如何使用keras后端计算显着图

import tensorflow as tf
from tensorflow.keras.applications.vgg16 import VGG16, preprocess_input, decode_predictions
from tensorflow.keras.preprocessing import image
import numpy as np
import matplotlib.pyplot as plt

# 加载预训练的VGG16模型
model = VGG16(weights='imagenet')

# 加载并预处理图像
img_path = 'path_to_your_image.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)

# 获取预测结果
preds = model.predict(x)
print('Predicted:', decode_predictions(preds, top=3)[0])

# 获取最后一层卷积层的输出
last_conv_layer = model.get_layer('block5_conv3')

# 计算梯度
grad_model = tf.keras.models.Model(
    [model.inputs], [last_conv_layer.output, model.output]
)
with tf.GradientTape() as tape:
    conv_outputs, predictions = grad_model(x)
    class_idx = np.argmax(predictions[0])
    loss = predictions[:, class_idx]

grads = tape.gradient(loss, conv_outputs)[0]
pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))

# 生成显着图
for i in range(512):
    conv_outputs[:, :, :, i] *= pooled_grads[i]

heatmap = tf.reduce_mean(conv_outputs, axis=-1)
heatmap = np.maximum(heatmap, 0)
heatmap /= np.max(heatmap)

# 可视化显着图
plt.matshow(heatmap)
plt.show()