| { 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; |
This dataset contains ~443k anime face images of size 256x256 drawn by ~7,000 artists, obtained from Danbooru
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:
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
| 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() |
| """ | |
| 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 |
| 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: |
| /** | |
| * DC3 (Difference Cover 3) 後綴陣列構造算法 | |
| * | |
| * 算法原理： | |
| * 1. 將所有後綴分為三類：Type A (i%3==0), Type B1 (i%3==1), Type B2 (i%3==2) | |
| * 2. 遞歸處理 Type B 後綴（佔總數的 2/3） | |
| * 3. 利用 Type B 的排序結果來排序 Type A 後綴 | |
| * 4. 歸併 Type A 和 Type B 得到完整的後綴陣列 | |
| * | |
| * 時間複雜度分析： |
| /usr/local/cuda/lib64 |
| # 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, |
