def __init__(self, k=1.0):
        self.k = k

    def weight_layer(self):
        return ExternalReweighting()

    def sample_weights(self, idxs, scores):
        N = len(scores)
        s = scores[idxs]
        epsilon = 1e-8
        s = s + epsilon  # 确保 s 为正数
        w = scores.sum() / N / s
        w_hat = w**self.k
        w_hat *= w.dot(s) / w_hat.dot(s)
        return w_hat

    @property
    def weight_size(self):
        return 1


class NoReweightingPolicy(ReweightingPolicy):
    """Set all sample weights to 1."""
    def weight_layer(self):
        return ExternalReweighting()

    def sample_weights(self, idxs, scores):
        return np.ones((len(idxs), 1))

    @property
    def weight_size(self):
        return 1


class CorrectingReweightingPolicy(ReweightingPolicy):
    """CorrectingReweightingPolicy aims to scale the sample weights according
    to the mistakes of the importance predictor

    Arguments
    ---------
    k: float
       The bias power used in all other reweighting schemes
    """
    def __init__(self, k=1.0):
        self.k = k
        self._biased_reweighting = BiasedReweightingPolicy(k)

    def weight_layer(self):
        return CorrectingReweighting()

    def sample_weights(self, idxs, scores):
        w = self._biased_reweighting.sample_weights(idxs, scores)

        return np.hstack([w, scores[idxs][:, np.newaxis]])

    @property
    def weight_size(self):
        return 2


class AdjustedBiasedReweighting(Layer):
    """Implement a Keras layer that using the sum of the weights and the number
    of samples it recomputes the weights.
    
    The specifics are the following:

    Given B = {i_1, i_2, ..., i_|B|} the mini-batch idexes, \\hat{s_i} the
    predicted score of sample i and k the max-loss bias constant.

        S_1 = \\sum_{i \\notin B} \\hat{s_i}
        S_2 = \\sum_{i \\in B} s_i
        a_i^{(1)} = \\frac{1}{N} (\\frac{S1 + S2}{s_i})^k \\forall i \\in B
        a_i^{(2)} = \\frac{1}{N} \\frac{S1 + S2}{s_i} \\forall i \\in B
        t = \\frac{\\sum_{i} a_i^{(2)} s_i }{\\sum_{i} a_i^{(1)} s_i}
        a_i = t a_i^{(1)} \\forall i \\in B
    """
    def __init__(self, k=1.0, **kwargs):
        self.k = k

        super(AdjustedBiasedReweighting, self).__init__(**kwargs)

    def build(self, input_shape):
        assert isinstance(input_shape, list)
        assert len(input_shape) == 2
        assert input_shape[0][1] == 1
        assert input_shape[1][1] == 2

        super(AdjustedBiasedReweighting, self).build(input_shape)

    def compute_output_shape(self, input_shape):
        return input_shape[0]

    def call(self, x):
        s, s_hat = x

        # Compute the variables defined in the class comment
        S2 = K.sum(s)
        S1 = s_hat[0, 1]
        N = s_hat[0, 0]

        # Compute the unbiased weights
        a2 = (S1 + S2) / N / s

        # Compute the biased weights and the scaling factor t
        a1 = K.pow(a2, self.k)
        sT = K.transpose(s)
        t = K.dot(sT, a2) / K.dot(sT, a1)

        return K.stop_gradient([a1 * t])[0]


class ExternalReweighting(Layer):
    """Use the provided input as sample weights"""
    def build(self, input_shape):
        super(ExternalReweighting, self).build(input_shape)

    def compute_output_shape(self, input_shape):
        return input_shape[0]

    def call(self, x):
        return K.stop_gradient(x[1])


class CorrectingReweighting(Layer):
    """Use the provided weights and the score to correct sample weights that
    were computed with a very wrong predicted score"""
    def __init__(self, min_decrease=0, max_increase=2, **kwargs):
        self.min_decrease = min_decrease
        self.max_increase = max_increase

        super(CorrectingReweighting, self).__init__(**kwargs)

    def build(self, input_shape):
        super(CorrectingReweighting, self).build(input_shape)

    def compute_output_shape(self, input_shape):
        return input_shape[0]

    def call(self, x):
        s, x1 = x
        a = x1[:, :1]
        s_hat = x1[:, 1:2]

        # Rescale the weights, making sure we mostly scale down
        a_hat = a * K.clip(s_hat / s, self.min_decrease, self.max_increase)

        # Scale again so that the reported loss is comparable to the other ones
        t = 1
        #sT = K.transpose(s)
        #t = K.dot(sT, a) / K.dot(sT, a_hat)

        return K.stop_gradient([a_hat * t])[0]


UNWEIGHTED = NoReweightingPolicy()
UNBIASED = BiasedReweightingPolicy(k=1.0)
上述是reweighting.py文件
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)

class GeneratorDataset(BaseDataset):
    """一个基于生成器的数据集，能够处理无法完全加载到内存中的大型数据集"""

    def __init__(self, train_gen, test_data, test_size=None):
        self.train_gen = train_gen
        self._x_test, self._y_test = test_data
        self._test_size_value = test_size

        # 假设生成器会生成相同形状的数据
        sample = next(train_gen)
        self._x_sample, self._y_sample = sample

    def _train_data(self, idxs=slice(None)):
        # 生成器无法通过索引获取特定的数据，因此这里返回整个生成器
        return self.train_gen

    def _train_size(self):
        # 生成器的数据大小未知，返回 None
        return None

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

    def _test_size(self):
        if self._test_size_value is not None:
            return self._test_size_value
        else:
            if isinstance(self._x_test, (list, tuple)):
                return len(self._x_test[0])
            else:
                return len(self._x_test)

    @property
    def shape(self):
        if isinstance(self._x_sample, (list, tuple)):
            return [xi.shape[1:] for xi in self._x_sample]
        else:
            return self._x_sample.shape[1:]

    @property
    def output_size(self):
        if self._y_sample.ndim == 1:
            return 1
        return self._y_sample.shape[1]
上述是datasets.py文件
仔细阅读上述文件，回答我一个问题：上述数据集划分是否有问题？为什么训练集得最后一轮准确率只有90%，而测试集准确率却有100%？这非常不正常，而且通过几轮测试发现测试集准确率都是100%