Fix mistakes (#6793)

* Fix mistake * Fix mistakes * Fix mistake * Fix mistake * Fix mistake * Update expand_dims.ts * Update pad.ts * Update concat.ts * Update gather.ts * Remove apostrophe * Fix mistake * Fix mistake * Fix mistake * Fix mistake * Fix mistake * Update graph_model.ts * Fix mistakes * Fix mistakes * Fix mistake * Fix mistake * Fix mistakes * Fix mistakes * Fix mistake * Fix mistakes * Fix mistakes * Fix mistakes * Fix mistake * Fix mistakes * Fix mistake * Fix mistake * Fix mistake * Update normalization.ts * Fix mistake * Fix mistakes * Fix mistake * Fix mistake * Fix mistakes * Fix mistake * Fix mistakes * Fix mistake * Fix mistake * Update exports_layers.ts * Update clip_by_value.ts * Fix mistake * Fix mistake * Fix mistake * Fix mistake * Update euclidean_norm.ts * Fix mistake * Fix mistake * Update norm.ts * Update euclidean_norm.ts * Fix mistake * Fix mistake * Fix mistake * Fix mistake * Fix mistake * Fix mistake * Fix mistake * Fix mistake * Fix mistake * Fix mistake * Update non_max_suppression_with_score.ts * Update non_max_suppression_with_score_async.ts * Fix mistake * Fix mistake * Fix mistake * Fix mistake * Fix mistake * Fix mistake * Update sparse_fill_empty_rows.ts * Update sparse_fill_empty_rows.ts * Fix mistake * Fix mistake * Fix mistake * Fix mistakes * Update compute_weighted_loss.ts * Update huber_loss.ts * Fix mistake * Fix mistake * Fix mistake * Fix mistake * Update readers.ts * Fix mistake * Fix mistakes * Fix mistakes * Update globals.ts * Fix mistakes * Fix mistake * Fix mistake * Fix mistake * Remove space * Update readers.ts * Update exports_layers.ts Co-authored-by: Jen Person <[email protected]> Co-authored-by: Ping Yu <[email protected]>
tensorflow · Sep 1, 2022 · b6d6bf7 · b6d6bf7
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diff --git a/tfjs-converter/src/executor/graph_model.ts b/tfjs-converter/src/executor/graph_model.ts
@@ -358,7 +358,7 @@ export class GraphModel<ModelURL extends Url = string | io.IOHandler> implements
    * Executes inference for the model for given input tensors.
    * @param inputs tensor, tensor array or tensor map of the inputs for the
    * model, keyed by the input node names.
-   * @param outputs output node name from the Tensorflow model, if no
+   * @param outputs output node name from the TensorFlow model, if no
    * outputs are specified, the default outputs of the model would be used.
    * You can inspect intermediate nodes of the model by adding them to the
    * outputs array.
@@ -382,7 +382,7 @@ export class GraphModel<ModelURL extends Url = string | io.IOHandler> implements
    * fashion, use this method when your model contains control flow ops.
    * @param inputs tensor, tensor array or tensor map of the inputs for the
    * model, keyed by the input node names.
-   * @param outputs output node name from the Tensorflow model, if no outputs
+   * @param outputs output node name from the TensorFlow model, if no outputs
    * are specified, the default outputs of the model would be used. You can
    * inspect intermediate nodes of the model by adding them to the outputs
    * array.

diff --git a/tfjs-converter/src/operations/custom_op/register.ts b/tfjs-converter/src/operations/custom_op/register.ts
@@ -21,7 +21,7 @@ import {OpExecutor, OpMapper} from '../types';
 const CUSTOM_OPS: {[key: string]: OpMapper} = {};
 
 /**
- * Register an Op for graph model executor. This allow you to register
+ * Register an Op for graph model executor. This allows you to register
  * TensorFlow custom op or override existing op.
  *
  * Here is an example of registering a new MatMul Op.
@@ -33,7 +33,7 @@ const CUSTOM_OPS: {[key: string]: OpMapper} = {};
  *
  * tf.registerOp('MatMul', customMatmul);
  * ```
- * The inputs and attrs of the node object is based on the TensorFlow op
+ * The inputs and attrs of the node object are based on the TensorFlow op
  * registry.
  *
  * @param name The Tensorflow Op name.

diff --git a/tfjs-core/src/browser_util.ts b/tfjs-core/src/browser_util.ts
@@ -25,7 +25,7 @@ const delayCallback: Function = (() => {
 })();
 
 /**
- * Returns a promise that resolve when a requestAnimationFrame has completed.
+ * Returns a promise that resolves when a requestAnimationFrame has completed.
  *
  * On Node.js this uses setImmediate instead of requestAnimationFrame.
  *

diff --git a/tfjs-core/src/globals.ts b/tfjs-core/src/globals.ts
@@ -364,7 +364,7 @@ export function registerBackend(
 /**
  * Gets the current backend. If no backends have been initialized, this will
  * attempt to initialize the best backend. Will throw an error if the highest
- * priority backend has async initialization, in which case, you should call
+ * priority backend has async initialization, in which case you should call
  * 'await tf.ready()' before running other code.
  *
  * @doc {heading: 'Backends'}

diff --git a/tfjs-core/src/gradients.ts b/tfjs-core/src/gradients.ts
@@ -141,7 +141,7 @@ function grads(f: (...args: Tensor[]) => Tensor): (
  * returns a metric you want to show.
  *
  * The result is a rich object with the following properties:
- * - grad: The gradient of `f(x)` w.r.t `x` (result of `tf.grad`).
+ * - grad: The gradient of `f(x)` w.r.t. `x` (result of `tf.grad`).
  * - value: The value returned by `f(x)`.
  *
  * ```js
@@ -187,7 +187,7 @@ function valueAndGrad<I extends Tensor, O extends Tensor>(f: (x: I) => O): (
  * returns a metric you want to show.
  *
  * The result is a rich object with the following properties:
- * - grads: The gradients of `f()` w.r.t each input (result of `tf.grads`).
+ * - grads: The gradients of `f()` w.r.t. each input (result of `tf.grads`).
  * - value: The value returned by `f(x)`.
  *
  * ```js
@@ -340,9 +340,9 @@ function variableGrads(f: () => Scalar, varList?: Variable[]):
  * `f`. When called, `g` returns `f().value`. In backward mode, custom gradients
  * with respect to each input of `f` are computed using `f().gradFunc`.
  *
- * The `save` function passsed to `f` should be used for saving tensors needed
+ * The `save` function passed to `f` should be used for saving tensors needed
  * in the gradient. And the `saved` passed to the `gradFunc` is a
- * `NamedTensorMap`, which contains those saved tensor.
+ * `NamedTensorMap`, which contains those saved tensors.
  *
  * ```js
  * const customOp = tf.customGrad((x, save) => {

diff --git a/tfjs-core/src/io/browser_files.ts b/tfjs-core/src/io/browser_files.ts
@@ -318,7 +318,7 @@ export function browserDownloads(fileNamePrefix = 'model'): IOHandler {
  *   loading from files that contain Keras-style models (i.e., `tf.Model`s), for
  *   which an `Array` of `File`s is expected (in that order):
  *   - A JSON file containing the model topology and weight manifest.
- *   - Optionally, One or more binary files containing the binary weights.
+ *   - Optionally, one or more binary files containing the binary weights.
  *     These files must have names that match the paths in the `weightsManifest`
  *     contained by the aforementioned JSON file, or errors will be thrown
  *     during loading. These weights files have the same format as the ones

diff --git a/tfjs-core/src/io/http.ts b/tfjs-core/src/io/http.ts
@@ -301,7 +301,7 @@ IORouterRegistry.registerLoadRouter(httpRouter);
  * https://gist.github.com/dsmilkov/1b6046fd6132d7408d5257b0976f7864
  * implements a server based on [flask](https://github.com/pallets/flask) that
  * can receive the request. Upon receiving the model artifacts via the requst,
- * this particular server reconsistutes instances of [Keras
+ * this particular server reconstitutes instances of [Keras
  * Models](https://keras.io/models/model/) in memory.
  *
  *

diff --git a/tfjs-core/src/io/model_management.ts b/tfjs-core/src/io/model_management.ts
@@ -210,7 +210,7 @@ async function listModels(): Promise<{[url: string]: ModelArtifactsInfo}> {
 }
 
 /**
- * Remove a model specified by URL from a reigstered storage medium.
+ * Remove a model specified by URL from a registered storage medium.
  *
  * ```js
  * // First create and save a model.

diff --git a/tfjs-core/src/ops/all.ts b/tfjs-core/src/ops/all.ts
@@ -31,7 +31,7 @@ import {op} from './operation';
  * Reduces the input along the dimensions given in `axes`. Unless `keepDims`
  * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in
  * `axes`. If `keepDims` is true, the reduced dimensions are retained with
- * length 1. If `axes` has no entries, all dimensions are reduced, and an
+ * length 1. If `axes` has no entries, all dimensions are reduced, and a
  * `tf.Tensor` with a single element is returned.
  *
  * ```js

diff --git a/tfjs-core/src/ops/any.ts b/tfjs-core/src/ops/any.ts
@@ -31,7 +31,7 @@ import {op} from './operation';
  * Reduces the input along the dimensions given in `axes`. Unless `keepDims`
  * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in
  * `axes`. If `keepDims` is true, the reduced dimensions are retained with
- * length 1. If `axes` has no entries, all dimensions are reduced, and an
+ * length 1. If `axes` has no entries, all dimensions are reduced, and a
  * `tf.Tensor` with a single element is returned.
  *
  * ```js

diff --git a/tfjs-core/src/ops/batch_to_space_nd.ts b/tfjs-core/src/ops/batch_to_space_nd.ts
@@ -58,7 +58,7 @@ import {op} from './operation';
  * blockShape[M-1], batch / prod(blockShape), x.shape[1], ...,
  * x.shape[N-1]]`
  *
- * 2. Permute dimensions of `reshaped`to produce `permuted` of shape `[batch /
+ * 2. Permute dimensions of `reshaped` to produce `permuted` of shape `[batch /
  * prod(blockShape),x.shape[1], blockShape[0], ..., x.shape[M],
  * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`
  *

diff --git a/tfjs-core/src/ops/batchnorm.ts b/tfjs-core/src/ops/batchnorm.ts
@@ -37,7 +37,7 @@ import {reshape} from './reshape';
  * Mean, variance, scale, and offset can be of two shapes:
  *   - The same shape as the input.
  *   - In the common case, the depth dimension is the last dimension of x, so
- *     the values would be an `tf.Tensor1D` of shape [depth].
+ *     the values would be a `tf.Tensor1D` of shape [depth].
  *
  * Also available are stricter rank-specific methods with the same signature
  * as this method that assert that parameters passed are of given rank

diff --git a/tfjs-core/src/ops/broadcast_to.ts b/tfjs-core/src/ops/broadcast_to.ts
@@ -31,7 +31,7 @@ import {reshape} from './reshape';
  * Broadcast an array to a compatible shape NumPy-style.
  *
  * The tensor's shape is compared to the broadcast shape from end to beginning.
- * Ones are prepended to the tensor's shape until is has the same length as
+ * Ones are prepended to the tensor's shape until it has the same length as
  * the broadcast shape. If input.shape[i]==shape[i], the (i+1)-th axis is
  * already broadcast-compatible. If input.shape[i]==1 and shape[i]==N, then
  * the input tensor is tiled N times along that axis (using tf.tile).

diff --git a/tfjs-core/src/ops/clip_by_value.ts b/tfjs-core/src/ops/clip_by_value.ts
@@ -34,8 +34,8 @@ import {op} from './operation';
  * x.clipByValue(-2, 3).print();  // or tf.clipByValue(x, -2, 3)
  * ```
  * @param x The input tensor.
- * @param clipValueMin Lower-bound of range to be clipped to.
- * @param clipValueMax Upper-bound of range to be clipped to.
+ * @param clipValueMin Lower bound of range to be clipped to.
+ * @param clipValueMax Upper bound of range to be clipped to.
  *
  * @doc {heading: 'Operations', subheading: 'Basic math'}
  */

diff --git a/tfjs-core/src/ops/concat.ts b/tfjs-core/src/ops/concat.ts
@@ -62,7 +62,7 @@ import {op} from './operation';
  * tf.concat([a, b], axis).print();
  * ```
  * @param tensors A list of tensors to concatenate.
- * @param axis The axis to concate along. Defaults to 0 (the first dim).
+ * @param axis The axis to concatenate along. Defaults to 0 (the first dim).
  *
  * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}
  */

diff --git a/tfjs-core/src/ops/diag.ts b/tfjs-core/src/ops/diag.ts
@@ -24,7 +24,7 @@ import {convertToTensor} from '../tensor_util_env';
 import {op} from './operation';
 
 /**
- * Returns a diagonal tensor with a given diagonal values.
+ * Returns a diagonal tensor with given diagonal values.
  *
  * Given a diagonal, this operation returns a tensor with the diagonal and
  * everything else padded with zeros.

diff --git a/tfjs-core/src/ops/einsum.ts b/tfjs-core/src/ops/einsum.ts
@@ -89,7 +89,7 @@ import {op} from './operation';
  *
  * - Does not support >2 input tensors.
  * - Does not support duplicate axes for any given input tensor. E.g., equation
- *   'ii->' is not suppoted.
+ *   'ii->' is not supported.
  * - The `...` notation is not supported.
  *
  * @param equation a string describing the contraction, in the same format as

diff --git a/tfjs-core/src/ops/erf.ts b/tfjs-core/src/ops/erf.ts
@@ -27,7 +27,7 @@ import {cast} from './cast';
 import {op} from './operation';
 
 /**
- * Computes gause error function of the input `tf.Tensor` element-wise:
+ * Computes Gauss error function of the input `tf.Tensor` element-wise:
  * `erf(x)`
  *
  * ```js

diff --git a/tfjs-core/src/ops/euclidean_norm.ts b/tfjs-core/src/ops/euclidean_norm.ts
@@ -22,7 +22,7 @@ import {norm} from './norm';
 import {op} from './operation';
 
 /**
- * Computes the euclidean norm of scalar, vectors, and matrices.
+ * Computes the Euclidean norm of scalar, vectors, and matrices.
  *
  * ```js
  * const x = tf.tensor1d([1, 2, 3, 4]);
@@ -34,12 +34,12 @@ import {op} from './operation';
  * @param axis Optional. If axis is null (the default), the input is
  * considered a vector and a single vector norm is computed over the entire
  * set of values in the Tensor, i.e. euclideanNorm(x) is equivalent
- * to euclideanNorm(x.reshape([-1])). If axis is a integer, the input
+ * to euclideanNorm(x.reshape([-1])). If axis is an integer, the input
  * is considered a batch of vectors, and axis determines the axis in x
  * over which to compute vector norms. If axis is a 2-tuple of integer it is
  * considered a batch of matrices and axis determines the axes in NDArray
  * over which to compute a matrix norm.
- * @param keepDims Optional. If true, the norm have the same dimensionality
+ * @param keepDims Optional. If true, the norm has the same dimensionality
  * as the input.
  *
  * @doc {heading: 'Operations', subheading: 'Matrices'}

diff --git a/tfjs-core/src/ops/expand_dims.ts b/tfjs-core/src/ops/expand_dims.ts
@@ -36,7 +36,7 @@ import {op} from './operation';
  * x.expandDims(axis).print();
  * ```
  *
- * @param x The input tensor whose dimensions to be expanded.
+ * @param x The input tensor whose dimensions are to be expanded.
  * @param axis The dimension index at which to insert shape of `1`. Defaults
  *     to 0 (the first dimension).
  *

diff --git a/tfjs-core/src/ops/gather.ts b/tfjs-core/src/ops/gather.ts
@@ -41,7 +41,7 @@ import {op} from './operation';
  *
  * x.gather(indices).print();
  * ```
- * @param x The input tensor whose slices to be gathered.
+ * @param x The input tensor whose slices are to be gathered.
  * @param indices The indices of the values to extract.
  * @param axis The axis over which to select values. Defaults to 0.
  * @param batchDims Optional. The number of batch dimensions. It must be less

diff --git a/tfjs-core/src/ops/gather_nd.ts b/tfjs-core/src/ops/gather_nd.ts
@@ -26,7 +26,7 @@ import {op} from './operation';
  * Gather slices from input tensor into a Tensor with shape specified by
  * `indices`.
  *
- * `indices` is an K-dimensional integer tensor, best thought of as a
+ * `indices` is a K-dimensional integer tensor, best thought of as a
  * (K-1)-dimensional tensor of indices into input, where each element defines a
  * slice of input:
  * output[\\(i_0, ..., i_{K-2}\\)] = input[indices[\\(i_0, ..., i_{K-2}\\)]]

diff --git a/tfjs-core/src/ops/image/crop_and_resize.ts b/tfjs-core/src/ops/image/crop_and_resize.ts
@@ -36,7 +36,7 @@ import {op} from '../operation';
  *     batch of images from which to take crops
  * @param boxes 2d float32 tensor of shape `[numBoxes, 4]`. Each entry is
  *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the normalized
- *     coordinates of the box in the boxInd[i]'th image in the batch
+ *     coordinates of the box in the `boxInd[i]`th image in the batch
  * @param boxInd 1d int32 tensor of shape `[numBoxes]` with values in range
  *     `[0, batch)` that specifies the image that the `i`-th box refers to.
  * @param cropSize 1d int32 tensor of 2 elements `[cropHeigh, cropWidth]`

diff --git a/tfjs-core/src/ops/image/non_max_suppression_padded.ts b/tfjs-core/src/ops/image/non_max_suppression_padded.ts
@@ -40,7 +40,7 @@ import {op} from '../operation';
  *     Defaults to 0.5 (50% box overlap).
  * @param scoreThreshold A threshold for deciding when to remove boxes based
  *     on score. Defaults to -inf, which means any score is accepted.
- * @param padToMaxOutputSize Defalts to false. If true, size of output
+ * @param padToMaxOutputSize Defaults to false. If true, size of output
  *     `selectedIndices` is padded to maxOutputSize.
  * @return A map with the following properties:
  *     - selectedIndices: A 1D tensor with the selected box indices.

diff --git a/tfjs-core/src/ops/image/non_max_suppression_padded_async.ts b/tfjs-core/src/ops/image/non_max_suppression_padded_async.ts
@@ -37,7 +37,7 @@ import {tensor1d} from '../tensor1d';
  *     Defaults to 0.5 (50% box overlap).
  * @param scoreThreshold A threshold for deciding when to remove boxes based
  *     on score. Defaults to -inf, which means any score is accepted.
- * @param padToMaxOutputSize Defalts to false. If true, size of output
+ * @param padToMaxOutputSize Defaults to false. If true, size of output
  *     `selectedIndices` is padded to maxOutputSize.
  * @return A map with the following properties:
  *     - selectedIndices: A 1D tensor with the selected box indices.

diff --git a/tfjs-core/src/ops/image/non_max_suppression_with_score.ts b/tfjs-core/src/ops/image/non_max_suppression_with_score.ts
@@ -30,7 +30,7 @@ import {op} from '../operation';
  * Performs non maximum suppression of bounding boxes based on
  * iou (intersection over union).
  *
- * This op also supports a Soft-NMS mode (c.f.
+ * This op also supports a Soft-NMS mode (cf.
  * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score
  * of other overlapping boxes, therefore favoring different regions of the image
  * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`

diff --git a/tfjs-core/src/ops/image/non_max_suppression_with_score_async.ts b/tfjs-core/src/ops/image/non_max_suppression_with_score_async.ts
@@ -26,7 +26,7 @@ import {tensor1d} from '../tensor1d';
  * Asynchronously performs non maximum suppression of bounding boxes based on
  * iou (intersection over union).
  *
- * This op also supports a Soft-NMS mode (c.f.
+ * This op also supports a Soft-NMS mode (cf.
  * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score
  * of other overlapping boxes, therefore favoring different regions of the image
  * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`

diff --git a/tfjs-core/src/ops/image/resize_nearest_neighbor.ts b/tfjs-core/src/ops/image/resize_nearest_neighbor.ts
@@ -38,8 +38,8 @@ import {reshape} from '../reshape';
  *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4
  *     corners of images and resized images. If false, rescale by
  *     `new_height / height`. Treat similarly the width dimension.
- * @param halfPixelCenters Defaults to `false`. Whether to assumes pixels are of
- *      half the actual dimensions, and yields more accurate resizes. This flag
+ * @param halfPixelCenters Defaults to `false`. Whether to assume pixels are of
+ *      half the actual dimensions, and yield more accurate resizes. This flag
  *      would also make the floating point coordinates of the top left pixel
  *      0.5, 0.5.
  *

diff --git a/tfjs-core/src/ops/image/transform.ts b/tfjs-core/src/ops/image/transform.ts
@@ -30,7 +30,7 @@ import {op} from '../operation';
  *
  * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.
  * @param transforms Projective transform matrix/matrices. A tensor1d of length
- *     8 or tensor of size N x 8. If one row of transforms is [a0, a1, a2, b0
+ *     8 or tensor of size N x 8. If one row of transforms is [a0, a1, a2, b0,
  *     b1, b2, c0, c1], then it maps the output point (x, y) to a transformed
  *     input point (x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k),
  *     where k = c0 x + c1 y + 1. The transforms are inverted compared to the

diff --git a/tfjs-core/src/ops/is_nan.ts b/tfjs-core/src/ops/is_nan.ts
@@ -25,7 +25,7 @@ import {TensorLike} from '../types';
 import {op} from './operation';
 
 /**
- * RReturns which elements of x are NaN.
+ * Returns which elements of x are NaN.
  *
  * ```js
  * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);

diff --git a/tfjs-core/src/ops/linalg/band_part.ts b/tfjs-core/src/ops/linalg/band_part.ts
@@ -41,7 +41,7 @@ import {zeros} from '../zeros';
  * `[I, J, K, ..., M, N]`, then the output is a tensor with the same shape where
  * `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`.
  * The indicator function
- * `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower))`
+ * `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower)`
  * `&& (num_upper < 0 || (n-m) <= num_upper)`
  *
  * ```js

diff --git a/tfjs-core/src/ops/linalg/gram_schmidt.ts b/tfjs-core/src/ops/linalg/gram_schmidt.ts
@@ -36,7 +36,7 @@ import {sum} from '../sum';
  * const x = tf.tensor2d([[1, 2], [3, 4]]);
  * let y = tf.linalg.gramSchmidt(x);
  * y.print();
- * console.log('Othogonalized:');
+ * console.log('Orthogonalized:');
  * y.dot(y.transpose()).print();  // should be nearly the identity matrix.
  * console.log('First row direction maintained:');
  * const data = await y.array();

diff --git a/tfjs-core/src/ops/log_sum_exp.ts b/tfjs-core/src/ops/log_sum_exp.ts
@@ -31,7 +31,7 @@ import {sub} from './sub';
 import {sum} from './sum';
 
 /**
- * Computes the log(sum(exp(elements across the reduction dimensions)).
+ * Computes the log(sum(exp(elements across the reduction dimensions))).
  *
  * Reduces the input along the dimensions given in `axis`. Unless `keepDims`
  * is true, the rank of the array is reduced by 1 for each entry in `axis`.

diff --git a/tfjs-core/src/ops/losses/compute_weighted_loss.ts b/tfjs-core/src/ops/losses/compute_weighted_loss.ts
@@ -32,7 +32,7 @@ import {sum} from '../sum';
 /**
  * Computes the weighted loss between two tensors.
  *
- * @param losses Tensor of shape `[batch_size, d1, ... dN]`.
+ * @param losses Tensor of shape `[batch_size, d1, ..., dN]`.
  * @param weights Tensor whose rank is either 0, or the same rank as
  *    `losses`, and must be broadcastable to `losses` (i.e., all
  *    dimensions must be either `1`, or the same as the corresponding

diff --git a/tfjs-core/src/ops/losses/huber_loss.ts b/tfjs-core/src/ops/losses/huber_loss.ts
@@ -32,7 +32,7 @@ import {sub} from '../sub';
 import {computeWeightedLoss} from './compute_weighted_loss';
 
 /**
- * Computes the huber loss between two tensors.
+ * Computes the Huber loss between two tensors.
  *
  * @param labels The ground truth output tensor, same dimensions as
  *    'predictions'.
@@ -41,7 +41,7 @@ import {computeWeightedLoss} from './compute_weighted_loss';
  *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions
  *    must be either `1`, or the same as the corresponding `losses`
  *    dimension).
- * @param delta Point where huber loss changes from quadratic to linear.
+ * @param delta Point where Huber loss changes from quadratic to linear.
  * @param reduction Type of reduction to apply to loss. Should be of type
  *    `Reduction`.
  *

diff --git a/tfjs-core/src/ops/max.ts b/tfjs-core/src/ops/max.ts
@@ -31,7 +31,7 @@ import {op} from './operation';
  * Reduces the input along the dimensions given in `axes`. Unless `keepDims`
  * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in
  * `axes`. If `keepDims` is true, the reduced dimensions are retained with
- * length 1. If `axes` has no entries, all dimensions are reduced, and an
+ * length 1. If `axes` has no entries, all dimensions are reduced, and a
  * `tf.Tensor` with a single element is returned.
  *
  * ```js
-Original file line number
+Diff line change
@@ Expand Up / @@ -25,7 +25,7 @@ const delayCallback: Function = (() => { @@
     })();
     /**
-     * Returns a promise that resolve when a requestAnimationFrame has completed.
+     * Returns a promise that resolves when a requestAnimationFrame has completed.
      *
      * On Node.js this uses setImmediate instead of requestAnimationFrame.
      *
@@ Expand Down @@