keras-gp | Gaussian Processes : Learning scalable deep | Machine Learning library

by alshedivat Python Version: 0.3.2 License: MIT

X-Ray Key Features Code Snippets Community Discussions(8)Vulnerabilities Install Support

kandi X-RAY | keras-gp Summary

keras-gp is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras, Neural Network applications. keras-gp has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Keras + Gaussian Processes: Learning scalable deep and recurrent kernels.

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keras-gp has a low active ecosystem.

It has 220 star(s) with 53 fork(s). There are 10 watchers for this library.

It had no major release in the last 12 months.

There are 9 open issues and 14 have been closed. On average issues are closed in 26 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of keras-gp is 0.3.2

Quality

keras-gp has 0 bugs and 0 code smells.

Security

keras-gp has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

keras-gp code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

keras-gp is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

keras-gp releases are available to install and integrate.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

keras-gp saves you 854 person hours of effort in developing the same functionality from scratch.

It has 1956 lines of code, 117 functions and 32 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed keras-gp and discovered the below as its top functions. This is intended to give you an instant insight into keras-gp implemented functionality, and help decide if they suit your requirements.

Load sensor sensors data
Convert geodetic to geodetic coordinates
Convert ECEF coordinates to enu
Constructs a list of polygon lines
Assemble the model
Assemble thenarx layer
Assemble a GPNX model
Predict from input data
Update the data
Update the grid
Train the model
Calculates the model
Performs a training loop
Set parameters
Evaluate the GP
Predict for X
Load configuration from file
Load GP configs from file
Train the GP model
Evaluate the model
Standardize the data
Compile the model
Assemble MLP
Finishes output GPs
Pad X and Y to sequences
Generate a loss function for a GP loss function

Get all kandi verified functions for this library.

keras-gp Key Features

No Key Features are available at this moment for keras-gp.

keras-gp Examples and Code Snippets

No Code Snippets are available at this moment for keras-gp.

Community Discussions

Trending Discussions on keras-gp

ImportError: cannot import name 'HeNormal' from 'keras.initializers'

tensorflow-gpu recognizes XLA-CPU instead of GPU

How to force keras to use tensorflow GPU backend

while implementing SEGNET using MaxPoolingWithArgmax2D and MaxUnpooling2D giving error

AttributeError: 'tuple' object has no attribute 'rank' when calling fit on a Keras model with custom generator

tensorflow 2 install on mac by conda

ValueError: An operation has `None` for gradient - Not using Custom

How to improve GPU usage in convolutional neural network?

QUESTION

ImportError: cannot import name 'HeNormal' from 'keras.initializers'

Asked 2021-Dec-29 at 23:08

If I try to run the line

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ANSWER

Answered 2021-Dec-29 at 23:08

I solved by replacing HeNormal with he_normal, since in initializers.py it was defined in this way.

I don't know why since in all the other devices I can import it with HeNormal

Source https://stackoverflow.com/questions/70525591

QUESTION

tensorflow-gpu recognizes XLA-CPU instead of GPU

Asked 2021-Nov-07 at 11:39

I am trying to install keras-gpu on PC with Tesla V100 and Windows Server 2019. I installed some version (2.4.3) and found that my GPU is not working. I need to install any 2.x.x version of keras with GPU support.

I have installed CUDA 10.1 cudnn 8.0.5 and after many attempts also tried 11.2 version with cudnn 8.1.1 (Also tried 11.5). And started searching version of tensorflow which can find my GPU.

for 10.1:

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ANSWER

Answered 2021-Nov-06 at 11:35

You could try installing tensorflow-gpu 2.2.x or 2.3.x which are compatible with CUDA 10.1, as can be checked in the tested build configurations below:

https://www.tensorflow.org/install/source#gpu

If you look at tested build configurations, you will see that tensorflow 2.4.0 is tested for CUDA 11.0. Looking at software requirements on tensorflow GPU support page (https://www.tensorflow.org/install/gpu#software_requirements) you can see that CUDA 11.2 seems to be recommended only for Tensorflow >= 2.5.0.

It is unlikely that your GPU is recognized as a 'XLA_CPU' device. Here 'XLA' stands for 'accelerated linear algebra' (https://www.tensorflow.org/xla). It's a domain specific compiler that can be used both for CPUs and GPUs. For more details you could take a look at this what is XLA_GPU and XLA_CPU for tensorflow. It is more likely that your GPU is simply not detected, as evidenced by this line in your output.

Source https://stackoverflow.com/questions/69863383

QUESTION

How to force keras to use tensorflow GPU backend

Asked 2021-Oct-08 at 16:10

I know this is one of the popular questions, but none of the solutions worked for me, so far.

I'm running a legacy code that is written in tensorflow v1.13.1 and keras v2.2.4. I cannot modify the code to run latest tensorflow version. Since keras has now been merged into tensorflow, I'm facing problems installing the specific versions of tensorflow and keras via pip. I found that anaconda has option to install keras and tensorflow with the above version. So, I installed it with

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ANSWER

Answered 2021-Oct-08 at 08:03

Let me start off with the assumption you're using an NVIDIA GPU. It's most likely due to you either not having CUDA installed or having the wrong version. Although, do double check to make sure you also have the latest NVIDIA drivers. If you look at this list you can see that tensorflow_gpu-1.13.1 uses CUDA version 10.0 and CudNN 7.4. To install those through anaconda use the following command.

Source https://stackoverflow.com/questions/69491795

QUESTION

while implementing SEGNET using MaxPoolingWithArgmax2D and MaxUnpooling2D giving error

Asked 2020-Nov-18 at 08:26

I am implementing SEGNET segmentation Network in python but getting the following error,

_Traceback (most recent call last): File "/scratch/pkasar.dbatu/training/NEW_SEGNET_updated_on_16_11_20.py", line 370, in model=segnet(input_shape=(256,256,3),n_labels=1) File "/scratch/pkasar.dbatu/training/NEW_SEGNET_updated_on_16_11_20.py", line 161, in segnet conv_14 = Convolution2D(512, (kernel, kernel), padding="same")(unpool_1) File "/home/pkasar.dbatu/.conda/envs/dl/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py", line 897, in call self._maybe_build(inputs) File "/home/pkasar.dbatu/.conda/envs/dl/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py", line 2416, in _maybe_build self.build(input_shapes) # pylint:disable=not-callable File "/home/pkasar.dbatu/.conda/envs/dl/lib/python3.7/site-packages/tensorflow/python/keras/layers/convolutional.py", line 153, in build input_channel = self._get_input_channel(input_shape) File "/home/pkasar.dbatu/.conda/envs/dl/lib/python3.7/site-packages/tensorflow/python/keras/layers/convolutional.py", line 293, in get_input_channel raise ValueError('The channel dimension of the inputs ' ValueError: The channel dimension of the inputs should be defined. Found None.

Tensorflow image is:- tensorflow 2.2.0 tensorflow-gpu 2.2.0 keras-base 2.4.3 keras-gpu 2.4.3 python 3.7.9 Please help me out Thank you in advance

The code snippet is as follows:-

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ANSWER

Answered 2020-Nov-17 at 09:05

You have input_shape=(256,256,3).

You must have (number_of_samples, nb_channels, rows, cols)

Source https://stackoverflow.com/questions/64869374

QUESTION

AttributeError: 'tuple' object has no attribute 'rank' when calling fit on a Keras model with custom generator

Asked 2020-Jul-13 at 19:11

I want to build a Neural Network with two inputs: for image data and for numeric data. So I wrote custom data generator for that. The train and validation dataframes contain 11 columns:

image_name — path to the image;
9 numeric features;
target — class for the item (last column).

The code for custom generator (based on this answer):

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ANSWER

Answered 2020-Jul-13 at 19:11

You need to convert all the individual objects returned by both the training and validation generators to Numpy arrays:

Source https://stackoverflow.com/questions/62744659

QUESTION

tensorflow 2 install on mac by conda

Asked 2020-May-29 at 08:44

Hi i'm trying to install tensorflow but getting package conflicts due to existing packages as numpy for example

Thanks for your help

This is part of the error massage:

UnsatisfiableError: The following specifications were found to be incompatible with each other:

Output in format: Requested package -> Available versions

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ANSWER

Answered 2020-May-29 at 08:44

Conda is designed to prevent these kinds of conflicts by creating separate, self-contained Python environments for different projects. Run these commands in your Terminal app:

Source https://stackoverflow.com/questions/62080823

QUESTION

ValueError: An operation has `None` for gradient - Not using Custom

Asked 2020-May-05 at 10:03

As per the title, I get this common error when trying to use Keras to do some Image Classification training. Unlike nearly all of the other examples, I am not trying to customise anything and simply using bog-standard keras functionality! Like this, who asks a similar question, but doesn't appear to have followed up.

I previously had an issue with this same project, but after having upgraded cudnn, and cudatoolkit (and relevant NVidia backends) I get this new error.

Conda List:

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ANSWER

Answered 2020-May-05 at 10:03

I think the problem is that you are clearing the session before training the model, doing this would make no sense, because clearing the session cleans the model structures in memory, so there would be no model representation in the TensorFlow side, making training fail.

So do not juse K.clear_session() in this case. It does not seem to be needed.

Source https://stackoverflow.com/questions/61585473

QUESTION

How to improve GPU usage in convolutional neural network?

Asked 2020-Jan-22 at 13:02

I am using keras library to implement CNN and Anaconda 3 (spyder 4) for execution.

I have used the command conda install -c anaconda keras-gpu It installed cudatoolkit-10.0.130 , cudnn-7.6.5 and tensorflow-gpu-2.0.0 But my code isn't working with tensorflow-gpu-2.0.0 so I have downgraded it to tensorflow-gpu-1.15.0 . (I have aslo installed latest CUDA toolkit on my machine but I dont know which one spyder is using , from my machine or conda environment ) Though my code is working fine but my GPU usage is %1 only. Am I installing something wrong like wrong combinations of Tensorflow and CUDA ? Actually I have tried most of the things mentioned online but I am not getting anywhere.

My system info : CPU : i7 9th gen GPU : RTX 2060 RAM : 16 GB OS : Windows 10

Is there any installation needed or any code changes to get my GPU working ? (I have executed one of the command like tf.config.list_physical_devices('GPU') to check my GPU and it is showing the positive result so tensorflow detecting my GPU but I have no idea why its is not using it for execution)

p.s : I have read online and most of the people talking about bottleneck due to CPU ( Even my CPU usage is low so it ll be appreciated if you tell me something to improve that as well ) , and solution they are asking to do is load your data so that GPU can be utilised efficiently. I am using image dataset so can you tell me how to preload the dataset or implement parallelism so that it can be fed to the GPU rather than giving it on fly. I am using keras as mentioned below so a code snippet which will be easy for newbie like me will be helpful to get kickstart.

Code :

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ANSWER

Answered 2020-Jan-22 at 12:04

As per the official documentation of TensorFlow, the following snippet should set the gpu memory usage:

Source https://stackoverflow.com/questions/59855790

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install keras-gp

KGP allows to build models in the same fashion as Keras, using the functional API. For example, a simple GP-RNN model can be built and compiled in just a few lines of code:. Note that KGP models support arbitrary off-the-shelf optimizers from Keras.
A quick tutorial that walks you through the key components of the library.
A few more examples.
KGP depends on Keras and requires either Theano or TensorFlow being installed. The GPML backend requires either MATLAB or Octave and a corresponding Python interface package: Oct2Py for Octave or the MATLAB engine for Python. Generally, MATLAB backend seems to provide faster runtime. However, if you compile the latest version of Octave with JIT and OpenBLAS support, the overhead gets reduced to minimum.