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enesayan / _dice.py
Created April 4, 2019 13:10 — forked from JDWarner/_dice.py
Dice coefficient between two boolean NumPy arrays or array-like data. This is commonly used as a set similarity measurement (though note it is not a true metric; it does not satisfy the triangle inequality). The dimensionality of the input is completely arbitrary, but `im1.shape` and `im2.shape` much be equal. This Gist is licensed under the mod…
"""
_dice.py : Dice coefficient for comparing set similarity.
"""
import numpy as np
def dice(im1, im2):
"""
@enesayan
enesayan / _jaccard.py
Created April 4, 2019 13:10 — forked from JDWarner/_jaccard.py
Jaccard coefficient between two boolean NumPy arrays or array-like data. This is commonly used as a set similarity metric, and it is a true metric. The dimensionality of the input is completely arbitrary, but `im1.shape` and `im2.shape` much be equal. This Gist is licensed under the modified BSD license, otherwise known as the 3-clause BSD.
"""
_jaccard.py : Jaccard metric for comparing set similarity.
"""
import numpy as np
def jaccard(im1, im2):
"""
@enesayan
enesayan / _dice.py
Created April 4, 2019 13:10 — forked from brunodoamaral/_dice.py
Dice coefficient between two boolean NumPy arrays or array-like data. This is commonly used as a set similarity measurement (though note it is not a true metric; it does not satisfy the triangle inequality). The dimensionality of the input is completely arbitrary, but `im1.shape` and `im2.shape` much be equal. This Gist is licensed under the mod…
def dice(im1, im2, empty_score=1.0):
"""
Computes the Dice coefficient, a measure of set similarity.
Parameters
----------
im1 : array-like, bool
Any array of arbitrary size. If not boolean, will be converted.
im2 : array-like, bool
Any other array of identical size. If not boolean, will be converted.
Returns
@enesayan
enesayan / A simple example: Confusion Matrix with Keras flow_from_directory.py
Created December 17, 2018 07:17 — forked from RyanAkilos/A simple example: Confusion Matrix with Keras flow_from_directory.py
import numpy as np
from keras import backend as K
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.preprocessing.image import ImageDataGenerator
from sklearn.metrics import classification_report, confusion_matrix
#Start
train_data_path = 'F://data//Train'
@kendricktan
kendricktan / youtube-dl-music.sh
Created July 30, 2018 13:52
Download youtube music via youtube-dl
youtube-dl --extract-audio --audio-format mp3 -o '%(title)s.%(ext)s' <URL>
@mjdietzx
mjdietzx / residual_network.py
Last active March 26, 2024 06:33
Clean and simple Keras implementation of residual networks (ResNeXt and ResNet) accompanying accompanying Deep Residual Learning: https://blog.waya.ai/deep-residual-learning-9610bb62c355.
"""
Clean and simple Keras implementation of network architectures described in:
- (ResNet-50) [Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385.pdf).
- (ResNeXt-50 32x4d) [Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/pdf/1611.05431.pdf).
Python 3.
"""
from keras import layers
from keras import models
@brunodoamaral
brunodoamaral / _dice.py
Last active March 16, 2024 18:00 — forked from JDWarner/_dice.py
Dice coefficient between two boolean NumPy arrays or array-like data. This is commonly used as a set similarity measurement (though note it is not a true metric; it does not satisfy the triangle inequality). The dimensionality of the input is completely arbitrary, but `im1.shape` and `im2.shape` much be equal. This Gist is licensed under the mod…
def dice(im1, im2, empty_score=1.0):
"""
Computes the Dice coefficient, a measure of set similarity.
Parameters
----------
im1 : array-like, bool
Any array of arbitrary size. If not boolean, will be converted.
im2 : array-like, bool
Any other array of identical size. If not boolean, will be converted.
Returns
@joelouismarino
joelouismarino / googlenet.py
Last active October 9, 2023 07:09
GoogLeNet in Keras
from __future__ import print_function
import imageio
from PIL import Image
import numpy as np
import keras
from keras.layers import Input, Dense, Conv2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Dropout, Flatten, Concatenate, Reshape, Activation
from keras.models import Model
from keras.regularizers import l2
from keras.optimizers import SGD
@fchollet
fchollet / classifier_from_little_data_script_2.py
Last active September 13, 2023 03:34
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
@kendricktan
kendricktan / gabor_filter.py
Created May 16, 2016 01:07
Gabor kernel filter example in python
import numpy as np
import cv2
# cv2.getGaborKernel(ksize, sigma, theta, lambda, gamma, psi, ktype)
# ksize - size of gabor filter (n, n)
# sigma - standard deviation of the gaussian function
# theta - orientation of the normal to the parallel stripes
# lambda - wavelength of the sunusoidal factor
# gamma - spatial aspect ratio
# psi - phase offset