Revert "Categoryencoding (#12)" (#14)

This reverts commit 2bb58dc.

tfjs-layers/src/exports_layers.ts

@@ -25,7 +25,7 @@ import {AveragePooling1D, AveragePooling2D, AveragePooling3D, GlobalAveragePooli
 import {GRU, GRUCell, GRUCellLayerArgs, GRULayerArgs, LSTM, LSTMCell, LSTMCellLayerArgs, LSTMLayerArgs, RNN, RNNCell, RNNLayerArgs, SimpleRNN, SimpleRNNCell, SimpleRNNCellLayerArgs, SimpleRNNLayerArgs, StackedRNNCells, StackedRNNCellsArgs} from './layers/recurrent';
 import {Bidirectional, BidirectionalLayerArgs, TimeDistributed, WrapperLayerArgs} from './layers/wrappers';
 import { Rescaling, RescalingArgs } from './layers/preprocessing/image_preprocessing';
-import { CategoryEncoding, CategoryEncodingArgs } from './layers/preprocessing/category_encoding';
+
 // TODO(cais): Add doc string to all the public static functions in this
 //   class; include exectuable JavaScript code snippets where applicable
 //   (b/74074458).
@@ -1729,44 +1729,3 @@ export function masking(args?: MaskingArgs) {
 export function rescaling(args?: RescalingArgs) {
   return new Rescaling(args);
 }
-
-/**
- * A preprocessing layer which encodes integer features.
- *
- * This layer provides options for condensing data into a categorical encoding
- * when the total number of tokens are known in advance. It accepts integer
- * values as inputs, and it outputs a dense or sparse representation of those
- * inputs.
- *
- * Arguments:
- *
- * numTokens: The total number of tokens the layer should support. All
- *  inputs to the layer must integers in the range `0 <= value <
- *  num_tokens`, or an error will be thrown.
- *
- * outputMode: Specification for the output of the layer.
- *  Defaults to `"multiHot". Values can be "oneHot", "multiHot" or
- *  "count", configuring the layer as follows:
- *
- *    oneHot: Encodes each individual element in the input into an
- *      array of numTokens size, containing a 1 at the element index. If
- *      the last dimension is size 1, will encode on that dimension. If the
- *      last dimension is not size 1, will append a new dimension for the
- *      encoded output.
- *
- *    multiHot: Encodes each sample in the input into a single array
- *     of `num_tokens` size, containing a 1 for each vocabulary term
- *     present in the sample. Treats the last dimension as the sample
- *     dimension, if input shape is `(..., sample_length)`, output shape
- *     will be `(..., numTokens)`.
- *
- *    count: Like "multiHot", but the int array contains a count of
- *     the number of times the token at that index appeared in the sample.
- *
- *  For all output modes, currently only output up to rank 2 is supported.
- *
- * @doc {heading: 'Layers', subheading: 'CategoryEncoding', namespace: 'layers'}
- */
-export function categoryEncoding(args: CategoryEncodingArgs) {
-  return new CategoryEncoding(args);
-}

tfjs-layers/src/layers/preprocessing/image_preprocessing.ts

@@ -24,7 +24,6 @@ export declare interface RescalingArgs extends LayerArgs {
  *
  * This rescales images by a scaling and offset factor
  */
-
 export class Rescaling extends Layer {
   /** @nocollapse */
   static className = 'Rescaling';

tfjs-layers/src/layers/preprocessing/image_preprocessing_test.ts

@@ -31,16 +31,16 @@ describeMathCPUAndGPU('Rescaling Layer', () => {
     const expectedOutputTensor = add(mul(intTensor, scale), offset);
     const scalingLayer = new Rescaling({scale, offset});
     const outputTensor = scalingLayer.apply(intTensor) as Tensor;
-    expect(outputTensor.dtype).toBe('float32');
-    expectTensorsClose(outputTensor, expectedOutputTensor);
+    expect(outputTensor.dtype).toBe('float32'); 
+    expectTensorsClose(outputTensor, expectedOutputTensor); 
   });
 
   it('Config holds correct name', () => {
     const scale = 1.0 / 127.5;
     const offset = -1.0;
     const scalingLayer = new Rescaling({scale, offset, name: 'Rescaling'});
     const config = scalingLayer.getConfig();
-    expect(config.name).toEqual('Rescaling');
+    expect(config.name).toEqual('Rescaling'); 
   });
 
 });