tuuzdu

{ stdenv, fetchurl }:

with stdenv.lib;

let
  arch =
    if stdenv.hostPlatform.system == "x86_64-linux" then "x64"
    else if stdenv.hostPlatform.system == "i686-linux" then "x86"
    else if stdenv.hostPlatform.system == "aarch64-linux" then "armhf"
    else throwSystem;

stormraiser

Danbooru Faces v0.1

Discription

This dataset contains ~443k anime face images of size 256x256 drawn by ~7,000 artists, obtained from Danbooru

Collection

We first downloaded JSON files of all existing posts numbered from 1 to 2,800,000 using their API. We filtered the posts by the following criteria:

jganzabal

This configuration worked for me, hope it helps

It is based on: https://becominghuman.ai/deep-learning-gaming-build-with-nvidia-titan-xp-and-macbook-pro-with-thunderbolt2-5ceee7167f8b

and on: https://stackoverflow.com/questions/44744737/tensorflow-mac-os-gpu-support

Hardware

joelthchao

import numpy as np
import tensorflow as tf
from keras.callbacks import TensorBoard
from keras.layers import Input, Dense
from keras.models import Model


def write_log(callback, names, logs, batch_no):
    for name, value in zip(names, logs):
        summary = tf.Summary()

t-ae

"""
The MIT License (MIT)

Copyright (c) 2018 Takehiro Araki.

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

cbaziotis

from keras import backend as K, initializers, regularizers, constraints
from keras.engine.topology import Layer


def dot_product(x, kernel):
    """
    Wrapper for dot product operation, in order to be compatible with both
    Theano and Tensorflow
    Args:

jacky860226

#define F(x) (x)/3+((x)%3==1?0:tb)
#define G(x) (x)<tb?(x)*3+1:((x)-tb)*3+2
int wa[3*maxn+5],wb[3*maxn+5],wv[3*maxn+5],c[maxn+5];
inline bool c0(int *s,int a,int b){
	return s[a]==s[b]&&s[a+1]==s[b+1]&&s[a+2]==s[b+2];
}
inline bool c12(int k,int *s,int a,int b){
	if(k==2)return s[a]<s[b]||s[a]==s[b]&&c12(1,s,a+1,b+1);
	else return s[a]<s[b]||s[a]==s[b]&&wv[a+1]<wv[b+1];
}

amchercashin

/usr/local/cuda/lib64

udibr

# variation to https://github.com/ryankiros/skip-thoughts/blob/master/decoding/search.py

def keras_rnn_predict(samples, empty=empty, rnn_model=model, maxlen=maxlen):
    """for every sample, calculate probability for every possible label
    you need to supply your RNN model and maxlen - the length of sequences it can handle
    """
    data = sequence.pad_sequences(samples, maxlen=maxlen, value=empty)
    return rnn_model.predict(data, verbose=0)

def beamsearch(predict=keras_rnn_predict,

ryerh

注意：本文内容适用于 Tmux 2.3 及以上的版本，但是绝大部分的特性低版本也都适用，鼠标支持、VI 模式、插件管理在低版本可能会与本文不兼容。

Tmux 快捷键 & 速查表 & 简明教程

启动新会话：

tmux [new -s 会话名 -n 窗口名]

恢复会话：