mirror of
https://github.com/wassname/keras-js.git
synced 2026-09-09 11:25:25 +08:00
implement Embedding layer, with tests
This commit is contained in:
Leon Chen
@@ -97,6 +97,9 @@
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<script src="/test/normalization/data_BatchNormalization.js"></script>
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<script src="/test/normalization/BatchNormalization.js"></script>
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<script src="/test/embeddings/data_Embedding.js"></script>
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<script src="/test/embeddings/Embedding.js"></script>
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<script>
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// mocha.checkLeaks();
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mocha.globals(['jQuery']);
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@@ -0,0 +1,248 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Using TensorFlow backend.\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"from keras.models import Model\n",
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"from keras.layers import Input\n",
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"from keras.layers.embeddings import Embedding\n",
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"from keras import backend as K"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def format_decimal(arr, places=6):\n",
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" return [round(x * 10**places) / 10**places for x in arr]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Embedding"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**[embeddings.Embedding.0] input_dim 5, output_dim 3, input_length=7, mask_zero=False, dropout=0.**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"W shape: (5, 3)\n",
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"W: [0.123907, 0.924736, 0.347325, 0.711111, -0.843888, -0.295, -0.702738, -0.469642, -0.024244, 0.962892, -0.00486, -0.636172, -0.318551, 0.114577, 0.674536]\n",
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"\n",
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"in shape: (7,)\n",
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"in: [1, 1, 1, 2, 0, 0, 3]\n",
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"out shape: (7, 3)\n",
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"out: [0.711111, -0.843888, -0.295, 0.711111, -0.843888, -0.295, 0.711111, -0.843888, -0.295, -0.702738, -0.469642, -0.024244, 0.123907, 0.924736, 0.347325, 0.123907, 0.924736, 0.347325, 0.962892, -0.00486, -0.636172]\n"
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]
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}
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],
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"source": [
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"input_dim = 5\n",
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"output_dim = 3\n",
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"input_length = 7\n",
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"data_in_shape = (input_length,)\n",
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"emb = Embedding(input_dim, output_dim, input_length=input_length, mask_zero=False, dropout=0.)\n",
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"\n",
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"layer_0 = Input(shape=data_in_shape)\n",
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"layer_1 = emb(layer_0)\n",
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"model = Model(input=layer_0, output=layer_1)\n",
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"\n",
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"# set weights to random (use seed for reproducibility)\n",
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"weights = []\n",
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"for i, w in enumerate(model.get_weights()):\n",
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" np.random.seed(1200 + i)\n",
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" weights.append(2 * np.random.random(w.shape) - 1)\n",
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"model.set_weights(weights)\n",
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"print('W shape:', weights[0].shape)\n",
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"print('W:', format_decimal(weights[0].ravel().tolist()))\n",
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"\n",
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"data_in = np.random.randint(0, input_dim - 1, data_in_shape)\n",
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"print('')\n",
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"print('in shape:', data_in_shape)\n",
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"print('in:', data_in.ravel().tolist())\n",
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"result = model.predict(np.array([data_in]))\n",
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"print('out shape:', result[0].shape)\n",
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"print('out:', format_decimal(result[0].ravel().tolist()))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**[embeddings.Embedding.1] input_dim 20, output_dim 5, input_length=10, mask_zero=True, dropout=0.**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"W shape: (20, 5)\n",
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"W: [-0.383154, 0.451816, 0.335889, 0.941217, 0.853643, -0.668432, 0.074737, -0.716747, -0.816627, 0.866955, -0.792696, 0.164527, 0.452684, 0.628771, -0.254798, -0.612431, 0.523026, -0.591887, -0.064898, 0.053117, -0.867557, 0.504834, 0.679206, -0.556031, 0.36241, 0.709619, 0.433085, 0.934247, 0.971585, -0.162598, 0.335228, -0.050332, 0.277192, 0.254304, -0.351008, 0.564886, 0.928261, -0.292494, -0.169661, 0.444558, 0.417255, 0.922794, -0.33368, 0.50358, -0.971797, -0.328566, -0.691183, -0.490174, 0.510752, -0.597686, 0.531579, 0.618621, -0.66854, 0.905422, 0.862149, 0.764005, -0.881062, 0.956836, 0.507665, -0.423755, -0.036345, -0.009073, -0.612606, 0.135378, -0.748721, -0.996749, -0.556871, 0.103616, -0.929907, 0.337223, 0.171248, -0.19617, -0.093472, -0.148244, -0.785821, 0.920643, -0.685849, -0.962657, -0.460922, -0.169631, -0.516211, -0.804437, -0.39244, 0.665189, 0.635366, 0.501686, 0.997166, -0.210946, 0.606477, -0.474869, 0.713225, 0.382837, -0.566294, 0.239002, 0.784362, -0.693148, -0.893049, 0.509413, -0.576682, -0.620533]\n",
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"\n",
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"in shape: (10,)\n",
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"in: [8, 6, 14, 8, 11, 17, 10, 18, 0, 13]\n",
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"out shape: (10, 5)\n",
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"out: [0.417255, 0.922794, -0.33368, 0.50358, -0.971797, 0.335228, -0.050332, 0.277192, 0.254304, -0.351008, 0.171248, -0.19617, -0.093472, -0.148244, -0.785821, 0.417255, 0.922794, -0.33368, 0.50358, -0.971797, 0.764005, -0.881062, 0.956836, 0.507665, -0.423755, 0.501686, 0.997166, -0.210946, 0.606477, -0.474869, 0.531579, 0.618621, -0.66854, 0.905422, 0.862149, 0.713225, 0.382837, -0.566294, 0.239002, 0.784362, -0.383154, 0.451816, 0.335889, 0.941217, 0.853643, -0.996749, -0.556871, 0.103616, -0.929907, 0.337223]\n"
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]
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}
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],
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"source": [
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"input_dim = 20\n",
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"output_dim = 5\n",
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"input_length = 10\n",
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"data_in_shape = (input_length,)\n",
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"emb = Embedding(input_dim, output_dim, input_length=input_length, mask_zero=True, dropout=0.)\n",
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"\n",
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"layer_0 = Input(shape=data_in_shape)\n",
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"layer_1 = emb(layer_0)\n",
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"model = Model(input=layer_0, output=layer_1)\n",
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"\n",
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"# set weights to random (use seed for reproducibility)\n",
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"weights = []\n",
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"for i, w in enumerate(model.get_weights()):\n",
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" np.random.seed(1210 + i)\n",
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" weights.append(2 * np.random.random(w.shape) - 1)\n",
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"model.set_weights(weights)\n",
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"print('W shape:', weights[0].shape)\n",
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"print('W:', format_decimal(weights[0].ravel().tolist()))\n",
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"\n",
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"data_in = np.random.randint(0, input_dim - 1, data_in_shape)\n",
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"print('')\n",
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"print('in shape:', data_in_shape)\n",
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"print('in:', data_in.ravel().tolist())\n",
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"result = model.predict(np.array([data_in]))\n",
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"print('out shape:', result[0].shape)\n",
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"print('out:', format_decimal(result[0].ravel().tolist()))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**[embeddings.Embedding.2] input_dim 33, output_dim 2, input_length=5, mask_zero=False, dropout=0.5**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"W shape: (33, 2)\n",
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"W: [-0.616375, -0.01389, 0.09463, 0.74616, 0.903976, 0.46756, 0.810735, -0.196574, 0.312078, 0.931824, -0.658021, -0.305244, 0.16033, -0.01051, -0.669953, -0.377897, 0.842174, -0.54385, -0.321611, 0.19085, 0.963801, 0.015549, 0.436929, -0.069782, 0.088399, -0.63811, 0.250636, 0.205862, 0.01425, 0.436179, -0.020659, 0.844792, -0.635104, -0.550835, 0.334066, -0.703519, -0.232756, 0.493263, -0.097717, -0.695123, 0.712031, 0.722442, -0.432591, 0.711508, 0.074558, 0.216026, 0.255217, 0.406698, -0.983917, -0.519696, -0.199758, 0.134159, -0.177873, -0.491386, 0.456317, 0.961498, 0.387117, 0.162469, 0.907715, -0.253443, -0.717262, -0.557943, 0.154291, 0.706519, 0.53983, -0.907997]\n",
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"\n",
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"in shape: (5,)\n",
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"in: [28, 9, 19, 30, 18]\n",
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"out shape: (5, 2)\n",
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"out: [0.387117, 0.162469, -0.321611, 0.19085, -0.097717, -0.695123, -0.717262, -0.557943, -0.232756, 0.493263]\n"
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]
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}
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],
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"source": [
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"input_dim = 33\n",
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"output_dim = 2\n",
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"input_length = 5\n",
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"data_in_shape = (input_length,)\n",
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"emb = Embedding(input_dim, output_dim, input_length=input_length, mask_zero=False, dropout=0.5)\n",
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"\n",
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"layer_0 = Input(shape=data_in_shape)\n",
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"layer_1 = emb(layer_0)\n",
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"model = Model(input=layer_0, output=layer_1)\n",
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"\n",
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"# set weights to random (use seed for reproducibility)\n",
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"weights = []\n",
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"for i, w in enumerate(model.get_weights()):\n",
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" np.random.seed(1220 + i)\n",
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" weights.append(2 * np.random.random(w.shape) - 1)\n",
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"model.set_weights(weights)\n",
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"print('W shape:', weights[0].shape)\n",
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"print('W:', format_decimal(weights[0].ravel().tolist()))\n",
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"\n",
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"data_in = np.random.randint(0, input_dim - 1, data_in_shape)\n",
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"print('')\n",
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"print('in shape:', data_in_shape)\n",
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"print('in:', data_in.ravel().tolist())\n",
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"result = model.predict(np.array([data_in]))\n",
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"print('out shape:', result[0].shape)\n",
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"print('out:', format_decimal(result[0].ravel().tolist()))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"anaconda-cloud": {},
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"kernelspec": {
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"display_name": "Python [default]",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}
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@@ -0,0 +1,49 @@
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import Layer from '../../engine/Layer'
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import Tensor from '../../Tensor'
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import ops from 'ndarray-ops'
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/**
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* Embedding layer class
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*/
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export default class Embedding extends Layer {
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/**
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* Creates a Embedding layer
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*/
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constructor (inputDim, outputDim, attrs = {}) {
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super(attrs)
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const {
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inputLength = 0,
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maskZero = false,
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dropout = 0.0
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} = attrs
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this.inputDim = inputDim
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this.outputDim = outputDim
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this.inputLength = inputLength
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// maskZero will be important for subsequence layers
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this.maskZero = maskZero
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// relevant only during training phase
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this.dropout = dropout
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// Layer weights specification
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this.params = ['W']
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}
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/**
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* Method for layer computational logic
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* @param {Tensor} x
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* @returns {Tensor} x
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*/
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call (x) {
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let y = new Tensor([], [x.tensor.shape[0], this.weights.W.tensor.shape[1]])
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for (let i = 0, len = x.tensor.shape[0]; i < len; i++) {
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ops.assign(y.tensor.pick(i, null), this.weights.W.tensor.pick(x.tensor.get(i), null))
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}
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x.tensor = y.tensor
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return x
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}
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}
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@@ -0,0 +1,5 @@
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import Embedding from './Embedding'
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export {
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Embedding
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}
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@@ -3,3 +3,4 @@ export * from './core'
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export * from './convolutional'
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export * from './pooling'
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export * from './normalization'
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export * from './embeddings'
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@@ -0,0 +1,51 @@
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/* eslint-env browser, mocha */
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describe.only('embeddings layer: Embedding', function () {
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const assert = chai.assert
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const styles = testGlobals.styles
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const logTime = testGlobals.logTime
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const stringifyCondensed = testGlobals.stringifyCondensed
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const approxEquals = KerasJS.testUtils.approxEquals
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const layers = KerasJS.layers
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const testParams = [
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{
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inputShape: [7],
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attrs: { inputDim: 5, outputDim: 3, inputLength: 7, maskZero: false, dropout: 0 }
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},
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{
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inputShape: [10],
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attrs: { inputDim: 20, outputDim: 5, inputLength: 10, maskZero: true, dropout: 0 }
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},
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{
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inputShape: [5],
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attrs: { inputDim: 33, outputDim: 2, inputLength: 5, maskZero: false, dropout: 0.5 }
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}
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]
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before(function () {
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console.log('\n%cembeddings layer: Embedding', styles.h1)
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})
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testParams.forEach(({ attrs }, i) => {
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const key = `embeddings.Embedding.${i}`
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const title = `[${key}] test: inputDim='${attrs.inputDim}', outputDim=${attrs.outputDim}, inputLength=${attrs.inputLength}, maskZero=${attrs.maskZero}, dropout=${attrs.dropout}`
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it(title, function () {
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console.log(`\n%c${title}`, styles.h3)
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let testLayer = new layers.Embedding(attrs)
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testLayer.setWeights(TEST_DATA[key].weights.map(w => new KerasJS.Tensor(w.data, w.shape)))
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let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
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console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
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const startTime = performance.now()
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t = testLayer.call(t)
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const endTime = performance.now()
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console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
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logTime(startTime, endTime)
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const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
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const shapeExpected = TEST_DATA[key].expected.shape
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assert.deepEqual(t.tensor.shape, shapeExpected)
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assert.isTrue(approxEquals(t.tensor, dataExpected))
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})
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})
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})
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@@ -0,0 +1,58 @@
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// TEST DATA
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// Keyed by mocha test ID
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// Python code for generating test data can be found in the matching jupyter notebook in folder `notebooks/`.
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||||
|
||||
(function () {
|
||||
var DATA = {
|
||||
'embeddings.Embedding.0': {
|
||||
input: {
|
||||
data: [1, 1, 1, 2, 0, 0, 3],
|
||||
shape: [7]
|
||||
},
|
||||
weights: [
|
||||
{
|
||||
data: [0.123907, 0.924736, 0.347325, 0.711111, -0.843888, -0.295, -0.702738, -0.469642, -0.024244, 0.962892, -0.00486, -0.636172, -0.318551, 0.114577, 0.674536],
|
||||
shape: [5, 3]
|
||||
}
|
||||
],
|
||||
expected: {
|
||||
data: [0.711111, -0.843888, -0.295, 0.711111, -0.843888, -0.295, 0.711111, -0.843888, -0.295, -0.702738, -0.469642, -0.024244, 0.123907, 0.924736, 0.347325, 0.123907, 0.924736, 0.347325, 0.962892, -0.00486, -0.636172],
|
||||
shape: [7, 3]
|
||||
}
|
||||
},
|
||||
'embeddings.Embedding.1': {
|
||||
input: {
|
||||
data: [8, 6, 14, 8, 11, 17, 10, 18, 0, 13],
|
||||
shape: [10]
|
||||
},
|
||||
weights: [
|
||||
{
|
||||
data: [-0.383154, 0.451816, 0.335889, 0.941217, 0.853643, -0.668432, 0.074737, -0.716747, -0.816627, 0.866955, -0.792696, 0.164527, 0.452684, 0.628771, -0.254798, -0.612431, 0.523026, -0.591887, -0.064898, 0.053117, -0.867557, 0.504834, 0.679206, -0.556031, 0.36241, 0.709619, 0.433085, 0.934247, 0.971585, -0.162598, 0.335228, -0.050332, 0.277192, 0.254304, -0.351008, 0.564886, 0.928261, -0.292494, -0.169661, 0.444558, 0.417255, 0.922794, -0.33368, 0.50358, -0.971797, -0.328566, -0.691183, -0.490174, 0.510752, -0.597686, 0.531579, 0.618621, -0.66854, 0.905422, 0.862149, 0.764005, -0.881062, 0.956836, 0.507665, -0.423755, -0.036345, -0.009073, -0.612606, 0.135378, -0.748721, -0.996749, -0.556871, 0.103616, -0.929907, 0.337223, 0.171248, -0.19617, -0.093472, -0.148244, -0.785821, 0.920643, -0.685849, -0.962657, -0.460922, -0.169631, -0.516211, -0.804437, -0.39244, 0.665189, 0.635366, 0.501686, 0.997166, -0.210946, 0.606477, -0.474869, 0.713225, 0.382837, -0.566294, 0.239002, 0.784362, -0.693148, -0.893049, 0.509413, -0.576682, -0.620533],
|
||||
shape: [20, 5]
|
||||
}
|
||||
],
|
||||
expected: {
|
||||
data: [0.417255, 0.922794, -0.33368, 0.50358, -0.971797, 0.335228, -0.050332, 0.277192, 0.254304, -0.351008, 0.171248, -0.19617, -0.093472, -0.148244, -0.785821, 0.417255, 0.922794, -0.33368, 0.50358, -0.971797, 0.764005, -0.881062, 0.956836, 0.507665, -0.423755, 0.501686, 0.997166, -0.210946, 0.606477, -0.474869, 0.531579, 0.618621, -0.66854, 0.905422, 0.862149, 0.713225, 0.382837, -0.566294, 0.239002, 0.784362, -0.383154, 0.451816, 0.335889, 0.941217, 0.853643, -0.996749, -0.556871, 0.103616, -0.929907, 0.337223],
|
||||
shape: [10, 5]
|
||||
}
|
||||
},
|
||||
'embeddings.Embedding.2': {
|
||||
input: {
|
||||
data: [28, 9, 19, 30, 18],
|
||||
shape: [5]
|
||||
},
|
||||
weights: [
|
||||
{
|
||||
data: [-0.616375, -0.01389, 0.09463, 0.74616, 0.903976, 0.46756, 0.810735, -0.196574, 0.312078, 0.931824, -0.658021, -0.305244, 0.16033, -0.01051, -0.669953, -0.377897, 0.842174, -0.54385, -0.321611, 0.19085, 0.963801, 0.015549, 0.436929, -0.069782, 0.088399, -0.63811, 0.250636, 0.205862, 0.01425, 0.436179, -0.020659, 0.844792, -0.635104, -0.550835, 0.334066, -0.703519, -0.232756, 0.493263, -0.097717, -0.695123, 0.712031, 0.722442, -0.432591, 0.711508, 0.074558, 0.216026, 0.255217, 0.406698, -0.983917, -0.519696, -0.199758, 0.134159, -0.177873, -0.491386, 0.456317, 0.961498, 0.387117, 0.162469, 0.907715, -0.253443, -0.717262, -0.557943, 0.154291, 0.706519, 0.53983, -0.907997],
|
||||
shape: [33, 2]
|
||||
}
|
||||
],
|
||||
expected: {
|
||||
data: [0.387117, 0.162469, -0.321611, 0.19085, -0.097717, -0.695123, -0.717262, -0.557943, -0.232756, 0.493263],
|
||||
shape: [5, 2]
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
window.TEST_DATA = Object.assign({}, window.TEST_DATA, DATA)
|
||||
})()
|
||||
Reference in New Issue
Block a user