pracheer commented on a change in pull request #9030: Fix Gan
URL: https://github.com/apache/incubator-mxnet/pull/9030#discussion_r156488669
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File path: docs/tutorials/unsupervised_learning/gan.md
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@@ -47,31 +47,31 @@ To complete this tutorial, you need:
- Python 2.7, and the following libraries for Python:
- Numpy - for matrix math
- OpenCV - for image manipulation
- - Scikit-learn - to easily get our dataset
- - Matplotlib - to visualize our output
+ - Scikit-learn - to easily get the MNIST dataset
+ - Matplotlib - to visualize the output
## The Data
-We need two pieces of data to train our DCGAN:
+We need two pieces of data to train the DCGAN:
1. Images of handwritten digits from the MNIST dataset
2. Random numbers from a normal distribution
-Our generator network will use the random numbers as the input to produce
images of handwritten digits, and out discriminator network will use images of
handwritten digits from the MNIST dataset to determine if images produced by
our generator are realistic.
+The Generator network will use the random numbers as the input to produce the
images of handwritten digits, and the Discriminator network will use images of
handwritten digits from the MNIST dataset to determine if images produced by
the Generator are realistic.
-We are going to use the python library, scikit-learn, to get the MNIST
dataset. Scikit-learn comes with a function that gets the dataset for us, which
we will then manipulate to create our training and testing inputs.
+We are going to use the python library, scikit-learn, to get the MNIST
dataset. Scikit-learn comes with a function that gets the dataset for us, which
we will then manipulate to create the training and testing inputs.
The MNIST dataset contains 70,000 images of handwritten digits. Each image is
28x28 pixels in size. To create random numbers, we're going to create a custom
MXNet data iterator, which will returns random numbers from a normal
distribution as we need then.
## Prepare the Data
### 1. Preparing the MNSIT dataset
-Let's start by preparing our handwritten digits from the MNIST dataset. We
import the fetch_mldata function from scikit-learn, and use it to get the MNSIT
dataset. Notice that it's shape is 70000x784. This contains the 70000 images on
every row and 784 pixels of each image in the columns of each row. Each image
is 28x28 pixels, but has been flattened so that all 784 images are represented
in a single list.
+Let us start by preparing the handwritten digits from the MNIST dataset. We
import the fetch_mldata function from scikit-learn, and use it to get the MNSIT
dataset. Notice that it's shape is 70000x784. This contains the 70000 images on
every row and 784 pixels of each image in the columns of each row. Each image
is 28x28 pixels, but has been flattened so that all 784 images are represented
in a single list.
```python
from sklearn.datasets import fetch_mldata
mnist = fetch_mldata('MNIST original')
```
-Next, we'll randomize the handwritten digits by using numpy to create random
permutations on the dataset on our rows (images). We'll then reshape our
dataset from 70000x786 to 70000x28x28, so that every image in our dataset is
arranged into a 28x28 grid, where each cell in the grid represents 1 pixel of
the image.
+Next, we'll randomize the handwritten digits by using numpy to create random
permutations on the dataset on the rows (images). We will then reshape the
dataset from 70000x786 to 70000x28x28, so that every image in the dataset is
arranged into a 28x28 grid, where each cell in the grid represents 1 pixel of
the image.
Review comment:
Just curious: Is there a guideline around when to use _we'll_ and when to
use _we will_. In this sentence, I see both.
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