@tensorflow/tfjs-layers
Version:
TensorFlow layers API in JavaScript
81 lines • 11.4 kB
JavaScript
/**
* @license
* Copyright 2022 CodeSmith 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 { Layer } from '../../engine/topology';
import { serialization, tidy } from '@tensorflow/tfjs-core';
import { greater, greaterEqual, max, min } from '@tensorflow/tfjs-core';
import { getExactlyOneShape, getExactlyOneTensor } from '../../utils/types_utils';
import { ValueError } from '../../errors';
import * as K from '../../backend/tfjs_backend';
import * as utils from './preprocessing_utils';
class CategoryEncoding extends Layer {
constructor(args) {
super(args);
this.numTokens = args.numTokens;
if (args.outputMode) {
this.outputMode = args.outputMode;
}
else {
this.outputMode = 'multiHot';
}
}
getConfig() {
const config = {
'numTokens': this.numTokens,
'outputMode': this.outputMode,
};
const baseConfig = super.getConfig();
Object.assign(config, baseConfig);
return config;
}
computeOutputShape(inputShape) {
inputShape = getExactlyOneShape(inputShape);
if (inputShape == null) {
return [this.numTokens];
}
if (this.outputMode === 'oneHot' && inputShape[inputShape.length - 1] !== 1) {
inputShape.push(this.numTokens);
return inputShape;
}
inputShape[inputShape.length - 1] = this.numTokens;
return inputShape;
}
call(inputs, kwargs) {
return tidy(() => {
inputs = getExactlyOneTensor(inputs);
if (inputs.dtype !== 'int32') {
inputs = K.cast(inputs, 'int32');
}
let countWeights;
if ((typeof kwargs['countWeights']) !== 'undefined') {
if (this.outputMode !== 'count') {
throw new ValueError(`countWeights is not used when outputMode !== count.
Received countWeights=${kwargs['countWeights']}`);
}
countWeights
= getExactlyOneTensor(kwargs['countWeights']);
}
const maxValue = max(inputs);
const minValue = min(inputs);
const greaterEqualMax = greater(this.numTokens, maxValue)
.bufferSync().get(0);
const greaterMin = greaterEqual(minValue, 0).bufferSync().get(0);
if (!(greaterEqualMax && greaterMin)) {
throw new ValueError('Input values must be between 0 < values <='
+ ` numTokens with numTokens=${this.numTokens}`);
}
return utils.encodeCategoricalInputs(inputs, this.outputMode, this.numTokens, countWeights);
});
}
}
/** @nocollapse */
CategoryEncoding.className = 'CategoryEncoding';
export { CategoryEncoding };
serialization.registerClass(CategoryEncoding);
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1] !== 1){\n      inputShape.push(this.numTokens);\n      return inputShape;\n    }\n\n    inputShape[inputShape.length - 1] = this.numTokens;\n    return inputShape;\n  }\n\n  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor[]|Tensor {\n    return tidy(() => {\n\n        inputs = getExactlyOneTensor(inputs);\n        if(inputs.dtype !== 'int32') {\n          inputs = K.cast(inputs, 'int32');\n      }\n\n        let countWeights: Tensor1D | Tensor2D;\n\n        if((typeof kwargs['countWeights']) !== 'undefined') {\n\n          if(this.outputMode !== 'count') {\n            throw new ValueError(\n              `countWeights is not used when outputMode !== count.\n              Received countWeights=${kwargs['countWeights']}`);\n          }\n\n          countWeights\n            =  getExactlyOneTensor(kwargs['countWeights']) as Tensor1D|Tensor2D;\n        }\n\n        const maxValue = max(inputs);\n        const minValue = min(inputs);\n        const greaterEqualMax = greater(this.numTokens, maxValue)\n                                                    .bufferSync().get(0);\n\n        const greaterMin = greaterEqual(minValue, 0).bufferSync().get(0);\n\n        if(!(greaterEqualMax && greaterMin)) {\n\n          throw new ValueError('Input values must be between 0 < values <='\n            + ` numTokens with numTokens=${this.numTokens}`);\n        }\n\n        return utils.encodeCategoricalInputs(inputs,\n          this.outputMode, this.numTokens, countWeights);\n    });\n  }\n}\n\nserialization.registerClass(CategoryEncoding);\n"]}