Can we visualize the embedding with multiple sprite images in tensorflow?

Question

What I mean is, can I, for example, construct 2 different sprite images and be able to choose one of them while viewing embeddings in 2D/3D space using TSNE/PCA?

In other words, when using the following code:

embedding.sprite.image_path = "Path/to/the/sprite_image.jpg"

Is there a way to add another sprite image?

So, when training a Conv Net to distinguish between MNIST digits, I not only need to view the 1,2,..9, and 0 in the 3D/2D space, instead, I would like to see where are the ones gathering in that space. Same for 2s, 3s and so on. so I need a unique color for the 1s, another one for the 2s and so on... I need to view this as in the following image:

source

Any help is much appreciated!

score 2 · Answer 1 · edited Sep 22 '21 at 17:27

2

There is an easier way to do this with filtering. You can just select the labels with a regex syntax:

If this is not what you are looking for, you could create a sprite image that assigns the same plain color image to each of your labels!

edited Sep 22 '21 at 17:27

Jeremy Caney

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answered Apr 16 '18 at 10:27

Ignacio Peletier

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score 1 · Answer 2 · edited Sep 22 '21 at 17:25

This functionality should come out of the box (without additional sprite images). See 'colour by' in the left sidepanel. You can toggle the A to switch sprite images on and off.

color by option

This run was produced with the example on the front page of the tensorboardX projector GitHub repo. https://github.com/lanpa/tensorboardX

You can also see a live demo with MNIST dataset (images and colours) at http://projector.tensorflow.org/

import torchvision.utils as vutils
import numpy as np
import torchvision.models as models
from torchvision import datasets
from tensorboardX import SummaryWriter

resnet18 = models.resnet18(False)
writer = SummaryWriter()
sample_rate = 44100
freqs = [262, 294, 330, 349, 392, 440, 440, 440, 440, 440, 440]

for n_iter in range(100):

    dummy_s1 = torch.rand(1)
    dummy_s2 = torch.rand(1)
    # data grouping by `slash`
    writer.add_scalar('data/scalar1', dummy_s1[0], n_iter)
    writer.add_scalar('data/scalar2', dummy_s2[0], n_iter)

    writer.add_scalars('data/scalar_group', {'xsinx': n_iter * np.sin(n_iter),
                                             'xcosx': n_iter * np.cos(n_iter),
                                             'arctanx': np.arctan(n_iter)}, n_iter)

    dummy_img = torch.rand(32, 3, 64, 64)  # output from network
    if n_iter % 10 == 0:
        x = vutils.make_grid(dummy_img, normalize=True, scale_each=True)
        writer.add_image('Image', x, n_iter)

        dummy_audio = torch.zeros(sample_rate * 2)
        for i in range(x.size(0)):
            # amplitude of sound should in [-1, 1]
            dummy_audio[i] = np.cos(freqs[n_iter // 10] * np.pi * float(i) / float(sample_rate))
        writer.add_audio('myAudio', dummy_audio, n_iter, sample_rate=sample_rate)

        writer.add_text('Text', 'text logged at step:' + str(n_iter), n_iter)

        for name, param in resnet18.named_parameters():
            writer.add_histogram(name, param.clone().cpu().data.numpy(), n_iter)

        # needs tensorboard 0.4RC or later
        writer.add_pr_curve('xoxo', np.random.randint(2, size=100), np.random.rand(100), n_iter)

dataset = datasets.MNIST('mnist', train=False, download=True)
images = dataset.test_data[:100].float()
label = dataset.test_labels[:100]

features = images.view(100, 784)
writer.add_embedding(features, metadata=label, label_img=images.unsqueeze(1))

# export scalar data to JSON for external processing
writer.export_scalars_to_json("./all_scalars.json")
writer.close()

There are some threads mentioning that this currently fails beyond a threshold number of datapoints. https://github.com/lanpa/tensorboardX

Can we visualize the embedding with multiple sprite images in tensorflow?

2 Answers2