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""" | |
_dice.py : Dice coefficient for comparing set similarity. | |
""" | |
import numpy as np | |
def dice(im1, im2): | |
""" |
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""" | |
_jaccard.py : Jaccard metric for comparing set similarity. | |
""" | |
import numpy as np | |
def jaccard(im1, im2): | |
""" |
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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 |
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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' |
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youtube-dl --extract-audio --audio-format mp3 -o '%(title)s.%(ext)s' <URL> |
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""" | |
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 |
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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 |
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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 |
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'''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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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 |
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