1 | /**
|
2 | * @license
|
3 | * Copyright 2018 Google LLC
|
4 | *
|
5 | * Use of this source code is governed by an MIT-style
|
6 | * license that can be found in the LICENSE file or at
|
7 | * https://opensource.org/licenses/MIT.
|
8 | * =============================================================================
|
9 | */
|
10 | /**
|
11 | * Exported functions.
|
12 | */
|
13 | import { CallbackConstructorRegistry } from './base_callbacks';
|
14 | import { Input, } from './engine/input_layer';
|
15 | import { LayersModel } from './engine/training';
|
16 | import { Sequential } from './models';
|
17 | export { loadLayersModel } from './models';
|
18 | // TODO(cais): Add doc string to all the public static functions in this
|
19 | // class; include exectuable JavaScript code snippets where applicable
|
20 | // (b/74074458).
|
21 | // LayersModel and related factory methods.
|
22 | /**
|
23 | * A model is a data structure that consists of `Layers` and defines inputs
|
24 | * and outputs.
|
25 | *
|
26 | * The key difference between `tf.model` and `tf.sequential` is that
|
27 | * `tf.model` is more generic, supporting an arbitrary graph (without
|
28 | * cycles) of layers. `tf.sequential` is less generic and supports only a linear
|
29 | * stack of layers.
|
30 | *
|
31 | * When creating a `tf.LayersModel`, specify its input(s) and output(s). Layers
|
32 | * are used to wire input(s) to output(s).
|
33 | *
|
34 | * For example, the following code snippet defines a model consisting of
|
35 | * two `dense` layers, with 10 and 4 units, respectively.
|
36 | *
|
37 | * ```js
|
38 | * // Define input, which has a size of 5 (not including batch dimension).
|
39 | * const input = tf.input({shape: [5]});
|
40 | *
|
41 | * // First dense layer uses relu activation.
|
42 | * const denseLayer1 = tf.layers.dense({units: 10, activation: 'relu'});
|
43 | * // Second dense layer uses softmax activation.
|
44 | * const denseLayer2 = tf.layers.dense({units: 4, activation: 'softmax'});
|
45 | *
|
46 | * // Obtain the output symbolic tensor by applying the layers on the input.
|
47 | * const output = denseLayer2.apply(denseLayer1.apply(input));
|
48 | *
|
49 | * // Create the model based on the inputs.
|
50 | * const model = tf.model({inputs: input, outputs: output});
|
51 | *
|
52 | * // The model can be used for training, evaluation and prediction.
|
53 | * // For example, the following line runs prediction with the model on
|
54 | * // some fake data.
|
55 | * model.predict(tf.ones([2, 5])).print();
|
56 | * ```
|
57 | * See also:
|
58 | * `tf.sequential`, `tf.loadLayersModel`.
|
59 | *
|
60 | * @doc {heading: 'Models', subheading: 'Creation'}
|
61 | */
|
62 | export function model(args) {
|
63 | return new LayersModel(args);
|
64 | }
|
65 | /**
|
66 | * Creates a `tf.Sequential` model. A sequential model is any model where the
|
67 | * outputs of one layer are the inputs to the next layer, i.e. the model
|
68 | * topology is a simple 'stack' of layers, with no branching or skipping.
|
69 | *
|
70 | * This means that the first layer passed to a `tf.Sequential` model should have
|
71 | * a defined input shape. What that means is that it should have received an
|
72 | * `inputShape` or `batchInputShape` argument, or for some type of layers
|
73 | * (recurrent, Dense...) an `inputDim` argument.
|
74 | *
|
75 | * The key difference between `tf.model` and `tf.sequential` is that
|
76 | * `tf.sequential` is less generic, supporting only a linear stack of layers.
|
77 | * `tf.model` is more generic and supports an arbitrary graph (without
|
78 | * cycles) of layers.
|
79 | *
|
80 | * Examples:
|
81 | *
|
82 | * ```js
|
83 | * const model = tf.sequential();
|
84 | *
|
85 | * // First layer must have an input shape defined.
|
86 | * model.add(tf.layers.dense({units: 32, inputShape: [50]}));
|
87 | * // Afterwards, TF.js does automatic shape inference.
|
88 | * model.add(tf.layers.dense({units: 4}));
|
89 | *
|
90 | * // Inspect the inferred shape of the model's output, which equals
|
91 | * // `[null, 4]`. The 1st dimension is the undetermined batch dimension; the
|
92 | * // 2nd is the output size of the model's last layer.
|
93 | * console.log(JSON.stringify(model.outputs[0].shape));
|
94 | * ```
|
95 | *
|
96 | * It is also possible to specify a batch size (with potentially undetermined
|
97 | * batch dimension, denoted by "null") for the first layer using the
|
98 | * `batchInputShape` key. The following example is equivalent to the above:
|
99 | *
|
100 | * ```js
|
101 | * const model = tf.sequential();
|
102 | *
|
103 | * // First layer must have a defined input shape
|
104 | * model.add(tf.layers.dense({units: 32, batchInputShape: [null, 50]}));
|
105 | * // Afterwards, TF.js does automatic shape inference.
|
106 | * model.add(tf.layers.dense({units: 4}));
|
107 | *
|
108 | * // Inspect the inferred shape of the model's output.
|
109 | * console.log(JSON.stringify(model.outputs[0].shape));
|
110 | * ```
|
111 | *
|
112 | * You can also use an `Array` of already-constructed `Layer`s to create
|
113 | * a `tf.Sequential` model:
|
114 | *
|
115 | * ```js
|
116 | * const model = tf.sequential({
|
117 | * layers: [tf.layers.dense({units: 32, inputShape: [50]}),
|
118 | * tf.layers.dense({units: 4})]
|
119 | * });
|
120 | * console.log(JSON.stringify(model.outputs[0].shape));
|
121 | * ```
|
122 | *
|
123 | * @doc {heading: 'Models', subheading: 'Creation'}
|
124 | */
|
125 | export function sequential(config) {
|
126 | return new Sequential(config);
|
127 | }
|
128 | /**
|
129 | * Used to instantiate an input to a model as a `tf.SymbolicTensor`.
|
130 | *
|
131 | * Users should call the `input` factory function for
|
132 | * consistency with other generator functions.
|
133 | *
|
134 | * Example:
|
135 | *
|
136 | * ```js
|
137 | * // Defines a simple logistic regression model with 32 dimensional input
|
138 | * // and 3 dimensional output.
|
139 | * const x = tf.input({shape: [32]});
|
140 | * const y = tf.layers.dense({units: 3, activation: 'softmax'}).apply(x);
|
141 | * const model = tf.model({inputs: x, outputs: y});
|
142 | * model.predict(tf.ones([2, 32])).print();
|
143 | * ```
|
144 | *
|
145 | * Note: `input` is only necessary when using `model`. When using
|
146 | * `sequential`, specify `inputShape` for the first layer or use `inputLayer`
|
147 | * as the first layer.
|
148 | *
|
149 | * @doc {heading: 'Models', subheading: 'Inputs'}
|
150 | */
|
151 | export function input(config) {
|
152 | return Input(config);
|
153 | }
|
154 | export function registerCallbackConstructor(verbosityLevel, callbackConstructor) {
|
155 | CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel, callbackConstructor);
|
156 | }
|
157 | //# sourceMappingURL=data:application/json;base64,{"version":3,"file":"exports.js","sourceRoot":"","sources":["../../../../../tfjs-layers/src/exports.ts"],"names":[],"mappings":"AAAA;;;;;;;;GAQG;AAEH;;GAEG;AAEH,OAAO,EAA0B,2BAA2B,EAAC,MAAM,kBAAkB,CAAC;AAEtF,OAAO,EAAC,KAAK,GAAe,MAAM,sBAAsB,CAAC;AAEzD,OAAO,EAAC,WAAW,EAAC,MAAM,mBAAmB,CAAC;AAC9C,OAAO,EAAC,UAAU,EAAiB,MAAM,UAAU,CAAC;AAEpD,OAAO,EAAC,eAAe,EAAC,MAAM,UAAU,CAAC;AAEzC,wEAAwE;AACxE,wEAAwE;AACxE,kBAAkB;AAElB,2CAA2C;AAE3C;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;GAuCG;AACH,MAAM,UAAU,KAAK,CAAC,IAAmB;IACvC,OAAO,IAAI,WAAW,CAAC,IAAI,CAAC,CAAC;AAC/B,CAAC;AAED;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;GA2DG;AACH,MAAM,UAAU,UAAU,CAAC,MAAuB;IAChD,OAAO,IAAI,UAAU,CAAC,MAAM,CAAC,CAAC;AAChC,CAAC;AAED;;;;;;;;;;;;;;;;;;;;;;GAsBG;AACH,MAAM,UAAU,KAAK,CAAC,MAAmB;IACvC,OAAO,KAAK,CAAC,MAAM,CAAC,CAAC;AACvB,CAAC;AAED,MAAM,UAAU,2BAA2B,CACvC,cAAsB,EACtB,mBAA4C;IAC9C,2BAA2B,CAAC,2BAA2B,CACnD,cAAc,EAAE,mBAAmB,CAAC,CAAC;AAC3C,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n\n/**\n * Exported functions.\n */\n\nimport {BaseCallbackConstructor, CallbackConstructorRegistry} from './base_callbacks';\nimport {ContainerArgs} from './engine/container';\nimport {Input, InputConfig,} from './engine/input_layer';\nimport {SymbolicTensor} from './engine/topology';\nimport {LayersModel} from './engine/training';\nimport {Sequential, SequentialArgs} from './models';\n\nexport {loadLayersModel} from './models';\n\n// TODO(cais): Add doc string to all the public static functions in this\n//   class; include exectuable JavaScript code snippets where applicable\n//   (b/74074458).\n\n// LayersModel and related factory methods.\n\n/**\n * A model is a data structure that consists of `Layers` and defines inputs\n * and outputs.\n *\n * The key difference between `tf.model` and `tf.sequential` is that\n * `tf.model` is more generic, supporting an arbitrary graph (without\n * cycles) of layers. `tf.sequential` is less generic and supports only a linear\n * stack of layers.\n *\n * When creating a `tf.LayersModel`, specify its input(s) and output(s). Layers\n * are used to wire input(s) to output(s).\n *\n * For example, the following code snippet defines a model consisting of\n * two `dense` layers, with 10 and 4 units, respectively.\n *\n * ```js\n * // Define input, which has a size of 5 (not including batch dimension).\n * const input = tf.input({shape: [5]});\n *\n * // First dense layer uses relu activation.\n * const denseLayer1 = tf.layers.dense({units: 10, activation: 'relu'});\n * // Second dense layer uses softmax activation.\n * const denseLayer2 = tf.layers.dense({units: 4, activation: 'softmax'});\n *\n * // Obtain the output symbolic tensor by applying the layers on the input.\n * const output = denseLayer2.apply(denseLayer1.apply(input));\n *\n * // Create the model based on the inputs.\n * const model = tf.model({inputs: input, outputs: output});\n *\n * // The model can be used for training, evaluation and prediction.\n * // For example, the following line runs prediction with the model on\n * // some fake data.\n * model.predict(tf.ones([2, 5])).print();\n * ```\n * See also:\n *   `tf.sequential`, `tf.loadLayersModel`.\n *\n * @doc {heading: 'Models', subheading: 'Creation'}\n */\nexport function model(args: ContainerArgs): LayersModel {\n  return new LayersModel(args);\n}\n\n/**\n * Creates a `tf.Sequential` model.  A sequential model is any model where the\n * outputs of one layer are the inputs to the next layer, i.e. the model\n * topology is a simple 'stack' of layers, with no branching or skipping.\n *\n * This means that the first layer passed to a `tf.Sequential` model should have\n * a defined input shape. What that means is that it should have received an\n * `inputShape` or `batchInputShape` argument, or for some type of layers\n * (recurrent, Dense...) an `inputDim` argument.\n *\n * The key difference between `tf.model` and `tf.sequential` is that\n * `tf.sequential` is less generic, supporting only a linear stack of layers.\n * `tf.model` is more generic and supports an arbitrary graph (without\n * cycles) of layers.\n *\n * Examples:\n *\n * ```js\n * const model = tf.sequential();\n *\n * // First layer must have an input shape defined.\n * model.add(tf.layers.dense({units: 32, inputShape: [50]}));\n * // Afterwards, TF.js does automatic shape inference.\n * model.add(tf.layers.dense({units: 4}));\n *\n * // Inspect the inferred shape of the model's output, which equals\n * // `[null, 4]`. The 1st dimension is the undetermined batch dimension; the\n * // 2nd is the output size of the model's last layer.\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * It is also possible to specify a batch size (with potentially undetermined\n * batch dimension, denoted by \"null\") for the first layer using the\n * `batchInputShape` key. The following example is equivalent to the above:\n *\n * ```js\n * const model = tf.sequential();\n *\n * // First layer must have a defined input shape\n * model.add(tf.layers.dense({units: 32, batchInputShape: [null, 50]}));\n * // Afterwards, TF.js does automatic shape inference.\n * model.add(tf.layers.dense({units: 4}));\n *\n * // Inspect the inferred shape of the model's output.\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * You can also use an `Array` of already-constructed `Layer`s to create\n * a `tf.Sequential` model:\n *\n * ```js\n * const model = tf.sequential({\n *   layers: [tf.layers.dense({units: 32, inputShape: [50]}),\n *            tf.layers.dense({units: 4})]\n * });\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * @doc {heading: 'Models', subheading: 'Creation'}\n */\nexport function sequential(config?: SequentialArgs): Sequential {\n  return new Sequential(config);\n}\n\n/**\n * Used to instantiate an input to a model as a `tf.SymbolicTensor`.\n *\n * Users should call the `input` factory function for\n * consistency with other generator functions.\n *\n * Example:\n *\n * ```js\n * // Defines a simple logistic regression model with 32 dimensional input\n * // and 3 dimensional output.\n * const x = tf.input({shape: [32]});\n * const y = tf.layers.dense({units: 3, activation: 'softmax'}).apply(x);\n * const model = tf.model({inputs: x, outputs: y});\n * model.predict(tf.ones([2, 32])).print();\n * ```\n *\n * Note: `input` is only necessary when using `model`. When using\n * `sequential`, specify `inputShape` for the first layer or use `inputLayer`\n * as the first layer.\n *\n * @doc {heading: 'Models', subheading: 'Inputs'}\n */\nexport function input(config: InputConfig): SymbolicTensor {\n  return Input(config);\n}\n\nexport function registerCallbackConstructor(\n    verbosityLevel: number,\n    callbackConstructor: BaseCallbackConstructor): void {\n  CallbackConstructorRegistry.registerCallbackConstructor(\n      verbosityLevel, callbackConstructor);\n}\n"]} |
\ | No newline at end of file |