brew install mariadb
brew services start mariadb

mkdir -p /usr/local/etc/my.cnf.d

after started

mysql -uroot
create database db1;
use db1;

CREATE USER 'user1'@'localhost' IDENTIFIED BY 'password';
GRANT ALL PRIVILEGES ON db1.* TO 'user1'@'localhost';

Also check

mariadb: missing /usr/local/etc/my.cnf.d, removed by brew prune

tensorflow/benchmarks

MacbookPro '13 Core i5-8259U CPU 2.30GHz
Python 3.6.6

This is my command and arguments

python tf_cnn_benchmarks.py --batch_size=512 --model=alexnet --data_format=NHWC --device=cpu

here the result

TensorFlow: 1.14
Model: alexnet
Dataset: imagenet (synthetic)
Mode: training
SingleSess: False
Batch size: 512 global
512 per device
Num batches: 100
Num epochs: 0.04
Devices: ['/cpu:0']
NUMA bind: False
Data format: NHWC
Optimizer: sgd
Variables: parameter_server
==========

Step Img/sec total_loss
1 images/sec: 47.8 +/- 0.0 (jitter = 0.0) nan
10 images/sec: 47.7 +/- 0.1 (jitter = 0.4) nan
20 images/sec: 47.7 +/- 0.1 (jitter = 0.3) nan
30 images/sec: 47.7 +/- 0.1 (jitter = 0.3) nan
40 images/sec: 47.7 +/- 0.1 (jitter = 0.3) nan
50 images/sec: 47.7 +/- 0.0 (jitter = 0.3) nan
60 images/sec: 47.7 +/- 0.0 (jitter = 0.4) nan
70 images/sec: 47.7 +/- 0.0 (jitter = 0.4) nan
80 images/sec: 47.6 +/- 0.0 (jitter = 0.4) nan
90 images/sec: 47.5 +/- 0.1 (jitter = 0.5) nan
100 images/sec: 47.5 +/- 0.1 (jitter = 0.5) nan
----------------------------------------------------------------
total images/sec: 47.52
----------------------------------------------------------------

Japanese version

Building and Installing TensorFlow from Source on Your Mac

$ python sr1_train.py                                                                                                                19-05-10
start: 2019-05-10 21:19:46.533988
Found 100 images belonging to 1 classes.
WARNING:tensorflow:From /Users/tak/.pyenv/versions/3.7.3/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, None, None, 64)    15616     
_________________________________________________________________
conv2d_1 (Conv2D)            (None, None, None, 32)    2080      
_________________________________________________________________
conv2d_2 (Conv2D)            (None, None, None, 3)     2403      
=================================================================
Total params: 20,099
Trainable params: 20,099
Non-trainable params: 0
_________________________________________________________________
WARNING:tensorflow:From /Users/tak/.pyenv/versions/3.7.3/lib/python3.7/site-packages/tensorflow/python/keras/utils/losses_utils.py:170: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
WARNING:tensorflow:From /Users/tak/.pyenv/versions/3.7.3/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
2019-05-10 21:19:47.656675: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
Found 1000 images belonging to 1 classes.
Epoch 1/10
100/100 [==============================] - 5s 47ms/sample - loss: 0.0215 - psnr: 16.4868
31/31 [==============================] - 137s 4s/step - loss: 0.0663 - psnr: 14.1137 - val_loss: 0.0226 - val_psnr: 16.4868
Epoch 2/10
100/100 [==============================] - 5s 47ms/sample - loss: 0.0122 - psnr: 18.7200
31/31 [==============================] - 133s 4s/step - loss: 0.0151 - psnr: 18.2903 - val_loss: 0.0139 - val_psnr: 18.7200
Epoch 3/10
100/100 [==============================] - 5s 48ms/sample - loss: 0.0097 - psnr: 19.6909
31/31 [==============================] - 136s 4s/step - loss: 0.0101 - psnr: 20.0132 - val_loss: 0.0112 - val_psnr: 19.6909
Epoch 4/10
100/100 [==============================] - 5s 47ms/sample - loss: 0.0079 - psnr: 20.5231
31/31 [==============================] - 135s 4s/step - loss: 0.0078 - psnr: 21.1236 - val_loss: 0.0094 - val_psnr: 20.5231
Epoch 5/10
100/100 [==============================] - 5s 48ms/sample - loss: 0.0068 - psnr: 21.1324
31/31 [==============================] - 136s 4s/step - loss: 0.0067 - psnr: 21.7721 - val_loss: 0.0082 - val_psnr: 21.1324
Epoch 6/10
100/100 [==============================] - 5s 48ms/sample - loss: 0.0063 - psnr: 21.4806
31/31 [==============================] - 135s 4s/step - loss: 0.0059 - psnr: 22.2806 - val_loss: 0.0076 - val_psnr: 21.4806
Epoch 7/10
100/100 [==============================] - 5s 48ms/sample - loss: 0.0061 - psnr: 21.6416
31/31 [==============================] - 135s 4s/step - loss: 0.0053 - psnr: 22.8222 - val_loss: 0.0074 - val_psnr: 21.6416
Epoch 8/10
100/100 [==============================] - 5s 48ms/sample - loss: 0.0059 - psnr: 21.7736
31/31 [==============================] - 135s 4s/step - loss: 0.0056 - psnr: 22.6185 - val_loss: 0.0071 - val_psnr: 21.7736
Epoch 9/10
100/100 [==============================] - 5s 47ms/sample - loss: 0.0055 - psnr: 22.0847
31/31 [==============================] - 135s 4s/step - loss: 0.0053 - psnr: 22.8264 - val_loss: 0.0067 - val_psnr: 22.0847
Epoch 10/10
100/100 [==============================] - 5s 48ms/sample - loss: 0.0055 - psnr: 22.0591
31/31 [==============================] - 135s 4s/step - loss: 0.0050 - psnr: 23.0951 - val_loss: 0.0067 - val_psnr: 22.0591
Saved model to disk
finish: 2019-05-10 21:42:19.937003

It took 23minutes, after updated.

$ python sr1_train.py                                                                                                                19-05-10
start: 2019-05-10 21:50:57.965290
Found 100 images belonging to 1 classes.
WARNING: Logging before flag parsing goes to stderr.
W0510 21:50:58.497403 4615673280 deprecation.py:506] From /Users/tak/.pyenv/versions/3.7.3/lib/python3.7/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, None, None, 64)    15616     
_________________________________________________________________
conv2d_1 (Conv2D)            (None, None, None, 32)    2080      
_________________________________________________________________
conv2d_2 (Conv2D)            (None, None, None, 3)     2403      
=================================================================
Total params: 20,099
Trainable params: 20,099
Non-trainable params: 0
_________________________________________________________________
Epoch 1/10
Found 1000 images belonging to 1 classes.
31/31 [==============================] - 86s 3s/step - loss: 0.0619 - psnr: 14.1663 - val_loss: 0.0226 - val_psnr: 16.5717
Epoch 2/10
31/31 [==============================] - 83s 3s/step - loss: 0.0152 - psnr: 18.2177 - val_loss: 0.0157 - val_psnr: 18.2270
Epoch 3/10
31/31 [==============================] - 83s 3s/step - loss: 0.0110 - psnr: 19.6592 - val_loss: 0.0121 - val_psnr: 19.4047
Epoch 4/10
31/31 [==============================] - 84s 3s/step - loss: 0.0082 - psnr: 20.9181 - val_loss: 0.0098 - val_psnr: 20.3380
Epoch 5/10
31/31 [==============================] - 84s 3s/step - loss: 0.0071 - psnr: 21.5562 - val_loss: 0.0089 - val_psnr: 20.8145
Epoch 6/10
31/31 [==============================] - 84s 3s/step - loss: 0.0066 - psnr: 21.8354 - val_loss: 0.0083 - val_psnr: 21.1056
Epoch 7/10
31/31 [==============================] - 84s 3s/step - loss: 0.0060 - psnr: 22.2487 - val_loss: 0.0086 - val_psnr: 20.8924
Epoch 8/10
31/31 [==============================] - 84s 3s/step - loss: 0.0065 - psnr: 21.9428 - val_loss: 0.0078 - val_psnr: 21.3960
Epoch 9/10
31/31 [==============================] - 84s 3s/step - loss: 0.0057 - psnr: 22.5164 - val_loss: 0.0075 - val_psnr: 21.5522
Epoch 10/10
31/31 [==============================] - 84s 3s/step - loss: 0.0053 - psnr: 22.8135 - val_loss: 0.0071 - val_psnr: 21.8321
Saved model to disk
finish: 2019-05-10 22:04:58.067069

It took 14minutes.

I got several enhancement.

start: 2019-05-06 14:56:52.219573
Found 100 images belonging to 1 classes.
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, None, None, 64)    15616     
_________________________________________________________________
conv2d_1 (Conv2D)            (None, None, None, 32)    2080      
_________________________________________________________________
conv2d_2 (Conv2D)            (None, None, None, 3)     2403      
=================================================================
Total params: 20,099
Trainable params: 20,099
Non-trainable params: 0
_________________________________________________________________
Epoch 1/50
Found 1000 images belonging to 1 classes.
2019-05-06 14:56:53.497344: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
31/31 [==============================] - 135s 4s/step - loss: 0.0433 - psnr: 15.1995 - val_loss: 0.0171 - val_psnr: 17.7111
Epoch 2/50
31/31 [==============================] - 131s 4s/step - loss: 0.0126 - psnr: 19.0475 - val_loss: 0.0110 - val_psnr: 19.6478
Epoch 3/50
31/31 [==============================] - 130s 4s/step - loss: 0.0085 - psnr: 20.7746 - val_loss: 0.0079 - val_psnr: 21.1229
Epoch 4/50
31/31 [==============================] - 130s 4s/step - loss: 0.0065 - psnr: 21.9326 - val_loss: 0.0067 - val_psnr: 21.8849
Epoch 5/50
31/31 [==============================] - 132s 4s/step - loss: 0.0059 - psnr: 22.3524 - val_loss: 0.0063 - val_psnr: 22.1181
Epoch 6/50
31/31 [==============================] - 139s 4s/step - loss: 0.0056 - psnr: 22.5829 - val_loss: 0.0063 - val_psnr: 22.1250
Epoch 7/50
31/31 [==============================] - 133s 4s/step - loss: 0.0053 - psnr: 22.7901 - val_loss: 0.0055 - val_psnr: 22.7345
Epoch 8/50
31/31 [==============================] - 133s 4s/step - loss: 0.0050 - psnr: 23.0820 - val_loss: 0.0056 - val_psnr: 22.6496
Epoch 9/50
31/31 [==============================] - 133s 4s/step - loss: 0.0049 - psnr: 23.1215 - val_loss: 0.0052 - val_psnr: 22.9906
Epoch 10/50
31/31 [==============================] - 132s 4s/step - loss: 0.0047 - psnr: 23.3423 - val_loss: 0.0052 - val_psnr: 23.0307
Epoch 11/50
31/31 [==============================] - 133s 4s/step - loss: 0.0046 - psnr: 23.4907 - val_loss: 0.0050 - val_psnr: 23.2160
Epoch 12/50
31/31 [==============================] - 132s 4s/step - loss: 0.0046 - psnr: 23.4611 - val_loss: 0.0049 - val_psnr: 23.2698
Epoch 13/50
31/31 [==============================] - 132s 4s/step - loss: 0.0047 - psnr: 23.3354 - val_loss: 0.0049 - val_psnr: 23.2364
Epoch 14/50
31/31 [==============================] - 132s 4s/step - loss: 0.0042 - psnr: 23.8382 - val_loss: 0.0046 - val_psnr: 23.5162
Epoch 15/50
31/31 [==============================] - 132s 4s/step - loss: 0.0041 - psnr: 23.9200 - val_loss: 0.0053 - val_psnr: 22.9095
Epoch 16/50
31/31 [==============================] - 132s 4s/step - loss: 0.0048 - psnr: 23.2758 - val_loss: 0.0048 - val_psnr: 23.3866
Epoch 17/50
31/31 [==============================] - 132s 4s/step - loss: 0.0041 - psnr: 23.9022 - val_loss: 0.0045 - val_psnr: 23.6750
Epoch 18/50
31/31 [==============================] - 132s 4s/step - loss: 0.0043 - psnr: 23.7698 - val_loss: 0.0047 - val_psnr: 23.4511
Epoch 19/50
31/31 [==============================] - 132s 4s/step - loss: 0.0043 - psnr: 23.7114 - val_loss: 0.0046 - val_psnr: 23.5902
Epoch 20/50
31/31 [==============================] - 133s 4s/step - loss: 0.0039 - psnr: 24.1346 - val_loss: 0.0044 - val_psnr: 23.7893
Epoch 21/50
31/31 [==============================] - 133s 4s/step - loss: 0.0039 - psnr: 24.1195 - val_loss: 0.0043 - val_psnr: 23.8258
Epoch 22/50
31/31 [==============================] - 132s 4s/step - loss: 0.0040 - psnr: 24.1192 - val_loss: 0.0042 - val_psnr: 23.9291
Epoch 23/50
31/31 [==============================] - 132s 4s/step - loss: 0.0042 - psnr: 23.8847 - val_loss: 0.0044 - val_psnr: 23.7253
Epoch 24/50
31/31 [==============================] - 132s 4s/step - loss: 0.0040 - psnr: 24.0788 - val_loss: 0.0042 - val_psnr: 23.9688
Epoch 25/50
31/31 [==============================] - 132s 4s/step - loss: 0.0041 - psnr: 23.9862 - val_loss: 0.0042 - val_psnr: 23.9878
Epoch 26/50
31/31 [==============================] - 132s 4s/step - loss: 0.0040 - psnr: 24.0689 - val_loss: 0.0053 - val_psnr: 22.8600
Epoch 27/50
31/31 [==============================] - 133s 4s/step - loss: 0.0038 - psnr: 24.3128 - val_loss: 0.0043 - val_psnr: 23.8061
Epoch 28/50
31/31 [==============================] - 143s 5s/step - loss: 0.0040 - psnr: 24.0586 - val_loss: 0.0043 - val_psnr: 23.8934
Epoch 29/50
31/31 [==============================] - 149s 5s/step - loss: 0.0038 - psnr: 24.2733 - val_loss: 0.0041 - val_psnr: 24.0404
Epoch 30/50
31/31 [==============================] - 138s 4s/step - loss: 0.0037 - psnr: 24.4183 - val_loss: 0.0040 - val_psnr: 24.1261
Epoch 31/50
31/31 [==============================] - 138s 4s/step - loss: 0.0039 - psnr: 24.1324 - val_loss: 0.0045 - val_psnr: 23.6627
Epoch 32/50
31/31 [==============================] - 141s 5s/step - loss: 0.0040 - psnr: 24.0463 - val_loss: 0.0046 - val_psnr: 23.6026
Epoch 33/50
31/31 [==============================] - 136s 4s/step - loss: 0.0039 - psnr: 24.1457 - val_loss: 0.0040 - val_psnr: 24.1586
Epoch 34/50
31/31 [==============================] - 133s 4s/step - loss: 0.0037 - psnr: 24.3215 - val_loss: 0.0040 - val_psnr: 24.1620
Epoch 35/50
31/31 [==============================] - 133s 4s/step - loss: 0.0037 - psnr: 24.3987 - val_loss: 0.0046 - val_psnr: 23.4779
Epoch 36/50
31/31 [==============================] - 133s 4s/step - loss: 0.0039 - psnr: 24.1829 - val_loss: 0.0040 - val_psnr: 24.1676
Epoch 37/50
31/31 [==============================] - 132s 4s/step - loss: 0.0037 - psnr: 24.4163 - val_loss: 0.0044 - val_psnr: 23.7255
Epoch 38/50
31/31 [==============================] - 133s 4s/step - loss: 0.0038 - psnr: 24.2440 - val_loss: 0.0041 - val_psnr: 24.1112
Epoch 39/50
31/31 [==============================] - 133s 4s/step - loss: 0.0038 - psnr: 24.3567 - val_loss: 0.0041 - val_psnr: 24.0044
Epoch 40/50
31/31 [==============================] - 135s 4s/step - loss: 0.0036 - psnr: 24.5425 - val_loss: 0.0040 - val_psnr: 24.1534
Epoch 41/50
31/31 [==============================] - 133s 4s/step - loss: 0.0037 - psnr: 24.4058 - val_loss: 0.0039 - val_psnr: 24.2825
Epoch 42/50
31/31 [==============================] - 133s 4s/step - loss: 0.0037 - psnr: 24.4329 - val_loss: 0.0040 - val_psnr: 24.1835
Epoch 43/50
31/31 [==============================] - 133s 4s/step - loss: 0.0037 - psnr: 24.3790 - val_loss: 0.0039 - val_psnr: 24.3117
Epoch 44/50
31/31 [==============================] - 133s 4s/step - loss: 0.0037 - psnr: 24.4384 - val_loss: 0.0039 - val_psnr: 24.2318
Epoch 45/50
31/31 [==============================] - 133s 4s/step - loss: 0.0036 - psnr: 24.5098 - val_loss: 0.0044 - val_psnr: 23.7762
Epoch 46/50
31/31 [==============================] - 133s 4s/step - loss: 0.0038 - psnr: 24.2288 - val_loss: 0.0038 - val_psnr: 24.3576
Epoch 47/50
31/31 [==============================] - 133s 4s/step - loss: 0.0036 - psnr: 24.4672 - val_loss: 0.0042 - val_psnr: 23.9391
Epoch 48/50
31/31 [==============================] - 133s 4s/step - loss: 0.0038 - psnr: 24.3314 - val_loss: 0.0039 - val_psnr: 24.2399
Epoch 49/50
31/31 [==============================] - 133s 4s/step - loss: 0.0034 - psnr: 24.6795 - val_loss: 0.0039 - val_psnr: 24.2486
Epoch 50/50
31/31 [==============================] - 133s 4s/step - loss: 0.0035 - psnr: 24.6281 - val_loss: 0.0039 - val_psnr: 24.3364
Saved model to disk
finish: 2019-05-06 16:48:15.757484

It took almost 2hours in iMac4k i7 5775R

I think resolution was a little bit increased, but got square pattern.

brew install zlib

export LDFLAGS="${LDFLAGS} -L/usr/local/opt/zlib/lib"
export CPPFLAGS="${CPPFLAGS} -I/usr/local/opt/zlib/include"
export PKG_CONFIG_PATH="${PKG_CONFIG_PATH} /usr/local/opt/zlib/lib/pkgconfig"

pyenv install 3.7.3
pyenv global 3.7.3

pip install numpy matplotlib keras Pillow tensorflow

pyenv/pyenv

ログインシェルをfishにしてみる

ログインシェルをfishにしてみる - Qiita

Shell Translation Dictionary

fish-shell/fish-shell

$ pyenv init
# Load pyenv automatically by appending
# the following to ~/.config/fish/config.fish:

status --is-interactive; and source (pyenv init -|psub)

status --is-interactive; and source (pyenv init -|psub)

Mouse disappear in VirtualBox

VirtualBox上のLinuxにGuestAdditionsをインストールして、共有フォルダを設定する

VirtualBox上のLinuxにGuestAdditionsをインストールして、共有フォルダを設定する - Qiita

Install VirtualBox Guest Additions in CentOS, RHEL & Fedora

Install VirtualBox Guest Additions in CentOS, RHEL & Fedora

sudo mkdir /mnt/guest_dir
sudo mount -t vboxsf host_dir /mnt/guest_dir

edit build.gradle

dependencies {
    testCompile group: 'junit', name: 'junit', version: '4.12'
    testCompile group: 'org.seleniumhq.selenium', name: 'selenium-java', version: '3.14.0'
}

download geckodriver to current directory

create java file

import org.junit.Test;
import org.openqa.selenium.By;
import org.openqa.selenium.WebDriver;
import org.openqa.selenium.firefox.FirefoxDriver;

public class SeleniumTest {

@Test
    public void test1() {
        System.setProperty("webdriver.gecko.driver", "./geckodriver");
        WebDriver driver = new FirefoxDriver();
        driver.get("http://localhost:8080/");
        driver.quit();
    }
}

plugins {
    id 'java'
}

group 'com.github.horitaku1124'
version '1.0-SNAPSHOT'

sourceCompatibility = 1.8

repositories {
    mavenCentral()
}

dependencies {
    compile 'org.apache.lucene:lucene-core:7.4.0'
    compile 'org.apache.lucene:lucene-queryparser:7.4.0'
}

Use these demo files

Location of the source

brew install mariadb
brew services start mariadb
brew services stop mariadb

sudo mysql.server start

or Ubuntu

sudo apt-get install mariadb-server mariadb-plugin-mroonga

setting on mariadb

> INSTALL SONAME 'ha_mroonga';
> SHOW ENGINES;
> CREATE FUNCTION last_insert_grn_id RETURNS INTEGER SONAME 'ha_mroonga.so';

Create table using mroonga

CREATE TABLE `title_indexed` (
  `id` int(11) NOT NULL,
  `title` TEXT DEFAULT '',
  FULLTEXT(`title`),
  PRIMARY KEY (`id`)
) ENGINE=Mroonga DEFAULT CHARSET=utf8;

Now, able to search

SELECT * FROM title_indexed WHERE MATCH(`title`) AGAINST('あいうえお');