Categories: Exception → Error

README.md

@@ -4,7 +4,7 @@
 
 The example code & slide deck for a talk I gave on getting started with AI. A link to the unlisted YouTube video is available upon request (because it contains my face, and this is a public repo) - see [my website](https://starbeamrainbowlabs.com/) for ways to get in touch.
 
-For advanced users, see also the `post-talk-improvements` branch, which contains a number of adjustments and changes to this codebase. These changes improve the model, but make it more complex to understand and hence are kept in a separate branch to allow regular users to look at the simpler / easier to understand codebase.
+This is the `post-talk-improvements` branch, which contains a number of adjustments and changes to this codebase. These changes improve the model, but make it more complex to understand and hence are kept in a separate branch to allow regular users to look at the simpler / easier to understand codebase.
 
 
 ## Dataset
@@ -16,6 +16,7 @@ The format is as follows:
 
 ```
 + dataset_dir/
+	+ categories.txt
 	+ train/
 		+ 1234,Comedy,Fantasy.jpg
 		+ 4321,Western,Short,Drama.jpg
@@ -28,6 +29,9 @@ The format is as follows:
 
 The filenames of the images take the following format: `ID,GENRE_1,GENRE_2,GENRE_N.jpg`.
 
+The `categories.txt` file should contain 1 category name per line (the order matters, as the line numbers - starting from 0 - are used as the numerical ids when training the model).
+
+
 ## System / User Requirements
  - [Node.js](https://nodejs.org/)
  - [NPM](https://www.npmjs.com/) (installed by default with Node.js)

package-lock.json

@@ -5,16 +5,16 @@
   "requires": true,
   "dependencies": {
     "@tensorflow/tfjs": {
-      "version": "2.7.0",
+      "version": "2.8.0",
-      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs/-/tfjs-2.7.0.tgz",
+      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs/-/tfjs-2.8.0.tgz",
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       "requires": {
-        "@tensorflow/tfjs-backend-cpu": "2.7.0",
+        "@tensorflow/tfjs-backend-cpu": "2.8.0",
-        "@tensorflow/tfjs-backend-webgl": "2.7.0",
+        "@tensorflow/tfjs-backend-webgl": "2.8.0",
-        "@tensorflow/tfjs-converter": "2.7.0",
+        "@tensorflow/tfjs-converter": "2.8.0",
-        "@tensorflow/tfjs-core": "2.7.0",
+        "@tensorflow/tfjs-core": "2.8.0",
-        "@tensorflow/tfjs-data": "2.7.0",
+        "@tensorflow/tfjs-data": "2.8.0",
-        "@tensorflow/tfjs-layers": "2.7.0",
+        "@tensorflow/tfjs-layers": "2.8.0",
         "argparse": "^1.0.10",
         "chalk": "^4.1.0",
         "core-js": "3",
@@ -23,9 +23,9 @@
       }
     },
     "@tensorflow/tfjs-backend-cpu": {
-      "version": "2.7.0",
+      "version": "2.8.0",
-      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-backend-cpu/-/tfjs-backend-cpu-2.7.0.tgz",
+      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-backend-cpu/-/tfjs-backend-cpu-2.8.0.tgz",
-      "integrity": "sha512-R6ORcWq3ub81ABvBZEZ8Ok5OOT59B4AsRe66ds7B/NK0nN+k6y37bR3ZDVjgkEKNWNvzB7ydODikge3GNmgQIQ==",
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       "requires": {
         "@types/seedrandom": "2.4.27",
         "seedrandom": "2.4.3"
@@ -39,11 +39,11 @@
       }
     },
     "@tensorflow/tfjs-backend-webgl": {
-      "version": "2.7.0",
+      "version": "2.8.0",
-      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-backend-webgl/-/tfjs-backend-webgl-2.7.0.tgz",
+      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-backend-webgl/-/tfjs-backend-webgl-2.8.0.tgz",
-      "integrity": "sha512-K7Rk5YTSWOZ969EZvh3w786daPn2ub4mA2JsX7mXKhBPUaOP9dKbBdLj9buCuMcu4zVq2pAp0QwpHSa4PHm3xg==",
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       "requires": {
-        "@tensorflow/tfjs-backend-cpu": "2.7.0",
+        "@tensorflow/tfjs-backend-cpu": "2.8.0",
         "@types/offscreencanvas": "~2019.3.0",
         "@types/seedrandom": "2.4.27",
         "@types/webgl-ext": "0.0.30",
@@ -59,14 +59,14 @@
       }
     },
     "@tensorflow/tfjs-converter": {
-      "version": "2.7.0",
+      "version": "2.8.0",
-      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-converter/-/tfjs-converter-2.7.0.tgz",
+      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-converter/-/tfjs-converter-2.8.0.tgz",
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+      "integrity": "sha512-GvVzPu0+gtgfq0+UAA2mHV7z5TT359TAUUJHOI7/dDSlfCMfLx5bSa0CamW7PIPkzSn9urljXTbocdeNAdpJKQ=="
     },
     "@tensorflow/tfjs-core": {
-      "version": "2.7.0",
+      "version": "2.8.0",
-      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-core/-/tfjs-core-2.7.0.tgz",
+      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-core/-/tfjs-core-2.8.0.tgz",
-      "integrity": "sha512-4w5zjK6C5nkLatHpzARVQNd5QKtIocJRwjZIwWcScT9z2z1dX4rVmDoUpYg1cdD4H+yRRdI0awRaI3SL34yy8Q==",
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       "requires": {
         "@types/offscreencanvas": "~2019.3.0",
         "@types/seedrandom": "2.4.27",
@@ -83,26 +83,26 @@
       }
     },
     "@tensorflow/tfjs-data": {
-      "version": "2.7.0",
+      "version": "2.8.0",
-      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-data/-/tfjs-data-2.7.0.tgz",
+      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-data/-/tfjs-data-2.8.0.tgz",
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       "requires": {
         "@types/node-fetch": "^2.1.2",
         "node-fetch": "~2.6.1"
       }
     },
     "@tensorflow/tfjs-layers": {
-      "version": "2.7.0",
+      "version": "2.8.0",
-      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-layers/-/tfjs-layers-2.7.0.tgz",
+      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-layers/-/tfjs-layers-2.8.0.tgz",
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     },
     "@tensorflow/tfjs-node": {
-      "version": "2.7.0",
+      "version": "2.8.0",
-      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-node/-/tfjs-node-2.7.0.tgz",
+      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-node/-/tfjs-node-2.8.0.tgz",
-      "integrity": "sha512-0cWplm7AE40gi2llqoAp+lD/0X3dVJ8kb7Arrqb5lMhShRWUFZpULH+F0fJI6Yax4LBTzBi2SZKGL/O8krZsxg==",
+      "integrity": "sha512-u62AqvIS9jaMrw09Hl/Dy1YKiqEkltQ1c+RoJuW0XE685bjRue7FRmkJy69+EvsnXLY6gvPR1y+LguTPql8ZwA==",
       "requires": {
-        "@tensorflow/tfjs": "2.7.0",
+        "@tensorflow/tfjs": "2.8.0",
-        "@tensorflow/tfjs-core": "2.7.0",
+        "@tensorflow/tfjs-core": "2.8.0",
         "adm-zip": "^0.4.11",
         "google-protobuf": "^3.9.2",
         "https-proxy-agent": "^2.2.1",
@@ -113,9 +113,9 @@
       }
     },
     "@types/node": {
-      "version": "14.14.7",
+      "version": "14.14.14",
-      "resolved": "https://registry.npmjs.org/@types/node/-/node-14.14.7.tgz",
+      "resolved": "https://registry.npmjs.org/@types/node/-/node-14.14.14.tgz",
-      "integrity": "sha512-Zw1vhUSQZYw+7u5dAwNbIA9TuTotpzY/OF7sJM9FqPOF3SPjKnxrjoTktXDZgUjybf4cWVBP7O8wvKdSaGHweg=="
+      "integrity": "sha512-UHnOPWVWV1z+VV8k6L1HhG7UbGBgIdghqF3l9Ny9ApPghbjICXkUJSd/b9gOgQfjM1r+37cipdw/HJ3F6ICEnQ=="
     },
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       "version": "2.5.7",
@@ -314,9 +314,9 @@
       "integrity": "sha1-PXz0Rk22RG6mRL9LOVB/mFEAjo4="
     },
     "core-js": {
-      "version": "3.7.0",
+      "version": "3.8.1",
-      "resolved": "https://registry.npmjs.org/core-js/-/core-js-3.7.0.tgz",
+      "resolved": "https://registry.npmjs.org/core-js/-/core-js-3.8.1.tgz",
-      "integrity": "sha512-NwS7fI5M5B85EwpWuIwJN4i/fbisQUwLwiSNUWeXlkAZ0sbBjLEvLvFLf1uzAUV66PcEPt4xCGCmOZSxVf3xzA=="
+      "integrity": "sha512-9Id2xHY1W7m8hCl8NkhQn5CufmF/WuR30BTRewvCXc1aZd3kMECwNZ69ndLbekKfakw9Rf2Xyc+QR6E7Gg+obg=="
     },
     "core-util-is": {
       "version": "1.0.2",
@@ -485,9 +485,9 @@
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     },
     "ini": {
-      "version": "1.3.5",
+      "version": "1.3.8",
-      "resolved": "https://registry.npmjs.org/ini/-/ini-1.3.5.tgz",
+      "resolved": "https://registry.npmjs.org/ini/-/ini-1.3.8.tgz",
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+      "integrity": "sha512-JV/yugV2uzW5iMRSiZAyDtQd+nxtUnjeLt0acNdw98kKLrvuRVyB80tsREOE7yvGVgalhZ6RNXCmEHkUKBKxew=="
     },
     "is-fullwidth-code-point": {
       "version": "1.0.0",
@@ -885,9 +885,9 @@
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     },
     "yargs": {
-      "version": "16.1.1",
+      "version": "16.2.0",
-      "resolved": "https://registry.npmjs.org/yargs/-/yargs-16.1.1.tgz",
+      "resolved": "https://registry.npmjs.org/yargs/-/yargs-16.2.0.tgz",
-      "integrity": "sha512-hAD1RcFP/wfgfxgMVswPE+z3tlPFtxG8/yWUrG2i17sTWGCGqWnxKcLTF4cUKDUK8fzokwsmO9H0TDkRbMHy8w==",
+      "integrity": "sha512-D1mvvtDG0L5ft/jGWkLpG1+m0eQxOfaBvTNELraWj22wSVUMWxZUvYgJYcKh6jGGIkJFhH4IZPQhR4TKpc8mBw==",
       "requires": {
         "cliui": "^7.0.2",
         "escalade": "^3.1.1",

package.json

@@ -20,7 +20,7 @@
   "author": "Starbeamrainbowlabs",
   "license": "MPL-2.0",
   "dependencies": {
-    "@tensorflow/tfjs-node": "^2.7.0",
+    "@tensorflow/tfjs-node": "^2.8.0",
     "applause-cli": "^1.5.1"
   }
 }

src/cli.mjs

@@ -13,7 +13,10 @@ 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");
+		.argument("output", "Path to the output directory to save the trained AI to")
+		.argument("image-size", "The width+height of input images (images are assumed to be square; default: 256)", 256, "integer")
+		.argument("cross-entropy", "Use categorical cross-entropy loss instead of mean-squared error (best if each image has only a single label)", false, "boolean")
+		.argument("activation", "Set the activation function for the CNN layer(s) (default: relu)", "relu", "string");
 	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");

src/lib/Categories.mjs

@@ -0,0 +1,40 @@
+"use strict";
+
+import fs from 'fs';
+
+/**
+ * Represents a list of categories.
+ */
+class Categories {
+	constructor(in_filename) {
+		this.filename = in_filename;
+		if(!fs.existsSync(this.filename))
+			throw new Error(`Error: No such file or directory '${this.filename}' (it should exist and have 1 category name per line).`);
+		
+		/**
+		 * A list of category names.
+		 * @type {string[]}
+		 */
+		this.values = fs.readFileSync(this.filename, "utf-8")
+			.replace(/\r\n?/, "\n")
+			.split("\n")
+			.map((line) => line.trim())
+			.filter((line) => line.length > 0);
+		
+		/**
+		 * The total number of categories.
+		 * @type {Number}
+		 */
+		this.count = this.values.length;
+	}
+	
+	to_id(cat_name) {
+		return this.values.indexOf(cat_name);
+	}
+	
+	to_name(id) {
+		return this.values[id];
+	}
+}
+
+export default Categories;

src/lib/DataPreprocessor.mjs

@@ -5,7 +5,7 @@ import path from 'path';
 
 import tf from '@tensorflow/tfjs-node';
 
-import genres from './Genres.mjs';
+import Categories from './Categories.mjs';
 
 class DataPreprocessor {
 	constructor(dir_input) {
@@ -14,21 +14,14 @@ class DataPreprocessor {
 		
 		this.dir_input_train = path.join(dir_input, "train");
 		this.dir_input_validate = path.join(dir_input, "validate");
+		this.file_input_cats = path.join(dir_input, "categories.txt");
 		
 		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;
+		this.cats = new Categories(this.file_input_cats);
-	}
-	
-	genre2id(genre_name) {
-		return genres.indexOf(genre_name);
-	}
-	
-	id2genre(id) {
-		return genres[id];
 	}
 	
 	async *data_from_dir(dirpath) {
@@ -37,37 +30,54 @@ class DataPreprocessor {
 		
 		let filenames = await fs.promises.readdir(dirpath);
 		for(let filename of filenames) {
-			let imagetensor = tf.node.decodeImage(
+			let imagebin = await fs.promises.readFile(path.join(dirpath, filename));
-				await fs.promises.readFile(path.join(dirpath, filename)),
+			let imagetensor = tf.tidy(() => {
-				3 // channels
+				// Normalise the data to be 0 - 1
-			);
+				return tf.node.decodeImage(
+					imagebin,
+					3 // channels
+				).cast("float32").div(255);
+			});
 			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]+$/, "")
+			let next_cats = filename.replace(/\.[a-zA-Z]+$/, "")
 				.split(",")
 				.slice(1)
-				.map(this.genre2id);
+				.map(this.cats.to_id.bind(this.cats));
 			
 			
-			let genres_pretensor = Array(this.genres_count).fill(0);
+			let pretensor = Array(this.cats.count).fill(0);
-			for(let genre_id of next_genres)
+			for(let cat_id of next_cats)
-				genres_pretensor[genre_id] = 1;
+				pretensor[cat_id] = 1;
 			
-			let genres_tensor = tf.tensor(genres_pretensor);
+			let cats_tensor = tf.tensor(pretensor);
 			
-			// console.log(`>>>>>>>>> output shapes: ${imagetensor.shape}, ${genres_tensor.shape}`);
+			// console.log(`>>>>>>>>> output shapes: [${imagetensor.shape}], [${cats_tensor.shape}]`);
 			
-			yield {
+			let result = {
 				xs: imagetensor,
-				ys: genres_tensor
+				ys: cats_tensor
 			};
+			// console.log(`[DEBUG] yielding xs`, result.xs.dataSync());
+			// console.log(`[DEBUG] ys`); result.ys.print();
+			// console.log(`--------------------`);
+			yield result;
 		}
 	}
 	
+	/**
+	 * Returns a Categories object instance that represents the list of
+	 * categories.
+	 * @return {Categories}
+	 */
+	categories() {
+		return this.cats;
+	}
+	
 	dataset_train() {
 		return tf.data.generator(this.data_from_dir.bind(this, this.dir_input_train));
 	}

src/lib/FilmPredictor.mjs

@@ -7,16 +7,20 @@ import tf from '@tensorflow/tfjs-node';
 // import tf from '@tensorflow/tfjs-node-gpu';
 // import tf from '@tensorflow/tfjs';
 
-import genres from './Genres.mjs';
+import make_model from './model.mjs';
 
 class FilmPredictor {
-	constructor(settings) {
+	constructor(settings, in_categories) {
 		this.settings = settings;
 		
-		this.genres = genres.length;
+		this.cats = in_categories;
+		
+		this.image_size = 256;
+		this.use_crossentropy = false;
+		this.activation = "relu";
 		
 		this.batch_size = 32;
-		this.prefetch = 4;
+		this.prefetch = 64;
 	}
 	
 	async init(dirpath = null) {
@@ -29,7 +33,17 @@ class FilmPredictor {
 			if(!fs.existsSync(this.dir_checkpoints))
 				await fs.promises.mkdir(this.dir_checkpoints, { recursive: true, mode: 0o755 });
 			
-			this.make_model();
+			await fs.promises.copyFile(
+				this.cats.filename,
+				path.join(this.settings.output, "categories.txt")
+			);
+			
+			this.model = make_model(
+				this.cats.count,
+				this.image_size,
+				this.use_crossentropy,
+				this.activation
+			);
 			
 			this.model.summary();
 		}
@@ -43,57 +57,6 @@ class FilmPredictor {
 		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);
@@ -142,7 +105,7 @@ class FilmPredictor {
 		for(let i = 0; i < arr.length; i++) {
 			if(arr[i] < 0.5)
 				continue;
-			result.push(genres[i]);
+			result.push(this.cats.values[i]);
 		}
 		return result;
 	}

src/lib/model.mjs

@@ -0,0 +1,67 @@
+"use strict";
+
+import tf from '@tensorflow/tfjs-node';
+
+export default function(cats_count, image_size, use_crossentropy = false, activation = "relu") {
+	console.error(`>>> Creating new model (activation = ${activation})`);
+	let model = tf.sequential();
+	
+	model.add(tf.layers.conv2d({
+		name: "conv2d_1",
+		dataFormat: "channelsLast",
+		inputShape: [ image_size, image_size, 3 ],
+		kernelSize: 5,
+		filters: 3,
+		strides: 1,
+		activation
+	}));
+	if(image_size > 32) {
+		model.add(tf.layers.conv2d({
+			name: "conv2d_2",
+			dataFormat: "channelsLast",
+			kernelSize: 5,
+			filters: 3,
+			strides: 2,
+			activation
+		}));
+		model.add(tf.layers.conv2d({
+			name: "conv2d_3",
+			dataFormat: "channelsLast",
+			kernelSize: 5,
+			filters: 3,
+			strides: 2,
+			activation
+		}));
+	}
+	
+	// Reshape and flatten
+	let cnn_stack_output_shape = model.layers[model.layers.length - 1].outputShape;
+	model.add(tf.layers.reshape({
+		name: "reshape",
+		targetShape: [
+			cnn_stack_output_shape[1] * cnn_stack_output_shape[2] * cnn_stack_output_shape[3]
+		]
+	}));
+	model.add(tf.layers.dense({
+		name: "dense",
+		units: cats_count,
+		activation: use_crossentropy ? "softmax" : "sigmoid" // If you're only predicting a single label at a time, then choose "softmax" instead
+	}));
+	
+	let 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 */ ]; 
+	
+	if(use_crossentropy) {
+		console.log(`>>> Using categorical cross-entropy loss`);
+		loss = "categoricalCrossentropy";
+		metrics = [ "accuracy", "categoricalCrossentropy", "categoricalAccuracy" ];
+	}
+	
+	model.compile({
+		optimizer: tf.train.adam(),
+		loss, 
+		metrics
+	});
+	
+	return model;
+}

src/subcommands/predict/predict.mjs

@@ -1,8 +1,10 @@
 "use strict";
 
+import path from 'path';
 import fs from 'fs';
 
 import FilmPredictor from '../../lib/FilmPredictor.mjs';
+import Categories from '../../lib/Categories.mjs';
 
 export default async function(settings) {
 	if(!fs.existsSync(settings.input)) {
@@ -14,7 +16,10 @@ export default async function(settings) {
 		process.exit(1);
 	}
 	
-	let model = new FilmPredictor(settings);
+	let model = new FilmPredictor(
+		settings,
+		new Categories(path.join(settings.ai_model, "categories.txt"))
+	);
 	await model.init(settings.ai_model); // We're training a new model here
 	
 	let result = await model.predict(settings.input);

src/subcommands/train/train.mjs

@@ -24,7 +24,10 @@ export default async function(settings) {
 		await fs.promises.mkdir(settings.output, { recursive: true, mode: 0o755 });
 	
 	let preprocessor = new DataPreprocessor(settings.input);
-	let model = new FilmPredictor(settings);
+	let model = new FilmPredictor(settings, preprocessor.cats);
+	model.image_size = settings.image_size;
+	model.use_crossentropy = settings.cross_entropy;
+	model.activation = settings.activation;
 	await model.init(); // We're training a new model here
 	
 	await model.train(