DataBloom

I’m reading through some academic code and I’ve come across a
line that looks like this

gen1 = tf.concat(3, [gen1, e6]) 

According to the Tensorflow
documentation, the signature is

tf.concat( values, axis, name='concat' ) 

which means the line I found doesn’t make any sense. I thought
that perhaps it was different in an older version, but it’s the
same in the
Tensorflow 1 documentation too!

When I switch the arguments, it works. Is there any explanation
for this madness?

Hello everyone, I am starting to use tensorflow and I would like
to know if you can recommend an updated tutorial on how to create
my own object detector using the tensorflow api (learning
transfer), thanks !!

My X_train has 316175 samples and I am trying to fit it on a
sequential model. But after running fit method, the number of
samples shows up to be 1236 during each epoch. Here’s how I am
fitting the model.

model.fit(x=X_train, y=y_train, epochs=25, batch_size=256, validation_data=(X_test,y_test)) 

I cannot understand why it is not using all the samples. Can
someone please help?

Hey,

I want to split my custom model e.g: class
MyModel(keras.Model):over multiply GPUs. and anything I’ve tried to
do cause OOM exception.

what and how should I do it?

thanks in advanced!

​

some background:

I’m trying to implement the followed paper:

Neural Audio Synthesis of Musical Notes with WaveNet
Autoencoders –https://arxiv.org/abs/1704.01279

but the large input (64K vector) with the deep WaveNet decoder
cause OOM exception.

​

I have multiply GPUs and when creating a layer I’m doing it
under a specific GPU but when I’m applying the gradients they all
located on the same GPU (the default one, GPU0) and that causes
OOM.

the train step is a custom one as well as the model.

So, i’ve made an autoencoder for the purpose of exracting useful
features from the images, a common task as far as i know, but this
is my first time actually doing this so i’m not sure how exactly
can i do it. I’ve fit a convolutional autoencoder on the set of
images, now i need to extract the output of a bottleneck layer for
each image i’ve got to construct a feature set of interest, how
should i do this? I used keras sequential way of writing the code
for the network