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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'
@enesayan
enesayan / residual_network.py
Created December 4, 2018 08:39 — forked from mjdietzx/residual_network.py
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