能否提供一个用Python实现的猫狗图像分类（二分类任务）的代码示例，用于识别猫和狗的图片？

当然可以，这里是一个简单的Python代码示例，使用了深度学习库Keras和预训练模型ResNet50来进行猫狗图像分类。首先，你需要安装`tensorflow`, `keras`, 和 `numpy` 等库。

import tensorflow as tf
from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
from tensorflow.keras.models import Model

# 加载预训练的ResNet50模型，去掉顶层的全连接层
base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))

# 添加自定义的分类层
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(1, activation='sigmoid')(x)

# 创建新的模型，输入是ResNet50的输入，输出是我们的预测层
model = Model(inputs=base_model.input, outputs=predictions)

# 冻结预训练模型的所有层
for layer in base_model.layers:
    layer.trainable = False

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

# 数据增强
train_datagen = ImageDataGenerator(preprocessing_function=preprocess_input,
                                   rescale=1./255,
                                   shear_range=0.2,
                                   zoom_range=0.2,
                                   horizontal_flip=True)

# 训练数据路径，替换为你实际的路径
train_generator = train_datagen.flow_from_directory(
    'path_to_train_data',
    target_size=(224, 224),
    batch_size=32,
    class_mode='binary'
)

# 验证数据路径
validation_generator = train_datagen.flow_from_directory(
    'path_to_validation_data',
    target_size=(224, 224),
    batch_size=32,
    class_mode='binary',
    shuffle=False  # 验证集不需要随机化
)

# 训练模型
history = model.fit(train_generator,
                    steps_per_epoch=len(train_generator),
                    epochs=10,
                    validation_data=validation_generator,
                    validation_steps=len(validation_generator))

#