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Update GAN FID/IS blog post #67

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vfdev-5 opened this issue Sep 9, 2021 · 0 comments · Fixed by #128
Closed

Update GAN FID/IS blog post #67

vfdev-5 opened this issue Sep 9, 2021 · 0 comments · Fixed by #128
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help wanted Extra attention is needed type: content Issues and PR related to Website content

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@vfdev-5
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vfdev-5 commented Sep 9, 2021

@gucifer please update the notebook and generate its markdown with the following fixes:

  1. Use # only once for the title and ##, ### etc for others. As title is specified in tags as title: ..., you have to update all headers by adding one more # to each of them.

  2. Fix this :

# Models for GAN

## Generator

The latent space dimension of input vectors for the generator is a key parameter of GAN.
....

## Generator

You have twice the header ## Generator

  1. Fix typos:
- dataset, emmiting events as it goes.
+ dataset, triggering events as it goes.

- Events - Events are emmited by the Engine
+ Events - Events are emitted by the Engine


- Event is emmited by the Engine. 
+ Event is emitted by the Engine.

By the way, the following sentence is a bit incorrect

Handlers - These are functions which can be configured so that they are triggered when a certain 
Event is emmited by the Engine. Ignite has a long list of pre defined Handlers such as checkpoint, 
early stopping, logging and built-in metrics.

=> Handlers are any functions and not only the ones under the link. Please fix the wording.

  1. Let's merge neighbouring cells together for better MD rendering.
    For example
In [ ]:
image_size = 64

In [ ]:
data_transform = transforms.Compose(
    [
        transforms.Resize(image_size),
        transforms.CenterCrop(image_size),
        transforms.ToTensor(),
        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
    ]
)

In [ ]:
train_dataset = ImageFolder(root="./data", transform=data_transform)

In [ ]:
test_dataset = torch.utils.data.Subset(train_dataset, torch.arange(3000))

=>

In [ ]:
image_size = 64

data_transform = transforms.Compose(
    [
        transforms.Resize(image_size),
        transforms.CenterCrop(image_size),
        transforms.ToTensor(),
        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
    ]
)

train_dataset = ImageFolder(root="./data", transform=data_transform)
test_dataset = torch.utils.data.Subset(train_dataset, torch.arange(3000))

Please apply that for all cells that are not separed with any text or output.

@ydcjeff ydcjeff added the type: content Issues and PR related to Website content label Sep 16, 2021
@vfdev-5 vfdev-5 added the help wanted Extra attention is needed label Oct 5, 2021
@trsvchn trsvchn mentioned this issue Nov 29, 2021
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