自注意力机制热力图代码

Sure! Here's an example code snippet for generating a heatmap visualization of the self-attention mechanism in a Transformer model:

import numpy as np
import matplotlib.pyplot as plt

def plot_attention_heatmap(attention_weights):
    # Normalize attention weights
    attention_weights = attention_weights / np.sum(attention_weights, axis=-1, keepdims=True)

    # Plot heatmap
    plt.figure(figsize=(8, 8))
    plt.imshow(attention_weights, cmap='hot', interpolation='nearest')
    plt.xlabel('Source')
    plt.ylabel('Target')
    plt.colorbar()
    plt.show()

# Example attention weights
attention_weights = np.array([[0.2, 0.3, 0.5],
                             [0.1, 0.6, 0.3],
                             [0.4, 0.2, 0.4]])

plot_attention_heatmap(attention_weights)

In this code, the `attention_weights` variable represents the attention weights calculated by the self-attention mechanism in a Transformer model. The `plot_attention_heatmap` function takes these weights as input and normalizes them before plotting a heatmap using matplotlib.

Note: This code is a simplified example and assumes the attention weights are already calculated. In a real Transformer model, you would need to extract the attention weights from the model's attention mechanism during inference.