Category: Misc

I am have searched a lot of tutorials and courses, most start
with a BERT model or some variation of it. I want to watch/ learn
how a transformer/ attention is trainned from scratch.

I want to try to build a attention/ transformer model for solved
games like chess, (ie I will have generate-able data)

I’m looking at reducing the costs of EfficientNet for a task
that only deals with greyscale data.

To do this, I need to reduce the number of filters across the
network by 1/3rd (RGB -> (B/W)), and train on COCO in
greyscale.

The
TensorFlow 2 Detection Model Zoo has
a link of training configs if you want to train from
scratch.

However, I can’t seem to find how I would edit the architecture
to reduce the number of channels.

I can see there’s an
official definition in Keras, however I’m unsure if this is
what’s used by the config.

If there was some way to load the saved model, and then edit
it’s structure that way, that could work. But I’m unsure if there’s
a better way to do this.

So at this point I’ve managed to get ahold of my checkpoint file
which is of type `DATA-00000-OF-00001` and there is also a similar
one that is of type `INDEX` and this file is significantly smaller
in terms of size. I would like to convert these two into a single
`*.pb` file. Is that possible?

Sorry for asking a question that’s not a direct TensorFlow issue
but 2.4 supports the 3000-series GPUs, so a lot of us are waiting
on Anaconda support, and was thinking maybe someone here can
remember how long it took Anaconda to support 2.3 after its
release, to give others an approximate timeframe on 2.4. Thanks.
)

https://reddit-username-generator.herokuapp.com/

Trained on ~400k usernames, this LSTM based approach can
generate pretty realistic looking reddit usernames. You can even
provide a start string like “PM_ME” (warning: lotta profanity).

At first I deployed with TensorFlow.js in React, but to learn
more I rewrote the backend with Flask instead.

Here’s the GitHub: https://github.com/dchen327/reddit-username-generator

Have fun!

I am trying to get my GPU to train. Gtx 1660 ti, tf 2.4.1, cuda
11.2, python 3.8.7

My NN was taking 15 minutes per epoch on some dummy data so I am
setting up GPU training. At one point I got through 13 epochs
before it got stuck (maybe ran out of memory?). Many github
resolutions later I am stuck at one of two errors:

CUBLAS_STATUS_ALLOC_FAILED CUDNN_STATUS_EXECUTION_FAILED

The only tickets I have found online have been resolved by
setting memory limit or setting “allow_growth” to true. Twice this
has gotten me past the first error, but isnt not working
consistently. Ultimately I end up at the second error either
way.

Has anyone encounter this and not had the widely reported
solution work? Thanks in advance if anyone can help me. Just spent
waaaaay too long trying to get this going and finally am out of
ways to google.

Git link https://github.com/ArimaValanImmanuel/posenet-python

I’m upgrading my tensorflow version and using their
tf_upgrade_v2 script. I don’t run into any issues but all my python
files register as “modified” in git, even when there are no
changes? I poked around google and some people mention that my file
permissions may be changing, so I set the core.filemode to false in
my .git/config and then retry to to the upgrade, but I am still
seeing file changes. I diffed the files and I see zero changes. I
believe it could be the EOL, but I tried setting core.autcrlf to
false as well and that still gives me all these files as modified.
Has anyone encountered this? Running ubuntu 20.04.1.

I have tf dataset with images and labels and want to convert it
to a Pandas DataFrame, since that’s the object required in an
AzureML pipeline designer.

I’m a beginner working with tensorflow and after googling for a
couple of hours I haven’t found anything.

I’d appreciate any tips on how to do this.

Autoencoders have a number of limitations for generative tasks.
That’s why they need a power-up to become Variational
Autoencoders. In my new video, I explain the first step to
transform an autoencoder into a VAE. Specifically, I discuss how
VAEs use multivariate normal distributions to encode input data
into a latent space and why this is awesome for generative tasks.
Don’t worry – I also explain what multivariate normal
distributions are!

This video is part of a series called “Generating Sound with
Neural Networks”. In this series, you’ll learn how to generate
sound from audio files and spectrograms 🎧 🎧 using Variational
Autoencoders 🤖 🤖

Here’s the video:

https://www.youtube.com/watch?v=b8AzCgY1gZI&list=PL-wATfeyAMNpEyENTc-tVH5tfLGKtSWPp&index=9