start implementation of activations

README.md

@@ -1,6 +1,6 @@
 # Keras.js
 
-Run trained Keras models in JavaScript. Works in the browser or Node.js.
+Run trained [Keras](https://github.com/fchollet/keras) models in your browser, GPU-powered using WebGL.
 
 ##### Why?
 
@@ -12,4 +12,4 @@ No. Why would you even want to?
 
 ### License
 
-MIT
+MIT

package.json

@@ -1,29 +1,28 @@
 {
   "name": "keras-js",
   "version": "0.0.1",
-  "description": "Run trained Keras models in JavaScript. Works in the browser or Node.js.",
-  "main": "lib/index.js",
+  "description": "Run trained Keras models in your browser, GPU-powered using WebGL.",
   "scripts": {
     "lint": "standard src test",
-    "test": "ava --verbose",
-    "check": "npm run lint && npm run test",
-    "compile": "rimraf lib && babel src -d lib",
-    "build": "echo \"No build step currently.\"",
-    "prepublish": "npm run check && npm run compile && npm run build"
+    "watch": "webpack src/index.js dist/keras.js --watch --config webpack.dev.config.js",
+    "build": "npm run lint && webpack --config webpack.prod.config.js",
+    "server": "http-server . -p 9001"
   },
   "repository": {
     "type": "git",
     "url": "git+https://github.com/transcranial/keras-js.git"
   },
   "keywords": [
-    "Keras",
+    "keras",
     "deep",
     "learning",
     "machine",
     "learning",
     "neural",
     "networks",
-    "javascript"
+    "javascript",
+    "webgl",
+    "gpu"
   ],
   "author": "Leon Chen",
   "license": "MIT",
@@ -32,39 +31,38 @@
   },
   "homepage": "https://github.com/transcranial/keras-js#readme",
   "dependencies": {
-    "vectorious": "^4.3.2"
+    "ndarray": "^1.0.18",
+    "ndarray-ops": "^1.2.2"
   },
   "devDependencies": {
-    "ava": "^0.16.0",
-    "babel-cli": "^6.11.4",
     "babel-core": "^6.13.2",
     "babel-eslint": "^6.1.2",
+    "babel-loader": "^6.2.5",
     "babel-plugin-transform-class-properties": "^6.11.5",
+    "babel-plugin-transform-object-rest-spread": "^6.8.0",
     "babel-preset-es2015": "^6.13.2",
-    "babel-register": "^6.11.6",
-    "rimraf": "^2.5.4",
-    "standard": "^8.0.0-beta.5"
+    "http-server": "^0.9.0",
+    "standard": "^8.0.0-beta.5",
+    "webpack": "^1.13.2"
   },
   "standard": {
-    "parser": "babel-eslint"
+    "parser": "babel-eslint",
+    "globals": [
+      "KerasJS",
+      "chai",
+      "testUtils",
+      "weblas",
+      "GPU",
+      "performance"
+    ]
   },
   "babel": {
     "presets": [
       "es2015"
     ],
     "plugins": [
-      "transform-class-properties"
+      "transform-class-properties",
+      "transform-object-rest-spread"
     ]
-  },
-  "ava": {
-    "files": [
-      "tests/**/*.js"
-    ],
-    "concurrency": 4,
-    "failFast": true,
-    "require": [
-      "babel-register"
-    ],
-    "babel": "inherit"
   }
 }

src/activations.js

@@ -0,0 +1,73 @@
+import ndarray from 'ndarray'
+import ops from 'ndarray-ops'
+
+/**
+ * Softmax activation function. In-place operation.
+ * @param {Tensor} x
+ * @returns {Tensor} `this`
+ */
+export function softmax (x) {
+  if (x.tensor.shape.length === 1) {
+    ops.expeq(x.tensor)
+    const sum = ops.sum(x.tensor)
+    ops.divseq(x.tensor, sum)
+  } else if (x.tensor.shape.length === 2) {
+    for (let i = 0; i < x.tensor.shape[0]; i++) {
+      ops.expeq(x.tensor.pick(i, null))
+      const sum = ops.sum(x.tensor.pick(i, null))
+      ops.divseq(x.tensor.pick(i, null), sum)
+    }
+  } else {
+    throw new Error(`[activations.softmax] tensor shape ${x.tensor.shape} not supported.`)
+  }
+  return this
+}
+
+export function softplus (x) {
+
+}
+
+export function softsign (x) {
+
+}
+
+/**
+ * ReLU activation function. In-place operation.
+ * @param {Tensor} x
+ * @param {Number} alpha
+ * @param {Number} maxValue
+ * @returns {Tensor} `this`
+ */
+export function relu (x, opts = {}) {
+  const { alpha = 0, maxValue = null } = opts
+  let neg
+  if (alpha !== 0) {
+    neg = ndarray(new x._type(x.tensor.data.length), x.tensor.shape)
+    ops.mins(neg, x.tensor, 0.0)
+    ops.mulseq(neg, alpha)
+  }
+  ops.maxseq(x.tensor, 0.0)
+  if (maxValue) {
+    ops.minseq(x.tensor, maxValue)
+  }
+  if (neg) {
+    ops.addeq(x.tensor, neg)
+  }
+  return this
+}
+
+export function tanh (x) {
+
+}
+
+export function sigmoid (x) {
+
+}
+
+export function hardSigmoid (x) {
+
+}
+
+export function linear (x) {
+  return x
+}

src/index.js

@@ -0,0 +1,7 @@
+import Tensor from './tensor'
+import * as activations from './activations'
+
+export {
+  Tensor,
+  activations
+}

src/tensor.js

@@ -0,0 +1,20 @@
+import ndarray from 'ndarray'
+
+export default class Tensor {
+  constructor (data, shape, options = {}) {
+    this._type = options.type || Float32Array
+    const TypedArray = this._type
+
+    if (shape.length && data.length !== shape.reduce((a, b) => a * b, 1)) {
+      throw new Error('Specified shape incompatible with data.')
+    }
+
+    if (data && data.length && data instanceof TypedArray) {
+      this.tensor = ndarray(data, shape)
+    } else if (data && data.length && data instanceof Array) {
+      this.tensor = ndarray(new TypedArray(data), shape)
+    } else {
+      this.tensor = ndarray(new TypedArray([]), [])
+    }
+  }
+}