Neural Network Hyperparameters

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np

np.random.seed(654)

n = 150
models = np.random.choice(['CNN', 'RNN', 'Transformer'], n)
data = {
    'Accuracy': np.where(models == 'Transformer', np.random.normal(0.92, 0.03, n),
                        np.where(models == 'CNN', np.random.normal(0.88, 0.04, n), np.random.normal(0.85, 0.05, n))),
    'Params (M)': np.where(models == 'Transformer', np.random.normal(125, 30, n),
                          np.where(models == 'CNN', np.random.normal(25, 8, n), np.random.normal(15, 5, n))),
    'Latency (ms)': np.where(models == 'Transformer', np.random.normal(50, 15, n),
                            np.where(models == 'CNN', np.random.normal(20, 5, n), np.random.normal(35, 10, n))),
    'Loss': 1 - np.where(models == 'Transformer', np.random.normal(0.92, 0.03, n),
                        np.where(models == 'CNN', np.random.normal(0.88, 0.04, n), np.random.normal(0.85, 0.05, n))) + np.random.normal(0, 0.02, n),
    'Model': models
}
df = pd.DataFrame(data)

plt.style.use('dark_background')
sns.set_style("darkgrid", {'axes.facecolor': '#0a0a0f', 'figure.facecolor': '#0a0a0f', 'grid.color': '#333333'})

palette = {'CNN': '#27D3F5', 'RNN': '#6CF527', 'Transformer': '#F527B0'}

g = sns.pairplot(
    df,
    hue='Model',
    palette=palette,
    height=2,
    kind='reg',
    diag_kind='kde',
    markers=['o', 's', 'D'],
    plot_kws={'scatter_kws': {'alpha': 0.65, 's': 50, 'edgecolor': 'white', 'linewidths': 0.4}, 'line_kws': {'linewidth': 2}},
    diag_kws={'alpha': 0.5, 'linewidth': 2.5}
)

g.fig.set_facecolor('#0a0a0f')
for ax in g.axes.flat:
    if ax:
        ax.set_facecolor('#0a0a0f')
        for spine in ax.spines.values():
            spine.set_color('#333333')
        ax.tick_params(colors='#888888')
        ax.xaxis.label.set_color('white')
        ax.yaxis.label.set_color('white')

g.fig.suptitle('Deep Learning Architecture Comparison', fontsize=14, fontweight='bold', color='white', y=1.02)
plt.setp(g._legend.get_title(), color='white')
plt.setp(g._legend.get_texts(), color='white')

plt.tight_layout()
plt.show()