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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>TensorFlow Reference</title>
<link href="https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:wght@400;600&family=Syne:wght@400;600;800&family=DM+Sans:wght@300;400;500&display=swap" rel="stylesheet">
<style>
:root {
--bg: #0e0f13;
--surface: #16181f;
--surface2: #1d2029;
--border: #272b38;
--text: #e8eaf2;
--muted: #7a7f96;
--code: #a8d8a8;
--c-tensor: #ff6f00;
--c-keras: #e91e63;
--c-data: #00bcd4;
--c-nn: #7c4dff;
--c-train: #4caf50;
--c-math: #ff9800;
--c-linalg: #2196f3;
--c-image: #9c27b0;
--c-io: #607d8b;
--c-dist: #f44336;
--c-saved: #009688;
--c-lite: #795548;
}
*, *::before, *::after { box-sizing: border-box; margin: 0; padding: 0; }
body {
background: var(--bg);
color: var(--text);
font-family: 'DM Sans', sans-serif;
font-size: 14px;
line-height: 1.6;
min-height: 100vh;
}
header {
background: var(--surface);
border-bottom: 1px solid var(--border);
padding: 32px 40px 24px;
position: sticky;
top: 0;
z-index: 100;
display: flex;
align-items: center;
gap: 24px;
flex-wrap: wrap;
}
.logo {
font-family: 'Syne', sans-serif;
font-weight: 800;
font-size: 22px;
letter-spacing: -0.5px;
color: var(--text);
display: flex;
align-items: center;
gap: 10px;
}
.logo span { color: var(--c-tensor); }
.back-link {
font-family: 'IBM Plex Mono', monospace;
font-size: 11px;
color: var(--muted);
text-decoration: none;
display: flex;
align-items: center;
gap: 5px;
transition: color 0.2s;
}
.back-link:hover { color: var(--c-tensor); }
.search-wrap { margin-left: auto; position: relative; }
#search {
background: var(--surface2);
border: 1px solid var(--border);
border-radius: 8px;
color: var(--text);
font-family: 'IBM Plex Mono', monospace;
font-size: 12px;
padding: 8px 14px 8px 36px;
width: 260px;
outline: none;
transition: border-color 0.2s;
}
#search:focus { border-color: var(--c-tensor); }
#search::placeholder { color: var(--muted); }
.search-icon { position: absolute; left: 11px; top: 50%; transform: translateY(-50%); color: var(--muted); font-size: 13px; pointer-events: none; }
.layout { display: grid; grid-template-columns: 220px 1fr; min-height: calc(100vh - 81px); }
nav {
background: var(--surface);
border-right: 1px solid var(--border);
padding: 24px 0;
position: sticky;
top: 81px;
height: calc(100vh - 81px);
overflow-y: auto;
scrollbar-width: thin;
scrollbar-color: var(--border) transparent;
}
nav::-webkit-scrollbar { width: 4px; }
nav::-webkit-scrollbar-thumb { background: var(--border); border-radius: 4px; }
.nav-label { font-family: 'IBM Plex Mono', monospace; font-size: 9px; font-weight: 600; letter-spacing: 1.5px; text-transform: uppercase; color: var(--muted); padding: 12px 20px 4px; }
.nav-item { display: flex; align-items: center; gap: 8px; padding: 6px 20px; cursor: pointer; transition: background 0.15s, color 0.15s; font-size: 13px; color: var(--muted); text-decoration: none; border-left: 2px solid transparent; }
.nav-item:hover { background: var(--surface2); color: var(--text); }
.nav-item.active { color: var(--text); border-left-color: var(--c-tensor); background: var(--surface2); }
.nav-dot { width: 7px; height: 7px; border-radius: 50%; flex-shrink: 0; }
main { padding: 40px; max-width: 1100px; }
.section { margin-bottom: 56px; scroll-margin-top: 100px; }
.section-header { display: flex; align-items: center; gap: 14px; margin-bottom: 20px; padding-bottom: 14px; border-bottom: 1px solid var(--border); flex-wrap: wrap; }
.section-icon { width: 36px; height: 36px; border-radius: 8px; display: flex; align-items: center; justify-content: center; font-size: 16px; flex-shrink: 0; }
.section-title { font-family: 'Syne', sans-serif; font-weight: 800; font-size: 20px; letter-spacing: -0.3px; }
.section-desc { color: var(--muted); font-size: 13px; margin-left: auto; font-style: italic; }
.fn-grid { display: grid; grid-template-columns: repeat(auto-fill, minmax(300px, 1fr)); gap: 10px; }
.fn-card {
background: var(--surface);
border: 1px solid var(--border);
border-radius: 10px;
padding: 14px 16px;
transition: border-color 0.2s, background 0.2s;
position: relative;
overflow: hidden;
}
.fn-card::before { content: ''; position: absolute; left: 0; top: 0; bottom: 0; width: 3px; background: var(--section-color, var(--c-tensor)); border-radius: 10px 0 0 10px; }
.fn-card:hover { background: var(--surface2); border-color: var(--section-color, var(--c-tensor)); }
.fn-name { font-family: 'IBM Plex Mono', monospace; font-size: 12.5px; font-weight: 600; color: var(--code); margin-bottom: 4px; }
.fn-name .prefix { color: var(--muted); font-weight: 400; }
.fn-sig { font-family: 'IBM Plex Mono', monospace; font-size: 10.5px; color: var(--muted); margin-bottom: 6px; white-space: nowrap; overflow: hidden; text-overflow: ellipsis; }
.fn-desc { font-size: 12px; color: #a0a4b8; line-height: 1.5; }
.fn-tags { display: flex; gap: 4px; margin-top: 8px; flex-wrap: wrap; }
.tag { font-family: 'IBM Plex Mono', monospace; font-size: 9px; padding: 2px 6px; border-radius: 4px; background: var(--surface2); border: 1px solid var(--border); color: var(--muted); letter-spacing: 0.3px; }
.subsection { margin-bottom: 24px; }
.subsection-title { font-family: 'Syne', sans-serif; font-size: 13px; font-weight: 600; color: var(--muted); text-transform: uppercase; letter-spacing: 1px; margin-bottom: 10px; display: flex; align-items: center; gap: 8px; }
.subsection-title::after { content: ''; flex: 1; height: 1px; background: var(--border); }
.hidden { display: none !important; }
@media (max-width: 768px) {
.layout { grid-template-columns: 1fr; }
nav { display: none; }
main { padding: 20px; }
#search { width: 180px; }
}
</style>
</head>
<body>
<header>
<div class="logo">
<svg width="28" height="28" viewBox="0 0 28 28" fill="none">
<rect width="28" height="28" rx="6" fill="#ff6f00" opacity="0.15"/>
<path d="M8 6 L8 22 M8 14 L16 10 M16 10 L20 8 M16 18 L20 20 M8 14 L16 18" stroke="#ff6f00" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
</svg>
TensorFlow <span>Reference</span>
</div>
<a class="back-link" href="index.html">← Back to index</a>
<div class="search-wrap">
<span class="search-icon">⌕</span>
<input id="search" type="text" placeholder="Search functions..." autocomplete="off">
</div>
</header>
<div class="layout">
<nav id="sidebar">
<div class="nav-label">Sections</div>
<a class="nav-item active" href="#tensors" onclick="setActive(this)">
<span class="nav-dot" style="background:var(--c-tensor)"></span> Tensors & Ops
</a>
<a class="nav-item" href="#keras-model" onclick="setActive(this)">
<span class="nav-dot" style="background:var(--c-keras)"></span> Keras — Models
</a>
<a class="nav-item" href="#keras-layers" onclick="setActive(this)">
<span class="nav-dot" style="background:var(--c-keras)"></span> Keras — Layers
</a>
<a class="nav-item" href="#keras-train" onclick="setActive(this)">
<span class="nav-dot" style="background:var(--c-train)"></span> Keras — Training
</a>
<a class="nav-item" href="#data" onclick="setActive(this)">
<span class="nav-dot" style="background:var(--c-data)"></span> tf.data
</a>
<a class="nav-item" href="#nn" onclick="setActive(this)">
<span class="nav-dot" style="background:var(--c-nn)"></span> tf.nn
</a>
<a class="nav-item" href="#math" onclick="setActive(this)">
<span class="nav-dot" style="background:var(--c-math)"></span> tf.math
</a>
<a class="nav-item" href="#linalg" onclick="setActive(this)">
<span class="nav-dot" style="background:var(--c-linalg)"></span> tf.linalg
</a>
<a class="nav-item" href="#image" onclick="setActive(this)">
<span class="nav-dot" style="background:var(--c-image)"></span> tf.image
</a>
<a class="nav-item" href="#autodiff" onclick="setActive(this)">
<span class="nav-dot" style="background:var(--c-train)"></span> Autodiff & tf.function
</a>
<a class="nav-item" href="#distribute" onclick="setActive(this)">
<span class="nav-dot" style="background:var(--c-dist)"></span> tf.distribute
</a>
<a class="nav-item" href="#io" onclick="setActive(this)">
<span class="nav-dot" style="background:var(--c-io)"></span> Saving & IO
</a>
<a class="nav-item" href="#lite" onclick="setActive(this)">
<span class="nav-dot" style="background:var(--c-lite)"></span> TFLite & TF-TRT
</a>
</nav>
<main id="main">
<!-- ═══════════════════════════════════════════════ -->
<!-- 1. TENSORS & OPS -->
<!-- ═══════════════════════════════════════════════ -->
<section class="section" id="tensors" style="--section-color: var(--c-tensor)">
<div class="section-header">
<div class="section-icon" style="background: color-mix(in srgb, var(--c-tensor) 15%, transparent)">⬡</div>
<div><div class="section-title" style="color:var(--c-tensor)">Tensors & Operations</div></div>
<div class="section-desc">Creation, shaping, indexing, and type casting</div>
</div>
<div class="subsection">
<div class="subsection-title">Core Types</div>
<div class="fn-grid">
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.</span>Tensor</div>
<div class="fn-sig"> .shape · .dtype · .numpy() · .device</div>
<div class="fn-desc">Immutable multi-dimensional array. Key attributes: <code style="color:var(--code)">.shape</code> (TensorShape), <code style="color:var(--code)">.dtype</code>, <code style="color:var(--code)">.numpy()</code> converts to NumPy array.</div>
<div class="fn-tags"><span class="tag">class</span><span class="tag">immutable</span></div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.</span>Variable</div>
<div class="fn-sig">(initial_value, trainable=True, name=None, dtype=None)</div>
<div class="fn-desc">Mutable tensor. Holds model weights. <code style="color:var(--code)">.assign(v)</code>, <code style="color:var(--code)">.assign_add(v)</code>, <code style="color:var(--code)">.assign_sub(v)</code> for in-place updates.</div>
<div class="fn-tags"><span class="tag">class</span><span class="tag">mutable</span><span class="tag">trainable</span></div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.</span>TensorSpec</div>
<div class="fn-sig">(shape, dtype=tf.float32, name=None)</div>
<div class="fn-desc">Describes a tensor's shape and dtype without holding data. Used in <code style="color:var(--code)">tf.function</code> signatures and <code style="color:var(--code)">tf.data</code>.</div>
<div class="fn-tags"><span class="tag">class</span></div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.</span>RaggedTensor</div>
<div class="fn-sig"> .from_row_lengths() / .from_value_rowids()</div>
<div class="fn-desc">Tensor with non-uniform row lengths — ideal for variable-length sequences (NLP). Supports most tf ops.</div>
<div class="fn-tags"><span class="tag">class</span><span class="tag">variable-length</span></div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.</span>SparseTensor</div>
<div class="fn-sig">(indices, values, dense_shape)</div>
<div class="fn-desc">Efficient representation for tensors with mostly zeros. Convert with <code style="color:var(--code)">tf.sparse.to_dense()</code>.</div>
<div class="fn-tags"><span class="tag">class</span><span class="tag">sparse</span></div>
</div>
</div>
</div>
<div class="subsection">
<div class="subsection-title">Creation</div>
<div class="fn-grid">
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.</span>constant</div>
<div class="fn-sig">(value, dtype=None, shape=None, name='Const')</div>
<div class="fn-desc">Creates a constant tensor from a Python value, list, or NumPy array. Result is immutable.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.</span>zeros</div>
<div class="fn-sig">(shape, dtype=tf.float32, name=None)</div>
<div class="fn-desc">Tensor of zeros. <code style="color:var(--code)">tf.zeros_like(x)</code> creates zeros with the same shape and dtype as <code style="color:var(--code)">x</code>.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.</span>ones</div>
<div class="fn-sig">(shape, dtype=tf.float32, name=None)</div>
<div class="fn-desc">Tensor of ones. <code style="color:var(--code)">tf.ones_like(x)</code> variant available.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.</span>fill</div>
<div class="fn-sig">(dims, value, name=None)</div>
<div class="fn-desc">Fills a tensor of shape <code style="color:var(--code)">dims</code> with scalar <code style="color:var(--code)">value</code>.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.</span>range</div>
<div class="fn-sig">(start, limit=None, delta=1, dtype=None)</div>
<div class="fn-desc">1-D tensor of evenly-spaced values. Mirrors Python <code style="color:var(--code)">range()</code>.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.</span>linspace</div>
<div class="fn-sig">(start, stop, num, axis=0, name=None)</div>
<div class="fn-desc">Evenly-spaced values over a closed interval [start, stop].</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.random.</span>normal</div>
<div class="fn-sig">(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None)</div>
<div class="fn-desc">Random values from a normal distribution. Use <code style="color:var(--code)">tf.random.set_seed(n)</code> for reproducibility.</div>
<div class="fn-tags"><span class="tag">random</span></div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.random.</span>uniform</div>
<div class="fn-sig">(shape, minval=0, maxval=None, dtype=tf.float32, seed=None)</div>
<div class="fn-desc">Random values from a uniform distribution.</div>
<div class="fn-tags"><span class="tag">random</span></div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.random.</span>shuffle</div>
<div class="fn-sig">(value, seed=None, name=None)</div>
<div class="fn-desc">Randomly shuffles a tensor along its first dimension.</div>
<div class="fn-tags"><span class="tag">random</span></div>
</div>
</div>
</div>
<div class="subsection">
<div class="subsection-title">Shape & Type</div>
<div class="fn-grid">
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.</span>reshape</div>
<div class="fn-sig">(tensor, shape, name=None)</div>
<div class="fn-desc">Returns tensor with new shape. Use <code style="color:var(--code)">-1</code> to infer one dimension. Data order unchanged.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.</span>transpose</div>
<div class="fn-sig">(a, perm=None, conjugate=False, name='transpose')</div>
<div class="fn-desc">Permutes dimensions. Default reverses all axes. <code style="color:var(--code)">perm=[0,2,1]</code> to swap last two dims.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.</span>expand_dims</div>
<div class="fn-sig">(input, axis, name=None)</div>
<div class="fn-desc">Inserts a new axis of size 1 at <code style="color:var(--code)">axis</code>. Equivalent to <code style="color:var(--code)">x[..., tf.newaxis]</code>.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.</span>squeeze</div>
<div class="fn-sig">(input, axis=None, name=None)</div>
<div class="fn-desc">Removes dimensions of size 1. <code style="color:var(--code)">axis</code> to target specific dims.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.</span>cast</div>
<div class="fn-sig">(x, dtype, name=None)</div>
<div class="fn-desc">Casts tensor to a new dtype (e.g. <code style="color:var(--code)">tf.float32 → tf.float16</code>).</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.</span>shape</div>
<div class="fn-sig">(input, out_type=tf.int32, name=None)</div>
<div class="fn-desc">Returns the <em>dynamic</em> shape as a 1-D tensor. Use <code style="color:var(--code)">x.shape</code> for static shape.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.</span>size</div>
<div class="fn-sig">(input, out_type=tf.int32, name=None)</div>
<div class="fn-desc">Returns the total number of elements in the tensor.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.</span>rank</div>
<div class="fn-sig">(input, name=None)</div>
<div class="fn-desc">Returns the number of dimensions (rank) as a scalar tensor.</div>
</div>
</div>
</div>
<div class="subsection">
<div class="subsection-title">Indexing & Slicing</div>
<div class="fn-grid">
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.</span>gather</div>
<div class="fn-sig">(params, indices, axis=None, batch_dims=0, name=None)</div>
<div class="fn-desc">Gathers slices from <code style="color:var(--code)">params</code> at <code style="color:var(--code)">indices</code>. Like fancy indexing in NumPy.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.</span>gather_nd</div>
<div class="fn-sig">(params, indices, batch_dims=0, name=None)</div>
<div class="fn-desc">Gathers elements using multi-dimensional indices. More powerful than <code style="color:var(--code)">tf.gather</code>.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.</span>slice</div>
<div class="fn-sig">(input_, begin, size, name=None)</div>
<div class="fn-desc">Extracts a sub-tensor starting at <code style="color:var(--code)">begin</code> with given <code style="color:var(--code)">size</code>.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.</span>boolean_mask</div>
<div class="fn-sig">(tensor, mask, axis=None, name='boolean_mask')</div>
<div class="fn-desc">Applies a boolean mask along an axis — selects elements where mask is True.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.</span>where</div>
<div class="fn-sig">(condition, x=None, y=None, name=None)</div>
<div class="fn-desc">Element-wise selection: returns <code style="color:var(--code)">x</code> where condition is True, else <code style="color:var(--code)">y</code>. With one arg, returns indices of True elements.</div>
</div>
</div>
</div>
<div class="subsection">
<div class="subsection-title">Combining & Splitting</div>
<div class="fn-grid">
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.</span>concat</div>
<div class="fn-sig">(values, axis, name='concat')</div>
<div class="fn-desc">Concatenates tensors along an existing axis. All dims except <code style="color:var(--code)">axis</code> must match.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.</span>stack</div>
<div class="fn-sig">(values, axis=0, name='stack')</div>
<div class="fn-desc">Stacks tensors along a <em>new</em> axis — increases rank by 1.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.</span>unstack</div>
<div class="fn-sig">(value, num=None, axis=0, name='unstack')</div>
<div class="fn-desc">Unpacks a tensor along an axis into a list of tensors — inverse of <code style="color:var(--code)">tf.stack</code>.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.</span>split</div>
<div class="fn-sig">(value, num_or_size_splits, axis=0, name='split')</div>
<div class="fn-desc">Splits a tensor into sub-tensors. Pass int for equal splits or list for custom sizes.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.</span>tile</div>
<div class="fn-sig">(input, multiples, name=None)</div>
<div class="fn-desc">Tiles a tensor by repeating it <code style="color:var(--code)">multiples</code> times along each dimension.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.</span>pad</div>
<div class="fn-sig">(tensor, paddings, mode='CONSTANT', constant_values=0)</div>
<div class="fn-desc">Pads a tensor. <code style="color:var(--code)">mode</code>: "CONSTANT", "REFLECT", "SYMMETRIC".</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.</span>repeat</div>
<div class="fn-sig">(input, repeats, axis=None, name=None)</div>
<div class="fn-desc">Repeats elements of a tensor. Mirrors <code style="color:var(--code)">np.repeat</code>.</div>
</div>
</div>
</div>
</section>
<!-- ═══════════════════════════════════════════════ -->
<!-- 2. KERAS — MODELS -->
<!-- ═══════════════════════════════════════════════ -->
<section class="section" id="keras-model" style="--section-color: var(--c-keras)">
<div class="section-header">
<div class="section-icon" style="background: color-mix(in srgb, var(--c-keras) 15%, transparent)">🧬</div>
<div><div class="section-title" style="color:var(--c-keras)">Keras — Models</div></div>
<div class="section-desc">Three ways to build a model</div>
</div>
<div class="subsection">
<div class="subsection-title">Model APIs</div>
<div class="fn-grid">
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.keras.</span>Sequential</div>
<div class="fn-sig">([layers])</div>
<div class="fn-desc">Simplest API — a linear stack of layers. Add with <code style="color:var(--code)">.add(layer)</code> or pass a list. No branching or multiple inputs/outputs.</div>
<div class="fn-tags"><span class="tag">beginner</span></div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">tf.keras.</span>Model</div>
<div class="fn-sig">(inputs, outputs, name=None)</div>
<div class="fn-desc">Functional API — define a DAG of layers. Pass symbolic tensors to build. Supports multiple inputs/outputs and shared layers.</div>
<div class="fn-tags"><span class="tag">functional api</span></div>
</div>
<div class="fn-card">
<div class="fn-name">class MyModel(tf.keras.Model):</div>
<div class="fn-sig"> __init__(self) / call(self, inputs, training=False)</div>
<div class="fn-desc">Subclassing API — define layers in <code style="color:var(--code)">__init__</code>, forward pass in <code style="color:var(--code)">call()</code>. Most flexible; required for dynamic architectures.</div>
<div class="fn-tags"><span class="tag">subclassing</span><span class="tag">advanced</span></div>
</div>
</div>
</div>
<div class="subsection">
<div class="subsection-title">Model methods</div>
<div class="fn-grid">
<div class="fn-card">
<div class="fn-name"><span class="prefix">model.</span>compile</div>
<div class="fn-sig">(optimizer, loss=None, metrics=None, loss_weights=None, run_eagerly=False, steps_per_execution=1, jit_compile=False)</div>
<div class="fn-desc">Configures the model for training. Must be called before <code style="color:var(--code)">fit()</code>. Pass string names or objects for optimizer/loss/metrics.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">model.</span>fit</div>
<div class="fn-sig">(x=None, y=None, batch_size=32, epochs=1, verbose='auto', callbacks=None, validation_split=0.0, validation_data=None, shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0, steps_per_epoch=None)</div>
<div class="fn-desc">Trains the model. Returns a <code style="color:var(--code)">History</code> object. <code style="color:var(--code)">x</code> can be NumPy arrays, <code style="color:var(--code)">tf.data.Dataset</code>, or generators.</div>
<div class="fn-tags"><span class="tag">training</span></div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">model.</span>evaluate</div>
<div class="fn-sig">(x=None, y=None, batch_size=32, verbose='auto', callbacks=None, return_dict=False)</div>
<div class="fn-desc">Computes loss and metrics on test data. Returns scalar(s) or dict.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">model.</span>predict</div>
<div class="fn-sig">(x, batch_size=32, verbose='auto', steps=None, callbacks=None)</div>
<div class="fn-desc">Runs inference. Returns NumPy array of predictions. No labels needed.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">model.</span>summary</div>
<div class="fn-sig">(line_length=None, positions=None, print_fn=None, expand_nested=False, show_trainable=False)</div>
<div class="fn-desc">Prints a table of layers, output shapes, and parameter counts.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">model.</span>get_weights / set_weights</div>
<div class="fn-sig">() / (weights)</div>
<div class="fn-desc">Get/set all weights as a list of NumPy arrays. Useful for manual weight transfer.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">model.</span>save</div>
<div class="fn-sig">(filepath, overwrite=True, save_format=None)</div>
<div class="fn-desc">Saves model to SavedModel (default) or HDF5 (<code style="color:var(--code)">.h5</code>) format. Includes architecture, weights, and optimizer state.</div>
<div class="fn-tags"><span class="tag">serialization</span></div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">model.</span>trainable_variables</div>
<div class="fn-sig">→ list[tf.Variable]</div>
<div class="fn-desc">All trainable weight variables. Used with <code style="color:var(--code)">GradientTape</code> for custom training loops.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">model.</span>train_on_batch</div>
<div class="fn-sig">(x, y, sample_weight=None, return_dict=False)</div>
<div class="fn-desc">Runs a single gradient update on one batch of data.</div>
</div>
</div>
</div>
</section>
<!-- ═══════════════════════════════════════════════ -->
<!-- 3. KERAS — LAYERS -->
<!-- ═══════════════════════════════════════════════ -->
<section class="section" id="keras-layers" style="--section-color: var(--c-keras)">
<div class="section-header">
<div class="section-icon" style="background: color-mix(in srgb, var(--c-keras) 15%, transparent)">🔲</div>
<div><div class="section-title" style="color:var(--c-keras)">Keras — Layers</div></div>
<div class="section-desc">All built-in layer types</div>
</div>
<div class="subsection">
<div class="subsection-title">Core / Dense</div>
<div class="fn-grid">
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>Dense</div>
<div class="fn-sig">(units, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, ...)</div>
<div class="fn-desc">Fully-connected layer: <code style="color:var(--code)">output = activation(input @ kernel + bias)</code>.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>Activation</div>
<div class="fn-sig">(activation, **kwargs)</div>
<div class="fn-desc">Applies an activation function as a standalone layer.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>Flatten</div>
<div class="fn-sig">(data_format=None, **kwargs)</div>
<div class="fn-desc">Flattens input to 1-D (keeping batch dim). Commonly used between conv and dense blocks.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>Reshape</div>
<div class="fn-sig">(target_shape, **kwargs)</div>
<div class="fn-desc">Reshapes input to <code style="color:var(--code)">target_shape</code>. Batch dim excluded.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>Lambda</div>
<div class="fn-sig">(function, output_shape=None, mask=None, arguments=None)</div>
<div class="fn-desc">Wraps an arbitrary function as a layer. Not serializable — prefer subclassing for production.</div>
<div class="fn-tags"><span class="tag">custom</span></div>
</div>
</div>
</div>
<div class="subsection">
<div class="subsection-title">Convolutional</div>
<div class="fn-grid">
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>Conv2D</div>
<div class="fn-sig">(filters, kernel_size, strides=(1,1), padding='valid', activation=None, use_bias=True, ...)</div>
<div class="fn-desc">2D convolution for image data. <code style="color:var(--code)">padding='same'</code> preserves spatial size.</div>
<div class="fn-tags"><span class="tag">image</span></div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>Conv1D</div>
<div class="fn-sig">(filters, kernel_size, strides=1, padding='valid', activation=None, ...)</div>
<div class="fn-desc">1D convolution for sequences/text.</div>
<div class="fn-tags"><span class="tag">sequence</span></div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>Conv3D</div>
<div class="fn-sig">(filters, kernel_size, strides=(1,1,1), padding='valid', activation=None, ...)</div>
<div class="fn-desc">3D convolution for volumetric data (video, 3D scans).</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>SeparableConv2D</div>
<div class="fn-sig">(filters, kernel_size, strides=(1,1), padding='valid', ...)</div>
<div class="fn-desc">Depthwise + pointwise convolution. Fewer params than Conv2D — used in MobileNet, Xception.</div>
<div class="fn-tags"><span class="tag">efficient</span></div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>Conv2DTranspose</div>
<div class="fn-sig">(filters, kernel_size, strides=(1,1), padding='valid', ...)</div>
<div class="fn-desc">Transposed convolution (deconvolution) — upsamples spatial dims. Used in decoders and GANs.</div>
<div class="fn-tags"><span class="tag">upsampling</span></div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>DepthwiseConv2D</div>
<div class="fn-sig">(kernel_size, strides=(1,1), padding='valid', depth_multiplier=1, ...)</div>
<div class="fn-desc">Per-channel convolution without mixing channels. First step in separable convolutions.</div>
</div>
</div>
</div>
<div class="subsection">
<div class="subsection-title">Pooling</div>
<div class="fn-grid">
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>MaxPooling2D</div>
<div class="fn-sig">(pool_size=(2,2), strides=None, padding='valid')</div>
<div class="fn-desc">Takes the max in each pooling window. Strides defaults to <code style="color:var(--code)">pool_size</code>.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>AveragePooling2D</div>
<div class="fn-sig">(pool_size=(2,2), strides=None, padding='valid')</div>
<div class="fn-desc">Average pooling — smoother than max pooling.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>GlobalMaxPooling2D</div>
<div class="fn-sig">(data_format=None, keepdims=False)</div>
<div class="fn-desc">Reduces spatial dims to 1 value per channel (global max). Outputs shape <code style="color:var(--code)">(batch, filters)</code>.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>GlobalAveragePooling2D</div>
<div class="fn-sig">(data_format=None, keepdims=False)</div>
<div class="fn-desc">Global average pooling — used in modern CNNs (ResNet, EfficientNet) instead of Flatten.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>MaxPooling1D / AveragePooling1D</div>
<div class="fn-sig">(pool_size=2, strides=None, padding='valid')</div>
<div class="fn-desc">1D pooling for sequence models.</div>
</div>
</div>
</div>
<div class="subsection">
<div class="subsection-title">Recurrent</div>
<div class="fn-grid">
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>LSTM</div>
<div class="fn-sig">(units, activation='tanh', recurrent_activation='sigmoid', return_sequences=False, return_state=False, stateful=False, dropout=0.0, recurrent_dropout=0.0, ...)</div>
<div class="fn-desc">Long Short-Term Memory. Set <code style="color:var(--code)">return_sequences=True</code> for stacked LSTMs.</div>
<div class="fn-tags"><span class="tag">sequence</span></div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>GRU</div>
<div class="fn-sig">(units, activation='tanh', recurrent_activation='sigmoid', return_sequences=False, return_state=False, stateful=False, dropout=0.0, ...)</div>
<div class="fn-desc">Gated Recurrent Unit — fewer params than LSTM, similar performance on many tasks.</div>
<div class="fn-tags"><span class="tag">sequence</span></div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>SimpleRNN</div>
<div class="fn-sig">(units, activation='tanh', return_sequences=False, return_state=False, stateful=False, dropout=0.0, ...)</div>
<div class="fn-desc">Basic RNN cell. Suffers from vanishing gradients — prefer LSTM/GRU in practice.</div>
<div class="fn-tags"><span class="tag">sequence</span></div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>Bidirectional</div>
<div class="fn-sig">(layer, merge_mode='concat', weights=None)</div>
<div class="fn-desc">Wrapper that runs an RNN both forward and backward and merges the outputs.</div>
<div class="fn-tags"><span class="tag">wrapper</span></div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>TimeDistributed</div>
<div class="fn-sig">(layer, **kwargs)</div>
<div class="fn-desc">Applies a layer to every time step independently. Wraps Dense, Conv2D, etc.</div>
<div class="fn-tags"><span class="tag">wrapper</span></div>
</div>
</div>
</div>
<div class="subsection">
<div class="subsection-title">Attention & Transformers</div>
<div class="fn-grid">
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>MultiHeadAttention</div>
<div class="fn-sig">(num_heads, key_dim, value_dim=None, dropout=0.0, use_bias=True, ...)</div>
<div class="fn-desc">Scaled dot-product multi-head attention. Call as <code style="color:var(--code)">layer(query, value, key, attention_mask)</code>.</div>
<div class="fn-tags"><span class="tag">transformer</span></div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>Attention</div>
<div class="fn-sig">(use_scale=False, score_mode='dot', dropout=0.0)</div>
<div class="fn-desc">Single-head attention (Bahdanau/Luong-style). Call as <code style="color:var(--code)">layer([query, value])</code>.</div>
<div class="fn-tags"><span class="tag">attention</span></div>
</div>
</div>
</div>
<div class="subsection">
<div class="subsection-title">Normalisation & Regularisation</div>
<div class="fn-grid">
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>BatchNormalization</div>
<div class="fn-sig">(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, ...)</div>
<div class="fn-desc">Normalises activations across the batch. Pass <code style="color:var(--code)">training=True</code> in call during training. Behaves differently at train vs inference.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>LayerNormalization</div>
<div class="fn-sig">(axis=-1, epsilon=0.001, center=True, scale=True, ...)</div>
<div class="fn-desc">Normalises across features (not batch). Preferred in Transformers — independent of batch size.</div>
<div class="fn-tags"><span class="tag">transformer</span></div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>GroupNormalization</div>
<div class="fn-sig">(groups=32, axis=-1, epsilon=0.001, ...)</div>
<div class="fn-desc">Normalises within channel groups. Works well with small batch sizes.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>Dropout</div>
<div class="fn-sig">(rate, noise_shape=None, seed=None)</div>
<div class="fn-desc">Sets <code style="color:var(--code)">rate</code> fraction of inputs to 0 during training. Pass <code style="color:var(--code)">training=True</code> in call.</div>
<div class="fn-tags"><span class="tag">regularization</span></div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>SpatialDropout2D</div>
<div class="fn-sig">(rate, data_format=None)</div>
<div class="fn-desc">Drops entire feature maps instead of individual activations — better for correlated spatial data.</div>
<div class="fn-tags"><span class="tag">regularization</span></div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>GaussianNoise</div>
<div class="fn-sig">(stddev, seed=None)</div>
<div class="fn-desc">Adds zero-centred Gaussian noise during training. Regularization for small datasets.</div>
<div class="fn-tags"><span class="tag">regularization</span></div>
</div>
</div>
</div>
<div class="subsection">
<div class="subsection-title">Embedding & Merging</div>
<div class="fn-grid">
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>Embedding</div>
<div class="fn-sig">(input_dim, output_dim, embeddings_initializer='uniform', mask_zero=False, ...)</div>
<div class="fn-desc">Turns integer indices into dense vectors of <code style="color:var(--code)">output_dim</code> dims. <code style="color:var(--code)">input_dim</code> = vocab size.</div>
<div class="fn-tags"><span class="tag">nlp</span></div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>Add / Subtract / Multiply</div>
<div class="fn-sig">([tensor_a, tensor_b])</div>
<div class="fn-desc">Element-wise merging layers. All inputs must have the same shape.</div>
<div class="fn-tags"><span class="tag">merge</span></div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>Concatenate</div>
<div class="fn-sig">(axis=-1)</div>
<div class="fn-desc">Concatenates a list of tensors along <code style="color:var(--code)">axis</code>.</div>
<div class="fn-tags"><span class="tag">merge</span></div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>Dot</div>
<div class="fn-sig">(axes, normalize=False)</div>
<div class="fn-desc">Computes dot product between two tensors along <code style="color:var(--code)">axes</code>. <code style="color:var(--code)">normalize=True</code> gives cosine similarity.</div>
<div class="fn-tags"><span class="tag">merge</span></div>
</div>
</div>
</div>
<div class="subsection">
<div class="subsection-title">Upsampling & Preprocessing</div>
<div class="fn-grid">
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>UpSampling2D</div>
<div class="fn-sig">(size=(2,2), data_format=None, interpolation='nearest')</div>
<div class="fn-desc">Repeats rows and columns. <code style="color:var(--code)">interpolation='bilinear'</code> for smoother upsampling.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>ZeroPadding2D</div>
<div class="fn-sig">(padding=(1,1), data_format=None)</div>
<div class="fn-desc">Adds rows/columns of zeros around spatial dimensions.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>Rescaling</div>
<div class="fn-sig">(scale, offset=0.0, **kwargs)</div>
<div class="fn-desc">Rescales inputs: <code style="color:var(--code)">output = input * scale + offset</code>. Use <code style="color:var(--code)">scale=1/255</code> to normalise images.</div>
<div class="fn-tags"><span class="tag">preprocessing</span></div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>Normalization</div>
<div class="fn-sig">(axis=-1, mean=None, variance=None)</div>
<div class="fn-desc">Normalises features to zero-mean/unit-variance. Call <code style="color:var(--code)">.adapt(data)</code> to compute stats from data.</div>
<div class="fn-tags"><span class="tag">preprocessing</span></div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>RandomFlip / RandomRotation / RandomZoom</div>
<div class="fn-sig">(mode / factor / ...)</div>
<div class="fn-desc">Built-in data augmentation layers. Only active during training. Part of the model — no separate augmentation pipeline needed.</div>
<div class="fn-tags"><span class="tag">augmentation</span></div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">layers.</span>TextVectorization</div>
<div class="fn-sig">(max_tokens=None, standardize='lower_and_strip_punctuation', split='whitespace', ngrams=None, output_mode='int', output_sequence_length=None, ...)</div>
<div class="fn-desc">Maps text strings to integer sequences. Call <code style="color:var(--code)">.adapt(text_data)</code> to build vocab.</div>
<div class="fn-tags"><span class="tag">nlp</span><span class="tag">preprocessing</span></div>
</div>
</div>
</div>
<div class="subsection">
<div class="subsection-title">Custom Layer Base Class</div>
<div class="fn-grid">
<div class="fn-card">
<div class="fn-name">class MyLayer(tf.keras.layers.Layer):</div>
<div class="fn-sig"> __init__ / build(input_shape) / call(inputs, training=False)</div>
<div class="fn-desc"><code style="color:var(--code)">build()</code> creates weights lazily on first call. <code style="color:var(--code)">call()</code> defines forward pass. <code style="color:var(--code)">get_config()</code> for serialization.</div>
<div class="fn-tags"><span class="tag">subclassing</span></div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">self.</span>add_weight</div>
<div class="fn-sig">(name, shape, initializer='zeros', trainable=True, regularizer=None, constraint=None)</div>
<div class="fn-desc">Creates a trainable or non-trainable weight inside a custom layer's <code style="color:var(--code)">build()</code> method.</div>
</div>
</div>
</div>
</section>
<!-- ═══════════════════════════════════════════════ -->
<!-- 4. KERAS — TRAINING -->
<!-- ═══════════════════════════════════════════════ -->
<section class="section" id="keras-train" style="--section-color: var(--c-train)">
<div class="section-header">
<div class="section-icon" style="background: color-mix(in srgb, var(--c-train) 15%, transparent)">⚙️</div>
<div><div class="section-title" style="color:var(--c-train)">Keras — Training</div></div>
<div class="section-desc">Optimizers, losses, metrics, callbacks, and regularizers</div>
</div>
<div class="subsection">
<div class="subsection-title">Optimizers</div>
<div class="fn-grid">
<div class="fn-card">
<div class="fn-name"><span class="prefix">optimizers.</span>Adam</div>
<div class="fn-sig">(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=False, ...)</div>
<div class="fn-desc">Adaptive Moment Estimation. The default starting optimizer for most tasks.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">optimizers.</span>AdamW</div>
<div class="fn-sig">(learning_rate=0.001, weight_decay=0.004, beta_1=0.9, beta_2=0.999, ...)</div>
<div class="fn-desc">Adam with decoupled weight decay. Preferred for Transformer fine-tuning.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">optimizers.</span>SGD</div>
<div class="fn-sig">(learning_rate=0.01, momentum=0.0, nesterov=False, ...)</div>
<div class="fn-desc">Stochastic Gradient Descent. <code style="color:var(--code)">nesterov=True</code> and <code style="color:var(--code)">momentum=0.9</code> often outperforms Adam for vision.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">optimizers.</span>RMSprop</div>
<div class="fn-sig">(learning_rate=0.001, rho=0.9, momentum=0.0, epsilon=1e-07, ...)</div>
<div class="fn-desc">Good for RNNs. Maintains a moving average of squared gradients.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">optimizers.schedules.</span>ExponentialDecay</div>
<div class="fn-sig">(initial_lr, decay_steps, decay_rate, staircase=False)</div>
<div class="fn-desc">Decays LR by <code style="color:var(--code)">decay_rate</code> every <code style="color:var(--code)">decay_steps</code>. Pass as <code style="color:var(--code)">learning_rate</code> to optimizer.</div>
<div class="fn-tags"><span class="tag">lr schedule</span></div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">optimizers.schedules.</span>CosineDecay</div>
<div class="fn-sig">(initial_lr, decay_steps, alpha=0.0, warmup_steps=0, warmup_target=None)</div>
<div class="fn-desc">Cosine annealing LR schedule. Standard for large model training.</div>
<div class="fn-tags"><span class="tag">lr schedule</span></div>
</div>
</div>
</div>
<div class="subsection">
<div class="subsection-title">Loss Functions</div>
<div class="fn-grid">
<div class="fn-card">
<div class="fn-name"><span class="prefix">losses.</span>SparseCategoricalCrossentropy</div>
<div class="fn-sig">(from_logits=False, ignore_class=None, reduction='sum_over_batch_size', name=None)</div>
<div class="fn-desc">Multi-class classification with integer labels. Use <code style="color:var(--code)">from_logits=True</code> when your last layer has no softmax.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">losses.</span>CategoricalCrossentropy</div>
<div class="fn-sig">(from_logits=False, label_smoothing=0.0, ...)</div>
<div class="fn-desc">Multi-class classification with one-hot labels. <code style="color:var(--code)">label_smoothing</code> for regularisation.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">losses.</span>BinaryCrossentropy</div>
<div class="fn-sig">(from_logits=False, label_smoothing=0.0, ...)</div>
<div class="fn-desc">Binary or multi-label classification. Output activation: sigmoid (or none + <code style="color:var(--code)">from_logits=True</code>).</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">losses.</span>MeanSquaredError</div>
<div class="fn-sig">(reduction='sum_over_batch_size', name='mean_squared_error')</div>
<div class="fn-desc">MSE regression loss.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">losses.</span>MeanAbsoluteError</div>
<div class="fn-sig">(reduction=..., name='mean_absolute_error')</div>
<div class="fn-desc">MAE — more robust to outliers than MSE.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">losses.</span>Huber</div>
<div class="fn-sig">(delta=1.0, reduction=..., name='huber_loss')</div>
<div class="fn-desc">Combines MSE (near zero) and MAE (large errors). Good for regression with outliers.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">losses.</span>KLDivergence</div>
<div class="fn-sig">(reduction=..., name='kl_divergence')</div>
<div class="fn-desc">Kullback-Leibler divergence. Used in VAEs and distribution matching tasks.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">losses.</span>CosineSimilarity</div>
<div class="fn-sig">(axis=-1, reduction=..., name='cosine_similarity')</div>
<div class="fn-desc">Returns negative cosine similarity (for minimisation). Used in contrastive/embedding learning.</div>
</div>
</div>
</div>
<div class="subsection">
<div class="subsection-title">Metrics</div>
<div class="fn-grid">
<div class="fn-card">
<div class="fn-name"><span class="prefix">metrics.</span>Accuracy / SparseCategoricalAccuracy / BinaryAccuracy</div>
<div class="fn-sig">(name, dtype)</div>
<div class="fn-desc">Classification accuracy variants. <code style="color:var(--code)">Sparse</code> for integer labels, <code style="color:var(--code)">Binary</code> for sigmoid outputs.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">metrics.</span>AUC</div>
<div class="fn-sig">(num_thresholds=200, curve='ROC', summation_method='interpolation', thresholds=None, multi_label=False, ...)</div>
<div class="fn-desc">Area Under the ROC or PR curve. <code style="color:var(--code)">curve='PR'</code> for precision-recall AUC.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">metrics.</span>Precision / Recall / F1Score</div>
<div class="fn-sig">(thresholds=None, top_k=None, class_id=None)</div>
<div class="fn-desc">Classification metrics. <code style="color:var(--code)">F1Score</code> added in Keras 2.12+.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">metrics.</span>MeanSquaredError / MeanAbsoluteError</div>
<div class="fn-sig">(name, dtype)</div>
<div class="fn-desc">Regression metrics tracked across batches.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">metrics.</span>Mean</div>
<div class="fn-sig">(name='mean', dtype=None)</div>
<div class="fn-desc">Tracks mean of arbitrary scalar values. Useful in custom training loops.</div>
</div>
<div class="fn-card">
<div class="fn-name">Custom metric pattern</div>
<div class="fn-sig">class MyMetric(tf.keras.metrics.Metric):</div>
<div class="fn-desc">Override <code style="color:var(--code)">update_state(y_true, y_pred, sample_weight)</code>, <code style="color:var(--code)">result()</code>, and <code style="color:var(--code)">reset_state()</code>.</div>
<div class="fn-tags"><span class="tag">custom</span></div>
</div>
</div>
</div>
<div class="subsection">
<div class="subsection-title">Callbacks</div>
<div class="fn-grid">
<div class="fn-card">
<div class="fn-name"><span class="prefix">callbacks.</span>ModelCheckpoint</div>
<div class="fn-sig">(filepath, monitor='val_loss', save_best_only=False, save_weights_only=False, mode='auto', save_freq='epoch', ...)</div>
<div class="fn-desc">Saves the model at each epoch or at best validation performance.</div>
<div class="fn-tags"><span class="tag">essential</span></div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">callbacks.</span>EarlyStopping</div>
<div class="fn-sig">(monitor='val_loss', min_delta=0, patience=0, mode='auto', baseline=None, restore_best_weights=False)</div>
<div class="fn-desc">Stops training when monitored metric stops improving. <code style="color:var(--code)">restore_best_weights=True</code> reloads the best checkpoint.</div>
<div class="fn-tags"><span class="tag">essential</span></div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">callbacks.</span>TensorBoard</div>
<div class="fn-sig">(log_dir='logs', histogram_freq=0, write_graph=True, write_images=False, update_freq='epoch', profile_batch=0, embeddings_freq=0, ...)</div>
<div class="fn-desc">Logs training metrics for TensorBoard visualisation. Run <code style="color:var(--code)">tensorboard --logdir logs</code>.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">callbacks.</span>ReduceLROnPlateau</div>
<div class="fn-sig">(monitor='val_loss', factor=0.1, patience=10, mode='auto', min_delta=0.0001, cooldown=0, min_lr=0.0)</div>
<div class="fn-desc">Reduces LR by <code style="color:var(--code)">factor</code> when metric plateaus. Essential for cyclic training.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">callbacks.</span>LearningRateScheduler</div>
<div class="fn-sig">(schedule, verbose=0)</div>
<div class="fn-desc">Calls <code style="color:var(--code)">schedule(epoch, current_lr)</code> to compute the new LR each epoch.</div>
</div>
<div class="fn-card">
<div class="fn-name"><span class="prefix">callbacks.</span>CSVLogger</div>
<div class="fn-sig">(filename, separator=',', append=False)</div>
<div class="fn-desc">Streams epoch results to a CSV file.</div>
</div>
<div class="fn-card">
<div class="fn-name">Custom callback pattern</div>
<div class="fn-sig">class MyCallback(tf.keras.callbacks.Callback):</div>
<div class="fn-desc">Override hooks: <code style="color:var(--code)">on_epoch_begin/end</code>, <code style="color:var(--code)">on_batch_begin/end</code>, <code style="color:var(--code)">on_train_begin/end</code>. Access <code style="color:var(--code)">self.model</code>.</div>
<div class="fn-tags"><span class="tag">custom</span></div>
</div>
</div>