gx

chenyc

2025-02-12 ea42ff3ebee1eeb3fb29423aa848a249441db81c

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
/**
 * @license
 * Copyright 2018 Google LLC
 *
 * Use of this source code is governed by an MIT-style
 * license that can be found in the LICENSE file or at
 * https://opensource.org/licenses/MIT.
 * =============================================================================
 */
import { argMax, clone, dispose, mul, reshape, tensor1d, tidy } from '@tensorflow/tfjs-core';
function standardizeSampleOrClassWeights(xWeight, outputNames, weightType) {
    const numOutputs = outputNames.length;
    if (xWeight == null || (Array.isArray(xWeight) && xWeight.length === 0)) {
        return outputNames.map(name => null);
    }
    if (numOutputs === 1) {
        if (Array.isArray(xWeight) && xWeight.length === 1) {
            return xWeight;
        }
        else if (typeof xWeight === 'object' && outputNames[0] in xWeight) {
            return [xWeight[outputNames[0]]];
        }
        else {
            return [xWeight];
        }
    }
    if (Array.isArray(xWeight)) {
        if (xWeight.length !== numOutputs) {
            throw new Error(`Provided ${weightType} is an array of ${xWeight.length} ` +
                `element(s), but the model has ${numOutputs} outputs. ` +
                `Make sure a set of weights is provided for each model output.`);
        }
        return xWeight;
    }
    else if (typeof xWeight === 'object' && Object.keys(xWeight).length > 0 &&
        typeof xWeight[Object.keys(xWeight)[0]] ===
            'object') {
        const output = [];
        outputNames.forEach(outputName => {
            if (outputName in xWeight) {
                output.push(xWeight[outputName]);
            }
            else {
                output.push(null);
            }
        });
        return output;
    }
    else {
        throw new Error(`The model has multiple (${numOutputs}) outputs, ` +
            `so ${weightType} must be either an array with ` +
            `${numOutputs} elements or an object with ${outputNames} keys. ` +
            `Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`);
    }
}
/**
 * Standardize class weighting objects.
 *
 * This function takes a single class-weighting object, an array of them,
 * or a map from output name to class-weighting object. It compares it to the
 * output name(s) of the model, base on which it outputs an array of
 * class-weighting objects of which the length matches the number of outputs.
 *
 * @param classWeight Input class-weighting object(s).
 * @param outputNames All output name(s) of the model.
 * @return An array of class-weighting objects. The length of the array matches
 *   the model's number of outputs.
 */
export function standardizeClassWeights(classWeight, outputNames) {
    return standardizeSampleOrClassWeights(classWeight, outputNames, 'classWeight');
}
export function standardizeSampleWeights(classWeight, outputNames) {
    return standardizeSampleOrClassWeights(classWeight, outputNames, 'sampleWeight');
}
/**
 * Standardize by-sample and/or by-class weights for training.
 *
 * Note that this function operates on one model output at a time. For a model
 * with multiple outputs, you must call this function multiple times.
 *
 * @param y The target tensor that the by-sample and/or by-class weight is for.
 *     The values of y are assumed to encode the classes, either directly
 *     as an integer index, or as one-hot encoding.
 * @param sampleWeight By-sample weights.
 * @param classWeight By-class weights: an object mapping class indices
 *     (integers) to a weight (float) to apply to the model's loss for the
 *     samples from this class during training. This can be useful to tell the
 *     model to "pay more attention" to samples from an under-represented class.
 * @param sampleWeightMode The mode for the sample weights.
 * @return A Promise of weight tensor, of which the size of the first dimension
 *     matches that of `y`.
 */
export async function standardizeWeights(y, sampleWeight, classWeight, sampleWeightMode) {
    if (sampleWeight != null || sampleWeightMode != null) {
        // TODO(cais): Once 'temporal' mode is implemented, document it in the doc
        // string.
        throw new Error('Support sampleWeight is not implemented yet');
    }
    if (classWeight != null) {
        // Apply class weights per sample.
        const yClasses = tidy(() => {
            if (y.shape.length === 1) {
                // Assume class indices.
                return clone(y);
            }
            else if (y.shape.length === 2) {
                if (y.shape[1] > 1) {
                    // Assume one-hot encoding of classes.
                    const axis = 1;
                    return argMax(y, axis);
                }
                else if (y.shape[1] === 1) {
                    // Class index.
                    return reshape(y, [y.shape[0]]);
                }
                else {
                    throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) ` +
                        `during handling of class weights. The size is expected to be ` +
                        `>= 1.`);
                }
            }
            else {
                throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during ` +
                    `handling of class weights. The rank is expected to be 1 or 2.`);
            }
        });
        const yClassIndices = Array.from(await yClasses.data());
        dispose(yClasses);
        const classSampleWeight = [];
        yClassIndices.forEach(classIndex => {
            if (classWeight[classIndex] == null) {
                throw new Error(`classWeight must contain all classes in the training data. ` +
                    `The class ${classIndex} exists in the data but not in ` +
                    `classWeight`);
            }
            else {
                classSampleWeight.push(classWeight[classIndex]);
            }
        });
        return tensor1d(classSampleWeight, 'float32');
    }
    else {
        return null;
    }
}
/**
 * Apply per-sample weights on the loss values from a number of samples.
 *
 * @param losses Loss tensor of shape `[batchSize]`.
 * @param sampleWeights Per-sample weight tensor of shape `[batchSize]`.
 * @returns Tensor of the same shape as`losses`.
 */
export function computeWeightedLoss(losses, sampleWeights) {
    return mul(losses, sampleWeights);
}
//# sourceMappingURL=data:application/json;base64,{"version":3,"file":"training_utils.js","sourceRoot":"","sources":["../../../../../../tfjs-layers/src/engine/training_utils.ts"],"names":[],"mappings":"AAAA;;;;;;;;GAQG;AAEH,OAAO,EAAC,MAAM,EAAE,KAAK,EAAE,OAAO,EAAE,GAAG,EAAE,OAAO,EAAoB,QAAQ,EAAE,IAAI,EAAC,MAAM,uBAAuB,CAAC;AAuB7G,SAAS,+BAA+B,CACpC,OAAiD,EAAE,WAAqB,EACxE,UAAwC;IAC1C,MAAM,UAAU,GAAG,WAAW,CAAC,MAAM,CAAC;IACtC,IAAI,OAAO,IAAI,IAAI,IAAI,CAAC,KAAK,CAAC,OAAO,CAAC,OAAO,CAAC,IAAI,OAAO,CAAC,MAAM,KAAK,CAAC,CAAC,EAAE;QACvE,OAAO,WAAW,CAAC,GAAG,CAAC,IAAI,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC;KACtC;IACD,IAAI,UAAU,KAAK,CAAC,EAAE;QACpB,IAAI,KAAK,CAAC,OAAO,CAAC,OAAO,CAAC,IAAI,OAAO,CAAC,MAAM,KAAK,CAAC,EAAE;YAClD,OAAO,OAAO,CAAC;SAChB;aAAM,IAAI,OAAO,OAAO,KAAK,QAAQ,IAAI,WAAW,CAAC,CAAC,CAAC,IAAI,OAAO,EAAE;YACnE,OAAO,CAAE,OAA0B,CAAC,WAAW,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;SACtD;aAAM;YACL,OAAO,CAAC,OAAsB,CAAC,CAAC;SACjC;KACF;IACD,IAAI,KAAK,CAAC,OAAO,CAAC,OAAO,CAAC,EAAE;QAC1B,IAAI,OAAO,CAAC,MAAM,KAAK,UAAU,EAAE;YACjC,MAAM,IAAI,KAAK,CACX,YAAY,UAAU,mBAAmB,OAAO,CAAC,MAAM,GAAG;gBAC1D,iCAAiC,UAAU,YAAY;gBACvD,+DAA+D,CAAC,CAAC;SACtE;QACD,OAAO,OAAO,CAAC;KAChB;SAAM,IACH,OAAO,OAAO,KAAK,QAAQ,IAAI,MAAM,CAAC,IAAI,CAAC,OAAO,CAAC,CAAC,MAAM,GAAG,CAAC;QAC9D,OAAQ,OAA0B,CAAC,MAAM,CAAC,IAAI,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC;YACvD,QAAQ,EAAE;QAChB,MAAM,MAAM,GAAkB,EAAE,CAAC;QACjC,WAAW,CAAC,OAAO,CAAC,UAAU,CAAC,EAAE;YAC/B,IAAI,UAAU,IAAI,OAAO,EAAE;gBACzB,MAAM,CAAC,IAAI,CAAE,OAA0B,CAAC,UAAU,CAAC,CAAC,CAAC;aACtD;iBAAM;gBACL,MAAM,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;aACnB;QACH,CAAC,CAAC,CAAC;QACH,OAAO,MAAM,CAAC;KACf;SAAM;QACL,MAAM,IAAI,KAAK,CACX,2BAA2B,UAAU,aAAa;YAClD,MAAM,UAAU,gCAAgC;YAChD,GAAG,UAAU,+BAA+B,WAAW,SAAS;YAChE,YAAY,UAAU,oBAAoB,IAAI,CAAC,SAAS,CAAC,OAAO,CAAC,EAAE,CAAC,CAAC;KAC1E;AACH,CAAC;AAED;;;;;;;;;;;;GAYG;AACH,MAAM,UAAU,uBAAuB,CACnC,WAAqD,EACrD,WAAqB;IACvB,OAAO,+BAA+B,CAClC,WAAW,EAAE,WAAW,EAAE,aAAa,CAAC,CAAC;AAC/C,CAAC;AAED,MAAM,UAAU,wBAAwB,CACpC,WAAqD,EACrD,WAAqB;IACvB,OAAO,+BAA+B,CAClC,WAAW,EAAE,WAAW,EAAE,cAAc,CAAC,CAAC;AAChD,CAAC;AAED;;;;;;;;;;;;;;;;;GAiBG;AACH,MAAM,CAAC,KAAK,UAAU,kBAAkB,CACpC,CAAS,EAAE,YAAqB,EAAE,WAAyB,EAC3D,gBAA6B;IAC/B,IAAI,YAAY,IAAI,IAAI,IAAI,gBAAgB,IAAI,IAAI,EAAE;QACpD,0EAA0E;QAC1E,UAAU;QACV,MAAM,IAAI,KAAK,CAAC,6CAA6C,CAAC,CAAC;KAChE;IAED,IAAI,WAAW,IAAI,IAAI,EAAE;QACvB,kCAAkC;QAClC,MAAM,QAAQ,GAAa,IAAI,CAAC,GAAG,EAAE;YACnC,IAAI,CAAC,CAAC,KAAK,CAAC,MAAM,KAAK,CAAC,EAAE;gBACxB,wBAAwB;gBACxB,OAAO,KAAK,CAAC,CAAC,CAAa,CAAC;aAC7B;iBAAM,IAAI,CAAC,CAAC,KAAK,CAAC,MAAM,KAAK,CAAC,EAAE;gBAC/B,IAAI,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,GAAG,CAAC,EAAE;oBAClB,sCAAsC;oBACtC,MAAM,IAAI,GAAG,CAAC,CAAC;oBACf,OAAO,MAAM,CAAC,CAAC,EAAE,IAAI,CAAC,CAAC;iBACxB;qBAAM,IAAI,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,CAAC,EAAE;oBAC3B,eAAe;oBACf,OAAO,OAAO,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;iBACjC;qBAAM;oBACL,MAAM,IAAI,KAAK,CACX,+CAA+C,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,IAAI;wBAC7D,+DAA+D;wBAC/D,OAAO,CAAC,CAAC;iBACd;aACF;iBAAM;gBACL,MAAM,IAAI,KAAK,CACX,yCAAyC,CAAC,CAAC,IAAI,WAAW;oBAC1D,+DAA+D,CAAC,CAAC;aACtE;QACH,CAAC,CAAC,CAAC;QAEH,MAAM,aAAa,GAAG,KAAK,CAAC,IAAI,CAAC,MAAM,QAAQ,CAAC,IAAI,EAAE,CAAC,CAAC;QACxD,OAAO,CAAC,QAAQ,CAAC,CAAC;QAClB,MAAM,iBAAiB,GAAa,EAAE,CAAC;QACvC,aAAa,CAAC,OAAO,CAAC,UAAU,CAAC,EAAE;YACjC,IAAI,WAAW,CAAC,UAAU,CAAC,IAAI,IAAI,EAAE;gBACnC,MAAM,IAAI,KAAK,CACX,6DAA6D;oBAC7D,aAAa,UAAU,iCAAiC;oBACxD,aAAa,CAAC,CAAC;aACpB;iBAAM;gBACL,iBAAiB,CAAC,IAAI,CAAC,WAAW,CAAC,UAAU,CAAC,CAAC,CAAC;aACjD;QACH,CAAC,CAAC,CAAC;QAEH,OAAO,QAAQ,CAAC,iBAAiB,EAAE,SAAS,CAAC,CAAC;KAC/C;SAAM;QACL,OAAO,IAAI,CAAC;KACb;AACH,CAAC;AAED;;;;;;GAMG;AACH,MAAM,UAAU,mBAAmB,CAAC,MAAc,EAAE,aAAqB;IACvE,OAAO,GAAG,CAAC,MAAM,EAAE,aAAa,CAAC,CAAC;AACpC,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\nimport {argMax, clone, dispose, mul, reshape, Tensor, Tensor1D, tensor1d, tidy} from '@tensorflow/tfjs-core';\n\n/**\n * For multi-class classification problems, this object is designed to store a\n * mapping from class index to the \"weight\" of the class, where higher weighted\n * classes have larger impact on loss, accuracy, and other metrics.\n *\n * This is useful for cases in which you want the model to \"pay more attention\"\n * to examples from an under-represented class, e.g., in unbalanced datasets.\n */\nexport type ClassWeight = {\n  [classIndex: number]: number\n};\n\n/**\n * Class weighting for a model with multiple outputs.\n *\n * This object maps each output name to a class-weighting object.\n */\nexport type ClassWeightMap = {\n  [outputName: string]: ClassWeight\n};\n\nfunction standardizeSampleOrClassWeights(\n    xWeight: ClassWeight|ClassWeight[]|ClassWeightMap, outputNames: string[],\n    weightType: 'sampleWeight'|'classWeight'): ClassWeight[] {\n  const numOutputs = outputNames.length;\n  if (xWeight == null || (Array.isArray(xWeight) && xWeight.length === 0)) {\n    return outputNames.map(name => null);\n  }\n  if (numOutputs === 1) {\n    if (Array.isArray(xWeight) && xWeight.length === 1) {\n      return xWeight;\n    } else if (typeof xWeight === 'object' && outputNames[0] in xWeight) {\n      return [(xWeight as ClassWeightMap)[outputNames[0]]];\n    } else {\n      return [xWeight as ClassWeight];\n    }\n  }\n  if (Array.isArray(xWeight)) {\n    if (xWeight.length !== numOutputs) {\n      throw new Error(\n          `Provided ${weightType} is an array of ${xWeight.length} ` +\n          `element(s), but the model has ${numOutputs} outputs. ` +\n          `Make sure a set of weights is provided for each model output.`);\n    }\n    return xWeight;\n  } else if (\n      typeof xWeight === 'object' && Object.keys(xWeight).length > 0 &&\n      typeof (xWeight as ClassWeightMap)[Object.keys(xWeight)[0]] ===\n          'object') {\n    const output: ClassWeight[] = [];\n    outputNames.forEach(outputName => {\n      if (outputName in xWeight) {\n        output.push((xWeight as ClassWeightMap)[outputName]);\n      } else {\n        output.push(null);\n      }\n    });\n    return output;\n  } else {\n    throw new Error(\n        `The model has multiple (${numOutputs}) outputs, ` +\n        `so ${weightType} must be either an array with ` +\n        `${numOutputs} elements or an object with ${outputNames} keys. ` +\n        `Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`);\n  }\n}\n\n/**\n * Standardize class weighting objects.\n *\n * This function takes a single class-weighting object, an array of them,\n * or a map from output name to class-weighting object. It compares it to the\n * output name(s) of the model, base on which it outputs an array of\n * class-weighting objects of which the length matches the number of outputs.\n *\n * @param classWeight Input class-weighting object(s).\n * @param outputNames All output name(s) of the model.\n * @return An array of class-weighting objects. The length of the array matches\n *   the model's number of outputs.\n */\nexport function standardizeClassWeights(\n    classWeight: ClassWeight|ClassWeight[]|ClassWeightMap,\n    outputNames: string[]): ClassWeight[] {\n  return standardizeSampleOrClassWeights(\n      classWeight, outputNames, 'classWeight');\n}\n\nexport function standardizeSampleWeights(\n    classWeight: ClassWeight|ClassWeight[]|ClassWeightMap,\n    outputNames: string[]): ClassWeight[] {\n  return standardizeSampleOrClassWeights(\n      classWeight, outputNames, 'sampleWeight');\n}\n\n/**\n * Standardize by-sample and/or by-class weights for training.\n *\n * Note that this function operates on one model output at a time. For a model\n * with multiple outputs, you must call this function multiple times.\n *\n * @param y The target tensor that the by-sample and/or by-class weight is for.\n *     The values of y are assumed to encode the classes, either directly\n *     as an integer index, or as one-hot encoding.\n * @param sampleWeight By-sample weights.\n * @param classWeight By-class weights: an object mapping class indices\n *     (integers) to a weight (float) to apply to the model's loss for the\n *     samples from this class during training. This can be useful to tell the\n *     model to \"pay more attention\" to samples from an under-represented class.\n * @param sampleWeightMode The mode for the sample weights.\n * @return A Promise of weight tensor, of which the size of the first dimension\n *     matches that of `y`.\n */\nexport async function standardizeWeights(\n    y: Tensor, sampleWeight?: Tensor, classWeight?: ClassWeight,\n    sampleWeightMode?: 'temporal'): Promise<Tensor> {\n  if (sampleWeight != null || sampleWeightMode != null) {\n    // TODO(cais): Once 'temporal' mode is implemented, document it in the doc\n    // string.\n    throw new Error('Support sampleWeight is not implemented yet');\n  }\n\n  if (classWeight != null) {\n    // Apply class weights per sample.\n    const yClasses: Tensor1D = tidy(() => {\n      if (y.shape.length === 1) {\n        // Assume class indices.\n        return clone(y) as Tensor1D;\n      } else if (y.shape.length === 2) {\n        if (y.shape[1] > 1) {\n          // Assume one-hot encoding of classes.\n          const axis = 1;\n          return argMax(y, axis);\n        } else if (y.shape[1] === 1) {\n          // Class index.\n          return reshape(y, [y.shape[0]]);\n        } else {\n          throw new Error(\n              `Encountered unexpected last-dimension size (${y.shape[1]}) ` +\n              `during handling of class weights. The size is expected to be ` +\n              `>= 1.`);\n        }\n      } else {\n        throw new Error(\n            `Unexpected rank of target (y) tensor (${y.rank}) during ` +\n            `handling of class weights. The rank is expected to be 1 or 2.`);\n      }\n    });\n\n    const yClassIndices = Array.from(await yClasses.data());\n    dispose(yClasses);\n    const classSampleWeight: number[] = [];\n    yClassIndices.forEach(classIndex => {\n      if (classWeight[classIndex] == null) {\n        throw new Error(\n            `classWeight must contain all classes in the training data. ` +\n            `The class ${classIndex} exists in the data but not in ` +\n            `classWeight`);\n      } else {\n        classSampleWeight.push(classWeight[classIndex]);\n      }\n    });\n\n    return tensor1d(classSampleWeight, 'float32');\n  } else {\n    return null;\n  }\n}\n\n/**\n * Apply per-sample weights on the loss values from a number of samples.\n *\n * @param losses Loss tensor of shape `[batchSize]`.\n * @param sampleWeights Per-sample weight tensor of shape `[batchSize]`.\n * @returns Tensor of the same shape as`losses`.\n */\nexport function computeWeightedLoss(losses: Tensor, sampleWeights: Tensor) {\n  return mul(losses, sampleWeights);\n}\n"]}