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Starbeamrainbowlabs 2020-09-15 18:32:51 +01:00
parent 5b583fc3a2
commit 151d218102
Signed by: sbrl
GPG key ID: 1BE5172E637709C2
10 changed files with 1104 additions and 1 deletions
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datasets/
output/
# Created by https://www.toptal.com/developers/gitignore/api/node,git,archives,images
# Edit at https://www.toptal.com/developers/gitignore?templates=node,git,archives,images

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}

6
package.json
View file

@ -18,5 +18,9 @@
"films"
],
"author": "Starbeamrainbowlabs",
"license": "MPL-2.0"
"license": "MPL-2.0",
"dependencies": {
"@tensorflow/tfjs-node": "^2.3.0",
"applause-cli": "^1.3.3"
}
}

41
src/cli.mjs Normal file
View file

@ -0,0 +1,41 @@
"use strict";
import path from 'path';
import CliParser from 'applause-cli';
import train from './subcommands/train/train.mjs';
import predict from './subcommands/predict/predict.mjs';
const __dirname = import.meta.url.slice(7, import.meta.url.lastIndexOf("/"));
export default async function () {
let cli = new CliParser(path.resolve(__dirname, "../package.json"));
cli.subcommand("train", "Trains a new AI")
.argument("input", "The input directory containing the training data", null, "string")
.argument("output", "Path to the output directory to save the trained AI to");
cli.subcommand("predict", "Predicts the genres of the specified image")
.argument("input", "Path to the input image")
.argument("ai-model", "Path to the saved AI model to load");
let settings = cli.parse(process.argv.slice(2));
if(cli.current_subcommand == null) {
cli.write_help_exit();
return;
}
switch(cli.current_subcommand) {
case "train":
await train(settings);
break;
case "predict":
await predict(settings);
break;
default:
console.error(`Error: Unknown subcommand '${cli.current_subcommand}' (try --help for usage information)`);
process.exit(1);
break;
}
}

10
src/index.mjs
View file

@ -0,0 +1,10 @@
#!/usr/bin/env node
"use strict";
import cli from './cli.mjs';
(async () => {
"use strict";
await cli();
})();

80
src/lib/DataPreprocessor.mjs Normal file
View file

@ -0,0 +1,80 @@
"use strict";
import fs from 'fs';
import path from 'path';
import tf from '@tensorflow/tfjs-node';
import genres from './Genres.mjs';
class DataPreprocessor {
constructor(dir_input) {
if(!fs.existsSync(dir_input))
throw new Error(`Error: The input directory '${dir_input}' does not exist.`);
this.dir_input_train = path.join(dir_input, "train");
this.dir_input_validate = path.join(dir_input, "validate");
if(!fs.existsSync(this.dir_input_train))
throw new Error(`Error: Failed to locate the directory containing the training data.`);
if(!fs.existsSync(this.dir_input_validate))
throw new Error(`Error: Failed to locate the directory containing the validation data.`);
this.genres_count = genres.length;
}
genre2id(genre_name) {
return genres.indexOf(genre_name);
}
id2genre(id) {
return genres[id];
}
async *data_from_dir(dirpath) {
if(!fs.existsSync(dirpath))
throw new Error(`[DataPreprocessor/dataset_dir] Error: The specified directory does not exist.`);
let filenames = await fs.promises.readdir(dirpath);
for(let filename of filenames) {
let imagetensor = tf.node.decodeImage(
await fs.promises.readFile(path.join(dirpath, filename)),
3 // channels
);
if(imagetensor.shape[0] == 256
&& imagetensor.shape[1] == 256
&& imagetensor.shape[0] == 3)
throw new Error(`Error: Loaded image has a shape of [${imagetensor.shape.join(", ")}], but a shape of [ 256, 256, 3 ] was expected.`);
// Strip the file extension, then split into an array of genres, and finally remove the id from the beginning
let next_genres = filename.replace(/\.[a-zA-Z]+$/, "")
.split(",")
.slice(1)
.map(this.genre2id);
let genres_pretensor = Array(this.genres_count).fill(0);
for(let genre_id of next_genres)
genres_pretensor[genre_id] = 1;
let genres_tensor = tf.tensor(genres_pretensor);
// console.log(`>>>>>>>>> output shapes: ${imagetensor.shape}, ${genres_tensor.shape}`);
yield {
xs: imagetensor,
ys: genres_tensor
};
}
}
dataset_train() {
return tf.data.generator(this.data_from_dir.bind(this, this.dir_input_train));
}
dataset_validate() {
return tf.data.generator(this.data_from_dir.bind(this, this.dir_input_validate));
}
}
export default DataPreprocessor;

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"use strict";
import path from 'path';
import fs from 'fs';
import tf from '@tensorflow/tfjs-node';
import genres from './Genres.mjs';
class FilmPredictor {
constructor(settings) {
this.settings = settings;
this.genres = genres.length;
this.batch_size = 32;
this.prefetch = 4;
}
async init(dirpath = null) {
if(!fs.existsSync(this.settings.output))
await fs.promises.mkdir(this.settings.output, { recursive: true, mode: 0o755 });
this.dir_checkpoints = path.join(this.settings.output, "checkpoints");
if(!fs.existsSync(this.dir_checkpoints))
await fs.promises.mkdir(this.dir_checkpoints, { recursive: true, mode: 0o755 });
if(dirpath !== null)
await this.load_model(dirpath);
else
this.make_model();
this.model.summary();
}
async load_model(dirpath) {
if(!fs.existsSync(dirpath)) {
throw new Error(`Error: The directory ${dirpath} doesn't exist.`);
}
console.error(`>>> Loading model from '${dirpath}'`);
this.model = await this.tf.loadLayersModel(`file://${dirpath}`);
console.error(`>>> Model loading complete`);
}
make_model() {
console.error(`>>> Creating new model`);
this.model = tf.sequential();
this.model.add(tf.layers.conv2d({
name: "conv2d_1",
dataFormat: "channelsLast",
inputShape: [ 256, 256, 3 ],
kernelSize: 5,
filters: 3,
strides: 2,
activation: "relu"
}));
this.model.add(tf.layers.conv2d({
name: "conv2d_2",
dataFormat: "channelsLast",
kernelSize: 5,
filters: 3,
strides: 2,
activation: "relu"
}));
this.model.add(tf.layers.conv2d({
name: "conv2d_3",
dataFormat: "channelsLast",
kernelSize: 5,
filters: 3,
strides: 2,
activation: "relu"
}));
// Reshape and flatten
let cnn_stack_output_shape = this.model.getLayer("conv2d_3").outputShape;
this.model.add(tf.layers.reshape({
name: "reshape",
targetShape: [
cnn_stack_output_shape[1] * cnn_stack_output_shape[2] * cnn_stack_output_shape[3]
]
}));
this.model.add(tf.layers.dense({
name: "dense",
units: this.genres,
activation: "sigmoid" // If you're only predicting a single label at a time, then choose "softmax" instead
}));
this.model.compile({
optimizer: tf.train.adam(),
loss: "meanSquaredError", // we want the root mean squared error, but Tensorflow.js doesn't have an option for that so we'll do it in post when graphing with jq
metrics: [ "mse" /* meanSquaredError */ ]
});
}
async train(dataset_train, dataset_validate) {
dataset_train = dataset_train.batch(this.batch_size).prefetch(this.prefetch);
dataset_validate = dataset_validate.batch(this.batch_size).prefetch(this.prefetch);
await this.model.fitDataset(dataset_train, {
epochs: 50,
verbose: 1,
validationData: dataset_validate,
yieldEvery: "batch",
shuffle: false,
callbacks: {
onEpochEnd: async (epoch, metrics) => {
console.error(`>>> Epoch ${epoch} complete, metrics:`, metrics);
let dir_output_checkpoint = `file://${path.join(this.dir_checkpoints, `${epoch.toString()}`)}`;
await Promise.all([
this.model.save(dir_output_checkpoint),
fs.promises.appendFile(
path.join(this.settings.output, `metrics.stream.json`), JSON.stringify(metrics) + "\n"
)
]);
}
}
})
}
async predict(image) {
}
}
export default FilmPredictor;

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"use strict";
export default [
"Action",
"Adventure",
"Animation",
"Biography",
"Comedy",
"Crime",
"Documentary",
"Drama",
"Family",
"Fantasy",
"Film-Noir",
"History",
"Horror",
"Music",
"Musical",
"Mystery",
"Romance",
"Sci-Fi",
"Short",
"Sport",
"Thriller",
"War",
"Western"
];

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"use strict";
export default async function(settings) {
}

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"use strict";
import fs from 'fs';
import DataPreprocessor from '../../lib/DataPreprocessor.mjs';
import FilmPredictor from '../../lib/FilmPredictor.mjs';
export default async function(settings) {
if(typeof settings.input !== "string") {
console.error("Error: No input directory specified");
process.exit(1);
}
if(typeof settings.output !== "string") {
console.error("Error: No output directory specified");
process.exit(1);
}
if(!fs.existsSync(settings.input)) {
console.error(`Error: The input directory '${settings.input}' doesn't exist (did you type it correctly?)`);
process.exit(1);
}
if(!fs.existsSync(settings.output))
await fs.promises.mkdir(settings.output, { recursive: true, mode: 0o755 });
let preprocessor = new DataPreprocessor(settings.input);
let model = new FilmPredictor(settings);
await model.init(); // We're training a new model here
await model.train(
preprocessor.dataset_train(),
preprocessor.dataset_validate()
);
}