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{ | ||
"tfjsVersion": "4.10.0" | ||
} |
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[ | ||
{ | ||
"name": "Tensors", | ||
"description": [ | ||
"<p>Tensors are the core datastructure of TensorFlow.js", | ||
"They are a generalization of vectors and matrices to potentially", | ||
"higher dimensions.</p>" | ||
], | ||
"subheadings": [ | ||
{ | ||
"name": "Creation", | ||
"description": [ | ||
"<p>We have utility functions for common cases like Scalar, 1D,", | ||
"2D, 3D and 4D tensors, as well a number of functions to initialize", | ||
"tensors in ways useful for machine learning.</p>" | ||
], | ||
"pin": [ | ||
"tensor", | ||
"scalar", | ||
"tensor1d", | ||
"tensor2d", | ||
"tensor3d", | ||
"tensor4d", | ||
"tensor5d", | ||
"tensor6d" | ||
] | ||
}, | ||
{ | ||
"name": "Classes", | ||
"description": [ | ||
"<p>", | ||
"This section shows the main Tensor related classes in TensorFlow.js and", | ||
"the methods we expose on them.", | ||
"</p>" | ||
], | ||
"pin": [ | ||
"Tensor", | ||
"Variable", | ||
"TensorBuffer" | ||
] | ||
}, | ||
{ | ||
"name": "Transformations", | ||
"description": [ | ||
"<p>This section describes some common Tensor", | ||
"transformations for reshaping and type-casting.</p>" | ||
] | ||
}, | ||
{ | ||
"name": "Slicing and Joining", | ||
"description": [ | ||
"<p>TensorFlow.js provides several operations", | ||
"to slice or extract parts of a tensor, or join multiple", | ||
"tensors together." | ||
] | ||
} | ||
] | ||
}, | ||
{ | ||
"name": "Models", | ||
"description": [ | ||
"<p>Models are one of the primary abstractions used in", | ||
"TensorFlow.js Layers. Models can be trained, evaluated, and used", | ||
"for prediction. A model's state (topology, and optionally, trained", | ||
"weights) can be restored from various formats.</p>", | ||
"<p>Models are a collection of Layers, see Model Creation for", | ||
"details about how Layers can be connected.</p>" | ||
], | ||
"subheadings": [ | ||
{ | ||
"name": "Creation", | ||
"description": [ | ||
"<p>There are two primary ways of creating models.</p>", | ||
"<ul><li>Sequential — Easiest, works if the models is a", | ||
"simple stack of each layer's input resting on the top of the", | ||
"previous layer's output.</li>", | ||
"<li>Model — Offers more control if the layers need to be", | ||
"wired together in graph-like ways — multiple 'towers',", | ||
"layers that skip a layer, etc.</li></ul>" | ||
], | ||
"pin": [ | ||
"sequential", | ||
"model" | ||
] | ||
}, | ||
{ | ||
"name": "Inputs", | ||
"description": [] | ||
}, | ||
{ | ||
"name": "Loading", | ||
"description": [], | ||
"pin": [ | ||
"loadGraphModel", | ||
"loadLayersModel" | ||
] | ||
} | ||
] | ||
}, | ||
{ | ||
"name": "Layers", | ||
"description": [ | ||
"<p>Layers are the primary building block for ", | ||
"constructing a Model. Each layer will typically perform some", | ||
"computation to transform its input to its output.</p>", | ||
"<p>Layers will automatically take care of creating and initializing", | ||
"the various internal variables/weights they need to function.</p>" | ||
], | ||
"subheadings": [ | ||
{ | ||
"name": "Advanced Activation", | ||
"description": [] | ||
}, | ||
{ | ||
"name": "Basic", | ||
"description": [] | ||
}, | ||
{ | ||
"name": "Convolutional", | ||
"description": [] | ||
}, | ||
{ | ||
"name": "Merge", | ||
"description": [] | ||
}, | ||
{ | ||
"name": "Normalization", | ||
"description": [] | ||
}, | ||
{ | ||
"name": "Pooling", | ||
"description": [] | ||
}, | ||
{ | ||
"name": "Recurrent", | ||
"description": [] | ||
}, | ||
{ | ||
"name": "Wrapper", | ||
"description": [] | ||
} | ||
] | ||
}, | ||
{ | ||
"name": "Operations", | ||
"description": [], | ||
"subheadings": [ | ||
{ | ||
"name": "Arithmetic", | ||
"description": [ | ||
"<p>To perform mathematical computation on Tensors, we use", | ||
"operations. Tensors are immutable, so all operations always return", | ||
"new Tensors and never modify input Tensors.</p>" | ||
], | ||
"pin": [ | ||
"add", | ||
"sub", | ||
"mul", | ||
"div" | ||
] | ||
}, | ||
{ | ||
"name": "Basic math" | ||
}, | ||
{ | ||
"name": "Matrices" | ||
}, | ||
{ | ||
"name": "Convolution" | ||
}, | ||
{ | ||
"name": "Reduction" | ||
}, | ||
{ | ||
"name": "Normalization" | ||
}, | ||
{ | ||
"name": "Images" | ||
}, | ||
{ | ||
"name": "RNN" | ||
}, | ||
{ | ||
"name": "Logical" | ||
} | ||
] | ||
}, | ||
{ | ||
"name": "Training", | ||
"description": [ | ||
"<p>We also provide an API to do perform training, and", | ||
"compute gradients. We compute gradients eagerly, users provide a function", | ||
"that is a combination of operations and we automatically differentiate", | ||
"that function's output with respect to its inputs.", | ||
"<p>For those familiar with TensorFlow, the API we expose exactly mirrors", | ||
"the TensorFlow Eager API.", | ||
"</p>" | ||
], | ||
"subheadings": [ | ||
{ | ||
"name": "Gradients", | ||
"pin": [ | ||
"grad", | ||
"grads", | ||
"valAndGrad", | ||
"valAndGrads", | ||
"customGrad" | ||
] | ||
}, | ||
{ | ||
"name": "Optimizers", | ||
"pin": [ | ||
"sgd", | ||
"momentum", | ||
"adagrad", | ||
"adadelta" | ||
] | ||
}, | ||
{ | ||
"name": "Losses" | ||
}, | ||
{ | ||
"name": "Classes" | ||
} | ||
] | ||
}, | ||
{ | ||
"name": "Performance", | ||
"description": [], | ||
"subheadings": [ | ||
{ | ||
"name": "Memory", | ||
"pin": [ | ||
"tidy" | ||
] | ||
}, | ||
{ | ||
"name": "Timing", | ||
"pin": [ | ||
"time" | ||
] | ||
} | ||
] | ||
}, | ||
{ | ||
"name": "Environment", | ||
"description": [ | ||
"<p>TensorFlow.js can run mathematical operations on", | ||
"different backends. Currently, we support WebGL and JavaScript", | ||
"CPU. By default, we choose the 'best' backend available, but", | ||
"allow users to customize their backend.</p>" | ||
], | ||
"subheadings": [] | ||
}, | ||
{ | ||
"name": "Constraints", | ||
"description": [ | ||
"<p>Constraints are added to attributes", | ||
"of a Layer (such as weights, kernels, or biases) at", | ||
"construction time to clamp, or otherwise enforce an allowed range,", | ||
"of values for different components of the Layer.</p>" | ||
], | ||
"subheadings": [] | ||
}, | ||
{ | ||
"name": "Initializers", | ||
"description": [ | ||
"<p>Initializers are used in Layers", | ||
"to establish the starting the values of weights, biases, kernels, ", | ||
"etc.</p>" | ||
], | ||
"subheadings": [] | ||
}, | ||
{ | ||
"name": "Regularizers", | ||
"description": [ | ||
"<p>Regularizers can be attached to various components", | ||
"of a Layer to add a 'scoring' function to help drive weights, or ", | ||
"other trainable values, away from excessively large values. They're", | ||
"typically used to promote a notion that a 'simpler' model is better", | ||
"than a complicated model, assuming equal performance.</p>" | ||
], | ||
"subheadings": [] | ||
}, | ||
{ | ||
"name": "Data", | ||
"description": [ | ||
"<p>TensorFlow.js Data provides simple APIs to load and parse data ", | ||
"from disk or over the web in a variety of formats, and to prepare ", | ||
"that data for use in machine learning models (e.g. via operations ", | ||
"like filter, map, shuffle, and batch)." | ||
], | ||
"subheadings": [ | ||
{ | ||
"name": "Creation" | ||
}, | ||
{ | ||
"name": "Operations" | ||
}, | ||
{ | ||
"name": "Classes" | ||
} | ||
] | ||
}, | ||
{ | ||
"name": "Visualization", | ||
"description": [ | ||
"<p>tfjs-vis is a companion library for TensorFlow.js that provides ", | ||
"in-browser visualization capabilities for training and understanding ", | ||
"models. <a href='/api_vis/latest/'>API docs for tfjs-vis are available here</a>" | ||
], | ||
"subheadings": [] | ||
} | ||
] |
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source/_data/api/4.10.0/tfjs-backend-webgl_src_flags_webgl_ts.json
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{ | ||
"docs": { | ||
"headings": [] | ||
}, | ||
"docLinkAliases": {}, | ||
"configInterfaceParamMap": {}, | ||
"inlineTypes": {}, | ||
"docTypeAliases": {} | ||
} |
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