diff --git a/index.html b/index.html index 93630ae..a31cedf 100644 --- a/index.html +++ b/index.html @@ -40,6 +40,10 @@ + + + + diff --git a/notebooks/core/Highway.ipynb b/notebooks/core/Highway.ipynb new file mode 100644 index 0000000..763e0aa --- /dev/null +++ b/notebooks/core/Highway.ipynb @@ -0,0 +1,253 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from keras.models import Model\n", + "from keras.layers import Input\n", + "from keras.layers.core import Highway\n", + "from keras import backend as K" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def format_decimal(arr, places=6):\n", + " return [round(x * 10**places) / 10**places for x in arr]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Highway" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**[core.Highway.0]**" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "W shape: (6, 6)\n", + "W: [0.176262, 0.795427, 0.783061, 0.631675, -0.928221, 0.383515, -0.242638, 0.037022, 0.315903, -0.6123, -0.455367, 0.437212, 0.566007, 0.700655, 0.55049, -0.926671, -0.766613, 0.502561, -0.521564, -0.490388, 0.715251, 0.899558, 0.123374, -0.642439, 0.540504, -0.015238, 0.262506, 0.678996, -0.077921, -0.00412, 0.358822, 0.301572, -0.46241, -0.865351, 0.54289, -0.038032]\n", + "b shape: (6,)\n", + "b: [-0.90255, -0.421781, 0.441933, -0.956768, -0.588154, -0.898453]\n", + "W_carry shape: (6, 6)\n", + "W_carry: [-0.583079, -0.036638, -0.158924, 0.718364, -0.657677, -0.322272, -0.458934, 0.382083, -0.559191, 0.623902, -0.978946, 0.122407, 0.627452, 0.490201, -0.621777, -0.987718, 0.544088, 0.915664, 0.403876, -0.404843, 0.535985, 0.376437, -0.225633, 0.230412, -0.14489, 0.168579, 0.405271, -0.77621, 0.84654, 0.977773, 0.354822, 0.59033, -0.941847, -0.644482, 0.749855, 0.489864]\n", + "b_carry shape: (6,)\n", + "b_carry: [0.034596, 0.893925, 0.53092, -0.435208, -0.557909, 0.372444]\n", + "\n", + "in shape: (6,)\n", + "in: [-0.665722, -0.215115, 0.236105, -0.17614, -0.99507, 0.768064]\n", + "out shape: (6,)\n", + "out: [-0.914347, -0.408456, -0.114281, -0.888056, -0.290505, -0.199544]\n" + ] + } + ], + "source": [ + "data_in_shape = (6,)\n", + "layer_0 = Input(shape=data_in_shape)\n", + "layer_1 = Highway(transform_bias=-2, activation='linear', bias=True)(layer_0)\n", + "model = Model(input=layer_0, output=layer_1)\n", + "\n", + "# set weights to random (use seed for reproducibility)\n", + "weights = []\n", + "for i, w in enumerate(model.get_weights()):\n", + " np.random.seed(20+i)\n", + " weights.append(2 * np.random.random(w.shape) - 1)\n", + "model.set_weights(weights)\n", + "print('W shape:', weights[0].shape)\n", + "print('W:', format_decimal(weights[0].ravel().tolist()))\n", + "print('b shape:', weights[1].shape)\n", + "print('b:', format_decimal(weights[1].ravel().tolist()))\n", + "print('W_carry shape:', weights[2].shape)\n", + "print('W_carry:', format_decimal(weights[2].ravel().tolist()))\n", + "print('b_carry shape:', weights[3].shape)\n", + "print('b_carry:', format_decimal(weights[3].ravel().tolist()))\n", + "\n", + "data_in = 2 * np.random.random(data_in_shape) - 1\n", + "print('')\n", + "print('in shape:', data_in_shape)\n", + "print('in:', format_decimal(data_in.ravel().tolist()))\n", + "result = model.predict(np.array([data_in]))\n", + "print('out shape:', result[0].shape)\n", + "print('out:', format_decimal(result[0].ravel().tolist()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**[core.Highway.1]**" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "W shape: (5, 5)\n", + "W: [0.288287, -0.238503, 0.326096, -0.672699, 0.925216, -0.306676, 0.983502, -0.529884, 0.171389, -0.18662, -0.727531, 0.088273, 0.036353, 0.53371, 0.8677, -0.820593, -0.608457, 0.988387, -0.529639, -0.522027, 0.2582, 0.469905, 0.376689, -0.937739, 0.805028]\n", + "b shape: (5,)\n", + "b: [-0.427892, 0.916211, 0.540626, 0.97374, -0.583669]\n", + "W_carry shape: (5, 5)\n", + "W_carry: [0.717779, -0.254578, 0.110258, 0.911313, 0.473339, 0.63241, -0.797827, 0.856976, 0.218218, 0.193107, -0.816432, -0.309628, 0.325505, -0.116573, 0.102976, 0.407425, 0.178802, -0.900134, 0.123584, 0.532717, 0.821817, -0.81418, 0.805043, -0.078079, -0.095963]\n", + "b_carry shape: (5,)\n", + "b_carry: [-0.50298, -0.100049, -0.178118, -0.479401, 0.740791]\n", + "\n", + "in shape: (5,)\n", + "in: [-0.62992, -0.960677, 0.906504, 0.360902, -0.026824]\n", + "out shape: (5,)\n", + "out: [-0.652907, -0.353259, 0.890362, 0.477292, -0.256096]\n" + ] + } + ], + "source": [ + "data_in_shape = (5,)\n", + "layer_0 = Input(shape=data_in_shape)\n", + "layer_1 = Highway(transform_bias=-5, activation='tanh', bias=True)(layer_0)\n", + "model = Model(input=layer_0, output=layer_1)\n", + "\n", + "# set weights to random (use seed for reproducibility)\n", + "weights = []\n", + "for i, w in enumerate(model.get_weights()):\n", + " np.random.seed(30+i)\n", + " weights.append(2 * np.random.random(w.shape) - 1)\n", + "model.set_weights(weights)\n", + "print('W shape:', weights[0].shape)\n", + "print('W:', format_decimal(weights[0].ravel().tolist()))\n", + "print('b shape:', weights[1].shape)\n", + "print('b:', format_decimal(weights[1].ravel().tolist()))\n", + "print('W_carry shape:', weights[2].shape)\n", + "print('W_carry:', format_decimal(weights[2].ravel().tolist()))\n", + "print('b_carry shape:', weights[3].shape)\n", + "print('b_carry:', format_decimal(weights[3].ravel().tolist()))\n", + "\n", + "data_in = 2 * np.random.random(data_in_shape) - 1\n", + "print('')\n", + "print('in shape:', data_in_shape)\n", + "print('in:', format_decimal(data_in.ravel().tolist()))\n", + "result = model.predict(np.array([data_in]))\n", + "print('out shape:', result[0].shape)\n", + "print('out:', format_decimal(result[0].ravel().tolist()))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "W shape: (4, 4)\n", + "W: [-0.184626, -0.889268, 0.57707, -0.42539, -0.099299, -0.392175, 0.052799, 0.247624, 0.553551, 0.372483, 0.961878, 0.201632, 0.627937, 0.41729, -0.944931, 0.808534]\n", + "W_carry shape: (4, 4)\n", + "W_carry: [-0.498153, -0.907808, 0.353632, -0.913061, -0.767153, 0.207731, -0.618139, 0.337031, 0.834896, -0.16244, -0.33548, -0.433933, -0.627435, -0.365779, -0.037663, -0.860959]\n", + "\n", + "in shape: (4,)\n", + "in: [0.409965, -0.370646, 0.490565, -0.203574]\n", + "out shape: (4,)\n", + "out: [0.482075, -0.04199, 0.593448, 0.031503]\n" + ] + } + ], + "source": [ + "data_in_shape = (4,)\n", + "layer_0 = Input(shape=data_in_shape)\n", + "layer_1 = Highway(transform_bias=1, activation='hard_sigmoid', bias=False)(layer_0)\n", + "model = Model(input=layer_0, output=layer_1)\n", + "\n", + "# set weights to random (use seed for reproducibility)\n", + "weights = []\n", + "for i, w in enumerate(model.get_weights()):\n", + " np.random.seed(40+i)\n", + " weights.append(2 * np.random.random(w.shape) - 1)\n", + "model.set_weights(weights)\n", + "print('W shape:', weights[0].shape)\n", + "print('W:', format_decimal(weights[0].ravel().tolist()))\n", + "print('W_carry shape:', weights[1].shape)\n", + "print('W_carry:', format_decimal(weights[1].ravel().tolist()))\n", + "\n", + "data_in = 2 * np.random.random(data_in_shape) - 1\n", + "print('')\n", + "print('in shape:', data_in_shape)\n", + "print('in:', format_decimal(data_in.ravel().tolist()))\n", + "result = model.predict(np.array([data_in]))\n", + "print('out shape:', result[0].shape)\n", + "print('out:', format_decimal(result[0].ravel().tolist()))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/core/MaxoutDense.ipynb b/notebooks/core/MaxoutDense.ipynb new file mode 100644 index 0000000..1e5352a --- /dev/null +++ b/notebooks/core/MaxoutDense.ipynb @@ -0,0 +1,176 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from keras.models import Model\n", + "from keras.layers import Input\n", + "from keras.layers.core import MaxoutDense\n", + "from keras import backend as K" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def format_decimal(arr, places=6):\n", + " return [round(x * 10**places) / 10**places for x in arr]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### MaxoutDense" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**[core.MaxoutDense.0] nb_feature=4, biase=True**" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "W shape: (4, 6, 3)\n", + "W: [0.542641, -0.958496, 0.267296, 0.497608, -0.002986, -0.550407, -0.603874, 0.521061, -0.661778, -0.82332, 0.37072, 0.906787, -0.992103, 0.024385, 0.625242, 0.225052, 0.443511, -0.416248, 0.835548, 0.429152, 0.085089, -0.71566, -0.253318, 0.348267, -0.116334, -0.131972, 0.235534, 0.026276, 0.300794, 0.202078, 0.610446, 0.043294, 0.817298, -0.361528, -0.819081, -0.3986, -0.772031, 0.657363, -0.906207, 0.252574, 0.095172, 0.638574, -0.602105, 0.713701, -0.296695, 0.509295, -0.408077, 0.767873, -0.348977, -0.669968, -0.214942, -0.813079, 0.642211, -0.697696, -0.231771, 0.888521, 0.975251, -0.087391, 0.652246, -0.497252, 0.194743, 0.805664, 0.069116, 0.180403, -0.921436, -0.285636, -0.840774, -0.38908, -0.338561, 0.547661, -0.920082, -0.141016]\n", + "b shape: (4, 3)\n", + "b: [0.542641, -0.958496, 0.267296, 0.497608, -0.002986, -0.550407, -0.603874, 0.521061, -0.661778, -0.82332, 0.37072, 0.906787]\n", + "\n", + "in shape: (6,)\n", + "in: [-0.992103, 0.024385, 0.625242, 0.225052, 0.443511, -0.416248]\n", + "out shape: (3,)\n", + "out: [0.090044, 0.227783, 0.435236]\n" + ] + } + ], + "source": [ + "data_in_shape = (6,)\n", + "layer_0 = Input(shape=data_in_shape)\n", + "layer_1 = MaxoutDense(3, nb_feature=4, bias=True)(layer_0)\n", + "model = Model(input=layer_0, output=layer_1)\n", + "\n", + "# set weights to random (use seed for reproducibility)\n", + "weights = []\n", + "for w in model.get_weights():\n", + " np.random.seed(10)\n", + " weights.append(2 * np.random.random(w.shape) - 1)\n", + "model.set_weights(weights)\n", + "print('W shape:', weights[0].shape)\n", + "print('W:', format_decimal(weights[0].ravel().tolist()))\n", + "print('b shape:', weights[1].shape)\n", + "print('b:', format_decimal(weights[1].ravel().tolist()))\n", + "\n", + "data_in = 2 * np.random.random(data_in_shape) - 1\n", + "print('')\n", + "print('in shape:', data_in_shape)\n", + "print('in:', format_decimal(data_in.ravel().tolist()))\n", + "result = model.predict(np.array([data_in]))\n", + "print('out shape:', result[0].shape)\n", + "print('out:', format_decimal(result[0].ravel().tolist()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**[core.MaxoutDense.1] nb_feature=7, biase=False**" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "W shape: (7, 6, 3)\n", + "W: [-0.639461, -0.96105, -0.073563, 0.449868, -0.159593, -0.029146, -0.974438, -0.025257, 0.883613, 0.70159, 0.459929, -0.782528, 0.787808, 0.714308, -0.669827, 0.264668, -0.959033, -0.766525, -0.367265, -0.684175, 0.517959, 0.636551, -0.310751, -0.362402, -0.776678, -0.832094, 0.425452, 0.199087, -0.888653, -0.040405, -0.196647, 0.695958, 0.435698, 0.204128, 0.104768, 0.898205, 0.973347, -0.323892, -0.520251, 0.592872, -0.872627, -0.270769, -0.859954, -0.361265, -0.859235, -0.419473, 0.580202, 0.810801, 0.585243, 0.123637, 0.232037, -0.277033, -0.662365, -0.127518, 0.465651, -0.874225, -0.958534, 0.541096, -0.400096, 0.402329, 0.469335, 0.865809, -0.199343, -0.283124, 0.613134, 0.528982, 0.305229, 0.621933, 0.28443, 0.914888, -0.332251, 0.476505, 0.899667, -0.331272, 0.223264, -0.26866, -0.076919, -0.849996, -0.961313, 0.519299, -0.094482, 0.245668, 0.479903, -0.162673, -0.264765, -0.661942, 0.587745, 0.666075, 0.468426, 0.750589, 0.296113, 0.392132, -0.613964, 0.535264, -0.331834, -0.124082, -0.36226, 0.136579, 0.317385, 0.151117, -0.363626, -0.563993, 0.689877, -0.395671, -0.123559, -0.817171, -0.398042, -0.82861, -0.312573, 0.418602, 0.947937, 0.25045, -0.461393, 0.313418, 0.253996, 0.651304, 0.067388, 0.822243, -0.168993, -0.427911, 0.038015, 0.8449, 0.447823, -0.012174, -0.005007, 0.297444]\n", + "\n", + "in shape: (6,)\n", + "in: [-0.104458, 0.101279, 0.94235, 0.864827, 0.681371, -0.745903]\n", + "out shape: (3,)\n", + "out: [1.043451, 2.068543, 0.396771]\n" + ] + } + ], + "source": [ + "data_in_shape = (6,)\n", + "layer_0 = Input(shape=data_in_shape)\n", + "layer_1 = MaxoutDense(3, nb_feature=7, bias=False)(layer_0)\n", + "model = Model(input=layer_0, output=layer_1)\n", + "\n", + "# set weights to random (use seed for reproducibility)\n", + "weights = []\n", + "for w in model.get_weights():\n", + " np.random.seed(11)\n", + " weights.append(2 * np.random.random(w.shape) - 1)\n", + "model.set_weights(weights)\n", + "print('W shape:', weights[0].shape)\n", + "print('W:', format_decimal(weights[0].ravel().tolist()))\n", + "\n", + "data_in = 2 * np.random.random(data_in_shape) - 1\n", + "print('')\n", + "print('in shape:', data_in_shape)\n", + "print('in:', format_decimal(data_in.ravel().tolist()))\n", + "result = model.predict(np.array([data_in]))\n", + "print('out shape:', result[0].shape)\n", + "print('out:', format_decimal(result[0].ravel().tolist()))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/src/layers/core/Highway.js b/src/layers/core/Highway.js new file mode 100644 index 0000000..5ee1d06 --- /dev/null +++ b/src/layers/core/Highway.js @@ -0,0 +1,67 @@ +import * as activations from '../../activations' +import Tensor from '../../Tensor' +import Layer from '../../engine/Layer' +import { gemv } from 'ndarray-blas-level2' +import ops from 'ndarray-ops' +import cwise from 'cwise' + +/** +* Highway layer class +* From Keras docs: Densely connected highway network, a natural extension of LSTMs to feedforward networks. +*/ +export default class Highway extends Layer { + /** + * Creates a Highway layer + * @param {number} outputDim - output dimension size + * @param {Object} [attrs] - layer attributes + */ + constructor (attrs = {}) { + super(attrs) + const { + transformBias = -2, + activation = 'linear', + bias = true + } = attrs + + this.transformBias = transformBias + this.activation = activations[activation] + this.bias = bias + + /** + * Layer weights specification + */ + this.params = this.bias ? ['W', 'b', 'W_carry', 'b_carry'] : ['W', 'W_carry'] + } + + _computeOutput = cwise({ + args: ['array', 'array', 'array'], + body: function (_x, _y, _transform) { + _x = _y * _transform + (1 - _transform) * _x + } + }) + + /** + * Method for layer computational logic + * @param {Tensor} x + * @returns {Tensor} x + */ + call = x => { + let y = new Tensor([], [this.weights.W.tensor.shape[1]]) + if (this.bias) { + ops.assign(y.tensor, this.weights.b.tensor) + } + gemv(1.0, this.weights.W.tensor.transpose(1, 0), x.tensor, 1.0, y.tensor) + this.activation(y) + + let transform = new Tensor([], [this.weights.W_carry.tensor.shape[1]]) + if (this.bias) { + ops.assign(transform.tensor, this.weights.b_carry.tensor) + } + gemv(1.0, this.weights.W_carry.tensor.transpose(1, 0), x.tensor, 1.0, transform.tensor) + activations.sigmoid(transform) + + this._computeOutput(x.tensor, y.tensor, transform.tensor) + + return x + } +} diff --git a/src/layers/core/MaxoutDense.js b/src/layers/core/MaxoutDense.js new file mode 100644 index 0000000..5351284 --- /dev/null +++ b/src/layers/core/MaxoutDense.js @@ -0,0 +1,56 @@ +import Tensor from '../../Tensor' +import Layer from '../../engine/Layer' +import { gemv } from 'ndarray-blas-level2' +import ops from 'ndarray-ops' + +/** +* MaxoutDense layer class +* From Keras docs: takes the element-wise maximum of nb_feature Dense(input_dim, output_dim) linear layers +* Note that `nb_feature` is implicit in the weights tensors, with shapes: +* - W: [nb_feature, input_dim, output_dim] +* - b: [nb_feature, output_dim] +*/ +export default class MaxoutDense extends Layer { + /** + * Creates a MaxoutDense layer + * @param {number} outputDim - output dimension size + * @param {Object} [attrs] - layer attributes + */ + constructor (outputDim, attrs = {}) { + super(attrs) + const { + inputDim = null, + bias = true + } = attrs + this.outputDim = outputDim + this.inputDim = inputDim + this.bias = bias + + /** + * Layer weights specification + */ + this.params = this.bias ? ['W', 'b'] : ['W'] + } + + /** + * Method for layer computational logic + * @param {Tensor} x + * @returns {Tensor} x + */ + call = x => { + const nbFeature = this.weights.W.tensor.shape[0] + + let featMax = new Tensor([], [this.outputDim]) + for (let i = 0; i < nbFeature; i++) { + let y = new Tensor([], [this.outputDim]) + if (this.bias) { + ops.assign(y.tensor, this.weights.b.tensor.pick(i, null)) + } + gemv(1.0, this.weights.W.tensor.pick(i, null, null).transpose(1, 0), x.tensor, 1.0, y.tensor) + ops.maxeq(featMax.tensor, y.tensor) + } + + x.tensor = featMax.tensor + return x + } +} diff --git a/src/layers/core/index.js b/src/layers/core/index.js index c7f09c1..e18356e 100644 --- a/src/layers/core/index.js +++ b/src/layers/core/index.js @@ -6,6 +6,8 @@ import Reshape from './Reshape' import Permute from './Permute' import RepeatVector from './RepeatVector' import Merge from './Merge' +import Highway from './Highway' +import MaxoutDense from './MaxoutDense' export { Dense, @@ -15,5 +17,7 @@ export { Reshape, Permute, RepeatVector, - Merge + Merge, + Highway, + MaxoutDense } diff --git a/test/core/Dense.js b/test/core/Dense.js index 0fd7fe9..46a1923 100644 --- a/test/core/Dense.js +++ b/test/core/Dense.js @@ -18,7 +18,7 @@ describe('core layer: Dense', function () { describe('CPU', function () { before(function () { - console.log('\n%cDense', styles.h2) + console.log('\n%cCPU', styles.h2) }) it('[core.Dense.0] [CPU] should produce expected values', function () { @@ -82,7 +82,7 @@ describe('core layer: Dense', function () { describe('CPU', function () { before(function () { - console.log('\n%cDense', styles.h2) + console.log('\n%cGPU', styles.h2) }) it('[core.Dense.3] [GPU] should produce expected values', function () { diff --git a/test/core/Highway.js b/test/core/Highway.js new file mode 100644 index 0000000..168a54e --- /dev/null +++ b/test/core/Highway.js @@ -0,0 +1,68 @@ +/* eslint-env browser, mocha */ + +describe('core layer: Highway', function () { + const assert = chai.assert + const styles = testGlobals.styles + const logTime = testGlobals.logTime + const stringifyCondensed = testGlobals.stringifyCondensed + const approxEquals = KerasJS.testUtils.approxEquals + const layers = KerasJS.layers + + before(function () { + console.log('\n%ccore layer: Highway', styles.h1) + }) + + it('[core.Highway.0] should produce expected values, transformBias=-2, activation=linear bias=true', function () { + const key = 'core.Highway.0' + console.log(`\n%c[${key}] transformBias=-2, activation=linear bias=true`, styles.h3) + let testLayer = new layers.Highway({ transformBias: -2, activation: 'linear', bias: true }) + testLayer.setWeights(TEST_DATA[key].weights.map(w => new KerasJS.Tensor(w.data, w.shape))) + let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape) + console.log('%cin', styles.h4, stringifyCondensed(t.tensor)) + const startTime = performance.now() + t = testLayer.call(t) + const endTime = performance.now() + console.log('%cout', styles.h4, stringifyCondensed(t.tensor)) + logTime(startTime, endTime) + const dataExpected = new Float32Array(TEST_DATA[key].expected.data) + const shapeExpected = TEST_DATA[key].expected.shape + assert.deepEqual(t.tensor.shape, shapeExpected) + assert.isTrue(approxEquals(t.tensor, dataExpected)) + }) + + it('[core.Highway.1] should produce expected values, transformBias=-5, activation=tanh bias=true', function () { + const key = 'core.Highway.1' + console.log(`\n%c[${key}] transformBias=-5, activation=tanh bias=true`, styles.h3) + let testLayer = new layers.Highway({ transformBias: -5, activation: 'tanh', bias: true }) + testLayer.setWeights(TEST_DATA[key].weights.map(w => new KerasJS.Tensor(w.data, w.shape))) + let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape) + console.log('%cin', styles.h4, stringifyCondensed(t.tensor)) + const startTime = performance.now() + t = testLayer.call(t) + const endTime = performance.now() + console.log('%cout', styles.h4, stringifyCondensed(t.tensor)) + logTime(startTime, endTime) + const dataExpected = new Float32Array(TEST_DATA[key].expected.data) + const shapeExpected = TEST_DATA[key].expected.shape + assert.deepEqual(t.tensor.shape, shapeExpected) + assert.isTrue(approxEquals(t.tensor, dataExpected)) + }) + + it('[core.Highway.2] should produce expected values, transformBias=1, activation=hardSigmoid bias=false', function () { + const key = 'core.Highway.2' + console.log(`\n%c[${key}] transformBias=1, activation=hardSigmoid bias=false`, styles.h3) + let testLayer = new layers.Highway({ transformBias: 1, activation: 'hardSigmoid', bias: false }) + testLayer.setWeights(TEST_DATA[key].weights.map(w => new KerasJS.Tensor(w.data, w.shape))) + let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape) + console.log('%cin', styles.h4, stringifyCondensed(t.tensor)) + const startTime = performance.now() + t = testLayer.call(t) + const endTime = performance.now() + console.log('%cout', styles.h4, stringifyCondensed(t.tensor)) + logTime(startTime, endTime) + const dataExpected = new Float32Array(TEST_DATA[key].expected.data) + const shapeExpected = TEST_DATA[key].expected.shape + assert.deepEqual(t.tensor.shape, shapeExpected) + assert.isTrue(approxEquals(t.tensor, dataExpected)) + }) +}) diff --git a/test/core/MaxoutDense.js b/test/core/MaxoutDense.js new file mode 100644 index 0000000..d84e1e6 --- /dev/null +++ b/test/core/MaxoutDense.js @@ -0,0 +1,50 @@ +/* eslint-env browser, mocha */ + +describe('core layer: MaxoutDense', function () { + const assert = chai.assert + const styles = testGlobals.styles + const logTime = testGlobals.logTime + const stringifyCondensed = testGlobals.stringifyCondensed + const approxEquals = KerasJS.testUtils.approxEquals + const layers = KerasJS.layers + + before(function () { + console.log('\n%ccore layer: MaxoutDense', styles.h1) + }) + + it('[core.MaxoutDense.0] should produce expected values, nbFeature=4, bias=true', function () { + const key = 'core.MaxoutDense.0' + console.log(`\n%c[${key}] nbFeature=4, bias=true`, styles.h3) + let testLayer = new layers.MaxoutDense(3) + testLayer.setWeights(TEST_DATA[key].weights.map(w => new KerasJS.Tensor(w.data, w.shape))) + let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape) + console.log('%cin', styles.h4, stringifyCondensed(t.tensor)) + const startTime = performance.now() + t = testLayer.call(t) + const endTime = performance.now() + console.log('%cout', styles.h4, stringifyCondensed(t.tensor)) + logTime(startTime, endTime) + const dataExpected = new Float32Array(TEST_DATA[key].expected.data) + const shapeExpected = TEST_DATA[key].expected.shape + assert.deepEqual(t.tensor.shape, shapeExpected) + assert.isTrue(approxEquals(t.tensor, dataExpected)) + }) + + it('[core.MaxoutDense.1] should produce expected values, nbFeature=7, bias=false', function () { + const key = 'core.MaxoutDense.1' + console.log(`\n%c[${key}] nbFeature=7, bias=false`, styles.h3) + let testLayer = new layers.MaxoutDense(3, { bias: false }) + testLayer.setWeights(TEST_DATA[key].weights.map(w => new KerasJS.Tensor(w.data, w.shape))) + let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape) + console.log('%cin', styles.h4, stringifyCondensed(t.tensor)) + const startTime = performance.now() + t = testLayer.call(t) + const endTime = performance.now() + console.log('%cout', styles.h4, stringifyCondensed(t.tensor)) + logTime(startTime, endTime) + const dataExpected = new Float32Array(TEST_DATA[key].expected.data) + const shapeExpected = TEST_DATA[key].expected.shape + assert.deepEqual(t.tensor.shape, shapeExpected) + assert.isTrue(approxEquals(t.tensor, dataExpected)) + }) +}) diff --git a/test/core/data_Highway.js b/test/core/data_Highway.js new file mode 100644 index 0000000..8f8f97e --- /dev/null +++ b/test/core/data_Highway.js @@ -0,0 +1,86 @@ +// TEST DATA +// Keyed by mocha test ID +// Python code for generating test data can be found in the matching jupyter notebook in folder `notebooks/`. + +(function () { + var DATA = { + 'core.Highway.0': { + input: { + data: [-0.665722, -0.215115, 0.236105, -0.17614, -0.99507, 0.768064], + shape: [6] + }, + weights: [ + { + data: [0.176262, 0.795427, 0.783061, 0.631675, -0.928221, 0.383515, -0.242638, 0.037022, 0.315903, -0.6123, -0.455367, 0.437212, 0.566007, 0.700655, 0.55049, -0.926671, -0.766613, 0.502561, -0.521564, -0.490388, 0.715251, 0.899558, 0.123374, -0.642439, 0.540504, -0.015238, 0.262506, 0.678996, -0.077921, -0.00412, 0.358822, 0.301572, -0.46241, -0.865351, 0.54289, -0.038032], + shape: [6, 6] + }, + { + data: [-0.90255, -0.421781, 0.441933, -0.956768, -0.588154, -0.898453], + shape: [6] + }, + { + data: [-0.583079, -0.036638, -0.158924, 0.718364, -0.657677, -0.322272, -0.458934, 0.382083, -0.559191, 0.623902, -0.978946, 0.122407, 0.627452, 0.490201, -0.621777, -0.987718, 0.544088, 0.915664, 0.403876, -0.404843, 0.535985, 0.376437, -0.225633, 0.230412, -0.14489, 0.168579, 0.405271, -0.77621, 0.84654, 0.977773, 0.354822, 0.59033, -0.941847, -0.644482, 0.749855, 0.489864], + shape: [6, 6] + }, + { + data: [0.034596, 0.893925, 0.53092, -0.435208, -0.557909, 0.372444], + shape: [6] + } + ], + expected: { + data: [-0.914347, -0.408456, -0.114281, -0.888056, -0.290505, -0.199544], + shape: [6] + } + }, + 'core.Highway.1': { + input: { + data: [-0.62992, -0.960677, 0.906504, 0.360902, -0.026824], + shape: [5] + }, + weights: [ + { + data: [0.288287, -0.238503, 0.326096, -0.672699, 0.925216, -0.306676, 0.983502, -0.529884, 0.171389, -0.18662, -0.727531, 0.088273, 0.036353, 0.53371, 0.8677, -0.820593, -0.608457, 0.988387, -0.529639, -0.522027, 0.2582, 0.469905, 0.376689, -0.937739, 0.805028], + shape: [5, 5] + }, + { + data: [-0.427892, 0.916211, 0.540626, 0.97374, -0.583669], + shape: [5] + }, + { + data: [0.717779, -0.254578, 0.110258, 0.911313, 0.473339, 0.63241, -0.797827, 0.856976, 0.218218, 0.193107, -0.816432, -0.309628, 0.325505, -0.116573, 0.102976, 0.407425, 0.178802, -0.900134, 0.123584, 0.532717, 0.821817, -0.81418, 0.805043, -0.078079, -0.095963], + shape: [5, 5] + }, + { + data: [-0.50298, -0.100049, -0.178118, -0.479401, 0.740791], + shape: [5] + } + ], + expected: { + data: [-0.652907, -0.353259, 0.890362, 0.477292, -0.256096], + shape: [5] + } + }, + 'core.Highway.2': { + input: { + data: [0.409965, -0.370646, 0.490565, -0.203574], + shape: [4] + }, + weights: [ + { + data: [-0.184626, -0.889268, 0.57707, -0.42539, -0.099299, -0.392175, 0.052799, 0.247624, 0.553551, 0.372483, 0.961878, 0.201632, 0.627937, 0.41729, -0.944931, 0.808534], + shape: [4, 4] + }, + { + data: [-0.498153, -0.907808, 0.353632, -0.913061, -0.767153, 0.207731, -0.618139, 0.337031, 0.834896, -0.16244, -0.33548, -0.433933, -0.627435, -0.365779, -0.037663, -0.860959], + shape: [4, 4] + } + ], + expected: { + data: [0.482075, -0.04199, 0.593448, 0.031503], + shape: [4] + } + } + } + + window.TEST_DATA = Object.assign({}, window.TEST_DATA, DATA) +})() diff --git a/test/core/data_MaxoutDense.js b/test/core/data_MaxoutDense.js new file mode 100644 index 0000000..dd8bfe7 --- /dev/null +++ b/test/core/data_MaxoutDense.js @@ -0,0 +1,46 @@ +// TEST DATA +// Keyed by mocha test ID +// Python code for generating test data can be found in the matching jupyter notebook in folder `notebooks/`. + +(function () { + var DATA = { + 'core.MaxoutDense.0': { + input: { + data: [-0.992103, 0.024385, 0.625242, 0.225052, 0.443511, -0.416248], + shape: [6] + }, + weights: [ + { + data: [0.542641, -0.958496, 0.267296, 0.497608, -0.002986, -0.550407, -0.603874, 0.521061, -0.661778, -0.82332, 0.37072, 0.906787, -0.992103, 0.024385, 0.625242, 0.225052, 0.443511, -0.416248, 0.835548, 0.429152, 0.085089, -0.71566, -0.253318, 0.348267, -0.116334, -0.131972, 0.235534, 0.026276, 0.300794, 0.202078, 0.610446, 0.043294, 0.817298, -0.361528, -0.819081, -0.3986, -0.772031, 0.657363, -0.906207, 0.252574, 0.095172, 0.638574, -0.602105, 0.713701, -0.296695, 0.509295, -0.408077, 0.767873, -0.348977, -0.669968, -0.214942, -0.813079, 0.642211, -0.697696, -0.231771, 0.888521, 0.975251, -0.087391, 0.652246, -0.497252, 0.194743, 0.805664, 0.069116, 0.180403, -0.921436, -0.285636, -0.840774, -0.38908, -0.338561, 0.547661, -0.920082, -0.141016], + shape: [4, 6, 3] + }, + { + data: [0.542641, -0.958496, 0.267296, 0.497608, -0.002986, -0.550407, -0.603874, 0.521061, -0.661778, -0.82332, 0.37072, 0.906787], + shape: [4, 3] + } + ], + expected: { + data: [0.090044, 0.227783, 0.435236], + shape: [3] + } + }, + 'core.MaxoutDense.1': { + input: { + data: [-0.104458, 0.101279, 0.94235, 0.864827, 0.681371, -0.745903], + shape: [6] + }, + weights: [ + { + data: [-0.639461, -0.96105, -0.073563, 0.449868, -0.159593, -0.029146, -0.974438, -0.025257, 0.883613, 0.70159, 0.459929, -0.782528, 0.787808, 0.714308, -0.669827, 0.264668, -0.959033, -0.766525, -0.367265, -0.684175, 0.517959, 0.636551, -0.310751, -0.362402, -0.776678, -0.832094, 0.425452, 0.199087, -0.888653, -0.040405, -0.196647, 0.695958, 0.435698, 0.204128, 0.104768, 0.898205, 0.973347, -0.323892, -0.520251, 0.592872, -0.872627, -0.270769, -0.859954, -0.361265, -0.859235, -0.419473, 0.580202, 0.810801, 0.585243, 0.123637, 0.232037, -0.277033, -0.662365, -0.127518, 0.465651, -0.874225, -0.958534, 0.541096, -0.400096, 0.402329, 0.469335, 0.865809, -0.199343, -0.283124, 0.613134, 0.528982, 0.305229, 0.621933, 0.28443, 0.914888, -0.332251, 0.476505, 0.899667, -0.331272, 0.223264, -0.26866, -0.076919, -0.849996, -0.961313, 0.519299, -0.094482, 0.245668, 0.479903, -0.162673, -0.264765, -0.661942, 0.587745, 0.666075, 0.468426, 0.750589, 0.296113, 0.392132, -0.613964, 0.535264, -0.331834, -0.124082, -0.36226, 0.136579, 0.317385, 0.151117, -0.363626, -0.563993, 0.689877, -0.395671, -0.123559, -0.817171, -0.398042, -0.82861, -0.312573, 0.418602, 0.947937, 0.25045, -0.461393, 0.313418, 0.253996, 0.651304, 0.067388, 0.822243, -0.168993, -0.427911, 0.038015, 0.8449, 0.447823, -0.012174, -0.005007, 0.297444], + shape: [7, 6, 3] + } + ], + expected: { + data: [1.043451, 2.068543, 0.396771], + shape: [3] + } + } + } + + window.TEST_DATA = Object.assign({}, window.TEST_DATA, DATA) +})()