python实现将模型中的多头注意力机制进行热图可视化
时间: 2024-05-01 16:23:59 浏览: 240
要将模型中的多头注意力机制进行热图可视化,可以使用以下步骤:
- 导入必要的库
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
- 定义函数获取注意力权重
def get_attention_weights(model, inputs):
# 获取注意力权重
attention_weights = []
for layer in model.layers:
if 'multi_head_attention' in layer.name:
weights = layer.get_weights()
attention = layer.get_attention_weights()(inputs, training=False)
attention_weights.append(attention.numpy())
return attention_weights
- 定义函数绘制热图
def plot_attention_weights(attention_weights, input_tokens, output_tokens):
# 绘制热图
fig, ax = plt.subplots(figsize=(16, 8))
ax.imshow(attention_weights, cmap='hot')
ax.set_xticks(np.arange(len(output_tokens)))
ax.set_yticks(np.arange(len(input_tokens)))
ax.set_xticklabels(output_tokens, fontsize=14)
ax.set_yticklabels(input_tokens, fontsize=14)
ax.set_xlabel('Output Tokens', fontsize=16)
ax.set_ylabel('Input Tokens', fontsize=16)
plt.show()
- 加载模型和数据,并获取注意力权重
# 加载模型
model = tf.keras.models.load_model('model.h5')
# 加载数据
input_data = np.load('input_data.npy')
output_data = np.load('output_data.npy')
# 获取注意力权重
attention_weights = get_attention_weights(model, input_data)
- 绘制热图
# 绘制第一组注意力权重
plot_attention_weights(attention_weights[0][0], input_tokens, output_tokens)
其中,input_tokens
和 output_tokens
是输入和输出的标记序列,可以通过预处理数据时保存的标记映射表进行获取。在绘制热图时,可以通过调整 figsize
参数来调整热图的大小。
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