基于LeNet5或AlexNet的动物图像分类代码

以下是基于AlexNet的动物图像分类代码示例：

import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# 数据集路径
train_dir = "path/to/train/directory"
validation_dir = "path/to/validation/directory"

# 定义图像生成器
train_datagen = ImageDataGenerator(
    rescale=1./255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True
)
validation_datagen = ImageDataGenerator(rescale=1./255)

# 生成训练数据
train_generator = train_datagen.flow_from_directory(
    train_dir,
    target_size=(224, 224),
    batch_size=32,
    class_mode='categorical'
)

# 生成验证数据
validation_generator = validation_datagen.flow_from_directory(
    validation_dir,
    target_size=(224, 224),
    batch_size=32,
    class_mode='categorical'
)

# 定义模型
model = tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(96, (11,11), strides=(4,4), activation='relu', input_shape=(224,224,3)),
    tf.keras.layers.MaxPooling2D((3,3), strides=(2,2)),
    tf.keras.layers.Conv2D(256, (5,5), activation='relu'),
    tf.keras.layers.MaxPooling2D((3,3), strides=(2,2)),
    tf.keras.layers.Conv2D(384, (3,3), activation='relu'),
    tf.keras.layers.Conv2D(384, (3,3), activation='relu'),
    tf.keras.layers.Conv2D(256, (3,3), activation='relu'),
    tf.keras.layers.MaxPooling2D((3,3), strides=(2,2)),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(4096, activation='relu'),
    tf.keras.layers.Dropout(0.5),
    tf.keras.layers.Dense(4096, activation='relu'),
    tf.keras.layers.Dropout(0.5),
    tf.keras.layers.Dense(4, activation='softmax')
])

# 编译模型
model.compile(
    loss='categorical_crossentropy',
    optimizer=tf.keras.optimizers.SGD(lr=0.01),
    metrics=['accuracy']
)

# 训练模型
model.fit(
    train_generator,
    steps_per_epoch=train_generator.n // train_generator.batch_size,
    epochs=50,
    validation_data=validation_generator,
    validation_steps=validation_generator.n // validation_generator.batch_size
)

注：上述代码中，我们使用的是在动物图像分类问题中表现较好的AlexNet模型，但是由于数据集和训练环境的不同，需要根据实际情况进行调整和优化。