CNN图像分类:它总是给我相同的预测

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# 假设我们有一个简单的CNN模型
model = Sequential([
    Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)),
    MaxPooling2D((2, 2)),
    Conv2D(64, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Conv2D(128, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Flatten(),
    Dense(128, activation='relu'),
    Dropout(0.5),
    Dense(10, activation='softmax')  # 假设有10个类别
])

# 编译模型
model.compile(optimizer=Adam(learning_rate=0.001),
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# 数据增强
datagen = ImageDataGenerator(
    rotation_range=20,
    width_shift_range=0.2,
    height_shift_range=0.2,
    horizontal_flip=True,
    validation_split=0.2)

train_generator = datagen.flow_from_directory(
    'path_to_training_data',
    target_size=(64, 64),
    batch_size=32,
    class_mode='sparse',
    subset='training')

validation_generator = datagen.flow_from_directory(
    'path_to_training_data',
    target_size=(64, 64),
    batch_size=32,
    class_mode='sparse',
    subset='validation')

# 训练模型
model.fit(train_generator,
          validation_data=validation_generator,
          epochs=50)