我在我的项目中设置了定制培训和测试功能，因此我可以对培训过程进行详细定制。我使用k-折叠交叉验证来评估我的模型。无论出于何种原因，模型在第一次折叠时正确训练，然后在第二次折叠时抛出此错误。 tensorflow.python.framework.errors_impl.FailedPreconditionError: Could not find variable _AnonymousVar13. This could mean that the variable has been deleted. In TF1, it can also mean the variable is uninitialized. Debug info: container=localhost, status=Not found: Resource localhost/_AnonymousVar13/N10tensorflow3VarE does not exist. [[node test_model/dense_2/Tensordot/ReadVariableOp (defined at training_example.py:33) ]] [Op:__inference__train_step_1082] 我不知道发生了什么事。我认为错误是由于初始化不好引起的，所以我 model.build() 使用输入形状。我也尝试过用空白Tensor初始化图的权重，但没有成功。我还在最后一行重置了后端，以防与名称冲突，但这并不能解决问题。

import numpy as np
import sklearn.model_selection
import tensorflow as tf
from tensorflow.python.keras.metrics import Mean, Precision, Recall
from tensorflow.python.keras.optimizer_v2.adam import Adam

n_splits = 5
batch_size = 16
n_epochs = 2
loss_function = tf.keras.losses.BinaryCrossentropy()
optimiser_fn = Adam
metrics = [
    Mean(name='loss'),
    Precision(name='prec'),
    Recall(name='recall'),
]
learning_rate = 1e-2
dense_outputs = [10,10]
activation = 'relu'

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

        self._dense_ops = [tf.keras.layers.Dense(o) for o in dense_outputs]
        self._output = tf.keras.layers.Dense(1)

    def call(self, inputs):
        hidden = inputs
        for l in self._dense_ops:
            hidden = l(hidden)
        return self._output(hidden)

def _load_fold_sets_for_training(fold, fold_idcs, features, labels, batch_size):
    # Get the indices for the sets.
    train_idcs, validation_idcs, _ = fold_idcs[fold]

    # Get the training data and labels.
    training_data = features[train_idcs]
    training_labels = labels[train_idcs]

    # Load the training, validation and testing sets.
    training_set = tf.data.Dataset.from_tensor_slices(
        (training_data, training_labels)
    )
    training_set = training_set.batch(batch_size, drop_remainder=False)

    validation_set = tf.data.Dataset.from_tensor_slices(
        (features[validation_idcs], labels[validation_idcs])
    )
    validation_set = validation_set.batch(batch_size, drop_remainder=False)

    return training_set, validation_set

@tf.function
def _train_step(batch_samples, batch_labels):
    batch_predictions = model(batch_samples, training=True)

    loss = loss_function(batch_predictions, batch_labels)

    gradients = tf.gradients(loss, model.trainable_variables)
    optimiser.apply_gradients(
        zip(gradients, model.trainable_variables)
    )

    batch_predictions = tf.sigmoid(batch_predictions)

    metrics[0].update_state(loss)
    [m.update_state(batch_labels, batch_predictions) for m in metrics[1:]]

@tf.function
def _inference_step(batch_samples, batch_labels):
    batch_predictions = model(batch_samples, training=False)

    loss = loss_function(batch_predictions, batch_labels)

    batch_predictions = tf.sigmoid(batch_predictions)

    metrics[0].update_state(loss)
    [m.update_state(batch_labels, batch_predictions) for m in metrics[1:]]

# Generate dataset.

features = np.random.rand(15,1440,1)
labels = np.random.rand(15,1440)

# Set up splits.

kfold = sklearn.model_selection.KFold(n_splits=n_splits, shuffle=True)
splits = []
for train_idcs, test_idcs in kfold.split(features):
    train_idcs, val_idcs = sklearn.model_selection.train_test_split(train_idcs)
    splits += [[train_idcs, val_idcs, test_idcs]]

fold = 0
while fold < n_splits:
    # Load datasets for fold.
    training_set, validation_set = _load_fold_sets_for_training(fold, splits, features, labels, batch_size)

    # Load model.
    model = TestModel()

    # Build model.
    model.build((1440, 1))

    # Initialise Adam optimiser.
    optimiser = optimiser_fn(learning_rate)

    epoch = 0
    while epoch < n_epochs:
        epoch += 1

        # Training.
        for batch_features, batch_labels in training_set: _train_step(batch_features, batch_labels)
        print(f'fold {fold}: epoch {epoch}:', ' '.join(f'train_{m.name}: {m.result():0.05f}' for m in metrics))

        # Validation.
        for batch_features, batch_labels in validation_set: _inference_step(batch_features, batch_labels)
        print(f'fold {fold}: epoch {epoch}:', ' '.join(f'val_{m.name}: {m.result():0.05f}' for m in metrics))

    tf.keras.backend.clear_session()

    fold += 1

有什么想法吗？