JossWhittle

#pragma once

#include <immintrin.h>

#define I32B __m256i
#define STORE_I32B(ptrD, ptrS) _mm256_store_si256(ptrD, *ptrS)

/**
 * Copies the content of one 32 byte aligned 
 * 64 byte buffer into another

JossWhittle

import numpy as np
from numba import njit, prange

BIT_COUNT_LOOKUP = np.array([bin(i).count('1') for i in range(256)]).astype(np.uint8)

@njit(fastmath=True, nopython=True, parallel=True)
def fast_tanimoto_matrix(fingerprints, progress):
    """
    Compute a symmetric Tanimoto similarity matrix over a set of fingerprints of size (N, F//8).
    Where N is the number of fingerprints, and F is the length of the boolean fingerprint.

JossWhittle

import os
import json
import urllib.request
from tqdm import tqdm
import multiprocessing

with urllib.request.urlopen('https://earthview.withgoogle.com/_api/photos.json') as f:
    slugs = list(map(lambda slug: slug | dict(id=slug['slug'].split('-')[-1]),
                     json.loads(f.read().decode('utf-8'))))

JossWhittle

JossWhittle

FROM frolvlad/alpine-python3

# Build and install numpy
RUN apk add --no-cache \
        --virtual=.build-dependencies \
        g++ gfortran file binutils \
        musl-dev python3-dev cython openblas-dev lapack-dev && \
    apk add libstdc++ openblas lapack && \
    \
    pip install --disable-pip-version-check --no-build-isolation --no-cache-dir numpy==1.18.5 && \

JossWhittle

--- Strings & Numbers ---

<String>        := <Char> | <Char> <String>
<Char>          := <Alpha> | <Digit> | <SpecialChar>
<Alpha>         := "A..Z" | "a..z"
<SpecialChar>   := "." | "," | ";" | ":" | "'" | " " | "#" | "$" | "?"

<Number>        := <NumberPart> | <Sign> <NumberPart>
<NumberPart>    := <IntPart> | <IntPart> "." <DecimalPart>
<IntPart>       := "0" | <NZDigit> | <NZDigit> <DecimalPart>

JossWhittle

# Keep last N weights and one weight every H hours, discard others
old_weight_paths = sorted(list(glob(os.path.join(weight_path, 'style_weights-*.h5'))))

# Iterate over weights from newest to oldest, discard oldest weights if multiple were saved that hour
prev_weight_age = -1
for old_weight_path in reversed(old_weight_paths[:-keep_last_n_weights]):

    # Age of this weight file in hours
    weight_age = int((time() - os.path.getmtime(old_weight_path)) // (60 * 60 * keep_weights_every_n_hours))

JossWhittle

class LinearWarmUpAndCosineDecay(tf.keras.optimizers.schedules.LearningRateSchedule):
    
    def __init__(self, initial_learning_rate, warmup_steps, total_steps, alpha, name=None):
        super(LinearWarmUpAndCosineDecay, self).__init__(name=name)
        self.warmup_steps = warmup_steps
        self.total_steps  = total_steps
        self.alpha        = alpha
        
        self.initial_learning_rate = initial_learning_rate
        self.min_learning_rate     = self.initial_learning_rate * self.alpha

JossWhittle

\usepackage{listings}

\usepackage{textcomp}
\usepackage[utf8]{inputenc}
\usepackage[TS1,T1]{fontenc}
\usepackage[english]{babel}
\usepackage{sourcecodepro}
\usepackage{scrextend}
\addtokomafont{labelinglabel}{\sffamily}
\pdfmapfile{=SourceCodePro.map}

JossWhittle

import numpy as np
import tensorflow as tf

class LogMetrics(tf.keras.callbacks.Callback):
    
    def __init__(self, log_dir, loss, metrics, steps, dataset, training=False):
        super(LogMetrics, self).__init__()
        self.log_dir     = log_dir
        self.metrics     = metrics
        self.steps       = steps