Taksir’s gists

bogdan-kulynych / install-cuda-10-bionic.sh

Last active April 7, 2026 06:06

Install CUDA 10 on Ubuntu 18.04

	# WARNING: These steps seem to not work anymore!

	#!/bin/bash

	# Purge existign CUDA first
	sudo apt --purge remove "cublas" "cuda"
	sudo apt --purge remove "nvidia*"

	# Install CUDA Toolkit 10
	wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.0.130-1_amd64.deb

adnan-bashir-fuaad / Project-Edit-Pipeline-Excerpt.py

Created August 22, 2018 14:47

Extracting the activations from layers with Keras and pickling them for future use with Scikit-Learn

	model = load_model('/home/adnan/Datasets/Network-weights/FCN-Classifier-Light-CIFAR-10/Classifier_CIFAR10_FCN_light_trained_models-97-0.7636.h5')

	layer_outputs = [layer.output for layer in model.layers]
	activation_model = Model(inputs=model.input, outputs=layer_outputs)


	def get_activations(img):
	layer_activations = list()
	activations = activation_model.predict(img)

Mahedi-61 / cuda_11.8_installation_on_Ubuntu_22.04

Last active January 17, 2026 07:58

Instructions for CUDA v11.8 and cuDNN 8.9.7 installation on Ubuntu 22.04 for PyTorch 2.1.2

	#!/bin/bash

	### steps ####
	# Verify the system has a cuda-capable gpu
	# Download and install the nvidia cuda toolkit and cudnn
	# Setup environmental variables
	# Verify the installation
	###

	### to verify your gpu is cuda enable check

mbednarski / w2v.ipynb

Created March 6, 2018 23:34

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Thimira / keras_bottleneck_multiclass.py

Last active April 13, 2021 05:52

Learn how to build a multi-class image classification system using bottleneck features from a pre-trained model in Keras to achieve transfer learning. Tutorial: https://www.codesofinterest.com/2017/08/bottleneck-features-multi-class-classification-keras.html

	'''
	Using Bottleneck Features for Multi-Class Classification in Keras

	We use this technique to build powerful (high accuracy without overfitting) Image Classification systems with small
	amount of training data.

	The full tutorial to get this code working can be found at the "Codes of Interest" Blog at the following link,
	https://www.codesofinterest.com/2017/08/bottleneck-features-multi-class-classification-keras.html

	Please go through the tutorial before attempting to run this code, as it explains how to setup your training data.

anonymous / multi_image.py

Created June 6, 2017 19:56

I am attempting to build a simple CNN that takes four 100x100 grayscale images as inputs and tells me which one contains a circle. I am having trouble with the interaction between my generator and my CNN inputs, my inputs expect an array of dimension 4 but get an array of shape (100, 100, 1) as expected.

	import numpy as np
	import os
	from keras.models import Model
	from keras.layers import Input
	from keras.layers import Conv2D
	from keras.layers import MaxPooling2D
	from keras.layers import Flatten
	from keras.layers import Dense
	from keras.layers.merge import concatenate

mjdietzx / waya-dl-setup.sh

Last active September 20, 2025 11:52

Install CUDA Toolkit v8.0 and cuDNN v6.0 on Ubuntu 16.04

	#!/bin/bash

	# install CUDA Toolkit v8.0
	# instructions from https://developer.nvidia.com/cuda-downloads (linux -> x86_64 -> Ubuntu -> 16.04 -> deb (network))
	CUDA_REPO_PKG="cuda-repo-ubuntu1604_8.0.61-1_amd64.deb"
	wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/${CUDA_REPO_PKG}
	sudo dpkg -i ${CUDA_REPO_PKG}
	sudo apt-get update
	sudo apt-get -y install cuda

soruly / compre.py

Last active June 15, 2023 14:38

Compare two images using OpenCV and SIFT in python

	import cv2
	import sys
	import os.path
	import numpy as np

	def drawMatches(img1, kp1, img2, kp2, matches):

	rows1 = img1.shape[0]
	cols1 = img1.shape[1]
	rows2 = img2.shape[0]

fchollet / classifier_from_little_data_script_3.py

Last active February 26, 2025 01:37

Fine-tuning a Keras model. Updated to the Keras 2.0 API.

	'''This script goes along the blog post
	"Building powerful image classification models using very little data"
	from blog.keras.io.
	It uses data that can be downloaded at:
	https://www.kaggle.com/c/dogs-vs-cats/data
	In our setup, we:
	- created a data/ folder
	- created train/ and validation/ subfolders inside data/
	- created cats/ and dogs/ subfolders inside train/ and validation/
	- put the cat pictures index 0-999 in data/train/cats

fchollet / classifier_from_little_data_script_1.py

Last active February 18, 2026 04:59

Updated to the Keras 2.0 API.

	'''This script goes along the blog post
	"Building powerful image classification models using very little data"
	from blog.keras.io.
	It uses data that can be downloaded at:
	https://www.kaggle.com/c/dogs-vs-cats/data
	In our setup, we:
	- created a data/ folder
	- created train/ and validation/ subfolders inside data/
	- created cats/ and dogs/ subfolders inside train/ and validation/
	- put the cat pictures index 0-999 in data/train/cats

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