resnet做垃圾分类的代码

下面是一个使用ResNet50模型进行垃圾分类的示例代码，使用Tensorflow框架实现，仅供参考：

import tensorflow as tf
import numpy as np
import os

# 设置图片大小和通道数
IMAGE_SIZE = 224
CHANNELS = 3

# 设置分类数和类别标签
NUM_CLASSES = 6
CLASS_NAMES = ['glass', 'metal', 'paper', 'plastic', 'trash', 'cardboard']

# 加载ResNet50模型
model = tf.keras.applications.ResNet50(weights='imagenet', include_top=False, input_shape=(IMAGE_SIZE, IMAGE_SIZE, CHANNELS))

# 冻结模型的前面层
for layer in model.layers:
    layer.trainable = False

# 添加全局平均池化层和全连接层
x = model.output
x = tf.keras.layers.GlobalAveragePooling2D()(x)
x = tf.keras.layers.Dense(1024, activation='relu')(x)
predictions = tf.keras.layers.Dense(NUM_CLASSES, activation='softmax')(x)

# 构造新模型
model_final = tf.keras.models.Model(inputs=model.input, outputs=predictions)

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

# 加载训练数据
train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255, validation_split=0.2)
train_generator = train_datagen.flow_from_directory(directory='path/to/training/data', target_size=(IMAGE_SIZE, IMAGE_SIZE), batch_size=32, class_mode='categorical', subset='training')
validation_generator = train_datagen.flow_from_directory(directory='path/to/training/data', target_size=(IMAGE_SIZE, IMAGE_SIZE), batch_size=32, class_mode='categorical', subset='validation')

# 训练模型
model_final.fit_generator(generator=train_generator, steps_per_epoch=train_generator.samples/train_generator.batch_size, validation_data=validation_generator, validation_steps=validation_generator.samples/validation_generator.batch_size, epochs=10)

# 评估模型
test_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255)
test_generator = test_datagen.flow_from_directory(directory='path/to/test/data', target_size=(IMAGE_SIZE, IMAGE_SIZE), batch_size=1, class_mode='categorical')
scores = model_final.evaluate_generator(test_generator, steps=test_generator.samples/test_generator.batch_size)
print('Accuracy: {}%'.format(scores[1]*100))

# 预测新图片
img_path = 'path/to/new/image'
img = tf.keras.preprocessing.image.load_img(img_path, target_size=(IMAGE_SIZE, IMAGE_SIZE))
img_array = tf.keras.preprocessing.image.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0)
img_array /= 255.
prediction = model_final.predict(img_array)
print('Prediction:', CLASS_NAMES[np.argmax(prediction)])

这个示例代码需要根据实际情况进行调整，比如修改图片路径、分类数、类别标签等。同时需要注意，这个模型训练需要一定的计算资源和时间，建议在GPU环境下进行。