kastnerkyle

def typical_top_k_filtering(logits, top_k=0, top_p=0.0, temperature=1.0, min_tokens_to_keep=1, filter_value=-1E12):
    """ Filter a distribution of logits using typicality, with optional top-k and/or nucleus (top-p) filtering
        Meister et. al. https://arxiv.org/abs/2202.00666
        Args:
            logits: logits distribution shape (..., vocabulary size)
            top_k >0: keep top k tokens with highest prob (top-k filtering).
            top_p >0.0: keep the top p tokens which compose cumulative probability mass top_p (nucleus filtering).
            min_tokens_to_keep >=1: always keep at least this many tokens through the top_p / nucleus sampling
    """
    # https://arxiv.org/abs/2202.00666

kastnerkyle

{
    "seconds_per_quarter": 0.5, 
    "parts_names": [
        "Soprano", 
        "Alto", 
        "Tenor", 
        "Bass"
    ], 
    "parts_cumulative_times": [
        [

kastnerkyle

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

kastnerkyle

kastnerkyle

#!/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

kastnerkyle

#!/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

kastnerkyle

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

kastnerkyle

Code

https://gist.github.com/victor-shepardson/5b3d3087dc2b4817b9bffdb8e87a57c4

Setup

I'm using Ubuntu 16.04 with a GTX 1060

install CUDA

kastnerkyle

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

kastnerkyle