I got an error ,IndexError: list index out of range. it worked on a other machine but after i transferred it to a other machine it doesn’t work anymore. Python: 3.8.5 tensorflow: 2.3.1 Traceback says: My code: I really cannot understand why such an error happens. What is wrong in my codes? How should I fix this? Answer Define the
Tag: tensorflow2.0
How can I select top-n elements from tensor without repeating elements?
I want to select top-n elements of 3 dimension tensor given the picked elements are all unique. All the elements are sorted by the 2nd column, and I’m selecting top-2 in the example below but I don’t want duplicates in there. Condition: No for loops or tf.map_fn() Here is the input and desired_output that I want: This is what I’m
Trying to extract patches from image and getting “UnimplementedError: Only support ksizes across space”
I was trying to split my image through 4 patches when I came through the following error: UnimplementedError: Only support ksizes across space Traceback: Answer After further research I was able to manage by changing from: To : And then reshape to obtain 3 channel images:
Could not load dynamic library ‘cudart64_101.dll’ on tensorflow CPU-only installation
I just installed the latest version of Tensorflow via pip install tensorflow and whenever I run a program, I get the log message: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library ‘cudart64_101.dll’; dlerror: cudart64_101.dll not found Is this bad? How do I fix the error? Answer Tensorflow 2.1+ What’s going on? With the new Tensorflow 2.1 release, the default tensorflow pip
Cannot use vggface-keras in Tensorflow 2.0
I am trying to use the keras-vggface library from https://github.com/rcmalli/keras-vggface to train a CNN. I have installed tensorflow 2.0.0-rc1, keras 2.3.1, cuda 10.1, cudnn 7.6.5 and the driver’s version is 418, the problem is that when i try to use the vggface model, as a convolutional base, i get an error, here is the code and the error Error! I
What is the difference between tf-nightly and tensorflow in PyPI?
What is the difference between tf-nightly and tensorflow in PyPI? Which one is reliable? https://pypi.org/project/tf-nightly/ https://pypi.org/project/tensorflow/ Answer Just to add to what Ben Souchet wrote: As its name suggests, the tf-nightly pip package is built and released to PyPI every night (barring any build failures, which happens rarely). As a result, you can see an almost once-per-day version update history.
Why is TensorFlow 2 much slower than TensorFlow 1?
It’s been cited by many users as the reason for switching to Pytorch, but I’ve yet to find a justification/explanation for sacrificing the most important practical quality, speed, for eager execution. Below is code benchmarking performance, TF1 vs. TF2 – with TF1 running anywhere from 47% to 276% faster. My question is: what is it, at the graph or hardware
How to generate CNN heatmaps using built-in Keras in TF2.0 (tf.keras)
I used to generate heatmaps for my Convolutional Neural Networks, based on the stand-alone Keras library on top of TensorFlow 1. That worked fine, however, after my switch to TF2.0 and built-in tf.keras implementation (with eager execution) I cannot use my old heatmap generation code any longer. So I re-wrote parts of my code for TF2.0 and ended up with
Tensorboard not found as magic function in jupyter
I want to run tensorboard in jupyter using the latest tensorflow 2.0.0a0. With the tensorboard version 1.13.1, and python 3.6. using … %tensorboard –logdir {logs_base_dir} I get the error : UsageError: Line magic function %tensorboard not found Do you have an idea what the problem could be? It seems that all versions are up to date and the command seems
How to tie weights between transposed layers?
I have try to tied weights in tensorflow 2.0 keras, with below code. but it shows this errors? does anyone know how to write tied weights dense layer ? Errors Answer It took much of my time to figure out, but I think this is the way of Tied Weights by subclassing Keras Dense layer. Hope it can help someone