Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Update info_gan.md #1

Merged
merged 1 commit into from
Nov 6, 2018
Merged

Update info_gan.md #1

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
20 changes: 11 additions & 9 deletions docs/tutorials/gluon/info_gan.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -88,10 +88,10 @@ split = int(len(images)*0.8)
test_images = images[split:]
test_filenames = filenames[split:]
train_images = images[:split]
train_filenames = filenames[:split]
train_filenames = filenames[:split]

train_data = mx.gluon.data.ArrayDataset(nd.concatenate(train_images))
train_dataloader = mx.gluon.data.DataLoader(train_data, batch_size=batch_size, shuffle=True, last_batch='rollover', num_workers=4)
train_data = gluon.data.ArrayDataset(nd.concatenate(train_images))
train_dataloader = gluon.data.DataLoader(train_data, batch_size=batch_size, shuffle=True, last_batch='rollover', num_workers=4)
```

## Generator
Expand Down Expand Up @@ -239,13 +239,13 @@ This function samples `c`, `z`, and concatenates them to create the generator in
def create_generator_input():

#create random noise
z = mx.nd.random_normal(0, 1, shape=(batch_size, z_dim), ctx=ctx)
z = nd.random_normal(0, 1, shape=(batch_size, z_dim), ctx=ctx)
label = nd.array(np.random.randint(n_categories, size=batch_size)).as_in_context(ctx)
c1 = nd.one_hot(label, depth=n_categories).as_in_context(ctx)
c2 = nd.random.uniform(-1, 1, shape=(batch_size, n_continuous)).as_in_context(ctx)

# concatenate random noise with c which will be the input of the generator
return mx.nd.concat(z, c1, c2, dim=1)
return nd.concat(z, c1, c2, dim=1)
```

Define the training loop.
Expand All @@ -265,8 +265,8 @@ with SummaryWriter(logdir='./logs/') as sw:
print("Epoch", epoch)
starttime = time.time()

d_error_epoch = mx.nd.zeros((1,), ctx=ctx)
g_error_epoch = mx.nd.zeros((1,), ctx=ctx)
d_error_epoch = nd.zeros((1,), ctx=ctx)
g_error_epoch = nd.zeros((1,), ctx=ctx)

for idx, data in enumerate(train_dataloader):
i = i + 1
Expand Down Expand Up @@ -372,11 +372,11 @@ Take some images from the test data, obtain its feature vector from `discriminat
```python
feature_size = 8192

features = mx.nd.zeros((len(test_images), feature_size), ctx=ctx)
features = nd.zeros((len(test_images), feature_size), ctx=ctx)

for idx, image in enumerate(test_images):

feature = discriminator(mx.nd.array(image))
feature = discriminator(nd.array(image))
feature = feature.reshape(feature_size,)
features[idx,:] = feature.copyto(ctx)

Expand Down Expand Up @@ -434,3 +434,5 @@ with open("imagetsne.json", 'w') as outfile:
Load the file with TSNEViewer. You can now inspect whether similiar looking images are grouped nearby or not.

<img src="https://raw.githubusercontent.com/NRauschmayr/web-data/master/mxnet/doc/tutorials/info_gan/tsne.png" style="width:800px;height:600px;">

<!-- INSERT SOURCE DOWNLOAD BUTTONS -->