写一份基于卷积神经网络动物分类的代码

好的，以下是一个基于卷积神经网络的动物分类代码，使用的是Python和TensorFlow框架：

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

# 创建数据生成器
train_datagen = ImageDataGenerator(
    rescale=1./255,
    rotation_range=20,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    fill_mode='nearest')

test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
    'train_dir',
    target_size=(224, 224),
    batch_size=32,
    class_mode='categorical')

validation_generator = test_datagen.flow_from_directory(
    'val_dir',
    target_size=(224, 224),
    batch_size=32,
    class_mode='categorical')

# 创建模型
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))

# 编译模型
model.compile(loss='categorical_crossentropy',
              optimizer=optimizers.RMSprop(lr=1e-4),
              metrics=['acc'])

# 创建回调函数
checkpoint_callback = callbacks.ModelCheckpoint(
    'model.h5', monitor='val_acc', save_best_only=True, mode='max')

earlystop_callback = callbacks.EarlyStopping(
    monitor='val_acc', patience=5, mode='max')

# 训练模型
history = model.fit(
    train_generator,
    steps_per_epoch=100,
    epochs=50,
    validation_data=validation_generator,
    validation_steps=50,
    callbacks=[checkpoint_callback, earlystop_callback])

# 保存模型
model.save('model.h5')

以上代码中，我们使用了ImageDataGenerator来生成训练集和验证集，使用了Sequential模型和Conv2D、MaxPooling2D、Flatten、Dense等层来构建卷积神经网络模型，使用了RMSprop优化器、categorical_crossentropy损失函数和acc指标来编译模型，使用了ModelCheckpoint和EarlyStopping回调函数来保存模型和提前结束训练。