szha closed pull request #9834: minor spelling tweaks for docs URL: https://github.com/apache/incubator-mxnet/pull/9834
This is a PR merged from a forked repository. As GitHub hides the original diff on merge, it is displayed below for the sake of provenance: As this is a foreign pull request (from a fork), the diff is supplied below (as it won't show otherwise due to GitHub magic): diff --git a/docs/tutorials/basic/data.md b/docs/tutorials/basic/data.md index 1a88242592..54ee334f97 100644 --- a/docs/tutorials/basic/data.md +++ b/docs/tutorials/basic/data.md @@ -416,7 +416,7 @@ data_iter = mx.io.ImageRecordIter( data_shape=(3, 227, 227), # output data shape. An 227x227 region will be cropped from the original image. batch_size=4, # number of samples per batch resize=256 # resize the shorter edge to 256 before cropping - # ... you can add more augumentation options as defined in ImageRecordIter. + # ... you can add more augmentation options as defined in ImageRecordIter. ) data_iter.reset() batch = data_iter.next() diff --git a/docs/tutorials/basic/image_io.md b/docs/tutorials/basic/image_io.md index 8d60ee8fc0..092affbc74 100644 --- a/docs/tutorials/basic/image_io.md +++ b/docs/tutorials/basic/image_io.md @@ -85,7 +85,7 @@ data_iter = mx.io.ImageRecordIter( data_shape=(3, 227, 227), # output data shape. An 227x227 region will be cropped from the original image. batch_size=4, # number of samples per batch resize=256 # resize the shorter edge to 256 before cropping - # ... you can add more augumentation options here. use help(mx.io.ImageRecordIter) to see all possible choices + # ... you can add more augmentation options here. use help(mx.io.ImageRecordIter) to see all possible choices ) data_iter.reset() batch = data_iter.next() diff --git a/docs/tutorials/basic/record_io.md b/docs/tutorials/basic/record_io.md index e415d9448b..9ba6fa6e25 100644 --- a/docs/tutorials/basic/record_io.md +++ b/docs/tutorials/basic/record_io.md @@ -2,7 +2,7 @@ This tutorial will walk through the python interface for reading and writing record io files. It can be useful when you need more more control over the -details of data pipeline. For example, when you need to augument image and label +details of data pipeline. For example, when you need to augment image and label together for detection and segmentation, or when you need a custom data iterator for triplet sampling and negative sampling. @@ -16,7 +16,7 @@ import numpy as np import matplotlib.pyplot as plt ``` -The relevent code is under `mx.recordio`. There are two classes: `MXRecordIO`, +The relevant code is under `mx.recordio`. There are two classes: `MXRecordIO`, which supports sequential read and write, and `MXIndexedRecordIO`, which supports random read and sequential write. diff --git a/docs/tutorials/gluon/customop.md b/docs/tutorials/gluon/customop.md index dbb1907bad..e10f3987ee 100644 --- a/docs/tutorials/gluon/customop.md +++ b/docs/tutorials/gluon/customop.md @@ -171,7 +171,7 @@ class DenseProp(mx.operator.CustomOpProp): ### Use CustomOp together with Block -Parameterized CustomOp are ususally used together with Blocks, which holds the parameter. +Parameterized CustomOp are usually used together with Blocks, which holds the parameter. ```python diff --git a/docs/tutorials/gluon/mnist.md b/docs/tutorials/gluon/mnist.md index 0bd616c369..fc2271999f 100644 --- a/docs/tutorials/gluon/mnist.md +++ b/docs/tutorials/gluon/mnist.md @@ -50,7 +50,7 @@ val_data = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size ## Approaches -We will cover a couple of approaches for performing the hand written digit recognition task. The first approach makes use of a traditional deep neural network architecture called Multilayer Percepton (MLP). We'll discuss its drawbacks and use that as a motivation to introduce a second more advanced approach called Convolution Neural Network (CNN) that has proven to work very well for image classification tasks. +We will cover a couple of approaches for performing the hand written digit recognition task. The first approach makes use of a traditional deep neural network architecture called Multilayer Perceptron (MLP). We'll discuss its drawbacks and use that as a motivation to introduce a second more advanced approach called Convolution Neural Network (CNN) that has proven to work very well for image classification tasks. Now, let's import required nn modules @@ -142,7 +142,7 @@ for i in range(epoch): z = net(x) # Computes softmax cross entropy loss. loss = gluon.loss.softmax_cross_entropy_loss(z, y) - # Backpropogate the error for one iteration. + # Backpropagate the error for one iteration. ag.backward([loss]) outputs.append(z) # Updates internal evaluation diff --git a/docs/tutorials/python/mnist.md b/docs/tutorials/python/mnist.md index 067ded96ab..e408ead5ae 100644 --- a/docs/tutorials/python/mnist.md +++ b/docs/tutorials/python/mnist.md @@ -44,7 +44,7 @@ val_iter = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size ``` ## Training -We will cover a couple of approaches for performing the hand written digit recognition task. The first approach makes use of a traditional deep neural network architecture called Multilayer Percepton (MLP). We'll discuss its drawbacks and use that as a motivation to introduce a second more advanced approach called Convolution Neural Network (CNN) that has proven to work very well for image classification tasks. +We will cover a couple of approaches for performing the hand written digit recognition task. The first approach makes use of a traditional deep neural network architecture called Multilayer Perceptron (MLP). We'll discuss its drawbacks and use that as a motivation to introduce a second more advanced approach called Convolution Neural Network (CNN) that has proven to work very well for image classification tasks. ### Multilayer Perceptron diff --git a/docs/tutorials/sparse/csr.md b/docs/tutorials/sparse/csr.md index bbe71ff40c..e66b10d998 100644 --- a/docs/tutorials/sparse/csr.md +++ b/docs/tutorials/sparse/csr.md @@ -13,7 +13,7 @@ For matrices of high sparsity (e.g. ~1% non-zeros = ~1% density), there are two - memory consumption is reduced significantly - certain operations are much faster (e.g. matrix-vector multiplication) -You may be familiar with the CSR storage format in [SciPy](https://www.scipy.org/) and will note the similarities in MXNet's implementation. However there are some additional competitive features in `CSRNDArray` inherited from `NDArray`, such as non-blocking asynchronous evaluation and automatic parallelization that are not available in SciPy's flavor of CSR. You can find further explainations for evaluation and parallization strategy in MXNet in the [NDArray tutorial](https://mxnet.incubator.apache.org/tutorials/basic/ndarray.html#lazy-evaluation-and-automatic-parallelization). +You may be familiar with the CSR storage format in [SciPy](https://www.scipy.org/) and will note the similarities in MXNet's implementation. However there are some additional competitive features in `CSRNDArray` inherited from `NDArray`, such as non-blocking asynchronous evaluation and automatic parallelization that are not available in SciPy's flavor of CSR. You can find further explanations for evaluation and parallelization strategy in MXNet in the [NDArray tutorial](https://mxnet.incubator.apache.org/tutorials/basic/ndarray.html#lazy-evaluation-and-automatic-parallelization). The introduction of `CSRNDArray` also brings a new attribute, `stype` as a holder for storage type info, to `NDArray`. You can query **ndarray.stype** now in addition to the oft-queried attributes such as **ndarray.shape**, **ndarray.dtype**, and **ndarray.context**. For a typical dense NDArray, the value of `stype` is **"default"**. For a `CSRNDArray`, the value of stype is **"csr"**. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services