{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "intro",
   "metadata": {},
   "source": [
    "# Activation Functions and Normalization\n",
    "\n",
    "Activation functions and normalization layers are critical components that enable deep neural networks to learn complex patterns and train stably.\n",
    "\n",
    "In this tutorial, you will learn:\n",
    "\n",
    "- 🎯 **Activation Functions** - ReLU, GELU, Sigmoid, Tanh and variants\n",
    "- 📊 **Batch Normalization** - Stabilizing training with BatchNorm\n",
    "- 📐 **Layer Normalization** - Alternative normalization for sequences\n",
    "- 🔲 **Group Normalization** - For small batch sizes\n",
    "- ⚖️ **When to use each** - Practical guidelines\n",
    "\n",
    "## Why Are These Important?\n",
    "\n",
    "**Activation Functions** introduce non-linearity, allowing networks to learn complex patterns  \n",
    "**Normalization Layers** stabilize training, accelerate convergence, and act as regularizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "imports",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T11:34:39.419797Z",
     "start_time": "2025-10-10T11:34:37.939252Z"
    },
    "execution": {
     "iopub.execute_input": "2026-05-30T16:20:11.670549Z",
     "iopub.status.busy": "2026-05-30T16:20:11.670347Z",
     "iopub.status.idle": "2026-05-30T16:20:11.361379Z",
     "shell.execute_reply": "2026-05-30T16:20:11.360332Z"
    }
   },
   "outputs": [],
   "source": [
    "import brainstate\n",
    "import jax.numpy as jnp\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "activation_intro",
   "metadata": {},
   "source": [
    "## 1. Activation Functions\n",
    "\n",
    "Activation functions determine the output of a neuron given its input.\n",
    "\n",
    "### ReLU Family\n",
    "\n",
    "#### Standard ReLU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "relu",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T11:34:39.824998Z",
     "start_time": "2025-10-10T11:34:39.425573Z"
    },
    "execution": {
     "iopub.execute_input": "2026-05-30T16:20:11.364127Z",
     "iopub.status.busy": "2026-05-30T16:20:11.363653Z",
     "iopub.status.idle": "2026-05-30T16:20:13.976286Z",
     "shell.execute_reply": "2026-05-30T16:20:13.975519Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input:  [-2 -1  0  1  2]\n",
      "Output: [0 0 0 1 2]\n",
      "\n",
      "✅ Advantages: Simple, fast, no gradient vanishing for positive values\n",
      "⚠️  Issue: 'Dying ReLU' problem when neurons always output 0\n"
     ]
    }
   ],
   "source": [
    "# ReLU: max(0, x)\n",
    "relu = brainstate.nn.ReLU()\n",
    "\n",
    "x = jnp.linspace(-3, 3, 100)\n",
    "y = relu(x)\n",
    "\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(x, y, linewidth=2, label='ReLU')\n",
    "plt.axhline(0, color='gray', linestyle='--', alpha=0.3)\n",
    "plt.axvline(0, color='gray', linestyle='--', alpha=0.3)\n",
    "plt.grid(alpha=0.3)\n",
    "plt.xlabel('Input')\n",
    "plt.ylabel('Output')\n",
    "plt.title('ReLU Activation', fontweight='bold')\n",
    "plt.legend()\n",
    "\n",
    "# Test with data\n",
    "test_input = jnp.array([-2, -1, 0, 1, 2])\n",
    "test_output = relu(test_input)\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.stem(test_input, test_output, basefmt=' ')\n",
    "plt.xlabel('Input')\n",
    "plt.ylabel('Output')\n",
    "plt.title('ReLU on Sample Points', fontweight='bold')\n",
    "plt.grid(alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(f\"Input:  {test_input}\")\n",
    "print(f\"Output: {test_output}\")\n",
    "print(\"\\n✅ Advantages: Simple, fast, no gradient vanishing for positive values\")\n",
    "print(\"⚠️  Issue: 'Dying ReLU' problem when neurons always output 0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "leaky_relu",
   "metadata": {},
   "source": [
    "#### Leaky ReLU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "leaky_relu_example",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T11:34:39.984362Z",
     "start_time": "2025-10-10T11:34:39.828971Z"
    },
    "execution": {
     "iopub.execute_input": "2026-05-30T16:20:13.978341Z",
     "iopub.status.busy": "2026-05-30T16:20:13.978045Z",
     "iopub.status.idle": "2026-05-30T16:20:14.151074Z",
     "shell.execute_reply": "2026-05-30T16:20:14.150314Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Advantage: Allows small gradient for negative values\n",
      "   Helps prevent dying ReLU problem\n"
     ]
    }
   ],
   "source": [
    "# LeakyReLU: max(alpha * x, x)\n",
    "leaky_relu = brainstate.nn.LeakyReLU(negative_slope=0.1)\n",
    "\n",
    "y_leaky = leaky_relu(x)\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.plot(x, y, linewidth=2, label='ReLU', alpha=0.5)\n",
    "plt.plot(x, y_leaky, linewidth=2, label='Leaky ReLU (α=0.1)')\n",
    "plt.axhline(0, color='gray', linestyle='--', alpha=0.3)\n",
    "plt.axvline(0, color='gray', linestyle='--', alpha=0.3)\n",
    "plt.grid(alpha=0.3)\n",
    "plt.xlabel('Input')\n",
    "plt.ylabel('Output')\n",
    "plt.title('ReLU vs Leaky ReLU', fontweight='bold')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "print(\"✅ Advantage: Allows small gradient for negative values\")\n",
    "print(\"   Helps prevent dying ReLU problem\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "modern_activations",
   "metadata": {},
   "source": [
    "### Modern Activation Functions\n",
    "\n",
    "#### GELU (Gaussian Error Linear Unit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "gelu",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T11:34:40.187218Z",
     "start_time": "2025-10-10T11:34:39.992930Z"
    },
    "execution": {
     "iopub.execute_input": "2026-05-30T16:20:14.152900Z",
     "iopub.status.busy": "2026-05-30T16:20:14.152734Z",
     "iopub.status.idle": "2026-05-30T16:20:14.404870Z",
     "shell.execute_reply": "2026-05-30T16:20:14.404154Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Used in Transformers (BERT, GPT)\n",
      "✅ Smooth, differentiable everywhere\n",
      "✅ Stochastic regularization properties\n"
     ]
    }
   ],
   "source": [
    "# GELU: Smooth, probabilistic activation\n",
    "gelu = brainstate.nn.GELU()\n",
    "\n",
    "y_gelu = gelu(x)\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.plot(x, y, linewidth=2, label='ReLU', alpha=0.5)\n",
    "plt.plot(x, y_gelu, linewidth=2, label='GELU')\n",
    "plt.axhline(0, color='gray', linestyle='--', alpha=0.3)\n",
    "plt.axvline(0, color='gray', linestyle='--', alpha=0.3)\n",
    "plt.grid(alpha=0.3)\n",
    "plt.xlabel('Input')\n",
    "plt.ylabel('Output')\n",
    "plt.title('ReLU vs GELU', fontweight='bold')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "print(\"✅ Used in Transformers (BERT, GPT)\")\n",
    "print(\"✅ Smooth, differentiable everywhere\")\n",
    "print(\"✅ Stochastic regularization properties\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sigmoid_tanh",
   "metadata": {},
   "source": [
    "### Classic Activations\n",
    "\n",
    "#### Sigmoid and Tanh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "sigmoid_tanh_example",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T11:34:40.529361Z",
     "start_time": "2025-10-10T11:34:40.200598Z"
    },
    "execution": {
     "iopub.execute_input": "2026-05-30T16:20:14.407337Z",
     "iopub.status.busy": "2026-05-30T16:20:14.407067Z",
     "iopub.status.idle": "2026-05-30T16:20:14.648564Z",
     "shell.execute_reply": "2026-05-30T16:20:14.647971Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigmoid:\n",
      "  ✅ Output range: (0, 1) - good for probabilities\n",
      "  ⚠️  Vanishing gradients for large |x|\n",
      "\n",
      "Tanh:\n",
      "  ✅ Output range: (-1, 1) - zero-centered\n",
      "  ⚠️  Also suffers from vanishing gradients\n",
      "  📝 Often used in RNN/LSTM gates\n"
     ]
    }
   ],
   "source": [
    "# Sigmoid: 1 / (1 + exp(-x))\n",
    "sigmoid = brainstate.nn.Sigmoid()\n",
    "\n",
    "# Tanh: (exp(x) - exp(-x)) / (exp(x) + exp(-x))\n",
    "tanh = brainstate.nn.Tanh()\n",
    "\n",
    "y_sigmoid = sigmoid(x)\n",
    "y_tanh = tanh(x)\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(x, y_sigmoid, linewidth=2, label='Sigmoid', color='blue')\n",
    "plt.axhline(0.5, color='gray', linestyle='--', alpha=0.3)\n",
    "plt.axvline(0, color='gray', linestyle='--', alpha=0.3)\n",
    "plt.grid(alpha=0.3)\n",
    "plt.xlabel('Input')\n",
    "plt.ylabel('Output')\n",
    "plt.title('Sigmoid: σ(x) = 1/(1+e⁻ˣ)', fontweight='bold')\n",
    "plt.legend()\n",
    "plt.ylim([-0.1, 1.1])\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(x, y_tanh, linewidth=2, label='Tanh', color='green')\n",
    "plt.axhline(0, color='gray', linestyle='--', alpha=0.3)\n",
    "plt.axvline(0, color='gray', linestyle='--', alpha=0.3)\n",
    "plt.grid(alpha=0.3)\n",
    "plt.xlabel('Input')\n",
    "plt.ylabel('Output')\n",
    "plt.title('Tanh: tanh(x)', fontweight='bold')\n",
    "plt.legend()\n",
    "plt.ylim([-1.1, 1.1])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"Sigmoid:\")\n",
    "print(\"  ✅ Output range: (0, 1) - good for probabilities\")\n",
    "print(\"  ⚠️  Vanishing gradients for large |x|\")\n",
    "print(\"\\nTanh:\")\n",
    "print(\"  ✅ Output range: (-1, 1) - zero-centered\")\n",
    "print(\"  ⚠️  Also suffers from vanishing gradients\")\n",
    "print(\"  📝 Often used in RNN/LSTM gates\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "activation_comparison",
   "metadata": {},
   "source": [
    "### Comparing All Activations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "all_activations",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T11:34:41.022564Z",
     "start_time": "2025-10-10T11:34:40.537638Z"
    },
    "execution": {
     "iopub.execute_input": "2026-05-30T16:20:14.650464Z",
     "iopub.status.busy": "2026-05-30T16:20:14.650230Z",
     "iopub.status.idle": "2026-05-30T16:20:15.161177Z",
     "shell.execute_reply": "2026-05-30T16:20:15.160345Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 Activation Function Guide:\n",
      "\n",
      "ReLU:        Default choice, fast, works well in most cases\n",
      "LeakyReLU:   When dying ReLU is a problem\n",
      "GELU:        Transformers, NLP models\n",
      "ELU:         Smooth variant of ReLU with negative values\n",
      "Sigmoid:     Output layer for binary classification\n",
      "Tanh:        RNN/LSTM gates, when zero-centered output needed\n"
     ]
    }
   ],
   "source": [
    "# Compare multiple activations\n",
    "activations = {\n",
    "    'ReLU': brainstate.nn.ReLU(),\n",
    "    'LeakyReLU': brainstate.nn.LeakyReLU(0.1),\n",
    "    'GELU': brainstate.nn.GELU(),\n",
    "    'ELU': brainstate.nn.ELU(),\n",
    "    'Sigmoid': brainstate.nn.Sigmoid(),\n",
    "    'Tanh': brainstate.nn.Tanh(),\n",
    "}\n",
    "\n",
    "fig, axes = plt.subplots(2, 3, figsize=(15, 8))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for idx, (name, activation) in enumerate(activations.items()):\n",
    "    y_act = activation(x)\n",
    "    axes[idx].plot(x, y_act, linewidth=2.5, color=f'C{idx}')\n",
    "    axes[idx].axhline(0, color='gray', linestyle='--', alpha=0.3)\n",
    "    axes[idx].axvline(0, color='gray', linestyle='--', alpha=0.3)\n",
    "    axes[idx].grid(alpha=0.3)\n",
    "    axes[idx].set_xlabel('Input', fontsize=10)\n",
    "    axes[idx].set_ylabel('Output', fontsize=10)\n",
    "    axes[idx].set_title(name, fontsize=12, fontweight='bold')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\\n📊 Activation Function Guide:\\n\")\n",
    "print(\"ReLU:        Default choice, fast, works well in most cases\")\n",
    "print(\"LeakyReLU:   When dying ReLU is a problem\")\n",
    "print(\"GELU:        Transformers, NLP models\")\n",
    "print(\"ELU:         Smooth variant of ReLU with negative values\")\n",
    "print(\"Sigmoid:     Output layer for binary classification\")\n",
    "print(\"Tanh:        RNN/LSTM gates, when zero-centered output needed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "softmax",
   "metadata": {},
   "source": [
    "### Softmax - For Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "softmax_example",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T11:34:41.424913Z",
     "start_time": "2025-10-10T11:34:41.045102Z"
    },
    "execution": {
     "iopub.execute_input": "2026-05-30T16:20:15.162699Z",
     "iopub.status.busy": "2026-05-30T16:20:15.162556Z",
     "iopub.status.idle": "2026-05-30T16:20:15.585309Z",
     "shell.execute_reply": "2026-05-30T16:20:15.584351Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logits:        [2.  1.  0.5 3.  0.1]\n",
      "Probabilities: [0.22427258 0.08250527 0.05004198 0.60963607 0.03354414]\n",
      "Sum of probs:  1.000000 (should be 1.0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "✅ Softmax converts logits to valid probability distribution\n",
      "   Use for multi-class classification output layer\n"
     ]
    }
   ],
   "source": [
    "# Softmax: converts logits to probabilities\n",
    "softmax = brainstate.nn.Softmax()\n",
    "\n",
    "# Example logits from a classifier\n",
    "logits = jnp.array([2.0, 1.0, 0.5, 3.0, 0.1])\n",
    "probs = softmax(logits)\n",
    "\n",
    "print(\"Logits:       \", logits)\n",
    "print(\"Probabilities:\", probs)\n",
    "print(f\"Sum of probs:  {jnp.sum(probs):.6f} (should be 1.0)\")\n",
    "\n",
    "# Visualize\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
    "\n",
    "classes = ['Cat', 'Dog', 'Bird', 'Fish', 'Horse']\n",
    "\n",
    "axes[0].bar(classes, logits, color='steelblue', alpha=0.7)\n",
    "axes[0].set_ylabel('Logit Value')\n",
    "axes[0].set_title('Raw Logits', fontweight='bold')\n",
    "axes[0].grid(axis='y', alpha=0.3)\n",
    "\n",
    "axes[1].bar(classes, probs, color='coral', alpha=0.7)\n",
    "axes[1].set_ylabel('Probability')\n",
    "axes[1].set_title('After Softmax', fontweight='bold')\n",
    "axes[1].set_ylim([0, 1])\n",
    "axes[1].grid(axis='y', alpha=0.3)\n",
    "\n",
    "# Add values on bars\n",
    "for i, v in enumerate(probs):\n",
    "    axes[1].text(i, v + 0.02, f'{v:.3f}', ha='center', fontsize=9)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\\n✅ Softmax converts logits to valid probability distribution\")\n",
    "print(\"   Use for multi-class classification output layer\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "normalization_intro",
   "metadata": {},
   "source": [
    "## 2. Normalization Layers\n",
    "\n",
    "Normalization stabilizes training by controlling the distribution of activations.\n",
    "\n",
    "### Batch Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "batchnorm",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T11:34:42.214285Z",
     "start_time": "2025-10-10T11:34:41.445945Z"
    },
    "execution": {
     "iopub.execute_input": "2026-05-30T16:20:15.587158Z",
     "iopub.status.busy": "2026-05-30T16:20:15.587012Z",
     "iopub.status.idle": "2026-05-30T16:20:16.876738Z",
     "shell.execute_reply": "2026-05-30T16:20:16.876205Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BatchNorm0d:\n",
      "BatchNorm0d(\n",
      "  in_size=(10,),\n",
      "  out_size=(10,),\n",
      "  affine=True,\n",
      "  bias_initializer=Constant(\n",
      "    value=0.0\n",
      "  ),\n",
      "  scale_initializer=Constant(\n",
      "    value=1.0\n",
      "  ),\n",
      "  dtype=<class 'numpy.float32'>,\n",
      "  track_running_stats=True,\n",
      "  momentum=Array(0.99, dtype=float32),\n",
      "  epsilon=Array(1.e-05, dtype=float32),\n",
      "  use_fast_variance=True,\n",
      "  feature_axes=(0,),\n",
      "  running_mean=BatchState(\n",
      "    value=ShapedArray(float32[10])\n",
      "  ),\n",
      "  running_var=BatchState(\n",
      "    value=ShapedArray(float32[10])\n",
      "  ),\n",
      "  weight=NormalizationParamState(\n",
      "    value={\n",
      "      'bias': ShapedArray(float32[10]),\n",
      "      'scale': ShapedArray(float32[10])\n",
      "    }\n",
      "  )\n",
      ")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Before BatchNorm:\n",
      "  Mean: [10.531552 10.173284  9.421941]...\n",
      "  Std:  [4.4984775 4.850637  4.945446 ]...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "After BatchNorm:\n",
      "  Mean: [ 2.0861626e-07 -5.5879354e-08  2.3096800e-07]...\n",
      "  Std:  [0.99999964 0.9999995  0.9999993 ]...\n",
      "\n",
      "✅ Normalizes to ~mean=0, ~std=1 across batch\n",
      "✅ Learns scale (γ) and shift (β) parameters\n"
     ]
    }
   ],
   "source": [
    "# BatchNorm: Normalizes across batch dimension\n",
    "brainstate.random.seed(42)\n",
    "batch_norm = brainstate.nn.BatchNorm0d((10,))\n",
    "\n",
    "print(\"BatchNorm0d:\")\n",
    "print(batch_norm)\n",
    "\n",
    "# Create batch of data with varying statistics\n",
    "batch_size = 32\n",
    "features = 10\n",
    "x = brainstate.random.randn(batch_size, features) * 5 + 10  # mean≈10, std≈5\n",
    "\n",
    "print()\n",
    "print(\"Before BatchNorm:\")\n",
    "print(f\"  Mean: {jnp.mean(x, axis=0)[:3]}...\")\n",
    "print(f\"  Std:  {jnp.std(x, axis=0)[:3]}...\")\n",
    "\n",
    "# Apply batch norm\n",
    "with brainstate.environ.context(fit=True):\n",
    "    y = batch_norm(x)\n",
    "\n",
    "print()\n",
    "print(\"After BatchNorm:\")\n",
    "print(f\"  Mean: {jnp.mean(y, axis=0)[:3]}...\")\n",
    "print(f\"  Std:  {jnp.std(y, axis=0)[:3]}...\")\n",
    "\n",
    "print()\n",
    "print(\"✅ Normalizes to ~mean=0, ~std=1 across batch\")\n",
    "print(\"✅ Learns scale (γ) and shift (β) parameters\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "batchnorm_visual",
   "metadata": {},
   "source": [
    "### Visualizing BatchNorm Effect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "batchnorm_visualization",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T11:34:43.223147Z",
     "start_time": "2025-10-10T11:34:42.223583Z"
    },
    "execution": {
     "iopub.execute_input": "2026-05-30T16:20:16.879070Z",
     "iopub.status.busy": "2026-05-30T16:20:16.878823Z",
     "iopub.status.idle": "2026-05-30T16:20:18.231071Z",
     "shell.execute_reply": "2026-05-30T16:20:18.230278Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BatchNorm standardizes different distributions to similar scale\n"
     ]
    }
   ],
   "source": [
    "# Generate data with different distributions\n",
    "brainstate.random.seed(0)\n",
    "x1 = brainstate.random.randn(1000) * 10 + 50  # High variance, high mean\n",
    "x2 = brainstate.random.randn(1000) * 2 - 5     # Low variance, negative mean\n",
    "\n",
    "# Create batch norm (treating as batch dimension)\n",
    "bn = brainstate.nn.BatchNorm0d((1,))\n",
    "\n",
    "# Reshape to (batch, features)\n",
    "x1_batch = x1[:, None]\n",
    "x2_batch = x2[:, None]\n",
    "\n",
    "# Apply normalization\n",
    "with brainstate.environ.context(fit=True):\n",
    "    y1 = bn(x1_batch).flatten()\n",
    "    y2 = bn(x2_batch).flatten()\n",
    "\n",
    "fig, axes = plt.subplots(2, 2, figsize=(12, 8))\n",
    "\n",
    "# Distribution 1 - before\n",
    "axes[0, 0].hist(np.array(x1), bins=50, alpha=0.7, color='blue', edgecolor='black')\n",
    "axes[0, 0].set_title('Distribution 1 - Before BN', fontweight='bold')\n",
    "axes[0, 0].set_xlabel('Value')\n",
    "axes[0, 0].set_ylabel('Frequency')\n",
    "axes[0, 0].axvline(jnp.mean(x1), color='red', linestyle='--', label=f'μ={jnp.mean(x1):.1f}')\n",
    "axes[0, 0].legend()\n",
    "\n",
    "# Distribution 1 - after\n",
    "axes[0, 1].hist(np.array(y1), bins=50, alpha=0.7, color='green', edgecolor='black')\n",
    "axes[0, 1].set_title('Distribution 1 - After BN', fontweight='bold')\n",
    "axes[0, 1].set_xlabel('Value')\n",
    "axes[0, 1].set_ylabel('Frequency')\n",
    "axes[0, 1].axvline(jnp.mean(y1), color='red', linestyle='--', label=f'μ={jnp.mean(y1):.2f}')\n",
    "axes[0, 1].legend()\n",
    "\n",
    "# Distribution 2 - before\n",
    "axes[1, 0].hist(np.array(x2), bins=50, alpha=0.7, color='blue', edgecolor='black')\n",
    "axes[1, 0].set_title('Distribution 2 - Before BN', fontweight='bold')\n",
    "axes[1, 0].set_xlabel('Value')\n",
    "axes[1, 0].set_ylabel('Frequency')\n",
    "axes[1, 0].axvline(jnp.mean(x2), color='red', linestyle='--', label=f'μ={jnp.mean(x2):.1f}')\n",
    "axes[1, 0].legend()\n",
    "\n",
    "# Distribution 2 - after\n",
    "axes[1, 1].hist(np.array(y2), bins=50, alpha=0.7, color='green', edgecolor='black')\n",
    "axes[1, 1].set_title('Distribution 2 - After BN', fontweight='bold')\n",
    "axes[1, 1].set_xlabel('Value')\n",
    "axes[1, 1].set_ylabel('Frequency')\n",
    "axes[1, 1].axvline(jnp.mean(y2), color='red', linestyle='--', label=f'μ={jnp.mean(y2):.2f}')\n",
    "axes[1, 1].legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"BatchNorm standardizes different distributions to similar scale\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "layernorm",
   "metadata": {},
   "source": [
    "### Layer Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "layernorm_example",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T11:34:45.380792Z",
     "start_time": "2025-10-10T11:34:45.023256Z"
    },
    "execution": {
     "iopub.execute_input": "2026-05-30T16:20:18.233387Z",
     "iopub.status.busy": "2026-05-30T16:20:18.233144Z",
     "iopub.status.idle": "2026-05-30T16:20:18.719913Z",
     "shell.execute_reply": "2026-05-30T16:20:18.718849Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LayerNorm:\n",
      "LayerNorm(\n",
      "  in_size=(10,),\n",
      "  out_size=(10,),\n",
      "  feature_axes=(0,),\n",
      "  reduction_axes=(-1,),\n",
      "  weight=NormalizationParamState(\n",
      "    value={\n",
      "      'bias': ShapedArray(float32[10]),\n",
      "      'scale': ShapedArray(float32[10])\n",
      "    }\n",
      "  ),\n",
      "  epsilon=1e-06,\n",
      "  dtype=<class 'numpy.float32'>,\n",
      "  use_bias=True,\n",
      "  use_scale=True,\n",
      "  bias_init=ZeroInit(\n",
      "    unit=Unit(\"1\")\n",
      "  ),\n",
      "  scale_init=Constant(\n",
      "    value=1.0\n",
      "  ),\n",
      "  use_fast_variance=True\n",
      ")\n",
      "\n",
      "Before LayerNorm (single sample):\n",
      "  Values: [ 3.0611591 13.874271  17.702465  19.20955    5.685323   5.9649186\n",
      "  8.997379  13.917359   5.0701327 11.3188305]\n",
      "  Mean: 10.480\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Std:  5.313\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "After LayerNorm:\n",
      "  Values: [-1.3963605   0.6388257   1.3593478   1.6430035  -0.9024544  -0.84983045\n",
      " -0.2790769   0.64693546 -1.0182422   0.15785426]\n",
      "  Mean: 0.000\n",
      "  Std:  1.000\n",
      "\n",
      "✅ LayerNorm works on single samples\n",
      "✅ Popular in Transformers and RNNs\n",
      "✅ Independent of batch size\n"
     ]
    }
   ],
   "source": [
    "# LayerNorm: Normalizes across features (not batch)\n",
    "layer_norm = brainstate.nn.LayerNorm((10,))\n",
    "\n",
    "print(\"LayerNorm:\")\n",
    "print(layer_norm)\n",
    "\n",
    "# Single sample\n",
    "x_single = brainstate.random.randn(10) * 5 + 10\n",
    "\n",
    "print()\n",
    "print(\"Before LayerNorm (single sample):\")\n",
    "print(f\"  Values: {x_single}\")\n",
    "print(f\"  Mean: {jnp.mean(x_single):.3f}\")\n",
    "print(f\"  Std:  {jnp.std(x_single):.3f}\")\n",
    "\n",
    "y_single = layer_norm(x_single)\n",
    "\n",
    "print()\n",
    "print(\"After LayerNorm:\")\n",
    "print(f\"  Values: {y_single}\")\n",
    "print(f\"  Mean: {jnp.mean(y_single):.3f}\")\n",
    "print(f\"  Std:  {jnp.std(y_single):.3f}\")\n",
    "\n",
    "print()\n",
    "print(\"✅ LayerNorm works on single samples\")\n",
    "print(\"✅ Popular in Transformers and RNNs\")\n",
    "print(\"✅ Independent of batch size\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "norm_comparison",
   "metadata": {},
   "source": [
    "### Comparing Normalization Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "norm_comparison_table",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-10T11:34:45.862243Z",
     "start_time": "2025-10-10T11:34:45.382805Z"
    },
    "execution": {
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    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 Normalization Method Comparison:\n",
      "\n",
      "      Method     Normalizes Over            Best For Batch Dependent          Typical Use\n",
      "   BatchNorm     Batch + Spatial CNNs, large batches             Yes               Vision\n",
      "   LayerNorm            Features  RNNs, Transformers              No                  NLP\n",
      "   GroupNorm              Groups       Small batches              No Vision (small batch)\n",
      "InstanceNorm Spatial per channel      Style transfer              No          GANs, Style\n",
      "\n",
      "\n",
      "🎯 Quick Guide:\n",
      "  • Large batch + CNN → BatchNorm\n",
      "  • Small batch + CNN → GroupNorm\n",
      "  • Sequences/RNN/Transformer → LayerNorm\n",
      "  • Single image inference → LayerNorm or GroupNorm\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "comparison = pd.DataFrame({\n",
    "    'Method': ['BatchNorm', 'LayerNorm', 'GroupNorm', 'InstanceNorm'],\n",
    "    'Normalizes Over': ['Batch + Spatial', 'Features', 'Groups', 'Spatial per channel'],\n",
    "    'Best For': ['CNNs, large batches', 'RNNs, Transformers', 'Small batches', 'Style transfer'],\n",
    "    'Batch Dependent': ['Yes', 'No', 'No', 'No'],\n",
    "    'Typical Use': ['Vision', 'NLP', 'Vision (small batch)', 'GANs, Style'],\n",
    "})\n",
    "\n",
    "print(\"\\n📊 Normalization Method Comparison:\\n\")\n",
    "print(comparison.to_string(index=False))\n",
    "\n",
    "print(\"\\n\\n🎯 Quick Guide:\")\n",
    "print(\"  • Large batch + CNN → BatchNorm\")\n",
    "print(\"  • Small batch + CNN → GroupNorm\")\n",
    "print(\"  • Sequences/RNN/Transformer → LayerNorm\")\n",
    "print(\"  • Single image inference → LayerNorm or GroupNorm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "practical_example",
   "metadata": {},
   "source": [
    "## 3. Putting It All Together\n",
    "\n",
    "Building a complete network with activations and normalization:"
   ]
  },
  {
   "cell_type": "code",
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   "id": "complete_network",
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    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modern CNN with GELU + BatchNorm + LayerNorm:\n",
      "ModernCNN(\n",
      "  conv1=Conv2d(\n",
      "    in_size=(32, 32, 3),\n",
      "    out_size=(32, 32, 64),\n",
      "    channel_first=False,\n",
      "    channels_last=True,\n",
      "    in_channels=3,\n",
      "    out_channels=64,\n",
      "    stride=(1, 1),\n",
      "    kernel_size=(3, 3),\n",
      "    lhs_dilation=(1, 1),\n",
      "    rhs_dilation=(1, 1),\n",
      "    groups=1,\n",
      "    dimension_numbers=ConvDimensionNumbers(lhs_spec=(0, 3, 1, 2), rhs_spec=(3, 2, 0, 1), out_spec=(0, 3, 1, 2)),\n",
      "    padding=SAME,\n",
      "    kernel_shape=(3, 3, 3, 64),\n",
      "    w_initializer=XavierNormal(\n",
      "      scale=1.0,\n",
      "      mode='fan_avg',\n",
      "      in_axis=-2,\n",
      "      out_axis=-1,\n",
      "      distribution='truncated_normal',\n",
      "      rng=RandomState(Array((), dtype=key<fry>) overlaying:\n",
      "      [1846506472 1430574090]),\n",
      "      unit=Unit(\"1\")\n",
      "    ),\n",
      "    weight=ParamState(\n",
      "      value={\n",
      "        'weight': ShapedArray(float32[3,3,3,64])\n",
      "      }\n",
      "    )\n",
      "  ),\n",
      "  bn1=BatchNorm2d(\n",
      "    in_size=(32, 32, 64),\n",
      "    out_size=(32, 32, 64),\n",
      "    affine=True,\n",
      "    bias_initializer=Constant(\n",
      "      value=0.0\n",
      "    ),\n",
      "    scale_initializer=Constant(\n",
      "      value=1.0\n",
      "    ),\n",
      "    dtype=<class 'numpy.float32'>,\n",
      "    track_running_stats=True,\n",
      "    momentum=Array(0.99, dtype=float32),\n",
      "    epsilon=Array(1.e-05, dtype=float32),\n",
      "    use_fast_variance=True,\n",
      "    feature_axes=(2,),\n",
      "    running_mean=BatchState(\n",
      "      value=ShapedArray(float32[1,1,64])\n",
      "    ),\n",
      "    running_var=BatchState(\n",
      "      value=ShapedArray(float32[1,1,64])\n",
      "    ),\n",
      "    weight=NormalizationParamState(\n",
      "      value={\n",
      "        'bias': ShapedArray(float32[1,1,64]),\n",
      "        'scale': ShapedArray(float32[1,1,64])\n",
      "      }\n",
      "    )\n",
      "  ),\n",
      "  act1=GELU(approximate=False),\n",
      "  pool1=MaxPool2d(\n",
      "    init_value=-inf,\n",
      "    computation=<function max at 0x760b73a14040>,\n",
      "    pool_dim=2,\n",
      "    return_indices=False,\n",
      "    kernel_size=(2, 2),\n",
      "    stride=(2, 2),\n",
      "    padding=VALID,\n",
      "    channel_axis=-1\n",
      "  ),\n",
      "  conv2=Conv2d(\n",
      "    in_size=(16, 16, 64),\n",
      "    out_size=(16, 16, 128),\n",
      "    channel_first=False,\n",
      "    channels_last=True,\n",
      "    in_channels=64,\n",
      "    out_channels=128,\n",
      "    stride=(1, 1),\n",
      "    kernel_size=(3, 3),\n",
      "    lhs_dilation=(1, 1),\n",
      "    rhs_dilation=(1, 1),\n",
      "    groups=1,\n",
      "    dimension_numbers=ConvDimensionNumbers(lhs_spec=(0, 3, 1, 2), rhs_spec=(3, 2, 0, 1), out_spec=(0, 3, 1, 2)),\n",
      "    padding=SAME,\n",
      "    kernel_shape=(3, 3, 64, 128),\n",
      "    w_initializer=XavierNormal(\n",
      "      scale=1.0,\n",
      "      mode='fan_avg',\n",
      "      in_axis=-2,\n",
      "      out_axis=-1,\n",
      "      distribution='truncated_normal',\n",
      "      rng=RandomState(Array((), dtype=key<fry>) overlaying:\n",
      "      [1846506472 1430574090]),\n",
      "      unit=Unit(\"1\")\n",
      "    ),\n",
      "    weight=ParamState(\n",
      "      value={\n",
      "        'weight': ShapedArray(float32[3,3,64,128])\n",
      "      }\n",
      "    )\n",
      "  ),\n",
      "  bn2=BatchNorm2d(\n",
      "    in_size=(16, 16, 128),\n",
      "    out_size=(16, 16, 128),\n",
      "    affine=True,\n",
      "    bias_initializer=Constant(\n",
      "      value=0.0\n",
      "    ),\n",
      "    scale_initializer=Constant(\n",
      "      value=1.0\n",
      "    ),\n",
      "    dtype=<class 'numpy.float32'>,\n",
      "    track_running_stats=True,\n",
      "    momentum=Array(0.99, dtype=float32),\n",
      "    epsilon=Array(1.e-05, dtype=float32),\n",
      "    use_fast_variance=True,\n",
      "    feature_axes=(2,),\n",
      "    running_mean=BatchState(\n",
      "      value=ShapedArray(float32[1,1,128])\n",
      "    ),\n",
      "    running_var=BatchState(\n",
      "      value=ShapedArray(float32[1,1,128])\n",
      "    ),\n",
      "    weight=NormalizationParamState(\n",
      "      value={\n",
      "        'bias': ShapedArray(float32[1,1,128]),\n",
      "        'scale': ShapedArray(float32[1,1,128])\n",
      "      }\n",
      "    )\n",
      "  ),\n",
      "  act2=GELU(approximate=False),\n",
      "  pool2=MaxPool2d(\n",
      "    in_size=(16, 16, 128),\n",
      "    out_size=(8, 8, 128),\n",
      "    init_value=-inf,\n",
      "    computation=<function max at 0x760b73a14040>,\n",
      "    pool_dim=2,\n",
      "    return_indices=False,\n",
      "    kernel_size=(2, 2),\n",
      "    stride=(2, 2),\n",
      "    padding=VALID,\n",
      "    channel_axis=-1\n",
      "  ),\n",
      "  fc1=Linear(\n",
      "    in_size=(8192,),\n",
      "    out_size=(256,),\n",
      "    weight=ParamState(\n",
      "      value={\n",
      "        'bias': ShapedArray(float32[256]),\n",
      "        'weight': ShapedArray(float32[8192,256])\n",
      "      }\n",
      "    )\n",
      "  ),\n",
      "  ln=LayerNorm(\n",
      "    in_size=(256,),\n",
      "    out_size=(256,),\n",
      "    feature_axes=(0,),\n",
      "    reduction_axes=(-1,),\n",
      "    weight=NormalizationParamState(\n",
      "      value={\n",
      "        'bias': ShapedArray(float32[256]),\n",
      "        'scale': ShapedArray(float32[256])\n",
      "      }\n",
      "    ),\n",
      "    epsilon=1e-06,\n",
      "    dtype=<class 'numpy.float32'>,\n",
      "    use_bias=True,\n",
      "    use_scale=True,\n",
      "    bias_init=ZeroInit(\n",
      "      unit=Unit(\"1\")\n",
      "    ),\n",
      "    scale_init=Constant(\n",
      "      value=1.0\n",
      "    ),\n",
      "    use_fast_variance=True\n",
      "  ),\n",
      "  act3=GELU(approximate=False),\n",
      "  dropout=Dropout(\n",
      "    prob=0.5,\n",
      "    broadcast_dims=()\n",
      "  ),\n",
      "  fc2=Linear(\n",
      "    in_size=(256,),\n",
      "    out_size=(10,),\n",
      "    weight=ParamState(\n",
      "      value={\n",
      "        'bias': ShapedArray(float32[10]),\n",
      "        'weight': ShapedArray(float32[256,10])\n",
      "      }\n",
      "    )\n",
      "  )\n",
      ")\n",
      "\n",
      "Input: (4, 32, 32, 3)\n",
      "Output: (4, 10)\n",
      "\n",
      "Logits: [ 3.325643   -1.4292734   0.7167487   1.7075737   0.22491142 -1.3628054\n",
      " -0.2709884  -0.27673513 -1.0504785  -1.4831703 ]\n"
     ]
    }
   ],
   "source": [
    "import brainunit as u\n",
    "\n",
    "\n",
    "class ModernCNN(brainstate.nn.Module):\n",
    "    \"\"\"CNN with modern activations and normalization.\"\"\"\n",
    "\n",
    "    def __init__(self, num_classes=10):\n",
    "        super().__init__()\n",
    "\n",
    "        # Block 1: Conv + BatchNorm + GELU\n",
    "        self.conv1 = brainstate.nn.Conv2d((32, 32, 3), out_channels=64, kernel_size=(3, 3), padding='SAME')\n",
    "        self.bn1 = brainstate.nn.BatchNorm2d((32, 32, 64))\n",
    "        self.act1 = brainstate.nn.GELU()\n",
    "        self.pool1 = brainstate.nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2))\n",
    "\n",
    "        # Block 2\n",
    "        self.conv2 = brainstate.nn.Conv2d((16, 16, 64), out_channels=128, kernel_size=(3, 3), padding='SAME')\n",
    "        self.bn2 = brainstate.nn.BatchNorm2d((16, 16, 128))\n",
    "        self.act2 = brainstate.nn.GELU()\n",
    "        self.pool2 = brainstate.nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2), in_size=self.bn2.out_size)\n",
    "\n",
    "        # Classifier\n",
    "        self.fc1 = brainstate.nn.Linear((128 * 8 * 8,), (256,))\n",
    "        self.ln = brainstate.nn.LayerNorm((256,))\n",
    "        self.act3 = brainstate.nn.GELU()\n",
    "        self.dropout = brainstate.nn.Dropout(prob=0.5)\n",
    "        self.fc2 = brainstate.nn.Linear((256,), (num_classes,))\n",
    "\n",
    "    def update(self, x):\n",
    "        # Block 1\n",
    "        x = self.conv1(x)\n",
    "        x = self.bn1(x)\n",
    "        x = self.act1(x)\n",
    "        x = self.pool1(x)\n",
    "\n",
    "        # Block 2\n",
    "        x = self.conv2(x)\n",
    "        x = self.bn2(x)\n",
    "        x = self.act2(x)\n",
    "        x = self.pool2(x)\n",
    "\n",
    "        # Classifier\n",
    "        x = u.math.flatten(x, start_axis=1)\n",
    "        x = self.fc1(x)\n",
    "        x = self.ln(x)\n",
    "        x = self.act3(x)\n",
    "        x = self.dropout(x)\n",
    "        x = self.fc2(x)\n",
    "\n",
    "        return x\n",
    "\n",
    "# Create and test\n",
    "brainstate.random.seed(0)\n",
    "model = ModernCNN(num_classes=10)\n",
    "\n",
    "# Forward pass\n",
    "x = brainstate.random.randn(4, 32, 32, 3)  # 4 images\n",
    "with brainstate.environ.context(fit=True) as env:\n",
    "    logits = model(x)\n",
    "\n",
    "print(\"Modern CNN with GELU + BatchNorm + LayerNorm:\")\n",
    "print(model)\n",
    "print()\n",
    "print(\"Input:\", x.shape)\n",
    "print(\"Output:\", logits.shape)\n",
    "print()\n",
    "print(\"Logits:\", logits[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "summary",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "In this tutorial, you learned:\n",
    "\n",
    "✅ **Activation Functions**\n",
    "  - ReLU family (ReLU, Leaky ReLU, ELU)\n",
    "  - Modern activations (GELU, SiLU)\n",
    "  - Classic activations (Sigmoid, Tanh)\n",
    "  - Softmax for classification\n",
    "\n",
    "✅ **Normalization Layers**\n",
    "  - BatchNorm for large-batch training\n",
    "  - LayerNorm for sequences and transformers\n",
    "  - GroupNorm for small-batch scenarios\n",
    "\n",
    "✅ **Practical Guidelines**\n",
    "  - When to use each activation\n",
    "  - When to use each normalization\n",
    "  - How to combine them effectively\n",
    "\n",
    "### Quick Reference Card\n",
    "\n",
    "| Task | Activation | Normalization |\n",
    "|------|-----------|---------------|\n",
    "| **CNN (large batch)** | ReLU/GELU | BatchNorm |\n",
    "| **CNN (small batch)** | ReLU/GELU | GroupNorm |\n",
    "| **Transformer/NLP** | GELU | LayerNorm |\n",
    "| **RNN/LSTM** | Tanh (gates) | LayerNorm |\n",
    "| **Binary output** | Sigmoid | - |\n",
    "| **Multi-class output** | Softmax | - |\n",
    "\n",
    "### Best Practices\n",
    "\n",
    "1. 🎯 **Use ReLU/GELU** as default activations\n",
    "2. 📊 **Add normalization** after conv/linear layers\n",
    "3. ⚡ **Order**: Conv/Linear → Norm → Activation\n",
    "4. 🔍 **Experiment** with activation functions\n",
    "5. 📝 **Use appropriate normalization** for your batch size\n",
    "\n",
    "### Next Steps\n",
    "\n",
    "Continue with:\n",
    "- **Recurrent Networks** - Handle sequential data\n",
    "- **Training** - Optimize with gradient descent\n",
    "- **Advanced Architectures** - ResNets, Transformers"
   ]
  }
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