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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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""" | |
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 |