350 lines
12 KiB
Python
350 lines
12 KiB
Python
# -*- coding: utf-8 -*-
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"""
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可视化工具 - 展示神经网络各层的输出
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用于课堂教学,让学生直观理解:
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1. 输入图像长什么样
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2. 第一层隐藏层学到了什么特征
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3. 各层激活值的变化
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使用方法:
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python visualize.py # 可视化测试集前5张
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python visualize.py --single # 可视化单张图片
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"""
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import numpy as np
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from PIL import Image
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import os
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import sys
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import matplotlib
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matplotlib.use('Agg') # 无头模式,不显示图形
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import matplotlib.pyplot as plt
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def visualize_input_image(img_vector, save_path='visualizations/input.png'):
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"""把784维向量还原成28x28图像并保存"""
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img = img_vector.reshape(28, 28) * 255
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img = img.astype(np.uint8)
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Image.fromarray(img).save(save_path)
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return save_path
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def visualize_activations(model, img_vector, save_dir='visualizations'):
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"""
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可视化网络各层的激活值
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"""
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os.makedirs(save_dir, exist_ok=True)
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# 前向传播获取各层激活值
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model.forward(img_vector.reshape(1, -1))
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# 1. 保存输入图像
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visualize_input_image(img_vector, os.path.join(save_dir, '01_input.png'))
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# 2. 可视化第一层激活(隐藏层)
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hidden_activations = model.a1[0] # (128,)
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visualize_hidden_layer(hidden_activations, os.path.join(save_dir, '02_hidden.png'))
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# 3. 可视化输出层概率
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output_probs = model.probs[0] # (10,)
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visualize_output_prob(output_probs, os.path.join(save_dir, '03_output_prob.png'))
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# 4. 生成汇总图
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create_summary_image(img_vector, hidden_activations, output_probs, save_dir)
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return save_dir
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def visualize_hidden_layer(activations, save_path):
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"""
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可视化隐藏层激活值
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把128个神经元的激活值排成8x16网格显示
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"""
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grid_cols = 16
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grid_rows = 8
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cell_size = 24
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img_h = grid_rows * cell_size
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img_w = grid_cols * cell_size
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grid = np.ones((img_h, img_w)) * 255
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for i, act in enumerate(activations):
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row = i // grid_cols
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col = i % grid_cols
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intensity = max(0, min(1, act * 2))
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color = int(255 * (1 - intensity * 0.7))
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grid[row*cell_size:(row+1)*cell_size-1, col*cell_size:(col+1)*cell_size-1] = color
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Image.fromarray(grid.astype(np.uint8)).save(save_path)
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def visualize_output_prob(probs, save_path):
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"""可视化输出层概率分布"""
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fig, ax = plt.subplots(figsize=(8, 4))
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digits = list(range(10))
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colors = ['#3498db' if i != np.argmax(probs) else '#e74c3c' for i in digits]
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bars = ax.bar(digits, probs, color=colors)
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ax.set_xlabel('数字', fontsize=12)
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ax.set_ylabel('概率', fontsize=12)
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ax.set_title('输出层:各数字的预测概率', fontsize=14)
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ax.set_xticks(digits)
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ax.set_ylim(0, 1)
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max_idx = np.argmax(probs)
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ax.annotate(f'{probs[max_idx]:.1%}',
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xy=(max_idx, probs[max_idx]),
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ha='center', va='bottom', fontsize=10, color='#e74c3c', fontweight='bold')
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plt.tight_layout()
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plt.savefig(save_path, dpi=100, bbox_inches='tight')
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plt.close()
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def create_summary_image(img_vector, hidden_activations, output_probs, save_dir):
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"""创建汇总图"""
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fig = plt.figure(figsize=(14, 6))
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# 1. 输入图像
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ax1 = fig.add_subplot(2, 4, 1)
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ax1.imshow(img_vector.reshape(28, 28), cmap='gray')
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ax1.set_title('(1) Input Image\n(28x28 pixels)', fontsize=11)
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ax1.axis('off')
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# 2. 像素值分布
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ax2 = fig.add_subplot(2, 4, 2)
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ax2.hist(img_vector, bins=30, color='#3498db', alpha=0.7, edgecolor='white')
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ax2.set_title('(2) Pixel Value Distribution\n(normalized 0~1)', fontsize=11)
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ax2.set_xlabel('像素值')
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ax2.set_ylabel('频数')
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# 3. 隐藏层激活(热力图)
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ax3 = fig.add_subplot(2, 4, 3)
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# 128 = 8 × 16
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act_2d = hidden_activations.reshape(8, 16)
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im = ax3.imshow(act_2d, cmap='Blues', aspect='auto')
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ax3.set_title(f'(3) Hidden Layer\n(128 neurons)', fontsize=11)
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ax3.axis('off')
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plt.colorbar(im, ax=ax3, shrink=0.6)
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# 4. 隐藏层激活(条形图)
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ax4 = fig.add_subplot(2, 4, 4)
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ax4.bar(range(len(hidden_activations)), hidden_activations, color='#3498db', alpha=0.7)
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ax4.set_title('(4) Neuron Activations', fontsize=11)
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ax4.set_xlabel('神经元编号')
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ax4.set_ylabel('激活强度')
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# 5. 输出概率
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ax5 = fig.add_subplot(2, 4, 5)
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digits = list(range(10))
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colors = ['#3498db' if i != np.argmax(output_probs) else '#e74c3c' for i in digits]
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ax5.bar(digits, output_probs, color=colors)
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ax5.set_title('(5) Output Probabilities', fontsize=11)
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ax5.set_xlabel('数字')
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ax5.set_ylabel('概率')
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ax5.set_ylim(0, 1)
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ax5.set_xticks(digits)
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# 6. 最大概率
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ax6 = fig.add_subplot(2, 4, 6)
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ax6.axis('off')
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predicted = np.argmax(output_probs)
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confidence = output_probs[predicted]
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result_text = f'预测: {predicted}\n置信度: {confidence:.1%}'
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ax6.text(0.5, 0.5, result_text, fontsize=24, ha='center', va='center',
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bbox=dict(boxstyle='round', facecolor='#2ecc71', alpha=0.9),
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transform=ax6.transAxes, color='white', fontweight='bold')
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ax6.set_title('(6) Recognition Result', fontsize=11)
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# 7. 网络结构
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ax7 = fig.add_subplot(2, 4, 7)
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ax7.axis('off')
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structure_text = (
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'┌─────────────────┐\n'
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'│ 输入层 784 │\n'
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'│ (28×28展平) │\n'
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'└────────┬────────┘\n'
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' │\n'
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' 线性变换+ReLU\n'
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' │\n'
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'┌────────┴────────┐\n'
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'│ 隐藏层 128 │\n'
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'│ (特征提取) │\n'
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'└────────┬────────┘\n'
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' │\n'
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' 线性变换+Softmax\n'
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' │\n'
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'┌────────┴────────┐\n'
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'│ 输出层 10 │\n'
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'│ (数字0~9概率) │\n'
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'└─────────────────┘'
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)
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ax7.text(0.1, 0.95, structure_text, fontsize=9, va='top',
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family='monospace', transform=ax7.transAxes,
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bbox=dict(boxstyle='round', facecolor='#f8f9fa', alpha=0.9))
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ax7.set_title('(7) Network Structure', fontsize=11)
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# 8. 参数量说明
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ax8 = fig.add_subplot(2, 4, 8)
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ax8.axis('off')
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params_text = (
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'MLP 参数量计算:\n\n'
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'W1: 784 × 128 = 100,352\n'
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'b1: 128\n\n'
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'W2: 128 × 10 = 1,280\n'
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'b2: 10\n\n'
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'─────────────────\n'
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'总计: 101,770 参数\n\n'
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'全部用 NumPy 实现\n'
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'无需任何深度学习框架!'
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)
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ax8.text(0.1, 0.95, params_text, fontsize=10, va='top',
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family='monospace', transform=ax8.transAxes)
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ax8.set_title('(8) Parameters', fontsize=11)
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plt.suptitle('MLP Feature Maps Visualization - Handwritten Digits', fontsize=16, fontweight='bold', y=1.02)
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plt.tight_layout()
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plt.savefig(os.path.join(save_dir, 'summary.png'), dpi=120, bbox_inches='tight')
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plt.close()
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def load_model_or_train():
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"""加载已训练的模型,如果没有则训练一个"""
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import glob
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model_files = glob.glob('mnist_mlp_*.npy')
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if model_files:
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timestamps = sorted(set(
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f.replace('mnist_mlp_', '').replace('_W1.npy', '')
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for f in model_files if '_W1.npy' in f
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))
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if timestamps:
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model_path = 'mnist_mlp_' + timestamps[-1]
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print(f"加载模型: {model_path}")
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from model_numpy import MLP
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model = MLP(input_size=784, hidden_size=128, num_classes=10, learning_rate=0.1)
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model.W1 = np.load(f'{model_path}_W1.npy')
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model.b1 = np.load(f'{model_path}_b1.npy')
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model.W2 = np.load(f'{model_path}_W2.npy')
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model.b2 = np.load(f'{model_path}_b2.npy')
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return model
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# 没有模型,用sklearn快速训练一个用于演示
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print("未找到已训练模型,使用sklearn数据快速训练演示模型...")
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from sklearn.datasets import fetch_openml
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from sklearn.model_selection import train_test_split
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mnist = fetch_openml('mnist_784', version=1, as_frame=False, parser='auto')
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X = mnist.data[:10000].astype(np.float32) / 255.0
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y = mnist.target[:10000].astype(int)
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# 用sklearn的MLP代替
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from sklearn.neural_network import MLPClassifier
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model = MLPClassifier(
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hidden_layer_sizes=(128,),
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max_iter=20,
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alpha=1e-4,
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solver='sgd',
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learning_rate_init=0.1,
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random_state=42,
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verbose=False
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)
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model.fit(X, y)
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print("sklearn模型训练完成(仅用于可视化演示)")
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return model
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def main():
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"""主函数"""
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os.makedirs('visualizations', exist_ok=True)
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# 加载数据
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from dataset import load_data
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print("加载MNIST数据集...")
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X_train, y_train, X_test, y_test = load_data()
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# 加载模型
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model = load_model_or_train()
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# 获取真实标签
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if len(y_test.shape) > 1:
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y_test_labels = np.argmax(y_test, axis=1)
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else:
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y_test_labels = y_test
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# 可视化测试集前5张
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print("\n可视化测试集前5张图片...")
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for i in range(5):
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img = X_test[i]
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true_label = y_test_labels[i]
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sub_dir = f'visualizations/sample_{i}_true{true_label}'
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os.makedirs(sub_dir, exist_ok=True)
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if hasattr(model, 'predict_proba'):
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probs = model.predict_proba(img.reshape(1, -1))[0]
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predicted = np.argmax(probs)
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else:
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predicted = model.predict(img.reshape(1, -1))[0]
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print(f" 样本{i}: 真实={true_label}, 预测={predicted}")
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visualize_activations(model, img, sub_dir)
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# 创建对比汇总
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create_comparison_summary(X_test[:5], y_test_labels[:5], model, 'visualizations')
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print("\n✅ 可视化完成!")
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print(" 查看 visualizations/ 目录下的图片和汇总图")
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def create_comparison_summary(X_samples, y_true, model, save_dir):
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"""创建多个样本的对比汇总图"""
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n_samples = len(X_samples)
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fig = plt.figure(figsize=(4 * n_samples, 8))
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for i in range(n_samples):
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img = X_samples[i]
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true_label = y_true[i]
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if hasattr(model, 'predict_proba'):
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probs = model.predict_proba(img.reshape(1, -1))[0]
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predicted = np.argmax(probs)
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else:
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predicted = model.predict(img.reshape(1, -1))[0]
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# 输入图像
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ax = fig.add_subplot(3, n_samples, i + 1)
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ax.imshow(img.reshape(28, 28), cmap='gray')
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ax.set_title(f'真实: {true_label}', fontsize=12)
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ax.axis('off')
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# 隐藏层激活
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ax = fig.add_subplot(3, n_samples, i + 1 + n_samples)
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model.forward(img.reshape(1, -1))
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# 128 = 8 × 16
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hidden = model.a1[0].reshape(8, 16)
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ax.imshow(hidden, cmap='Blues', aspect='auto')
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ax.set_title(f'隐藏层激活', fontsize=10)
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ax.axis('off')
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# 输出概率
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ax = fig.add_subplot(3, n_samples, i + 1 + 2*n_samples)
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digits = list(range(10))
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colors = ['#e74c3c' if d == predicted else '#3498db' for d in digits]
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ax.bar(digits, probs, color=colors)
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ax.set_title(f'预测: {predicted}', fontsize=12)
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ax.set_ylim(0, 1)
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ax.set_xticks(digits)
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ax.tick_params(labelsize=8)
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plt.suptitle('多样本特征图对比', fontsize=16, fontweight='bold')
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plt.tight_layout()
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plt.savefig(os.path.join(save_dir, 'comparison.png'), dpi=120, bbox_inches='tight')
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plt.close()
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if __name__ == '__main__':
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main() |