My Elasticsearch cheatsheet with example usage via rest api (still a work-in-progress)
Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.
$ python -m SimpleHTTPServer 8000
This is the Keras model of VGG-Face.
It has been obtained through the following method:
- vgg-face-keras:directly convert the vgg-face matconvnet model to keras model
- vgg-face-keras-fc:first convert vgg-face caffe model to mxnet model,and then convert it to keras model
Details about the network architecture can be found in the following paper:
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##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
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from __future__ import division | |
from numpy.fft import rfft | |
from numpy import argmax, mean, diff, log, nonzero | |
from scipy.signal import blackmanharris, correlate | |
from time import time | |
import sys | |
try: | |
import soundfile as sf | |
except ImportError: | |
from scikits.audiolab import flacread |
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def dice_loss(pred, target): | |
"""This definition generalize to real valued pred and target vector. | |
This should be differentiable. | |
pred: tensor with first dimension as batch | |
target: tensor with first dimension as batch | |
""" | |
smooth = 1. |
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#converts all midi files in the current folder | |
import glob | |
import os | |
import music21 | |
#converting everything into the key of C major or A minor | |
# major conversions | |
majors = dict([("A-", 4),("A", 3),("B-", 2),("B", 1),("C", 0),("D-", -1),("D", -2),("E-", -3),("E", -4),("F", -5),("G-", 6),("G", 5)]) |
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