图像理解如何创建

import tensorflow as tf
from tensorflow.keras import layers, models
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# 数据准备
datagen = ImageDataGenerator(rescale=1./255, validation_split=0.2)

train_generator = datagen.flow_from_directory(
    'path_to_dataset',
    target_size=(150, 150),
    batch_size=32,
    class_mode='binary',
    subset='training'
)

validation_generator = datagen.flow_from_directory(
    'path_to_dataset',
    target_size=(150, 150),
    batch_size=32,
    class_mode='binary',
    subset='validation'
)

# 模型构建
model = models.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(128, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Flatten(),
    layers.Dense(512, activation='relu'),
    layers.Dense(1, activation='sigmoid')
])

# 模型编译
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# 模型训练
model.fit(train_generator, epochs=10, validation_data=validation_generator)