Kyle Kastner kastnerkyle

## bwv101.7.C-minor-transposed.json
{
    "seconds_per_quarter": 0.5,
    "parts_names": [
        "Soprano",
        "Alto",
        "Tenor",
        "Bass"
    ],
    "parts_cumulative_times": [
        [

## batch_ar_example.py
import numpy as np

# make a minibatch of time, batch, features
# time length 7
# batch size 2
# feature dimension 4:
# 1:4, 10:14, 20:24, 30:34, etc for first minibatch element
# 5:8, 15:18, etc second minibatch el
n_features = 4
n_timesteps = 7

## Kiritan singing voice synthesis demo.ipynb

      
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                kastnerkyle
                / Kiritan singing voice synthesis demo.ipynb
            
            
              Created May 3, 2020
                — forked from r9y9/Kiritan singing voice synthesis demo.ipynb
            
              
                Neural_network_based_singing_voice_synthesis_demo_using_kiritan_singing_database_(Japanese)
              
          
      View Kiritan singing voice synthesis demo.ipynb
  
    
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## make_audiobook.py
#!/usr/bin/env python3
"""
To use:
1. install/set-up the google cloud api and dependencies listed on https://github.com/GoogleCloudPlatform/python-docs-samples/tree/master/texttospeech/cloud-client
2. install pandoc and pypandoc, also tqdm
3. create and download a service_account.json ("Service account key") from https://console.cloud.google.com/apis/credentials
4. run GOOGLE_APPLICATION_CREDENTIALS=service_account.json python make_audiobook.py book_name.epub
"""
import re
import sys

## make_audiobook.py
#!/usr/bin/env python3
"""
To use:
1. install/set-up the google cloud api and dependencies listed on https://github.com/GoogleCloudPlatform/python-docs-samples/tree/master/texttospeech/cloud-client
2. install pandoc and pypandoc, also tqdm
3. create and download a service_account.json ("Service account key") from https://console.cloud.google.com/apis/credentials
4. run GOOGLE_APPLICATION_CREDENTIALS=service_account.json python make_audiobook.py book_name.epub
"""
import re
import sys

## newsci-headlines
Some planets may orbit a supermassive black hole instead of a star
This is almost certainly not what Denisovans looked like
UN climate summit: Scientists' messages to world leaders
A hat that zaps the scalp with electricity helps reverse male balding
Man sees the world in miniature after a stroke damages his brain
Do dads matter? Anna Machin on the fascinating science of fatherhood
People like the idea of a carbon tax - if the money is put to good use
Climate change will boost risk of extreme flooding in northern Europe
Fast swimming fish robot could perform underwater surveillance
Ad Astra: Pirates and space monkeys can't save dull space psychodrama

## pytorch-glumpy.md

      
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                kastnerkyle
                / pytorch-glumpy.md
            
            
              Created Mar 4, 2020
                — forked from victor-shepardson/pytorch-glumpy.md
            
              
                using pycuda and glumpy to draw pytorch GPU tensors to the screen without copying to host memory
              
          
      View pytorch-glumpy.md
  
      
    Code
https://gist.github.com/victor-shepardson/5b3d3087dc2b4817b9bffdb8e87a57c4
Setup
I'm using Ubuntu 16.04 with a GTX 1060
install CUDA

  
## exact_pg.py
import jax
import jax.numpy as np
from jax import grad, jit
from jax.scipy.special import logsumexp


def dadashi_fig2d():
    """ Figure 2 d) of
    ''The Value Function Polytope in Reinforcement Learning''
    by Dadashi et al. (2019) https://arxiv.org/abs/1901.11524

## grover_large.ipynb

      
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                kastnerkyle
                / grover_large.ipynb
            
            
              Last active Jun 26, 2019
            
              
                Copy of Grover large from https://colab.research.google.com/drive/1edS5Mw_3GSVOs_UfueMayMcRQAPEDmm8?offline=true&sandboxMode=true#scrollTo=Uuh_3mE-fxNZ&forceEdit=true&offline=true&sandboxMode=true

              
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## vloss_single_mcts.py
# Author: Kyle Kastner
# License: BSD 3-Clause

# based on minigo implementation
# https://github.com/tensorflow/minigo/blob/master/mcts.py
# Useful discussion of the benefits
# http://www.moderndescartes.com/essays/agz/
# single player tweaks based on
# https://tmoer.github.io/AlphaZero/
	{
	"seconds_per_quarter": 0.5,
	"parts_names": [
	"Soprano",
	"Alto",
	"Tenor",
	"Bass"
	],
	"parts_cumulative_times": [
	[
	import numpy as np

	# make a minibatch of time, batch, features
	# time length 7
	# batch size 2
	# feature dimension 4:
	# 1:4, 10:14, 20:24, 30:34, etc for first minibatch element
	# 5:8, 15:18, etc second minibatch el
	n_features = 4
	n_timesteps = 7
	#!/usr/bin/env python3
	"""
	To use:
	1. install/set-up the google cloud api and dependencies listed on https://github.com/GoogleCloudPlatform/python-docs-samples/tree/master/texttospeech/cloud-client
	2. install pandoc and pypandoc, also tqdm
	3. create and download a service_account.json ("Service account key") from https://console.cloud.google.com/apis/credentials
	4. run GOOGLE_APPLICATION_CREDENTIALS=service_account.json python make_audiobook.py book_name.epub
	"""
	import re
	import sys
	Some planets may orbit a supermassive black hole instead of a star
	This is almost certainly not what Denisovans looked like
	UN climate summit: Scientists' messages to world leaders
	A hat that zaps the scalp with electricity helps reverse male balding
	Man sees the world in miniature after a stroke damages his brain
	Do dads matter? Anna Machin on the fascinating science of fatherhood
	People like the idea of a carbon tax - if the money is put to good use
	Climate change will boost risk of extreme flooding in northern Europe
	Fast swimming fish robot could perform underwater surveillance
	Ad Astra: Pirates and space monkeys can't save dull space psychodrama
	import jax
	import jax.numpy as np
	from jax import grad, jit
	from jax.scipy.special import logsumexp


	def dadashi_fig2d():
	""" Figure 2 d) of
	''The Value Function Polytope in Reinforcement Learning''
	by Dadashi et al. (2019) https://arxiv.org/abs/1901.11524
	# Author: Kyle Kastner
	# License: BSD 3-Clause

	# based on minigo implementation
	# https://github.com/tensorflow/minigo/blob/master/mcts.py
	# Useful discussion of the benefits
	# http://www.moderndescartes.com/essays/agz/
	# single player tweaks based on
	# https://tmoer.github.io/AlphaZero/