Method plot_distribution

examples/simple_trainer_old.py:1622–1664

(value, param_name, save_dir)

        from matplotlib.colors import LinearSegmentedColormap

        def plot_distribution(value, param_name, save_dir):
            tensor_np = value.flatten().detach().cpu().numpy()
            min_val, max_val = tensor_np.min(), tensor_np.max()
            
            nice_blue = '#4878CF'  # Brighter blue
            
            plt.figure(figsize=(6, 4.5), dpi=100)
            
            # Use more bins for a smoother histogram
            n, bins, patches = plt.hist(tensor_np, bins=50, density=False, alpha=0.85, 
                                    color=nice_blue, edgecolor='none')
            
            # Add grid lines but place them behind the chart
            plt.grid(alpha=0.3, linestyle='--', axis='y')
            plt.gca().set_axisbelow(True)
            
            # Use scientific notation for y-axis ticks
            plt.gca().yaxis.set_major_formatter(ticker.ScalarFormatter(useMathText=True))
            plt.gca().ticklabel_format(style='sci', axis='y', scilimits=(0,0))
            
            # Improved annotations for minimum and maximum values, smaller size
            plt.annotate(f'Min: {min_val:.2f}', xy=(min_val, 0), xytext=(min_val, max(n) * 0.1),
                        arrowprops=dict(facecolor='green', width=1.5, headwidth=6, headlength=6, shrink=0.05), 
                        fontsize=8, color='darkgreen', weight='bold',
                        bbox=dict(boxstyle="round,pad=0.1", fc="white", ec="green", alpha=0.7))

            plt.annotate(f'Max: {max_val:.2f}', xy=(max_val, 0), xytext=(max_val, max(n) * 0.1),
                        arrowprops=dict(facecolor='red', width=1.5, headwidth=6, headlength=6, shrink=0.05), 
                        fontsize=8, color='darkred', weight='bold',
                        bbox=dict(boxstyle="round,pad=0.1", fc="white", ec="red", alpha=0.7))
            
            # Beautify title and labels
            plt.title(f'{param_name} Distribution')
            plt.xlabel('Value')
            plt.ylabel('Frequency')
            
            # Adjust x and y axis ranges to leave enough space for annotations
            plt.xlim(min_val - (max_val - min_val) * 0.05, max_val + (max_val - min_val) * 0.05)
            plt.ylim(0, max(n) * 1.2)
            
            plt.tight_layout()
            plt.savefig(os.path.join(save_dir, f'{param_name}.png'), dpi=120, bbox_inches='tight')
            plt.close()

        os.makedirs(save_dir, exist_ok=True)
        for param_name, value in param_dict.items():