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import torch | |
from torchvision import datasets | |
class ImageFolderWithPaths(datasets.ImageFolder): | |
"""Custom dataset that includes image file paths. Extends | |
torchvision.datasets.ImageFolder | |
""" | |
# override the __getitem__ method. this is the method that dataloader calls | |
def __getitem__(self, index): |
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""" | |
Example TensorFlow script for finetuning a VGG model on your own data. | |
Uses tf.contrib.data module which is in release v1.2 | |
Based on PyTorch example from Justin Johnson | |
(https://gist.github.com/jcjohnson/6e41e8512c17eae5da50aebef3378a4c) | |
Required packages: tensorflow (v1.2) | |
Download the weights trained on ImageNet for VGG: | |
``` | |
wget http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz |
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def test_transaction(*args): | |
create_sql = "INSERT INTO user (name) VALUES (%s)" | |
transaction.set_autocommit(False, using='uic') | |
try: | |
cursor = connections['uic'].cursor() | |
for u in args: | |
cursor.execute(create_sql,[u]) | |
except IntegrityError as e: | |
transaction.rollback(using='uic') | |
else: |
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import numpy as np | |
def softmax(x): | |
""" | |
Compute softmax values for each sets of scores in x. | |
Rows are scores for each class. | |
Columns are predictions (samples). | |
""" | |
scoreMatExp = np.exp(np.asarray(x)) |
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# The MIT License (MIT) | |
# Copyright (c) 2016 Vladimir Ignatev | |
# | |
# Permission is hereby granted, free of charge, to any person obtaining | |
# a copy of this software and associated documentation files (the "Software"), | |
# to deal in the Software without restriction, including without limitation | |
# the rights to use, copy, modify, merge, publish, distribute, sublicense, | |
# and/or sell copies of the Software, and to permit persons to whom the Software | |
# is furnished to do so, subject to the following conditions: | |
# |
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/** | |
* Creates a unique filename by appending a number | |
* | |
* i.e. if image.jpg already exists, returns | |
* image2.jpg | |
*/ | |
function uniqueFilename($path, $name, $ext) { | |
$output = $name; | |
$basename = basename($name, '.' . $ext); |
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import itertools | |
def jaccard(labels1, labels2): | |
""" | |
Computes the Jaccard similarity between two sets of clustering labels. | |
The value returned is between 0 and 1, inclusively. A value of 1 indicates | |
perfect agreement between two clustering algorithms, whereas a value of 0 | |
indicates no agreement. For details on the Jaccard index, see: | |
http://en.wikipedia.org/wiki/Jaccard_index |
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# check.packages function: install and load multiple R packages. | |
# Check to see if packages are installed. Install them if they are not, then load them into the R session. | |
check.packages <- function(pkg){ | |
new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])] | |
if (length(new.pkg)) | |
install.packages(new.pkg, dependencies = TRUE) | |
sapply(pkg, require, character.only = TRUE) | |
} | |
# Usage example |