[JAVA] [LSTM text generation] Use ml5js
Day 24 of Javascript Advent Calendar 2019
What is LSTM?
The first character is given to the model that learned the sentence data, and the next character is guessed.
Library used
ml5js ml5 It uses the tensorflow model and makes it easy to handle with js.
tensorflow It is used for model generation. tensorflow
Advance preparation
What to install
- Python --Needed to run tensorflow
- Anaconda --Python distribution. A collection of libraries used in machine learning and scientific calculations. Python itself is also installed.
- pip --Used to install Python packages (TensorFlow is included with pip)
- nodejs --ml5 library Used to move the front end side
I installed Python with Anaconda and TensorFlow with pip.
environment
OS:windows 10 (It's a gaming PC. I thought it would take a certain amount of load.)
Training set
Download here training-charRNN
git clone https://github.com/ml5js/training-charRNN.git
Prepare the data
This is the most difficult place both later and earlier, but I will prepare appropriate text data so that I can move it for the time being.
Preparing Japanese data is difficult to separate phrases, so let's use English data. I will summarize Japanese at a later date.
File name: input.txt Location: training-lstm-master / [any_holder_name] /
Enter the text in input.txt and save it.
reference data
Free English novels and stories are available. If you want to use it as a trial, I think you can just prepare the original data from here. Project Gutenberg
Train
--Launch Anaconda Prompt --Move to the directory of the training set.
cd training-lstm-master
--Train the created data
python train.py --data_path=./[any_holder_name]/input.txt
After this training starts and ends, a file is generated in models / input /.
Generated files
- embedding
- manifest.json
- rnnlm_multi_rnn_cell_cell_0_basic_lstm_cell_bias
- rnnlm_multi_rnn_cell_cell_0_basic_lstm_cell_kernel
- rnnlm_multi_rnn_cell_cell_1_basic_lstm_cell_bias
- rnnlm_multi_rnn_cell_cell_1_basic_lstm_cell_kernel
- rnnlm_softmax_b
- rnnlm_softmax_w
- Variable
- vocab.json
We will use this file group in ml5-examples which will be explained later.
If an error occurs
I had the latest version of tensorflow, but I was angry. (I'm sorry for the error content at that time, but it has become a thousand winds.)
I had no choice but to put ** tensorflow 1.15.0 **.
I have to rewrite it to a 1-series function, and the following of train.py is modified.
# hide logs
tf.logging.set_verbosity(tf.logging.ERROR)
↓
# hide logs
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
I haven't talked about the front end yet, so I'm worried that this is good for Advent ...
front end
I finally got to the story of javascript. Now, let's use the model created earlier.
Move files
Download here ml5-examples
git clone https://github.com/ml5js/ml5-examples.git
I'm using the nodejs package, so it's the usual one
npm install
Move files
Move the files under models / input / generated earlier.
Destination https://github.com/ml5js/ml5-examples/tree/release/p5js/CharRNN/CharRNN_Text/models/woolf
- It is recommended that the input folder be a folder name that matches the text.
Example) Text about cats ml5-examples-master/p5js/CharRNN/CharRNN_Text/models/cat
Text about fortune-telling ml5-examples-master/p5js/CharRNN/CharRNN_Text/models/horoscope
Specify model
Since models is specified in sketch.js, replace "woolf" with the folder name created under models earlier.
charRNN = ml5.charRNN('./models/woolf/', modelReady);
https://github.com/ml5js/ml5-examples/blob/release/p5js/CharRNN/CharRNN_Text/sketch.js#L24
Launch local server
python 3 series
python -m http.server
python 2 series
python -m SimpleHTTPServer
If you open http: // localhost: 8000 / you will see the directory.
Use CharRNN Text
Click pl5js
Click CharRNN
Click CharRNN_Text
You will reach the Generator screen.
seed text: The first character to give length: number of characters you want to generate temperature: Weight / depth
seed text:happy length：100 Sentence generated at temperature: 0.5
happy and the other grown herself, 'I was than the bottle my to little sing how the poor comly up and gut
seed text:happy length：100 Sentence generated by temperature: 1
happys! All spomes wife a 'How finE it?' said 'Hares" should neven backed as much had right gaim--'
The text is incoherent.
What sentence did I use as data? You can find hints in the latter generated text.
Hint "Rabbit".
Answer: Alice's Adventures in Wonderland
The latter sentence has a temperature of 1, so it should be more like Alice's Adventures in Wonderland than the former (temperature: 0.5).
Certainly, I feel that the "rabbit" and "rights" have that kind of feeling.
I want to find out more about ml5.CharRNN.
If you use mecab and prepare Japanese sentence data, you may be able to use Japanese sentences! I heard that, so I will take time to try it. It's not completely about javascript lol
Reference article
-[4_1_2: LSTM training (creating "Alice in Wonderland" model) ml5.js JavaScript](https://himco.jp/2018/12/31/4_1_2%EF%BC%9Alstm%E3%81 % AE% E8% A8% 93% E7% B7% B4% E3% 80% 8C% E4% B8% 8D% E6% 80% 9D% E8% AD% B0% E3% 81% AE% E5% 9B% BD % E3% 81% AE% E3% 82% A2% E3% 83% AA% E3% 82% B9% E3% 80% 8D% E3% 83% A2% E3% 83% 87% E3% 83% AB% E3 % 81% AE% E4% BD% 9C% E6% 88% 90 /) -[4_1_1: LSTM text generation sample ml5.js JavaScript](https://himco.jp/2018/12/30/4_1_1%EF%BC%9Alstm-%E6%96%87%E7%AB%A0%E7 % 94% 9F% E6% 88% 90% E3% 82% B5% E3% 83% B3% E3% 83% 97% E3% 83% AB /)