当我试图训练CNN时，我得到了以下错误。

---------------------------------------------------------------------------
    ValueError                                Traceback (most recent call last)
    <ipython-input-1-8308471ad202> in <cell line: 89>()
         87 tf.data.experimental.enable_debug_mode()
         88 # Train the model
    ---> 89 model.fit(
         90     train_generator,
         91     steps_per_epoch=train_generator.samples // batch_size,
    
    1 frames
    /usr/local/lib/python3.10/dist-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
       1695                 logs = tf_utils.sync_to_numpy_or_python_type(logs)
       1696                 if logs is None:
    -> 1697                     raise ValueError(
       1698                         "Unexpected result of `train_function` "
       1699                         "(Empty logs). Please use "
    
    ValueError: Unexpected result of `train_function` (Empty logs). Please use `Model.compile(..., run_eagerly=True)`, or `tf.config.run_functions_eagerly(True)` for more information of where went wrong, or file a issue/bug to `tf.keras`.

我已经把我的图片存储在谷歌驱动器中，并使用单独的.csv文件来介绍带有标签名称的图片。在模型编译之前，它运行得很好。然后它发生了上述错误。顺便说一句，我对这个方法很陌生。我的代码就像下面的代码。

import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import pandas as pd
import os

from google.colab import drive
drive.mount('/content/gdrive')
%cd /content/gdrive/My Drive/

# Set the paths to your dataset directories
train_dir = './train'
test_dir = './test'

test_items = os.listdir(test_dir)
print("Items in test_dir:")
for item in test_items:
    print(item)

# Load the data into a dataframe
train_df = pd.read_csv('./train.csv')
test_df = pd.read_csv('./test.csv')

# Define image parameters
img_width, img_height = 150, 150
input_shape = (img_width, img_height, 3)  # Adjust for grayscale images

# Define hyperparameters
batch_size = 32
epochs = 10
learning_rate = 0.001

# Create data generators with data augmentation for training and validation
train_datagen = ImageDataGenerator(
    rescale=1./255,
    rotation_range=10,
    width_shift_range=0.1,
    height_shift_range=0.1,
    shear_range=0.1,
    zoom_range=0.1,
    horizontal_flip=True,
    fill_mode='nearest'
)

test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_dataframe(
    train_df,
    x_col='filename',
    y_col='fat_percentage',
    directory=train_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='raw'
)

test_generator = test_datagen.flow_from_dataframe(
    test_df,
    x_col='filename',
    y_col='fat_percentage',
    directory=test_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='raw'
)

# Define the CNN architecture
model = tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape),
    tf.keras.layers.MaxPooling2D((2, 2)),
    tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
    tf.keras.layers.MaxPooling2D((2, 2)),
    tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),
    tf.keras.layers.MaxPooling2D((2, 2)),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(1)  # No activation function for regression task
])

# Compile the model
model.compile(
    loss='mse',
    optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate),
    metrics=['mae'],
)

tf.config.run_functions_eagerly(True)
tf.data.experimental.enable_debug_mode()
# Train the model
model.fit(
    train_generator,
    steps_per_epoch=train_generator.samples // batch_size,
    epochs=epochs
)

# Evaluate the model on the test set
test_loss, test_mae = model.evaluate(test_generator, verbose=2)
print(f'Test Loss: {test_loss:.4f}')
print(f'Test MAE: {test_mae:.4f}')

是什么导致了这个错误？