我要用googlenet和tensorflow和keras来识别岩石图像的种类的代码，有七类岩石图像，每类岩石图像有几百张照片，然后还要对这七类图像制作数据集标签的代码

以下是使用GoogLeNet和TensorFlow和Keras进行岩石图像分类的代码示例：

导入需要的库和模块：

import tensorflow as tf
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.optimizers import Adam
import numpy as np
import os

设置训练和测试数据集的路径和其他参数：

train_dir = 'path/to/train/directory'
test_dir = 'path/to/test/directory'
img_width, img_height = 224, 224
batch_size = 32
epochs = 20

定义模型：

model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(img_width, img_height, 3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(256, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(7, activation='softmax'))

编译模型：

model.compile(loss='categorical_crossentropy',
              optimizer=Adam(lr=0.0001, decay=1e-6),
              metrics=['accuracy'])

使用ImageDataGenerator生成数据：

train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(
    rescale=1./255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

test_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
    train_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical')

test_generator = test_datagen.flow_from_directory(
    test_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical')

训练模型：

history = model.fit(
    train_generator,
    steps_per_epoch=train_generator.samples // batch_size,
    epochs=epochs,
    validation_data=test_generator,
    validation_steps=test_generator.samples // batch_size)

制作数据集标签的代码示例：

class_names = sorted(os.listdir(train_dir))
class_labels = {class_name: i for i, class_name in enumerate(class_names)}

# Save class labels to a file
with open('class_labels.txt', 'w') as file:
    for class_name, class_label in class_labels.items():
        file.write(f'{class_name}: {class_label}\n')

然后，您可以使用训练好的模型来预测新的岩石图像的种类。