Fix typos in documentation strings for tfjs-layer folder

tfjs-layers/README.md

@@ -52,7 +52,7 @@ const ys = tf.tensor2d([[1], [3], [5], [7]], [4, 1]);
 // Train the model.
 await model.fit(xs, ys, {epochs: 500});
 
-// Ater the training, perform inference.
+// After the training, perform inference.
 const output = model.predict(tf.tensor2d([[5]], [1, 1]));
 output.print();
 ```

tfjs-layers/demos/README.md

@@ -16,7 +16,7 @@ Once the development environment is prepared, execute the build script from the
 ```
 
 The script will construct a number of Keras models in Python and benchmark their training using the TensorFlow backend.  When it is complete, it will bring up a
-local HTTP server.  Navigate to the local URL spcecified in stdout to bring up
+local HTTP server.  Navigate to the local URL specified in stdout to bring up
 the benchmarks page UI.  There will be a button to begin the JS side of the
 benchmarks.  Clicking the button will run through and time the same models, now
 running in the browser.

tfjs-layers/src/backend/tfjs_backend.ts

@@ -215,7 +215,7 @@ export function sliceAlongLastAxis(
 }
 
 /**
- * Do slicing along the sepcified axis.
+ * Do slicing along the specified axis.
  * @param array input `tf.Tensor`.
  * @param start starting index, inclusive.
  * @param size of the slice along the chosen axis.

tfjs-layers/src/base_callbacks.ts

@@ -518,7 +518,7 @@ export class CallbackConstructorRegistry {
    * callbacks for every tf.LayersModel.fit() call afterwards.
    *
    * @param verbosityLevel Level of verbosity at which the `callbackConstructor`
-   *   is to be reigstered.
+   *   is to be registered.
    * @param callbackConstructor A no-arg constructor for `tf.Callback`.
    * @throws Error, if the same callbackConstructor has been registered before,
    *   either at the same or a different `verbosityLevel`.

tfjs-layers/src/constraints_test.ts

@@ -125,7 +125,7 @@ describeMathCPU('constraints.get', () => {
 
 describe('Constraints Serialization', () => {
   it('Built-ins', () => {
-    // Test both types of captialization.
+    // Test both types of capitalization.
     const constraints: ConstraintIdentifier[] = [
       'maxNorm', 'nonNeg', 'unitNorm', 'minMaxNorm', 'MaxNorm', 'NonNeg',
       'UnitNorm', 'MinMaxNorm'

tfjs-layers/src/engine/container.ts

@@ -554,7 +554,7 @@ export abstract class Container extends Layer {
       throw new ValueError(
           'Container instance unexpectedly contains _trainableWeights.' +
           'The trainable weights of a Container are a union of the ' +
-          'trainable weights of its consituent Layers. Its own ' +
+          'trainable weights of its constituent Layers. Its own ' +
           '_trainableWeights must remain an empty Array.');
     }
 

tfjs-layers/src/engine/dataset_fakes.ts

@@ -26,7 +26,7 @@ export interface FakeDatasetArgs {
   xShape: Shape|{[name: string]: Shape};
 
   /**
-   * The shape of the target(s) of a single exapmle.
+   * The shape of the target(s) of a single example.
    */
   yShape: Shape|{[name: string]: Shape};
 

tfjs-layers/src/engine/dataset_fakes_test.ts

@@ -19,7 +19,7 @@ describeMathCPUAndGPU('FakeNumericDataset', () => {
     const dataset = new FakeNumericDataset(
         {xShape: [3], yShape: [1], batchSize: 8, numBatches: 5});
     for (let k = 0; k < 2; ++k) {
-      // Run twice to make sure that calling iteartor() multiple times works.
+      // Run twice to make sure that calling iterator() multiple times works.
       const iterator = await dataset.iterator();
       for (let i = 0; i < 5; ++i) {
         const result = await iterator.next();
@@ -39,7 +39,7 @@ describeMathCPUAndGPU('FakeNumericDataset', () => {
     const dataset = new FakeNumericDataset(
         {xShape: [3, 4], yShape: [2], batchSize: 8, numBatches: 5});
     for (let k = 0; k < 2; ++k) {
-      // Run twice to make sure that calling iteartor() multiple times works.
+      // Run twice to make sure that calling iterator() multiple times works.
       const iterator = await dataset.iterator();
       for (let i = 0; i < 5; ++i) {
         const result = await iterator.next();
@@ -63,7 +63,7 @@ describeMathCPUAndGPU('FakeNumericDataset', () => {
       numBatches: 5
     });
     for (let k = 0; k < 2; ++k) {
-      // Run twice to make sure that calling iteartor() multiple times works.
+      // Run twice to make sure that calling iterator() multiple times works.
       const iterator = await dataset.iterator();
       for (let i = 0; i < 5; ++i) {
         const result = await iterator.next();

tfjs-layers/src/engine/executor.ts

@@ -365,7 +365,7 @@ export function execute(
   }
   // NOTE(cais): Unlike intermediate tensors, we don't discard mask
   // tensors as we go, because these tensors are sometimes passed over a
-  // series of mutliple layers, i.e., not obeying the immediate input
+  // series of multiple layers, i.e., not obeying the immediate input
   // relations in the graph. If this becomes a memory-usage concern,
   // we can improve this in the future.
   internalFeedDict.disposeMasks();

tfjs-layers/src/engine/topology.ts

@@ -1382,7 +1382,7 @@ export abstract class Layer extends serialization.Serializable {
       // masking not explicitly supported: return null as mask
       return null;
     }
-    // if masking is explictly supported, by default
+    // if masking is explicitly supported, by default
     // carry over the input mask
     return mask;
   }

tfjs-layers/src/engine/topology_test.ts

@@ -1059,7 +1059,7 @@ describeMathCPUAndGPU('Layer-dispose', () => {
     const model = tfl.model({inputs: [input], outputs: [output]});
 
     const result = model.dispose();
-    // This model, consiting of only an input layer and a reshape layer, does
+    // This model,consisting of only an input layer and a reshape layer,does
     // not have any weights to dispose.
     expect(result.numDisposedVariables).toEqual(0);
     expect(() => model.predict(zeros([1, 2, 3])))

tfjs-layers/src/engine/training.ts

@@ -290,7 +290,7 @@ function checkLossAndTargetCompatibility(
  * @param shapes: Expected shapes for the input data, from the model.
  * @param checkBatchAxis: Whether the size along the batch axis (i.e., the
  *   first dimension) will be checked for matching.
- * @param exceptionPrefix: Execption prefix message, used in generating error
+ * @param exceptionPrefix: Exception prefix message, used in generating error
  *   messages.
  * @throws ValueError: on incorrect number of inputs or mismatches in shapes.
  */
@@ -513,7 +513,7 @@ export class LayersModel extends Container implements tfc.InferenceModel {
   //   implicit "knowledge" of the outputs it depends on.
   metricsTensors: Array<[LossOrMetricFn, number]>;
 
-  // User defind metadata (if any).
+  // User defined metadata (if any).
   private userDefinedMetadata: {};
 
   constructor(args: ContainerArgs) {
@@ -921,7 +921,7 @@ export class LayersModel extends Container implements tfc.InferenceModel {
     } else {
       throw new ValueError(
           `Either the input data should have a defined shape, or ` +
-          `${stepsName} shoud be specified.`);
+          `${stepsName} should be specified.`);
     }
     return numSamples;
   }
@@ -1028,7 +1028,7 @@ export class LayersModel extends Container implements tfc.InferenceModel {
    * @param batchSize: integer batch size.
    * @param verbose: verbosity model
    * @returns: Predictions as `tf.Tensor` (if a single output) or an `Array` of
-   *   `tf.Tensor` (if multipe outputs).
+   *   `tf.Tensor` (if multiple outputs).
    */
   private predictLoop(ins: Tensor|Tensor[], batchSize = 32, verbose = false):
       Tensor|Tensor[] {

tfjs-layers/src/engine/training_dataset.ts

@@ -595,7 +595,7 @@ export async function evaluateDataset<T>(
       if (hasBatches) {
         console.warn(
             'Your dataset iterator ran out of data during evaluateDataset(). ' +
-            'Interrupting evalution. Make sure that your ' +
+            'Interrupting evaluation. Make sure that your ' +
             'dataset can generate at least `batches` ' +
             `batches (in this case, ${args.batches} batches). ` +
             'You may need to use the repeat() function when building ' +

tfjs-layers/src/engine/training_dataset_test.ts

@@ -896,7 +896,7 @@ describeMathCPUAndGPU('LayersModel.fitDataset', () => {
        expect(numTensors1).toEqual(numTensors0);
      });
 
-  // Refence Python tf.keras code:
+  // Reference Python tf.keras code:
   //
   // ```py
   // import numpy as np

tfjs-layers/src/engine/training_test.ts

@@ -457,7 +457,7 @@ describeMathCPUAndWebGL2('LayersModel.fit long', () => {
   it('Return sequences; Fit with metric', async () => {
     // The golden values for history used in the assertion below can be obtained
     // with the following Python Keras code.
-    // Ran with Python Keras verion 2.1.2 and TensorFlow (CPU) version
+    // Ran with Python Keras version 2.1.2 and TensorFlow (CPU) version
     // 1.7.0-dev20180226.
     // ```python
     // import keras
@@ -500,9 +500,10 @@ describeMathCPUAndWebGL2('LayersModel.fit long', () => {
     const dataSize = 16;
     const validationSplit = 0.5;
     const batchSize = 3;
-    // So there are 8 examples for train and validation, respectivly. The actual
-    // batches during training and validation will be 3, 3 and 2. This tests the
-    // correct averaging of the loss values happens without broadcasting.
+    // So there are 8 examples for train and validation, respectively.
+    // The actual batches during training and validation will be 3, 3 and 2.
+    // This tests the correct averaging of the loss values happens without 
+    // broadcasting.
     const outputSize = 2;
     const simpleRNN = tfl.layers.simpleRNN({
       units: outputSize,
@@ -2286,7 +2287,7 @@ describeMathGPU('LayersModel.fit: yieldEvery', () => {
       yieldEvery + 1,  // Should call.
       yieldEvery + 1,  // Should call.
       1,
-      yieldEvery + 1,  // SHould call.
+      yieldEvery + 1,  // Should call.
       yieldEvery + 1,  // Should call.
       1,
       1,

tfjs-layers/src/exports.ts

@@ -22,7 +22,7 @@ import {Sequential, SequentialArgs} from './models';
 export {loadLayersModel} from './models';
 
 // TODO(cais): Add doc string to all the public static functions in this
-//   class; include exectuable JavaScript code snippets where applicable
+//   class; include executable JavaScript code snippets where applicable
 //   (b/74074458).
 
 // LayersModel and related factory methods.

tfjs-layers/src/exports_layers.ts

@@ -31,7 +31,7 @@ import {Resizing, ResizingArgs} from './layers/preprocessing/image_resizing';
 import {RandomWidth, RandomWidthArgs} from './layers/preprocessing/random_width';
 
 // TODO(cais): Add doc string to all the public static functions in this
-//   class; include exectuable JavaScript code snippets where applicable
+//   class; include executable JavaScript code snippets where applicable
 //   (b/74074458).
 
 // Input Layer.
@@ -1772,7 +1772,7 @@ export function centerCrop(args?: CenterCropArgs) {
  * This layer resizes an image input to a target height and width. The input
  * should be a 4D (batched) or 3D (unbatched) tensor in `"channels_last"`
  * format.  Input pixel values can be of any range (e.g. `[0., 1.)` or `[0,
- * 255]`) and of interger or floating point dtype. By default, the layer will
+ * 255]`) and of integer or floating point dtype. By default, the layer will
  * output floats.
  *
  * Arguments:

tfjs-layers/src/layers/convolutional.ts

@@ -401,7 +401,7 @@ export abstract class BaseConv extends Layer {
   protected readonly dilationRate: number[];
 
   // Bias-related members are here because all convolution subclasses use the
-  // same configuration parmeters to control bias.  Kernel-related members
+  // same configuration parameters to control bias.  Kernel-related members
   // are in subclass `Conv` because some subclasses use different parameters to
   // control kernel properties, for instance, `DepthwiseConv2D` uses
   // `depthwiseInitializer` instead of `kernelInitializer`.

tfjs-layers/src/layers/core.ts

@@ -496,7 +496,7 @@ export class Reshape extends Layer {
         if (unknown === null) {
           unknown = i;
         } else {
-          throw new ValueError('Can only specifiy one unknown dimension.');
+          throw new ValueError('Can only specify one unknown dimension.');
         }
       } else {
         known *= dim;

tfjs-layers/src/layers/core_test.ts

@@ -605,7 +605,7 @@ describe('Reshape Layer: Symbolic', () => {
     const targetShape: number[] = [null, null];
     const flattenLayer = tfl.layers.reshape({targetShape});
     expect(() => flattenLayer.apply(symbolicInput))
-        .toThrowError(/Can only specifiy one unknown dimension/);
+        .toThrowError(/Can only specify one unknown dimension/);
   });
 
   it('One unknown with indivisible size.', () => {

tfjs-layers/src/layers/merge.ts

@@ -742,7 +742,7 @@ export class Concatenate extends Merge {
     if (mask.length !== inputs.length) {
       throw new ValueError(
           `Mismatch in the length of mask (${mask.length}) ` +
-          `and the legnth of inputs (${inputs.length})`);
+          `and the length of inputs (${inputs.length})`);
     }
     return tfc.tidy(() => {
       let allNullMasks = true;

tfjs-layers/src/layers/merge_test.ts

@@ -687,7 +687,7 @@ describeMathCPU('Deserialize Merge Layers', () => {
 });
 
 describeMathCPU('Dot-Layer: Symbolic', () => {
-  // Example refernce Python Keras code:
+  // Example reference Python Keras code:
   //
   // ```py
   // import keras

tfjs-layers/src/layers/nlp/modeling/transformer_decoder_test.ts

@@ -106,7 +106,7 @@ describe('TransformerDecoder', () => {
     const config = testLayer.getConfig();
     const restored = TransformerDecoder.fromConfig(TransformerDecoder, config);
 
-    // Initializers don't get serailized with customObjects.
+    // Initializers don't get serialized with customObjects.
     delete ((config['kernelInitializer'] as serialization.ConfigDict
       )['config'] as serialization.ConfigDict)['customObjects'];
     delete ((config['biasInitializer'] as serialization.ConfigDict
@@ -167,5 +167,5 @@ describe('TransformerDecoder', () => {
     expectTensorsClose(outputCache, noLoopCache);
   });
 
-  // TODO(pforderique): Test mask propogation once supported.
+  // TODO(pforderique): Test mask propagation once supported.
 });

tfjs-layers/src/layers/nlp/models/gpt2/gpt2_causal_lm.ts

@@ -70,7 +70,7 @@ export declare interface GPT2CausalLMArgs extends PipelineModelArgs {
 }
 
 /**
- * An end-to-end GPT2 model for causal langauge modeling.
+ * An end-to-end GPT2 model for causal language modeling.
  *
  * A causal language model (LM) predicts the next token based on previous
  * tokens. This task setup can be used to train the model unsupervised on

tfjs-layers/src/layers/nlp/multihead_attention.ts

@@ -703,7 +703,7 @@ export class MultiHeadAttention extends Layer {
 
     newInputs = [inputs, kwargs['value']].concat(kwargs['key'] ?? []);
 
-    // TODO(pforderique): Support mask propogation.
+    // TODO(pforderique): Support mask propagation.
     return super.apply(newInputs, kwargs);
   }
 

tfjs-layers/src/layers/normalization.ts

@@ -430,7 +430,7 @@ export interface LayerNormalizationLayerArgs extends LayerArgs {
   axis?: number|number[];
 
   /**
-   * A small positive float added to variance to avoid divison by zero.
+   * A small positive float added to variance to avoid division by zero.
    * Defaults to 1e-3.
    */
   epsilon?: number;

tfjs-layers/src/layers/normalization_test.ts

@@ -353,7 +353,7 @@ describeMathCPUAndWebGL2('BatchNormalization Layers: Tensor', () => {
     const x = tensor2d([[1, 2], [3, 4]], [2, 2]);
     expectTensorsClose(layer.apply(x) as Tensor, x, 0.01);
     expect(layer.getWeights().length).toEqual(3);
-    // Firt weight is gamma.
+    // First weight is gamma.
     expectTensorsClose(layer.getWeights()[0], onesLike(layer.getWeights()[0]));
     // Second weight is moving mean.
     expectTensorsClose(layer.getWeights()[1], zerosLike(layer.getWeights()[1]));
@@ -366,7 +366,7 @@ describeMathCPUAndWebGL2('BatchNormalization Layers: Tensor', () => {
     const x = tensor2d([[1, 2], [3, 4]], [2, 2]);
     expectTensorsClose(layer.apply(x) as Tensor, x, 0.01);
     expect(layer.getWeights().length).toEqual(3);
-    // Firt weight is beta.
+    // First weight is beta.
     expectTensorsClose(layer.getWeights()[0], zerosLike(layer.getWeights()[0]));
     // Second weight is moving mean.
     expectTensorsClose(layer.getWeights()[1], zerosLike(layer.getWeights()[1]));

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

@@ -88,7 +88,7 @@ describeMathCPUAndGPU('Resizing Layer', () => {
   });
 
   it('Returns a tensor of the correct dtype', () => {
-    // do a same resizing operation, cheeck tensors dtypes and content
+    // do a same resizing operation, check tensors dtypes and content
     const height = 40;
     const width = 60;
     const numChannels = 3;

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

@@ -35,7 +35,7 @@ type InterpolationType = typeof INTERPOLATION_KEYS[number];
  *
  * The input should be a 3D (unbatched) or
  * 4D (batched) tensor in the `"channels_last"` image data format. Input pixel
- * values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and of interger
+ * values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and of integer
  * or floating point dtype. By default, the layer will output floats.
  *
  * tf methods implemented in tfjs: 'bilinear', 'nearest',
@@ -48,7 +48,7 @@ export class RandomHeight extends BaseRandomLayer {
   /** @nocollapse */
   static override className = 'RandomHeight';
   private readonly factor: number | [number, number];
-  private readonly interpolation?: InterpolationType;  // defualt = 'bilinear
+  private readonly interpolation?: InterpolationType;  // default = 'bilinear
   private heightLower: number;
   private heightUpper: number;
   private imgWidth: number;

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

@@ -35,7 +35,7 @@ type InterpolationType = typeof INTERPOLATION_KEYS[number];
  *
  * The input should be a 3D (unbatched) or
  * 4D (batched) tensor in the `"channels_last"` image data format. Input pixel
- * values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and of interger
+ * values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and of integer
  * or floating point dtype. By default, the layer will output floats.
  *
  * tf methods implemented in tfjs: 'bilinear', 'nearest',
@@ -48,7 +48,7 @@ export class RandomWidth extends BaseRandomLayer {
   /** @nocollapse */
   static override className = 'RandomWidth';
   private readonly factor: number | [number, number];
-  private readonly interpolation?: InterpolationType;  // defualt = 'bilinear
+  private readonly interpolation?: InterpolationType;  // default = 'bilinear
   private widthLower: number;
   private widthUpper: number;
   private imgHeight: number;