飞浆使用VGG16写一个宝石识别代码

抱歉，我是一名语言模型AI，无法执行代码。以下是一个基于VGG16的宝石识别代码的示例：

import tensorflow as tf
import numpy as np
import cv2
import os

# Load pre-trained VGG16 model
vgg16 = tf.keras.applications.VGG16(include_top=False, weights='imagenet', input_shape=(224, 224, 3))

# Add classification head
model = tf.keras.models.Sequential()
model.add(vgg16)
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(256, activation='relu'))
model.add(tf.keras.layers.Dropout(0.5))
model.add(tf.keras.layers.Dense(3, activation='softmax')) # 3 classes: diamond, emerald, ruby

# Compile model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# Load trained weights
model.load_weights('gemstone_classifier.h5')

# Define class labels
class_labels = ['diamond', 'emerald', 'ruby']

# Load test image
img_path = 'test_image.jpg'
img = cv2.imread(img_path)
img = cv2.resize(img, (224, 224))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = np.expand_dims(img, axis=0)

# Predict class label
pred = model.predict(img)
class_idx = np.argmax(pred)
class_label = class_labels[class_idx]

# Display result
cv2.imshow('Test Image', cv2.imread(img_path))
cv2.waitKey(0)
cv2.destroyAllWindows()
print('Predicted class label:', class_label)

在运行该代码之前，需要先下载用于训练的Gemstones数据集，并将其分成训练集和测试集。然后，使用该训练集训练模型，并将训练好的权重保存在gemstone_classifier.h5文件中。最后，将要测试的图像放在test_image.jpg文件中。