{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial 3: Meta-Learning with Surrogate Hyperparameters\n",
    "\n",
    "This tutorial explores **meta-learning** for spiking neural networks by optimizing surrogate gradient hyperparameters. We'll cover:\n",
    "\n",
    "1. **What is Meta-Learning?** - Learning to learn\n",
    "2. **Hyperparameter Derivatives** - Taking gradients through surrogate parameters\n",
    "3. **Basic Meta-Learning** - Simple examples\n",
    "4. **Practical Applications** - Task-adaptive and network-adaptive surrogates\n",
    "5. **Multi-Parameter Optimization** - Complex surrogates\n",
    "6. **Advanced Techniques** - Per-layer adaptation, schedules\n",
    "7. **Real-World Examples** - MNIST with learned surrogates\n",
    "\n",
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-20T14:14:07.094429Z",
     "start_time": "2025-11-20T14:14:04.170721Z"
    }
   },
   "source": [
    "import jax\n",
    "import jax.numpy as jnp\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import braintools.surrogate as surrogate\n",
    "import brainstate\n",
    "\n",
    "# Set up plotting\n",
    "plt.rcParams['figure.figsize'] = (12, 6)\n",
    "plt.rcParams['font.size'] = 11\n",
    "\n",
    "print(\"Setup complete!\")\n",
    "print(f\"JAX version: {jax.__version__}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup complete!\n",
      "JAX version: 0.7.2\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. What is Meta-Learning for SNNs?\n",
    "\n",
    "**Meta-learning** (\"learning to learn\") optimizes the learning algorithm itself, not just the model parameters.\n",
    "\n",
    "### Traditional Training\n",
    "```\n",
    "Fixed surrogate (e.g., α=4.0) → Train network weights → Evaluate\n",
    "```\n",
    "\n",
    "### Meta-Learning\n",
    "```\n",
    "Trainable surrogate (α=?) → Train network weights → Evaluate → Update α\n",
    "```\n",
    "\n",
    "### Why Meta-Learn Surrogate Hyperparameters?\n",
    "\n",
    "1. **Task-Specific Optimization**: Different tasks may benefit from different gradient shapes\n",
    "2. **Network Architecture**: Deeper networks may need wider gradients\n",
    "3. **Data-Driven**: Learn optimal surrogates from data, not hand-tuning\n",
    "4. **Adaptive Training**: Surrogate can change during training (annealing)\n",
    "\n",
    "### The Key Insight\n",
    "\n",
    "JAX's automatic differentiation works through hyperparameters!\n",
    "\n",
    "$$\n",
    "\\frac{\\partial \\mathcal{L}}{\\partial \\alpha} = \\frac{\\partial \\mathcal{L}}{\\partial \\sigma'(x; \\alpha)} \\frac{\\partial \\sigma'(x; \\alpha)}{\\partial \\alpha}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-20T14:14:07.677018Z",
     "start_time": "2025-11-20T14:14:07.102452Z"
    }
   },
   "source": [
    "print(\"=== META-LEARNING CONCEPT ===\\n\")\n",
    "\n",
    "# Demonstrate gradient through hyperparameter\n",
    "def loss_with_surrogate(alpha, x, target):\n",
    "    \"\"\"Loss function that depends on surrogate hyperparameter.\"\"\"\n",
    "    sg = surrogate.Sigmoid(alpha=alpha)\n",
    "    output = sg(x)\n",
    "    return jnp.mean((output - target) ** 2)\n",
    "\n",
    "# Test data\n",
    "x_test = jnp.array([0.5, 1.0, 1.5])\n",
    "target_test = jnp.array([1.0, 1.0, 1.0])\n",
    "alpha_test = 4.0\n",
    "\n",
    "# Compute gradients\n",
    "loss_value = loss_with_surrogate(alpha_test, x_test, target_test)\n",
    "grad_alpha = jax.grad(loss_with_surrogate, argnums=0)(alpha_test, x_test, target_test)\n",
    "\n",
    "print(f\"Loss with α={alpha_test}: {loss_value:.6f}\")\n",
    "print(f\"Gradient ∂L/∂α: {grad_alpha:.6f}\")\n",
    "print(f\"\\n✅ We can compute gradients with respect to α!\")\n",
    "print(f\"✅ This enables meta-learning of surrogate hyperparameters!\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== META-LEARNING CONCEPT ===\n",
      "\n",
      "Loss with α=4.0: 0.000000\n",
      "Gradient ∂L/∂α: 0.000000\n",
      "\n",
      "✅ We can compute gradients with respect to α!\n",
      "✅ This enables meta-learning of surrogate hyperparameters!\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Visualizing Hyperparameter Derivatives\n",
    "\n",
    "Let's visualize how the alpha parameter affects both the gradient and its derivative."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-20T14:14:08.499955Z",
     "start_time": "2025-11-20T14:14:07.686736Z"
    }
   },
   "source": [
    "print(\"=== HYPERPARAMETER DERIVATIVE VISUALIZATION ===\\n\")\n",
    "\n",
    "# Define a simple loss function\n",
    "def simple_loss(alpha, x):\n",
    "    \"\"\"Simple loss: sum of surrogate outputs.\"\"\"\n",
    "    return jnp.sum(surrogate.sigmoid(x, alpha=alpha))\n",
    "\n",
    "# Test different alpha values\n",
    "alphas = jnp.linspace(0.5, 8.0, 50)\n",
    "x_vals = jnp.array([0.0, 0.5, 1.0])\n",
    "\n",
    "# Compute loss and gradient for each alpha\n",
    "losses = jax.vmap(lambda a: simple_loss(a, x_vals))(alphas)\n",
    "grads = jax.vmap(lambda a: jax.grad(simple_loss, argnums=0)(a, x_vals))(alphas)\n",
    "\n",
    "# Visualize\n",
    "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
    "\n",
    "# Loss landscape\n",
    "axes[0].plot(alphas, losses, 'b-', linewidth=3, marker='o', markersize=4)\n",
    "axes[0].set_xlabel('Alpha (α)', fontsize=12)\n",
    "axes[0].set_ylabel('Loss', fontsize=12)\n",
    "axes[0].set_title('Loss vs Surrogate Hyperparameter', fontsize=14, fontweight='bold')\n",
    "axes[0].grid(True, alpha=0.3)\n",
    "axes[0].axvline(4.0, color='red', linestyle='--', alpha=0.5, label='α=4.0 (common)')\n",
    "axes[0].legend(fontsize=11)\n",
    "\n",
    "# Gradient w.r.t. alpha\n",
    "axes[1].plot(alphas, grads, 'r-', linewidth=3, marker='o', markersize=4)\n",
    "axes[1].axhline(0, color='black', linestyle='-', alpha=0.3)\n",
    "axes[1].axvline(4.0, color='red', linestyle='--', alpha=0.5, label='α=4.0 (common)')\n",
    "axes[1].set_xlabel('Alpha (α)', fontsize=12)\n",
    "axes[1].set_ylabel('Gradient ∂L/∂α', fontsize=12)\n",
    "axes[1].set_title('Hyperparameter Gradient', fontsize=14, fontweight='bold')\n",
    "axes[1].grid(True, alpha=0.3)\n",
    "axes[1].legend(fontsize=11)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\\nKey Observations:\")\n",
    "print(\"• Loss changes with alpha → we can optimize it!\")\n",
    "print(\"• Gradient ∂L/∂α tells us how to update alpha\")\n",
    "print(\"• Negative gradient → increase alpha\")\n",
    "print(\"• Positive gradient → decrease alpha\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== HYPERPARAMETER DERIVATIVE VISUALIZATION ===\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ],
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     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Key Observations:\n",
      "• Loss changes with alpha → we can optimize it!\n",
      "• Gradient ∂L/∂α tells us how to update alpha\n",
      "• Negative gradient → increase alpha\n",
      "• Positive gradient → decrease alpha\n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How Alpha Affects the Surrogate Gradient\n",
    "\n",
    "Let's see how changing alpha affects the gradient shape and magnitude:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-20T14:14:11.472773Z",
     "start_time": "2025-11-20T14:14:10.629497Z"
    }
   },
   "source": [
    "print(\"=== ALPHA IMPACT ON GRADIENT ===\\n\")\n",
    "\n",
    "x = jnp.linspace(-2, 2, 1000)\n",
    "alphas_to_show = [1.0, 2.0, 4.0, 8.0]\n",
    "\n",
    "fig, axes = plt.subplots(2, 2, figsize=(16, 10))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for idx, alpha in enumerate(alphas_to_show):\n",
    "    # Compute surrogate gradient\n",
    "    sg = surrogate.Sigmoid(alpha=alpha)\n",
    "    grad = sg.surrogate_grad(x)\n",
    "    \n",
    "    # Compute derivative of gradient w.r.t. alpha at x=0\n",
    "    def grad_at_point(a, x_val):\n",
    "        return surrogate.Sigmoid(alpha=a).surrogate_grad(x_val)\n",
    "    \n",
    "    dgrad_dalpha_at_0 = jax.grad(grad_at_point, argnums=0)(alpha, 0.0)\n",
    "    \n",
    "    axes[idx].plot(x, grad, 'b-', linewidth=3, label=f'σ\\'(x; α={alpha})')\n",
    "    axes[idx].fill_between(x, 0, grad, alpha=0.3, color='blue')\n",
    "    axes[idx].axvline(0, color='red', linestyle='--', alpha=0.5)\n",
    "    axes[idx].set_xlabel('Input (x)', fontsize=12)\n",
    "    axes[idx].set_ylabel('Gradient Value', fontsize=12)\n",
    "    axes[idx].set_title(f'α = {alpha}  |  Peak grad = {jnp.max(grad):.3f}  |  ∂σ\\'/∂α|ₓ₌₀ = {dgrad_dalpha_at_0:.3f}', \n",
    "                       fontsize=11, fontweight='bold')\n",
    "    axes[idx].legend(fontsize=10)\n",
    "    axes[idx].grid(True, alpha=0.3)\n",
    "    \n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\\nEffect of Alpha:\")\n",
    "print(\"• Higher α → narrower, taller gradient\")\n",
    "print(\"• Peak gradient value increases with α\")\n",
    "print(\"• ∂σ'/∂α is positive → gradient increases with α\")\n",
    "print(\"\\n✅ We can measure how sensitive the gradient is to α!\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== ALPHA IMPACT ON GRADIENT ===\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x1000 with 4 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Effect of Alpha:\n",
      "• Higher α → narrower, taller gradient\n",
      "• Peak gradient value increases with α\n",
      "• ∂σ'/∂α is positive → gradient increases with α\n",
      "\n",
      "✅ We can measure how sensitive the gradient is to α!\n"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Basic Meta-Learning: Optimizing Alpha\n",
    "\n",
    "Let's implement a simple meta-learning loop to find the optimal alpha value for a task.\n",
    "\n",
    "### Scenario\n",
    "We want to train a simple spiking neuron, and we'll meta-learn the best alpha value."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-20T14:14:40.363052Z",
     "start_time": "2025-11-20T14:14:11.485602Z"
    }
   },
   "source": [
    "print(\"=== BASIC META-LEARNING: OPTIMIZING ALPHA ===\\n\")\n",
    "\n",
    "# Simple spiking neuron model\n",
    "def spiking_neuron(weights, inputs, alpha, tau=10.0, dt=0.1, n_steps=50):\n",
    "    \"\"\"\n",
    "    Simple LIF neuron with trainable weights and surrogate alpha.\n",
    "    \n",
    "    Parameters:\n",
    "    - weights: synaptic weights\n",
    "    - inputs: input spike trains\n",
    "    - alpha: surrogate gradient hyperparameter\n",
    "    \"\"\"\n",
    "    v = 0.0\n",
    "    spike_count = 0.0\n",
    "    sg = surrogate.Sigmoid(alpha=alpha)\n",
    "    \n",
    "    for t in range(n_steps):\n",
    "        # Synaptic input\n",
    "        i_syn = jnp.dot(weights, inputs[t])\n",
    "        \n",
    "        # Membrane dynamics\n",
    "        dv = (-v + i_syn) / tau * dt\n",
    "        v = v + dv\n",
    "        \n",
    "        # Generate spike\n",
    "        spike = sg(v - 1.0)\n",
    "        spike_count = spike_count + spike\n",
    "        \n",
    "        # Reset\n",
    "        v = v * (1.0 - spike)\n",
    "    \n",
    "    return spike_count\n",
    "\n",
    "# Generate synthetic data\n",
    "key = jax.random.PRNGKey(0)\n",
    "n_inputs = 10\n",
    "n_steps = 50\n",
    "n_samples = 20\n",
    "\n",
    "# Create input patterns and targets\n",
    "key, subkey = jax.random.split(key)\n",
    "inputs = jax.random.bernoulli(subkey, 0.3, (n_samples, n_steps, n_inputs)).astype(float)\n",
    "targets = jax.random.uniform(key, (n_samples,)) * 10  # Target spike counts\n",
    "\n",
    "# Loss function\n",
    "def loss_fn(weights, alpha, inputs, targets):\n",
    "    \"\"\"Loss depends on both weights AND alpha.\"\"\"\n",
    "    def single_loss(inp, tgt):\n",
    "        pred = spiking_neuron(weights, inp, alpha)\n",
    "        return (pred - tgt) ** 2\n",
    "    \n",
    "    return jnp.mean(jax.vmap(single_loss)(inputs, targets))\n",
    "\n",
    "# Initialize\n",
    "key, subkey = jax.random.split(key)\n",
    "weights = jax.random.normal(subkey, (n_inputs,)) * 0.1\n",
    "alpha = 2.0  # Initial alpha\n",
    "\n",
    "# Meta-learning loop\n",
    "n_meta_steps = 50\n",
    "lr_weights = 0.01\n",
    "lr_alpha = 0.1\n",
    "\n",
    "history = {\n",
    "    'loss': [],\n",
    "    'alpha': [],\n",
    "    'weights_norm': []\n",
    "}\n",
    "\n",
    "print(\"Starting meta-learning...\\n\")\n",
    "print(f\"{'Step':<6} {'Loss':<12} {'Alpha':<12} {'|Weights|':<12}\")\n",
    "print(\"-\" * 50)\n",
    "\n",
    "for step in range(n_meta_steps):\n",
    "    # Compute loss and gradients\n",
    "    loss_val, (grad_w, grad_a) = jax.value_and_grad(loss_fn, argnums=(0, 1))(\n",
    "        weights, alpha, inputs, targets\n",
    "    )\n",
    "    \n",
    "    # Update weights (inner loop)\n",
    "    weights = weights - lr_weights * grad_w\n",
    "    \n",
    "    # Update alpha (outer loop - meta-learning)\n",
    "    alpha = alpha - lr_alpha * grad_a\n",
    "    alpha = jnp.clip(alpha, 0.5, 10.0)  # Keep alpha in reasonable range\n",
    "    \n",
    "    # Record history\n",
    "    history['loss'].append(float(loss_val))\n",
    "    history['alpha'].append(float(alpha))\n",
    "    history['weights_norm'].append(float(jnp.linalg.norm(weights)))\n",
    "    \n",
    "    if step % 10 == 0:\n",
    "        print(f\"{step:<6} {loss_val:<12.6f} {alpha:<12.6f} {jnp.linalg.norm(weights):<12.6f}\")\n",
    "\n",
    "print(f\"\\n✅ Meta-learning complete!\")\n",
    "print(f\"\\nFinal Results:\")\n",
    "print(f\"• Learned alpha: {alpha:.4f}\")\n",
    "print(f\"• Final loss: {history['loss'][-1]:.6f}\")\n",
    "print(f\"• Initial loss: {history['loss'][0]:.6f}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== BASIC META-LEARNING: OPTIMIZING ALPHA ===\n",
      "\n",
      "Starting meta-learning...\n",
      "\n",
      "Step   Loss         Alpha        |Weights|   \n",
      "--------------------------------------------------\n",
      "0      33.388409    0.500000     0.277916    \n",
      "10     33.388409    9.684774     0.348439    \n",
      "20     33.388409    9.190311     0.358104    \n",
      "30     33.388409    8.329274     0.375764    \n",
      "40     33.388409    4.970298     0.454572    \n",
      "\n",
      "✅ Meta-learning complete!\n",
      "\n",
      "Final Results:\n",
      "• Learned alpha: 8.0207\n",
      "• Final loss: 33.388409\n",
      "• Initial loss: 33.388409\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-20T14:14:41.157481Z",
     "start_time": "2025-11-20T14:14:40.455860Z"
    }
   },
   "source": [
    "# Visualize meta-learning progress\n",
    "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
    "\n",
    "steps = np.arange(len(history['loss']))\n",
    "\n",
    "# Loss over time\n",
    "axes[0].plot(steps, history['loss'], 'b-', linewidth=2.5, marker='o', markersize=4)\n",
    "axes[0].set_xlabel('Meta-Learning Step', fontsize=12)\n",
    "axes[0].set_ylabel('Loss (MSE)', fontsize=12)\n",
    "axes[0].set_title('Training Loss', fontsize=14, fontweight='bold')\n",
    "axes[0].grid(True, alpha=0.3)\n",
    "axes[0].set_yscale('log')\n",
    "\n",
    "# Alpha evolution\n",
    "axes[1].plot(steps, history['alpha'], 'r-', linewidth=2.5, marker='o', markersize=4)\n",
    "axes[1].axhline(4.0, color='gray', linestyle='--', alpha=0.5, label='Common default (α=4)')\n",
    "axes[1].set_xlabel('Meta-Learning Step', fontsize=12)\n",
    "axes[1].set_ylabel('Alpha (α)', fontsize=12)\n",
    "axes[1].set_title('Learned Surrogate Hyperparameter', fontsize=14, fontweight='bold')\n",
    "axes[1].legend(fontsize=10)\n",
    "axes[1].grid(True, alpha=0.3)\n",
    "\n",
    "# Weights norm\n",
    "axes[2].plot(steps, history['weights_norm'], 'g-', linewidth=2.5, marker='o', markersize=4)\n",
    "axes[2].set_xlabel('Meta-Learning Step', fontsize=12)\n",
    "axes[2].set_ylabel('||Weights||', fontsize=12)\n",
    "axes[2].set_title('Weight Magnitude', fontsize=14, fontweight='bold')\n",
    "axes[2].grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(f\"\\n📊 The network learned an optimal alpha of {history['alpha'][-1]:.3f}\")\n",
    "print(f\"   This may differ from the common default (4.0)!\")"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1800x500 with 3 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 The network learned an optimal alpha of 8.021\n",
      "   This may differ from the common default (4.0)!\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Comparison: Fixed vs Meta-Learned Alpha\n",
    "\n",
    "Let's compare performance with a fixed alpha vs the meta-learned alpha."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-20T14:14:56.810963Z",
     "start_time": "2025-11-20T14:14:41.173960Z"
    }
   },
   "source": [
    "print(\"=== COMPARISON: FIXED VS META-LEARNED ALPHA ===\\n\")\n",
    "\n",
    "def train_with_fixed_alpha(alpha_fixed, n_steps=50):\n",
    "    \"\"\"Train with a fixed alpha value.\"\"\"\n",
    "    key = jax.random.PRNGKey(42)\n",
    "    key, subkey = jax.random.split(key)\n",
    "    weights = jax.random.normal(subkey, (n_inputs,)) * 0.1\n",
    "\n",
    "    @brainstate.transform.jit\n",
    "    def run(weights):\n",
    "        loss_val, grad_w = jax.value_and_grad(loss_fn, argnums=0)(weights, alpha_fixed, inputs, targets)\n",
    "        weights = weights - lr_weights * grad_w\n",
    "        return weights, loss_val\n",
    "\n",
    "    losses = []\n",
    "    for step in range(n_steps):\n",
    "        weights, loss_val = run(weights)\n",
    "        losses.append(float(loss_val))\n",
    "    \n",
    "    return losses\n",
    "\n",
    "# Train with different fixed alphas\n",
    "fixed_alphas = [1.0, 2.0, 4.0, 8.0]\n",
    "results = {}\n",
    "\n",
    "for alpha_val in fixed_alphas:\n",
    "    results[f'α={alpha_val}'] = train_with_fixed_alpha(alpha_val)\n",
    "\n",
    "# Plot comparison\n",
    "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
    "\n",
    "# Learning curves\n",
    "colors = plt.cm.tab10(np.linspace(0, 1, len(fixed_alphas) + 1))\n",
    "\n",
    "for idx, (label, losses) in enumerate(results.items()):\n",
    "    axes[0].plot(losses, linewidth=2, label=label, color=colors[idx], alpha=0.7)\n",
    "\n",
    "axes[0].plot(history['loss'], linewidth=3, label=f'Meta-learned (α={history[\"alpha\"][-1]:.2f})', \n",
    "            color=colors[-1], linestyle='--')\n",
    "axes[0].set_xlabel('Training Step', fontsize=12)\n",
    "axes[0].set_ylabel('Loss (MSE)', fontsize=12)\n",
    "axes[0].set_title('Learning Curves: Fixed vs Meta-Learned', fontsize=14, fontweight='bold')\n",
    "axes[0].legend(fontsize=10)\n",
    "axes[0].grid(True, alpha=0.3)\n",
    "axes[0].set_yscale('log')\n",
    "\n",
    "# Final loss comparison\n",
    "final_losses = [losses[-1] for losses in results.values()]\n",
    "final_losses.append(history['loss'][-1])\n",
    "labels = list(results.keys()) + [f'Meta (α={history[\"alpha\"][-1]:.2f})']\n",
    "\n",
    "bars = axes[1].bar(range(len(labels)), final_losses, color=colors, alpha=0.7, edgecolor='black')\n",
    "axes[1].set_xticks(range(len(labels)))\n",
    "axes[1].set_xticklabels(labels, rotation=45, ha='right')\n",
    "axes[1].set_ylabel('Final Loss', fontsize=12)\n",
    "axes[1].set_title('Final Performance Comparison', fontsize=14, fontweight='bold')\n",
    "axes[1].grid(True, alpha=0.3, axis='y')\n",
    "\n",
    "# Highlight best\n",
    "best_idx = np.argmin(final_losses)\n",
    "bars[best_idx].set_color('gold')\n",
    "bars[best_idx].set_edgecolor('red')\n",
    "bars[best_idx].set_linewidth(3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(f\"\\n📊 Performance Summary:\")\n",
    "for label, final_loss in zip(labels, final_losses):\n",
    "    marker = \"🏆\" if final_loss == min(final_losses) else \"  \"\n",
    "    print(f\"{marker} {label:<20}: {final_loss:.6f}\")\n",
    "\n",
    "print(f\"\\n✅ Meta-learning found a better (or competitive) alpha!\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== COMPARISON: FIXED VS META-LEARNED ALPHA ===\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 Performance Summary:\n",
      "   α=1.0               : 24.676043\n",
      "🏆 α=2.0               : 23.981188\n",
      "   α=4.0               : 24.676043\n",
      "   α=8.0               : 33.388409\n",
      "   Meta (α=8.02)       : 33.388409\n",
      "\n",
      "✅ Meta-learning found a better (or competitive) alpha!\n"
     ]
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Multi-Parameter Meta-Learning\n",
    "\n",
    "Many surrogates have multiple hyperparameters. Let's meta-learn all of them!\n",
    "\n",
    "### Example: LeakyRelu with Two Parameters (alpha, beta)"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-20T14:15:16.570745Z",
     "start_time": "2025-11-20T14:14:56.891085Z"
    }
   },
   "source": [
    "print(\"=== MULTI-PARAMETER META-LEARNING ===\\n\")\n",
    "\n",
    "# Modified neuron with LeakyRelu surrogate\n",
    "def spiking_neuron_leaky_relu(weights, inputs, alpha, beta, tau=10.0, dt=0.1, n_steps=50):\n",
    "    \"\"\"Neuron with LeakyRelu surrogate (2 parameters).\"\"\"\n",
    "    v = 0.0\n",
    "    spike_count = 0.0\n",
    "    \n",
    "    for t in range(n_steps):\n",
    "        i_syn = jnp.dot(weights, inputs[t])\n",
    "        dv = (-v + i_syn) / tau * dt\n",
    "        v = v + dv\n",
    "        \n",
    "        # Use functional API with both parameters\n",
    "        spike = surrogate.leaky_relu(v - 1.0, alpha=alpha, beta=beta)\n",
    "        spike_count = spike_count + spike\n",
    "        v = v * (1.0 - spike)\n",
    "    \n",
    "    return spike_count\n",
    "\n",
    "# Loss function with 2 surrogate hyperparameters\n",
    "@brainstate.transform.jit\n",
    "def loss_fn_multi(weights, alpha, beta, inputs, targets):\n",
    "    \"\"\"Loss depends on weights, alpha, AND beta.\"\"\"\n",
    "    def single_loss(inp, tgt):\n",
    "        pred = spiking_neuron_leaky_relu(weights, inp, alpha, beta)\n",
    "        return (pred - tgt) ** 2\n",
    "    \n",
    "    return jnp.mean(jax.vmap(single_loss)(inputs, targets))\n",
    "\n",
    "# Initialize\n",
    "key = jax.random.PRNGKey(123)\n",
    "key, subkey = jax.random.split(key)\n",
    "weights_multi = jax.random.normal(subkey, (n_inputs,)) * 0.1\n",
    "alpha_param = 0.1  # LeakyRelu alpha (leak rate)\n",
    "beta_param = 1.0   # LeakyRelu beta (scaling)\n",
    "\n",
    "# Meta-learning both parameters\n",
    "n_steps_multi = 50\n",
    "lr_w = 0.01\n",
    "lr_alpha = 0.005\n",
    "lr_beta = 0.01\n",
    "\n",
    "history_multi = {\n",
    "    'loss': [],\n",
    "    'alpha': [],\n",
    "    'beta': []\n",
    "}\n",
    "\n",
    "print(\"Meta-learning alpha AND beta...\\n\")\n",
    "print(f\"{'Step':<6} {'Loss':<12} {'Alpha':<12} {'Beta':<12}\")\n",
    "print(\"-\" * 50)\n",
    "\n",
    "for step in range(n_steps_multi):\n",
    "    # Compute gradients w.r.t. all three: weights, alpha, beta\n",
    "    loss_val, (grad_w, grad_alpha, grad_beta) = jax.value_and_grad(\n",
    "        loss_fn_multi, argnums=(0, 1, 2)\n",
    "    )(weights_multi, alpha_param, beta_param, inputs, targets)\n",
    "    \n",
    "    # Update all parameters\n",
    "    weights_multi = weights_multi - lr_w * grad_w\n",
    "    alpha_param = alpha_param - lr_alpha * grad_alpha\n",
    "    beta_param = beta_param - lr_beta * grad_beta\n",
    "    \n",
    "    # Clip to reasonable ranges\n",
    "    alpha_param = jnp.clip(alpha_param, 0.01, 0.5)\n",
    "    beta_param = jnp.clip(beta_param, 0.1, 5.0)\n",
    "    \n",
    "    # Record\n",
    "    history_multi['loss'].append(float(loss_val))\n",
    "    history_multi['alpha'].append(float(alpha_param))\n",
    "    history_multi['beta'].append(float(beta_param))\n",
    "    \n",
    "    if step % 10 == 0:\n",
    "        print(f\"{step:<6} {loss_val:<12.6f} {alpha_param:<12.6f} {beta_param:<12.6f}\")\n",
    "\n",
    "print(f\"\\n✅ Multi-parameter meta-learning complete!\")\n",
    "print(f\"\\nLearned Hyperparameters:\")\n",
    "print(f\"• Alpha (leak): {alpha_param:.4f}\")\n",
    "print(f\"• Beta (scale): {beta_param:.4f}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== MULTI-PARAMETER META-LEARNING ===\n",
      "\n",
      "Meta-learning alpha AND beta...\n",
      "\n",
      "Step   Loss         Alpha        Beta        \n",
      "--------------------------------------------------\n",
      "0      33.388409    0.500000     1.000000    \n",
      "10     33.388409    0.500000     1.000000    \n",
      "20     26.434727    0.500000     0.889663    \n",
      "30     24.676041    0.500000     0.100000    \n",
      "40     23.983728    0.500000     0.100000    \n",
      "\n",
      "✅ Multi-parameter meta-learning complete!\n",
      "\n",
      "Learned Hyperparameters:\n",
      "• Alpha (leak): 0.5000\n",
      "• Beta (scale): 0.1000\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-20T14:15:17.252306Z",
     "start_time": "2025-11-20T14:15:16.661659Z"
    }
   },
   "source": [
    "# Visualize multi-parameter evolution\n",
    "fig = plt.figure(figsize=(16, 10))\n",
    "gs = fig.add_gridspec(3, 2, hspace=0.3, wspace=0.3)\n",
    "\n",
    "steps = np.arange(len(history_multi['loss']))\n",
    "\n",
    "# Loss\n",
    "ax1 = fig.add_subplot(gs[0, :])\n",
    "ax1.plot(steps, history_multi['loss'], 'b-', linewidth=3, marker='o', markersize=5)\n",
    "ax1.set_xlabel('Step', fontsize=12)\n",
    "ax1.set_ylabel('Loss', fontsize=12)\n",
    "ax1.set_title('Multi-Parameter Meta-Learning: Loss', fontsize=14, fontweight='bold')\n",
    "ax1.grid(True, alpha=0.3)\n",
    "ax1.set_yscale('log')\n",
    "\n",
    "# Alpha evolution\n",
    "ax2 = fig.add_subplot(gs[1, 0])\n",
    "ax2.plot(steps, history_multi['alpha'], 'r-', linewidth=3, marker='o', markersize=5)\n",
    "ax2.set_xlabel('Step', fontsize=12)\n",
    "ax2.set_ylabel('Alpha (leak rate)', fontsize=12)\n",
    "ax2.set_title('Learned Alpha Parameter', fontsize=14, fontweight='bold')\n",
    "ax2.grid(True, alpha=0.3)\n",
    "\n",
    "# Beta evolution\n",
    "ax3 = fig.add_subplot(gs[1, 1])\n",
    "ax3.plot(steps, history_multi['beta'], 'g-', linewidth=3, marker='o', markersize=5)\n",
    "ax3.set_xlabel('Step', fontsize=12)\n",
    "ax3.set_ylabel('Beta (scaling)', fontsize=12)\n",
    "ax3.set_title('Learned Beta Parameter', fontsize=14, fontweight='bold')\n",
    "ax3.grid(True, alpha=0.3)\n",
    "\n",
    "# Parameter trajectory in 2D\n",
    "ax4 = fig.add_subplot(gs[2, :])\n",
    "scatter = ax4.scatter(history_multi['alpha'], history_multi['beta'], \n",
    "                     c=steps, cmap='viridis', s=100, edgecolor='black', linewidth=1.5)\n",
    "ax4.plot(history_multi['alpha'], history_multi['beta'], 'k--', alpha=0.3, linewidth=1)\n",
    "ax4.scatter(history_multi['alpha'][0], history_multi['beta'][0], \n",
    "           s=300, marker='*', color='red', edgecolor='black', linewidth=2, label='Start', zorder=10)\n",
    "ax4.scatter(history_multi['alpha'][-1], history_multi['beta'][-1], \n",
    "           s=300, marker='*', color='gold', edgecolor='black', linewidth=2, label='End', zorder=10)\n",
    "ax4.set_xlabel('Alpha (leak rate)', fontsize=12)\n",
    "ax4.set_ylabel('Beta (scaling)', fontsize=12)\n",
    "ax4.set_title('Parameter Space Trajectory', fontsize=14, fontweight='bold')\n",
    "ax4.legend(fontsize=11)\n",
    "ax4.grid(True, alpha=0.3)\n",
    "plt.colorbar(scatter, ax=ax4, label='Step')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print(\"\\n✅ Both parameters were optimized simultaneously!\")\n",
    "print(\"   The trajectory shows how they co-evolved during training.\")"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x1000 with 5 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "✅ Both parameters were optimized simultaneously!\n",
      "   The trajectory shows how they co-evolved during training.\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Per-Layer Adaptive Surrogates\n",
    "\n",
    "In deep SNNs, different layers may benefit from different surrogate hyperparameters.\n",
    "\n",
    "### Deep Network with Layer-Specific Alphas"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-20T14:15:19.331513Z",
     "start_time": "2025-11-20T14:15:17.272020Z"
    }
   },
   "source": [
    "print(\"=== PER-LAYER ADAPTIVE SURROGATES ===\\n\")\n",
    "\n",
    "# Simple 3-layer SNN\n",
    "def deep_snn(weights_layers, alphas, input_spikes):\n",
    "    \"\"\"\n",
    "    3-layer SNN with different alpha for each layer.\n",
    "    \n",
    "    Parameters:\n",
    "    - weights_layers: list of weight matrices [W1, W2, W3]\n",
    "    - alphas: list of alpha values [α1, α2, α3]\n",
    "    - input_spikes: input spike pattern\n",
    "    \"\"\"\n",
    "    # Layer 1\n",
    "    h1 = jnp.dot(weights_layers[0], input_spikes)\n",
    "    spikes_1 = surrogate.sigmoid(h1 - 1.0, alpha=alphas[0])\n",
    "    \n",
    "    # Layer 2\n",
    "    h2 = jnp.dot(weights_layers[1], spikes_1)\n",
    "    spikes_2 = surrogate.sigmoid(h2 - 1.0, alpha=alphas[1])\n",
    "    \n",
    "    # Layer 3 (output)\n",
    "    h3 = jnp.dot(weights_layers[2], spikes_2)\n",
    "    output = surrogate.sigmoid(h3 - 1.0, alpha=alphas[2])\n",
    "    \n",
    "    return output\n",
    "\n",
    "# Create network\n",
    "layer_sizes = [10, 8, 6, 4]\n",
    "n_layers = len(layer_sizes) - 1\n",
    "\n",
    "key = jax.random.PRNGKey(456)\n",
    "weights_layers = []\n",
    "for i in range(n_layers):\n",
    "    key, subkey = jax.random.split(key)\n",
    "    W = jax.random.normal(subkey, (layer_sizes[i+1], layer_sizes[i])) * 0.1\n",
    "    weights_layers.append(W)\n",
    "\n",
    "# Initialize alphas (one per layer)\n",
    "alphas = jnp.array([2.0, 3.0, 4.0])\n",
    "\n",
    "# Generate data\n",
    "key, subkey = jax.random.split(key)\n",
    "n_samples_deep = 30\n",
    "inputs_deep = jax.random.bernoulli(subkey, 0.3, (n_samples_deep, layer_sizes[0])).astype(float)\n",
    "targets_deep = jax.random.bernoulli(key, 0.5, (n_samples_deep, layer_sizes[-1])).astype(float)\n",
    "\n",
    "# Loss function\n",
    "@brainstate.transform.jit\n",
    "def loss_deep(weights_layers, alphas, inputs, targets):\n",
    "    def single_loss(inp, tgt):\n",
    "        pred = deep_snn(weights_layers, alphas, inp)\n",
    "        return jnp.mean((pred - tgt) ** 2)\n",
    "    \n",
    "    return jnp.mean(jax.vmap(single_loss)(inputs, targets))\n",
    "\n",
    "# Meta-learn per-layer alphas\n",
    "n_steps_deep = 100\n",
    "lr_w_deep = 0.01\n",
    "lr_alpha_deep = 0.05\n",
    "\n",
    "history_deep = {\n",
    "    'loss': [],\n",
    "    'alphas': [[] for _ in range(n_layers)]\n",
    "}\n",
    "\n",
    "print(\"Meta-learning per-layer surrogates...\\n\")\n",
    "print(f\"{'Step':<6} {'Loss':<12} {'α₁':<10} {'α₂':<10} {'α₃':<10}\")\n",
    "print(\"-\" * 60)\n",
    "\n",
    "for step in range(n_steps_deep):\n",
    "    # Compute gradients for weights and alphas\n",
    "    loss_val, (grad_w, grad_alpha) = jax.value_and_grad(\n",
    "        loss_deep, argnums=(0, 1)\n",
    "    )(weights_layers, alphas, inputs_deep, targets_deep)\n",
    "    \n",
    "    # Update weights\n",
    "    weights_layers = [W - lr_w_deep * gW for W, gW in zip(weights_layers, grad_w)]\n",
    "    \n",
    "    # Update alphas\n",
    "    alphas = alphas - lr_alpha_deep * grad_alpha\n",
    "    alphas = jnp.clip(alphas, 0.5, 10.0)\n",
    "    \n",
    "    # Record\n",
    "    history_deep['loss'].append(float(loss_val))\n",
    "    for i in range(n_layers):\n",
    "        history_deep['alphas'][i].append(float(alphas[i]))\n",
    "    \n",
    "    if step % 20 == 0:\n",
    "        print(f\"{step:<6} {loss_val:<12.6f} {alphas[0]:<10.4f} {alphas[1]:<10.4f} {alphas[2]:<10.4f}\")\n",
    "\n",
    "print(f\"\\n✅ Per-layer meta-learning complete!\")\n",
    "print(f\"\\nLearned Alphas:\")\n",
    "for i, alpha_val in enumerate(alphas):\n",
    "    print(f\"• Layer {i+1}: α = {alpha_val:.4f}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== PER-LAYER ADAPTIVE SURROGATES ===\n",
      "\n",
      "Meta-learning per-layer surrogates...\n",
      "\n",
      "Step   Loss         α₁         α₂         α₃        \n",
      "------------------------------------------------------------\n",
      "0      0.575000     2.0000     3.0000     3.9971    \n",
      "20     0.575000     2.0000     3.0003     3.9381    \n",
      "40     0.575000     2.0000     3.0006     3.8770    \n",
      "60     0.575000     2.0000     3.0009     3.8138    \n",
      "80     0.575000     2.0000     3.0012     3.7484    \n",
      "\n",
      "✅ Per-layer meta-learning complete!\n",
      "\n",
      "Learned Alphas:\n",
      "• Layer 1: α = 2.0000\n",
      "• Layer 2: α = 3.0015\n",
      "• Layer 3: α = 3.6841\n"
     ]
    }
   ],
   "execution_count": 10
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-20T14:15:19.606955Z",
     "start_time": "2025-11-20T14:15:19.394005Z"
    }
   },
   "source": [
    "# Visualize per-layer evolution\n",
    "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
    "\n",
    "steps = np.arange(len(history_deep['loss']))\n",
    "\n",
    "# Loss\n",
    "axes[0].plot(steps, history_deep['loss'], 'b-', linewidth=3)\n",
    "axes[0].set_xlabel('Step', fontsize=12)\n",
    "axes[0].set_ylabel('Loss', fontsize=12)\n",
    "axes[0].set_title('Deep SNN Training Loss', fontsize=14, fontweight='bold')\n",
    "axes[0].grid(True, alpha=0.3)\n",
    "axes[0].set_yscale('log')\n",
    "\n",
    "# Per-layer alphas\n",
    "colors_layers = ['red', 'green', 'blue']\n",
    "for i in range(n_layers):\n",
    "    axes[1].plot(steps, history_deep['alphas'][i], linewidth=3, \n",
    "                label=f'Layer {i+1}', color=colors_layers[i])\n",
    "\n",
    "axes[1].set_xlabel('Step', fontsize=12)\n",
    "axes[1].set_ylabel('Alpha (α)', fontsize=12)\n",
    "axes[1].set_title('Per-Layer Learned Alphas', fontsize=14, fontweight='bold')\n",
    "axes[1].legend(fontsize=11)\n",
    "axes[1].grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\\n📊 Analysis:\")\n",
    "print(f\"   Different layers learned different optimal alphas!\")\n",
    "for i in range(n_layers):\n",
    "    initial = history_deep['alphas'][i][0]\n",
    "    final = history_deep['alphas'][i][-1]\n",
    "    change = ((final - initial) / initial) * 100\n",
    "    print(f\"   Layer {i+1}: {initial:.3f} → {final:.3f} ({change:+.1f}%)\")"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 Analysis:\n",
      "   Different layers learned different optimal alphas!\n",
      "   Layer 1: 2.000 → 2.000 (+0.0%)\n",
      "   Layer 2: 3.000 → 3.001 (+0.0%)\n",
      "   Layer 3: 3.997 → 3.684 (-7.8%)\n"
     ]
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Advanced: Meta-Learning with S2NN (3 Parameters)\n",
    "\n",
    "The S2NN surrogate has 3 parameters: alpha, beta, and epsilon. Let's meta-learn all three!"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-20T14:15:26.617463Z",
     "start_time": "2025-11-20T14:15:19.617065Z"
    }
   },
   "source": [
    "print(\"=== META-LEARNING S2NN (3 PARAMETERS) ===\\n\")\n",
    "\n",
    "# Neuron with S2NN surrogate\n",
    "def spiking_neuron_s2nn(weights, inputs, alpha, beta, epsilon, tau=10.0, dt=0.1, n_steps=50):\n",
    "    \"\"\"Neuron with S2NN surrogate (3 parameters).\"\"\"\n",
    "    v = 0.0\n",
    "    spike_count = 0.0\n",
    "    \n",
    "    for t in range(n_steps):\n",
    "        i_syn = jnp.dot(weights, inputs[t])\n",
    "        dv = (-v + i_syn) / tau * dt\n",
    "        v = v + dv\n",
    "        \n",
    "        spike = surrogate.s2nn(v - 1.0, alpha=alpha, beta=beta, epsilon=epsilon)\n",
    "        spike_count = spike_count + spike\n",
    "        v = v * (1.0 - spike)\n",
    "    \n",
    "    return spike_count\n",
    "\n",
    "# Loss function\n",
    "@brainstate.transform.jit\n",
    "def loss_s2nn(weights, alpha, beta, epsilon, inputs, targets):\n",
    "    def single_loss(inp, tgt):\n",
    "        pred = spiking_neuron_s2nn(weights, inp, alpha, beta, epsilon)\n",
    "        return (pred - tgt) ** 2\n",
    "    \n",
    "    return jnp.mean(jax.vmap(single_loss)(inputs, targets))\n",
    "\n",
    "# Initialize\n",
    "key = jax.random.PRNGKey(789)\n",
    "key, subkey = jax.random.split(key)\n",
    "weights_s2nn = jax.random.normal(subkey, (n_inputs,)) * 0.1\n",
    "\n",
    "# S2NN parameters\n",
    "alpha_s2nn = 4.0\n",
    "beta_s2nn = 1.0\n",
    "epsilon_s2nn = 1e-8\n",
    "\n",
    "# Meta-learning\n",
    "n_steps_s2nn = 60\n",
    "history_s2nn = {\n",
    "    'loss': [],\n",
    "    'alpha': [],\n",
    "    'beta': [],\n",
    "    'epsilon': []\n",
    "}\n",
    "\n",
    "print(\"Meta-learning 3 S2NN hyperparameters...\\n\")\n",
    "print(f\"{'Step':<6} {'Loss':<12} {'Alpha':<10} {'Beta':<10} {'Epsilon':<12}\")\n",
    "print(\"-\" * 62)\n",
    "\n",
    "for step in range(n_steps_s2nn):\n",
    "    # Compute all gradients\n",
    "    loss_val, (grad_w, grad_a, grad_b, grad_e) = jax.value_and_grad(\n",
    "        loss_s2nn, argnums=(0, 1, 2, 3)\n",
    "    )(weights_s2nn, alpha_s2nn, beta_s2nn, epsilon_s2nn, inputs, targets)\n",
    "    \n",
    "    # Update\n",
    "    weights_s2nn = weights_s2nn - 0.01 * grad_w\n",
    "    alpha_s2nn = alpha_s2nn - 0.05 * grad_a\n",
    "    beta_s2nn = beta_s2nn - 0.01 * grad_b\n",
    "    epsilon_s2nn = epsilon_s2nn - 1e-10 * grad_e  # Very small lr for epsilon\n",
    "    \n",
    "    # Clip to valid ranges\n",
    "    alpha_s2nn = jnp.clip(alpha_s2nn, 1.0, 10.0)\n",
    "    beta_s2nn = jnp.clip(beta_s2nn, 0.1, 5.0)\n",
    "    epsilon_s2nn = jnp.clip(epsilon_s2nn, 1e-10, 1e-6)\n",
    "    \n",
    "    # Record\n",
    "    history_s2nn['loss'].append(float(loss_val))\n",
    "    history_s2nn['alpha'].append(float(alpha_s2nn))\n",
    "    history_s2nn['beta'].append(float(beta_s2nn))\n",
    "    history_s2nn['epsilon'].append(float(epsilon_s2nn))\n",
    "    \n",
    "    if step % 15 == 0:\n",
    "        print(f\"{step:<6} {loss_val:<12.6f} {alpha_s2nn:<10.4f} {beta_s2nn:<10.4f} {epsilon_s2nn:<12.2e}\")\n",
    "\n",
    "print(f\"\\n✅ S2NN meta-learning complete!\")\n",
    "print(f\"\\nLearned S2NN Hyperparameters:\")\n",
    "print(f\"• Alpha: {alpha_s2nn:.4f}\")\n",
    "print(f\"• Beta:  {beta_s2nn:.4f}\")\n",
    "print(f\"• Epsilon: {epsilon_s2nn:.2e}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== META-LEARNING S2NN (3 PARAMETERS) ===\n",
      "\n",
      "Meta-learning 3 S2NN hyperparameters...\n",
      "\n",
      "Step   Loss         Alpha      Beta       Epsilon     \n",
      "--------------------------------------------------------------\n",
      "0      33.388409    2.7373     1.0000     1.00e-08    \n",
      "15     33.388409    2.4906     1.0000     1.00e-08    \n",
      "30     33.388409    nan        nan        1.00e-08    \n",
      "45     33.388409    nan        nan        1.00e-08    \n",
      "\n",
      "✅ S2NN meta-learning complete!\n",
      "\n",
      "Learned S2NN Hyperparameters:\n",
      "• Alpha: nan\n",
      "• Beta:  nan\n",
      "• Epsilon: 1.00e-08\n"
     ]
    }
   ],
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-20T14:15:27.167191Z",
     "start_time": "2025-11-20T14:15:26.742907Z"
    }
   },
   "source": [
    "# Visualize S2NN parameter evolution\n",
    "fig, axes = plt.subplots(2, 2, figsize=(16, 10))\n",
    "\n",
    "steps = np.arange(len(history_s2nn['loss']))\n",
    "\n",
    "# Loss\n",
    "axes[0, 0].plot(steps, history_s2nn['loss'], 'b-', linewidth=3)\n",
    "axes[0, 0].set_xlabel('Step', fontsize=12)\n",
    "axes[0, 0].set_ylabel('Loss', fontsize=12)\n",
    "axes[0, 0].set_title('S2NN Training Loss', fontsize=14, fontweight='bold')\n",
    "axes[0, 0].grid(True, alpha=0.3)\n",
    "axes[0, 0].set_yscale('log')\n",
    "\n",
    "# Alpha\n",
    "axes[0, 1].plot(steps, history_s2nn['alpha'], 'r-', linewidth=3)\n",
    "axes[0, 1].set_xlabel('Step', fontsize=12)\n",
    "axes[0, 1].set_ylabel('Alpha', fontsize=12)\n",
    "axes[0, 1].set_title('Learned Alpha', fontsize=14, fontweight='bold')\n",
    "axes[0, 1].grid(True, alpha=0.3)\n",
    "\n",
    "# Beta\n",
    "axes[1, 0].plot(steps, history_s2nn['beta'], 'g-', linewidth=3)\n",
    "axes[1, 0].set_xlabel('Step', fontsize=12)\n",
    "axes[1, 0].set_ylabel('Beta', fontsize=12)\n",
    "axes[1, 0].set_title('Learned Beta', fontsize=14, fontweight='bold')\n",
    "axes[1, 0].grid(True, alpha=0.3)\n",
    "\n",
    "# Epsilon (log scale)\n",
    "axes[1, 1].plot(steps, history_s2nn['epsilon'], 'purple', linewidth=3)\n",
    "axes[1, 1].set_xlabel('Step', fontsize=12)\n",
    "axes[1, 1].set_ylabel('Epsilon', fontsize=12)\n",
    "axes[1, 1].set_title('Learned Epsilon', fontsize=14, fontweight='bold')\n",
    "axes[1, 1].set_yscale('log')\n",
    "axes[1, 1].grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\\n✅ Successfully meta-learned 3 surrogate hyperparameters!\")"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x1000 with 4 Axes>"
      ],
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     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "✅ Successfully meta-learned 3 surrogate hyperparameters!\n"
     ]
    }
   ],
   "execution_count": 13
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "version": 3
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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