deeplearning | Python implementation of Deep Learning book | Machine Learning library
kandi X-RAY | deeplearning Summary
kandi X-RAY | deeplearning Summary
Python implementation of Deep Learning book. Ian Goodfellow, Yoshua Bengio and Aaron Courville, MIT Press, 2016.
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def enable_mixed_precision_graph_rewrite_v1(opt, loss_scale='dynamic'):
"""Enable mixed precision via a graph rewrite.
Mixed precision is the use of both float32 and float16 data types when
training a model to improve performance. This is achi
Community Discussions
Trending Discussions on deeplearning
QUESTION
I'm trying to use a Google Container Registry public image as a base image for a Docker build. I'm currently using gcr.io/deeplearning-platform-release/tf-gpu.2-8
, but the same issue occurs with gcr.io/deeplearning-platform-release/tf-gpu.2-6
and gcr.io/deeplearning-platform-release/base-cu113
; see this Google page for reference.
If I just have the following 2 lines in my Dockerfile it crashes on the 2nd line:
...ANSWER
Answered 2022-Mar-31 at 13:53The site developer.download.nvidia.com must have been down. Trying again this morning works.
QUESTION
I'm trying to analyze my tensorflow application. The training runs well, but I get Failed to load libcupti (is it installed and accessible?)
if I open the Profile-Tab in Tensorboard.
My configuration is:
- Windows 10
- Python 3.9.7
- Tensorflow 2.6.0
- CUDA Toolkit 11.2
- cuDNN 8.1.1 (installed as here by copying files as described)
- Visual Studio Professional 2019
CUDA_PATH
is C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2
My Path-Variable contains:
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\bin
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\libnvvp
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\extras\CUPTI\lib64
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\include
C:\Program Files\NVIDIA Corporation\Nsight Systems 2020.4.3\target-windows-x64
conda list
(only relevant packages):
ANSWER
Answered 2022-Mar-21 at 18:36Hidden in the log output of jupyter I found an this error message: Could not load dynamic library 'cupti64_113.dll': dlerror: cupti64_113.dll not found
With this error message and that hint I was able to solve the problem:
I copied cupti64_2020.3.0.dll
in C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\extras\CUPTI\lib64
and renamed it to cupti64_113.dll
and now the profiler works.
QUESTION
I am trying to create a form which will be filled and photographed later on. An issue that I am facing is that of alignment. I came across some deep learning solutions which detect the corners of form. But this is a lot of times inaccurate in my use case where the sheet of paper is folded-reopened/crumpled. I also don't have a lot of flexibility/hard-coding options in the deeplearning process.
Are there any patterns which OpenCV can detect with ~100% accuracy no matter the orientation of the pattern? I will be putting different patterns on 4 corners of the sheet. I am thinking of using the inbuilt template matching function or other pattern recognition algorithms. There are some common patters like a big '+' sign or a star etc that I am trying to avoid. I also tried putting barcodes on the corners because they are also detected fairly easily(Not concerned with the contents of the barcode only their relative positioning). But depending on the quality of image the barcode isn't always detected.
...ANSWER
Answered 2022-Mar-10 at 11:22ArUco markers sound like the best option for you, they can easily be implemented in OpenCV.
Aruco example and documentation:https://docs.opencv.org/4.x/d5/dae/tutorial_aruco_detection.html
Python example: https://pyimagesearch.com/2020/12/21/detecting-aruco-markers-with-opencv-and-python/
QUESTION
I am trying to run a Custom Training Job in Google Cloud Platform's Vertex AI Training service.
The job is based on a tutorial from Google that fine-tunes a pre-trained BERT model (from HuggingFace).
When I use the gcloud
CLI tool to auto-package my training code into a Docker image and deploy it to the Vertex AI Training service like so:
ANSWER
Answered 2022-Mar-01 at 08:34The image size shown in the UI is the virtual size of the image. It is the compressed total image size that will be downloaded over the network. Once the image is pulled, it will be extracted and the resulting size will be bigger. In this case, the PyTorch image's virtual size is 6.8 GB while the actual size is 17.9 GB.
Also, when a docker push
command is executed, the progress bars show the uncompressed size. The actual amount of data that’s pushed will be compressed before sending, so the uploaded size will not be reflected by the progress bar.
To cut down the size of the docker image, custom containers can be used. Here, only the necessary components can be configured which would result in a smaller docker image. More information on custom containers here.
QUESTION
My dataset is formed by 32 columns, but after seeking for the important features I found that 4 of them are the most important ones so I wanted to work just on them but this error faced me:
This is my code:
...ANSWER
Answered 2022-Jan-22 at 18:07You have this line of code
QUESTION
I'm able to install the desired version of TensorRT from official nvidia guide (https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html#maclearn-net-repo-install)
...ANSWER
Answered 2022-Jan-18 at 13:25It's quite easy to "install" custom plugin if you registered it. So the steps are the following:
Install tensorRT
QUESTION
I have a question about flattening image matrixes in this case (64 x 64 pix x 3) to a vector (12288 x 1).
I understand that each image pixel is in a (64 X 64) matrix, and if I'm right, each element of this matrix is a vector of length 3, holding R,G,B data for that single pixel. So the first following row is R, G, B values for the top-left pixel:
...ANSWER
Answered 2022-Jan-07 at 17:47I think that it depends on your model design. If you design your model inputs with three arrays for three channels (R, G, B), you can try my way below. We need to separate it first and reshape it later.
QUESTION
Using the h2o package for R, I created a set of base models using AutoML with StackedEnsemble's disabled. Thus, the set of models only contains the base models that AutoML generates by default (GLM, GBM, XGBoost, DeepLearning, and DRF). Using these base models I was able to successfully train a default stacked ensemble manually using the h2o.stackedEnsemble function (i.e., a GLM with default params). I exported the model as a MOJO, shutdown the H2O cluster, restarted R, initialized a new H2O cluster, imported the stacked ensemble MOJO, and successfully generated predictions on a new validation set.
So far so good.
Next, I did the exact same thing following the exact same process, but this time I made one change: I trained the stacked ensemble with all pairwise interactions between the base models. The interactions were created automatically by feeding a list of the base model Ids to the interaction metalearner_parameter. The model appeared to train without issue and (as I described above) was able to export it as a MOJO, restart the h2o cluster, restart R, and import the MOJO. However, when I attempt to generate predictions on the same validation set I used above I get the following error:
...ANSWER
Answered 2022-Jan-06 at 14:54Unfortunately, H2O-3 doesn't currently support exporting GLM with interactions as MOJO. There's a bug that allows the GLM to be exported with interactions but the MOJO doesn't work correctly - the interactions are replaced by missing values. This should be fixed in the next release (3.36.0.2) - it will not allow to export that MOJO in the first place.
There's not much other than writing the stacked ensemble in R (base model predictions preprocessing (e.g., interaction creation) and then feeding it to the h2o.glm) that you can do. There is now an unmaintained package h2oEnsemble that might be helpful for that. You can also use another metalearner model that is more flexible, e.g., GBM.
QUESTION
I am working on a Kaggle notebook and whenever I run a cell that references the TensorFlow module at all, it prints out a huge warning about some sort of settings but still works. I looked up how to suppress warnings from TensorFlow, and everything I found said to do the following:
...ANSWER
Answered 2021-Dec-09 at 15:47So I managed to fix the problem with the following line:
QUESTION
How do I change the batch size in VGG16? I'm trying to address an issue of exceeding memory constraints by 10% by doing this.
Error:
...ANSWER
Answered 2021-Dec-03 at 22:24you are already using batch_size = 1.
- check if you are using gpu by checking the logs when you are importing tensorflow.
- try to resize the image before predicting with
tf.image.resize(image, [small_height,small_width,N_channels])
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