mirror of
https://github.com/wassname/keras-js.git
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add ZeroPadding1D/2D/3D layers, with tests
This commit is contained in:
Leon Chen
@@ -56,6 +56,12 @@
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<script src="/test/convolutional/UpSampling2D.js"></script>
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<script src="/test/convolutional/data_UpSampling3D.js"></script>
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<script src="/test/convolutional/UpSampling3D.js"></script>
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<script src="/test/convolutional/data_ZeroPadding1D.js"></script>
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<script src="/test/convolutional/ZeroPadding1D.js"></script>
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<script src="/test/convolutional/data_ZeroPadding2D.js"></script>
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<script src="/test/convolutional/ZeroPadding2D.js"></script>
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<script src="/test/convolutional/data_ZeroPadding3D.js"></script>
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<script src="/test/convolutional/ZeroPadding3D.js"></script>
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<script>
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// mocha.checkLeaks();
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@@ -0,0 +1,166 @@
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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.convolutional import ZeroPadding1D\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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"### ZeroPadding1D"
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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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"**[convolutional.ZeroPadding1D.0] padding 1 on 3x5 input**"
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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": 3,
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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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"\n",
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"in shape: (3, 5)\n",
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"in: [-0.412987, -0.139246, 0.567466, -0.512174, -0.33603, 0.585584, 0.453472, 0.942013, -0.609538, -0.894302, 0.711927, -0.126057, 0.677549, 0.676991, 0.471487]\n",
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"out shape: (5, 5)\n",
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"out: [0.0, 0.0, 0.0, 0.0, 0.0, -0.412987, -0.139246, 0.567466, -0.512174, -0.33603, 0.585584, 0.453472, 0.942013, -0.609538, -0.894302, 0.711927, -0.126057, 0.677549, 0.676991, 0.471487, 0.0, 0.0, 0.0, 0.0, 0.0]\n"
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]
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}
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],
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"source": [
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"data_in_shape = (3, 5)\n",
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"L = ZeroPadding1D(padding=1)\n",
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"\n",
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"layer_0 = Input(shape=data_in_shape)\n",
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"layer_1 = L(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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"np.random.seed(240)\n",
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"data_in = 2 * np.random.random(data_in_shape) - 1\n",
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"print('')\n",
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"print('in shape:', data_in_shape)\n",
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"print('in:', format_decimal(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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"**[convolutional.ZeroPadding1D.0] padding 3 on 4x4 input**"
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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": 4,
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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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"\n",
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"in shape: (4, 4)\n",
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"in: [0.229895, 0.156613, -0.66952, -0.996975, 0.45773, 0.684298, -0.999213, 0.276751, -0.484373, -0.506163, 0.353904, 0.513668, -0.981594, 0.78914, 0.603978, 0.204066]\n",
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"out shape: (10, 4)\n",
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"out: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.229895, 0.156613, -0.66952, -0.996975, 0.45773, 0.684298, -0.999213, 0.276751, -0.484373, -0.506163, 0.353904, 0.513668, -0.981594, 0.78914, 0.603978, 0.204066, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n"
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]
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}
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],
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"source": [
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"data_in_shape = (4, 4)\n",
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"L = ZeroPadding1D(padding=3)\n",
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"\n",
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"layer_0 = Input(shape=data_in_shape)\n",
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"layer_1 = L(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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"np.random.seed(241)\n",
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"data_in = 2 * np.random.random(data_in_shape) - 1\n",
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"print('')\n",
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"print('in shape:', data_in_shape)\n",
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"print('in:', format_decimal(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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"kernelspec": {
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"display_name": "Python 3",
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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": 0
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}
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@@ -0,0 +1,256 @@
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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.convolutional import ZeroPadding2D\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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"### ZeroPadding2D"
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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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"**[convolutional.ZeroPadding2D.0] padding (1,1) on 3x5x2 input, dim_ordering=tf**"
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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": 3,
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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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"\n",
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"in shape: (3, 5, 2)\n",
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"in: [-0.570441, -0.454673, -0.285321, 0.237249, 0.282682, 0.428035, 0.160547, -0.332203, 0.546391, 0.272735, 0.010827, -0.763164, -0.442696, 0.381948, -0.676994, 0.753553, -0.031788, 0.915329, -0.738844, 0.269075, 0.434091, 0.991585, -0.944288, 0.258834, 0.162138, 0.565201, -0.492094, 0.170854, -0.139788, -0.710674]\n",
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"out shape: (5, 7, 2)\n",
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"out: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.570441, -0.454673, -0.285321, 0.237249, 0.282682, 0.428035, 0.160547, -0.332203, 0.546391, 0.272735, 0.0, 0.0, 0.0, 0.0, 0.010827, -0.763164, -0.442696, 0.381948, -0.676994, 0.753553, -0.031788, 0.915329, -0.738844, 0.269075, 0.0, 0.0, 0.0, 0.0, 0.434091, 0.991585, -0.944288, 0.258834, 0.162138, 0.565201, -0.492094, 0.170854, -0.139788, -0.710674, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n"
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]
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}
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],
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"source": [
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"data_in_shape = (3, 5, 2)\n",
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"L = ZeroPadding2D(padding=(1, 1), dim_ordering='tf')\n",
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"\n",
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"layer_0 = Input(shape=data_in_shape)\n",
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"layer_1 = L(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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"np.random.seed(250)\n",
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"data_in = 2 * np.random.random(data_in_shape) - 1\n",
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"print('')\n",
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"print('in shape:', data_in_shape)\n",
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"print('in:', format_decimal(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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"**[convolutional.ZeroPadding2D.1] padding (1,1) on 3x5x2 input, dim_ordering=th**"
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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": 4,
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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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"\n",
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"in shape: (3, 5, 2)\n",
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"in: [-0.570441, -0.454673, -0.285321, 0.237249, 0.282682, 0.428035, 0.160547, -0.332203, 0.546391, 0.272735, 0.010827, -0.763164, -0.442696, 0.381948, -0.676994, 0.753553, -0.031788, 0.915329, -0.738844, 0.269075, 0.434091, 0.991585, -0.944288, 0.258834, 0.162138, 0.565201, -0.492094, 0.170854, -0.139788, -0.710674]\n",
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"out shape: (3, 7, 4)\n",
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"out: [0.0, 0.0, 0.0, 0.0, 0.0, -0.570441, -0.454673, 0.0, 0.0, -0.285321, 0.237249, 0.0, 0.0, 0.282682, 0.428035, 0.0, 0.0, 0.160547, -0.332203, 0.0, 0.0, 0.546391, 0.272735, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.010827, -0.763164, 0.0, 0.0, -0.442696, 0.381948, 0.0, 0.0, -0.676994, 0.753553, 0.0, 0.0, -0.031788, 0.915329, 0.0, 0.0, -0.738844, 0.269075, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.434091, 0.991585, 0.0, 0.0, -0.944288, 0.258834, 0.0, 0.0, 0.162138, 0.565201, 0.0, 0.0, -0.492094, 0.170854, 0.0, 0.0, -0.139788, -0.710674, 0.0, 0.0, 0.0, 0.0, 0.0]\n"
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]
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}
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],
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"source": [
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"data_in_shape = (3, 5, 2)\n",
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"L = ZeroPadding2D(padding=(1, 1), dim_ordering='th')\n",
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"\n",
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"layer_0 = Input(shape=data_in_shape)\n",
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"layer_1 = L(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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"np.random.seed(250)\n",
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"data_in = 2 * np.random.random(data_in_shape) - 1\n",
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"print('')\n",
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"print('in shape:', data_in_shape)\n",
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"print('in:', format_decimal(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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"**[convolutional.ZeroPadding2D.2] padding (3,2) on 2x6x4 input, dim_ordering=tf**"
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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": 5,
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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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"\n",
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"in shape: (2, 6, 4)\n",
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"in: [-0.989173, -0.133618, -0.505338, 0.023259, 0.503982, -0.303769, -0.436321, 0.793911, 0.416102, 0.806405, -0.098342, -0.738022, -0.982676, 0.805073, 0.741244, -0.941634, -0.253526, -0.136544, -0.295772, 0.207565, -0.517246, -0.686963, -0.176235, -0.354111, -0.862411, -0.969822, 0.200074, 0.290718, -0.038623, 0.294839, 0.247968, 0.557946, -0.455596, 0.6624, 0.879529, -0.466772, 0.40423, 0.213794, 0.645662, -0.044634, -0.552595, 0.771242, -0.131944, -0.172725, 0.700856, -0.001994, 0.606737, -0.593306]\n",
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"out shape: (8, 10, 4)\n",
|
||||
"out: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.989173, -0.133618, -0.505338, 0.023259, 0.503982, -0.303769, -0.436321, 0.793911, 0.416102, 0.806405, -0.098342, -0.738022, -0.982676, 0.805073, 0.741244, -0.941634, -0.253526, -0.136544, -0.295772, 0.207565, -0.517246, -0.686963, -0.176235, -0.354111, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.862411, -0.969822, 0.200074, 0.290718, -0.038623, 0.294839, 0.247968, 0.557946, -0.455596, 0.6624, 0.879529, -0.466772, 0.40423, 0.213794, 0.645662, -0.044634, -0.552595, 0.771242, -0.131944, -0.172725, 0.700856, -0.001994, 0.606737, -0.593306, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"data_in_shape = (2, 6, 4)\n",
|
||||
"L = ZeroPadding2D(padding=(3, 2), dim_ordering='tf')\n",
|
||||
"\n",
|
||||
"layer_0 = Input(shape=data_in_shape)\n",
|
||||
"layer_1 = L(layer_0)\n",
|
||||
"model = Model(input=layer_0, output=layer_1)\n",
|
||||
"\n",
|
||||
"# set weights to random (use seed for reproducibility)\n",
|
||||
"np.random.seed(252)\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": [
|
||||
"**[convolutional.ZeroPadding2D.3] padding (3,2) on 2x6x4 input, dim_ordering=th**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"in shape: (2, 6, 4)\n",
|
||||
"in: [-0.989173, -0.133618, -0.505338, 0.023259, 0.503982, -0.303769, -0.436321, 0.793911, 0.416102, 0.806405, -0.098342, -0.738022, -0.982676, 0.805073, 0.741244, -0.941634, -0.253526, -0.136544, -0.295772, 0.207565, -0.517246, -0.686963, -0.176235, -0.354111, -0.862411, -0.969822, 0.200074, 0.290718, -0.038623, 0.294839, 0.247968, 0.557946, -0.455596, 0.6624, 0.879529, -0.466772, 0.40423, 0.213794, 0.645662, -0.044634, -0.552595, 0.771242, -0.131944, -0.172725, 0.700856, -0.001994, 0.606737, -0.593306]\n",
|
||||
"out shape: (2, 12, 8)\n",
|
||||
"out: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.989173, -0.133618, -0.505338, 0.023259, 0.0, 0.0, 0.0, 0.0, 0.503982, -0.303769, -0.436321, 0.793911, 0.0, 0.0, 0.0, 0.0, 0.416102, 0.806405, -0.098342, -0.738022, 0.0, 0.0, 0.0, 0.0, -0.982676, 0.805073, 0.741244, -0.941634, 0.0, 0.0, 0.0, 0.0, -0.253526, -0.136544, -0.295772, 0.207565, 0.0, 0.0, 0.0, 0.0, -0.517246, -0.686963, -0.176235, -0.354111, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.862411, -0.969822, 0.200074, 0.290718, 0.0, 0.0, 0.0, 0.0, -0.038623, 0.294839, 0.247968, 0.557946, 0.0, 0.0, 0.0, 0.0, -0.455596, 0.6624, 0.879529, -0.466772, 0.0, 0.0, 0.0, 0.0, 0.40423, 0.213794, 0.645662, -0.044634, 0.0, 0.0, 0.0, 0.0, -0.552595, 0.771242, -0.131944, -0.172725, 0.0, 0.0, 0.0, 0.0, 0.700856, -0.001994, 0.606737, -0.593306, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"data_in_shape = (2, 6, 4)\n",
|
||||
"L = ZeroPadding2D(padding=(3, 2), dim_ordering='th')\n",
|
||||
"\n",
|
||||
"layer_0 = Input(shape=data_in_shape)\n",
|
||||
"layer_1 = L(layer_0)\n",
|
||||
"model = Model(input=layer_0, output=layer_1)\n",
|
||||
"\n",
|
||||
"# set weights to random (use seed for reproducibility)\n",
|
||||
"np.random.seed(252)\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
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
@@ -0,0 +1,34 @@
|
||||
import Layer from '../../engine/Layer'
|
||||
import Tensor from '../../Tensor'
|
||||
import ops from 'ndarray-ops'
|
||||
|
||||
/**
|
||||
* ZeroPadding1D layer class
|
||||
*/
|
||||
export default class ZeroPadding1D extends Layer {
|
||||
/**
|
||||
* Creates a ZeroPadding1D activation layer
|
||||
* @param {number} padding - length of padding
|
||||
*/
|
||||
constructor (padding = 1, attrs = {}) {
|
||||
super(attrs)
|
||||
this.padding = padding
|
||||
}
|
||||
|
||||
/**
|
||||
* Method for layer computational logic
|
||||
* @param {Tensor} x
|
||||
* @returns {Tensor} x
|
||||
*/
|
||||
call = x => {
|
||||
const inputShape = x.tensor.shape
|
||||
const outputShape = [inputShape[0] + this.padding * 2, inputShape[1]]
|
||||
let y = new Tensor([], outputShape)
|
||||
ops.assign(
|
||||
y.tensor.hi(inputShape[0] + this.padding, inputShape[1]).lo(this.padding, 0),
|
||||
x.tensor
|
||||
)
|
||||
x.tensor = y.tensor
|
||||
return x
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,56 @@
|
||||
import Layer from '../../engine/Layer'
|
||||
import Tensor from '../../Tensor'
|
||||
import ops from 'ndarray-ops'
|
||||
|
||||
/**
|
||||
* ZeroPadding2D layer class
|
||||
*/
|
||||
export default class ZeroPadding2D extends Layer {
|
||||
/**
|
||||
* Creates a ZeroPadding2D activation layer
|
||||
* @param {number} padding - size of padding
|
||||
*/
|
||||
constructor (padding = [1, 1], attrs = {}) {
|
||||
super(attrs)
|
||||
const {
|
||||
dimOrdering = 'tf'
|
||||
} = attrs
|
||||
|
||||
this.padding = padding
|
||||
this.dimOrdering = dimOrdering
|
||||
}
|
||||
|
||||
/**
|
||||
* Method for layer computational logic
|
||||
* @param {Tensor} x
|
||||
* @returns {Tensor} x
|
||||
*/
|
||||
call = x => {
|
||||
// convert to tf ordering
|
||||
if (this.dimOrdering === 'th') {
|
||||
x.tensor = x.tensor.transpose(1, 2, 0)
|
||||
}
|
||||
|
||||
const inputShape = x.tensor.shape
|
||||
const outputShape = [
|
||||
inputShape[0] + this.padding[0] * 2,
|
||||
inputShape[1] + this.padding[1] * 2,
|
||||
inputShape[2]
|
||||
]
|
||||
let y = new Tensor([], outputShape)
|
||||
ops.assign(
|
||||
y.tensor
|
||||
.hi(inputShape[0] + this.padding[0], inputShape[1] + this.padding[1], inputShape[2])
|
||||
.lo(this.padding[0], this.padding[1], 0),
|
||||
x.tensor
|
||||
)
|
||||
x.tensor = y.tensor
|
||||
|
||||
// convert back to th ordering if necessary
|
||||
if (this.dimOrdering === 'th') {
|
||||
x.tensor = x.tensor.transpose(2, 0, 1)
|
||||
}
|
||||
|
||||
return x
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,62 @@
|
||||
import Layer from '../../engine/Layer'
|
||||
import Tensor from '../../Tensor'
|
||||
import ops from 'ndarray-ops'
|
||||
|
||||
/**
|
||||
* ZeroPadding3D layer class
|
||||
*/
|
||||
export default class ZeroPadding3D extends Layer {
|
||||
/**
|
||||
* Creates a ZeroPadding3D activation layer
|
||||
* @param {number} padding - size of padding
|
||||
*/
|
||||
constructor (padding = [1, 1, 1], attrs = {}) {
|
||||
super(attrs)
|
||||
const {
|
||||
dimOrdering = 'tf'
|
||||
} = attrs
|
||||
|
||||
this.padding = padding
|
||||
this.dimOrdering = dimOrdering
|
||||
}
|
||||
|
||||
/**
|
||||
* Method for layer computational logic
|
||||
* @param {Tensor} x
|
||||
* @returns {Tensor} x
|
||||
*/
|
||||
call = x => {
|
||||
// convert to tf ordering
|
||||
if (this.dimOrdering === 'th') {
|
||||
x.tensor = x.tensor.transpose(1, 2, 3, 0)
|
||||
}
|
||||
|
||||
const inputShape = x.tensor.shape
|
||||
const outputShape = [
|
||||
inputShape[0] + this.padding[0] * 2,
|
||||
inputShape[1] + this.padding[1] * 2,
|
||||
inputShape[2] + this.padding[2] * 2,
|
||||
inputShape[3]
|
||||
]
|
||||
let y = new Tensor([], outputShape)
|
||||
ops.assign(
|
||||
y.tensor
|
||||
.hi(
|
||||
inputShape[0] + this.padding[0],
|
||||
inputShape[1] + this.padding[1],
|
||||
inputShape[2] + this.padding[2],
|
||||
inputShape[3]
|
||||
)
|
||||
.lo(this.padding[0], this.padding[1], this.padding[2], 0),
|
||||
x.tensor
|
||||
)
|
||||
x.tensor = y.tensor
|
||||
|
||||
// convert back to th ordering if necessary
|
||||
if (this.dimOrdering === 'th') {
|
||||
x.tensor = x.tensor.transpose(3, 0, 1, 2)
|
||||
}
|
||||
|
||||
return x
|
||||
}
|
||||
}
|
||||
@@ -4,6 +4,9 @@ import Convolution3D from './Convolution3D'
|
||||
import UpSampling1D from './UpSampling1D'
|
||||
import UpSampling2D from './UpSampling2D'
|
||||
import UpSampling3D from './UpSampling3D'
|
||||
import ZeroPadding1D from './ZeroPadding1D'
|
||||
import ZeroPadding2D from './ZeroPadding2D'
|
||||
import ZeroPadding3D from './ZeroPadding3D'
|
||||
|
||||
export {
|
||||
Convolution1D,
|
||||
@@ -11,5 +14,8 @@ export {
|
||||
Convolution3D,
|
||||
UpSampling1D,
|
||||
UpSampling2D,
|
||||
UpSampling3D
|
||||
UpSampling3D,
|
||||
ZeroPadding1D,
|
||||
ZeroPadding2D,
|
||||
ZeroPadding3D
|
||||
}
|
||||
|
||||
@@ -0,0 +1,48 @@
|
||||
/* eslint-env browser, mocha */
|
||||
|
||||
describe('convolutional layer: ZeroPadding1D', 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%cconvolutional layer: ZeroPadding1D', styles.h1)
|
||||
})
|
||||
|
||||
it(`[convolutional.ZeroPadding1D.0] padding 1 on 3x5 input`, function () {
|
||||
const key = `convolutional.ZeroPadding1D.0`
|
||||
console.log(`\n%c[${key}] padding 1 on 3x5 input`, styles.h3)
|
||||
let testLayer = new layers.ZeroPadding1D(1)
|
||||
let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
|
||||
console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
|
||||
const startTime = performance.now()
|
||||
t = testLayer.call(t)
|
||||
const endTime = performance.now()
|
||||
console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
|
||||
logTime(startTime, endTime)
|
||||
const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
|
||||
const shapeExpected = TEST_DATA[key].expected.shape
|
||||
assert.deepEqual(t.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
||||
})
|
||||
|
||||
it(`[convolutional.ZeroPadding1D.1] padding 3 on 4x4 input`, function () {
|
||||
const key = `convolutional.ZeroPadding1D.1`
|
||||
console.log(`\n%c[${key}] padding 3 on 4x4 input`, styles.h3)
|
||||
let testLayer = new layers.ZeroPadding1D(3)
|
||||
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))
|
||||
})
|
||||
})
|
||||
@@ -0,0 +1,82 @@
|
||||
/* eslint-env browser, mocha */
|
||||
|
||||
describe('convolutional layer: ZeroPadding2D', 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%cconvolutional layer: ZeroPadding2D', styles.h1)
|
||||
})
|
||||
|
||||
it(`[convolutional.ZeroPadding2D.0] padding 1,1 on 3x5x2 input, dimOrdering=tf`, function () {
|
||||
const key = `convolutional.ZeroPadding2D.0`
|
||||
console.log(`\n%c[${key}] padding 1,1 on 3x5x2 input, dimOrdering=tf`, styles.h3)
|
||||
let testLayer = new layers.ZeroPadding2D([1, 1], { dimOrdering: 'tf' })
|
||||
let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
|
||||
console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
|
||||
const startTime = performance.now()
|
||||
t = testLayer.call(t)
|
||||
const endTime = performance.now()
|
||||
console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
|
||||
logTime(startTime, endTime)
|
||||
const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
|
||||
const shapeExpected = TEST_DATA[key].expected.shape
|
||||
assert.deepEqual(t.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
||||
})
|
||||
|
||||
it(`[convolutional.ZeroPadding2D.1] padding 1,1 on 3x5x2 input, dimOrdering=th`, function () {
|
||||
const key = `convolutional.ZeroPadding2D.1`
|
||||
console.log(`\n%c[${key}] padding 1,1 on 3x5x2 input, dimOrdering=th`, styles.h3)
|
||||
let testLayer = new layers.ZeroPadding2D([1, 1], { dimOrdering: 'th' })
|
||||
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(`[convolutional.ZeroPadding2D.2] padding 3,2 on 2x6x4 input, dimOrdering=tf`, function () {
|
||||
const key = `convolutional.ZeroPadding2D.2`
|
||||
console.log(`\n%c[${key}] padding 3,2 on 2x6x4 input, dimOrdering=tf`, styles.h3)
|
||||
let testLayer = new layers.ZeroPadding2D([3, 2], { dimOrdering: 'tf' })
|
||||
let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
|
||||
console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
|
||||
const startTime = performance.now()
|
||||
t = testLayer.call(t)
|
||||
const endTime = performance.now()
|
||||
console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
|
||||
logTime(startTime, endTime)
|
||||
const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
|
||||
const shapeExpected = TEST_DATA[key].expected.shape
|
||||
assert.deepEqual(t.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
||||
})
|
||||
|
||||
it(`[convolutional.ZeroPadding2D.3] padding 3,2 on 2x6x4 input, dimOrdering=th`, function () {
|
||||
const key = `convolutional.ZeroPadding2D.3`
|
||||
console.log(`\n%c[${key}] padding 3,2 on 2x6x4 input, dimOrdering=th`, styles.h3)
|
||||
let testLayer = new layers.ZeroPadding2D([3, 2], { dimOrdering: 'th' })
|
||||
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))
|
||||
})
|
||||
})
|
||||
@@ -0,0 +1,82 @@
|
||||
/* eslint-env browser, mocha */
|
||||
|
||||
describe('convolutional layer: ZeroPadding3D', 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%cconvolutional layer: ZeroPadding3D', styles.h1)
|
||||
})
|
||||
|
||||
it(`[convolutional.ZeroPadding3D.0] padding 1,1,1 on 3x5x2x2 input, dimOrdering=tf`, function () {
|
||||
const key = `convolutional.ZeroPadding3D.0`
|
||||
console.log(`\n%c[${key}] padding 1,1,1 on 3x5x2x2 input, dimOrdering=tf`, styles.h3)
|
||||
let testLayer = new layers.ZeroPadding3D([1, 1, 1], { dimOrdering: 'tf' })
|
||||
let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
|
||||
console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
|
||||
const startTime = performance.now()
|
||||
t = testLayer.call(t)
|
||||
const endTime = performance.now()
|
||||
console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
|
||||
logTime(startTime, endTime)
|
||||
const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
|
||||
const shapeExpected = TEST_DATA[key].expected.shape
|
||||
assert.deepEqual(t.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
||||
})
|
||||
|
||||
it(`[convolutional.ZeroPadding3D.1] padding 1,1,1 on 3x5x2x2 input, dimOrdering=th`, function () {
|
||||
const key = `convolutional.ZeroPadding3D.1`
|
||||
console.log(`\n%c[${key}] padding 1,1,1 on 3x5x2x2 input, dimOrdering=th`, styles.h3)
|
||||
let testLayer = new layers.ZeroPadding3D([1, 1, 1], { dimOrdering: 'th' })
|
||||
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(`[convolutional.ZeroPadding3D.2] padding 3,2,2 on 3x2x1x4 input, dimOrdering=tf`, function () {
|
||||
const key = `convolutional.ZeroPadding3D.2`
|
||||
console.log(`\n%c[${key}] padding 3,2,2 on 3x2x1x4 input, dimOrdering=tf`, styles.h3)
|
||||
let testLayer = new layers.ZeroPadding3D([3, 2, 2], { dimOrdering: 'tf' })
|
||||
let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
|
||||
console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
|
||||
const startTime = performance.now()
|
||||
t = testLayer.call(t)
|
||||
const endTime = performance.now()
|
||||
console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
|
||||
logTime(startTime, endTime)
|
||||
const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
|
||||
const shapeExpected = TEST_DATA[key].expected.shape
|
||||
assert.deepEqual(t.tensor.shape, shapeExpected)
|
||||
assert.isTrue(approxEquals(t.tensor, dataExpected))
|
||||
})
|
||||
|
||||
it(`[convolutional.ZeroPadding3D.3] padding 3,2,2 on 3x2x1x4 input, dimOrdering=th`, function () {
|
||||
const key = `convolutional.ZeroPadding3D.3`
|
||||
console.log(`\n%c[${key}] padding 3,2,2 on 3x2x1x4 input, dimOrdering=th`, styles.h3)
|
||||
let testLayer = new layers.ZeroPadding3D([3, 2, 2], { dimOrdering: 'th' })
|
||||
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))
|
||||
})
|
||||
})
|
||||
@@ -0,0 +1,30 @@
|
||||
// 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 = {
|
||||
'convolutional.ZeroPadding1D.0': {
|
||||
input: {
|
||||
data: [-0.412987, -0.139246, 0.567466, -0.512174, -0.33603, 0.585584, 0.453472, 0.942013, -0.609538, -0.894302, 0.711927, -0.126057, 0.677549, 0.676991, 0.471487],
|
||||
shape: [3, 5]
|
||||
},
|
||||
expected: {
|
||||
data: [0.0, 0.0, 0.0, 0.0, 0.0, -0.412987, -0.139246, 0.567466, -0.512174, -0.33603, 0.585584, 0.453472, 0.942013, -0.609538, -0.894302, 0.711927, -0.126057, 0.677549, 0.676991, 0.471487, 0.0, 0.0, 0.0, 0.0, 0.0],
|
||||
shape: [5, 5]
|
||||
}
|
||||
},
|
||||
'convolutional.ZeroPadding1D.1': {
|
||||
input: {
|
||||
data: [0.229895, 0.156613, -0.66952, -0.996975, 0.45773, 0.684298, -0.999213, 0.276751, -0.484373, -0.506163, 0.353904, 0.513668, -0.981594, 0.78914, 0.603978, 0.204066],
|
||||
shape: [4, 4]
|
||||
},
|
||||
expected: {
|
||||
data: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.229895, 0.156613, -0.66952, -0.996975, 0.45773, 0.684298, -0.999213, 0.276751, -0.484373, -0.506163, 0.353904, 0.513668, -0.981594, 0.78914, 0.603978, 0.204066, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
||||
shape: [10, 4]
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
window.TEST_DATA = Object.assign({}, window.TEST_DATA, DATA)
|
||||
})()
|
||||
@@ -0,0 +1,50 @@
|
||||
// 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 = {
|
||||
'convolutional.ZeroPadding2D.0': {
|
||||
input: {
|
||||
data: [-0.570441, -0.454673, -0.285321, 0.237249, 0.282682, 0.428035, 0.160547, -0.332203, 0.546391, 0.272735, 0.010827, -0.763164, -0.442696, 0.381948, -0.676994, 0.753553, -0.031788, 0.915329, -0.738844, 0.269075, 0.434091, 0.991585, -0.944288, 0.258834, 0.162138, 0.565201, -0.492094, 0.170854, -0.139788, -0.710674],
|
||||
shape: [3, 5, 2]
|
||||
},
|
||||
expected: {
|
||||
data: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.570441, -0.454673, -0.285321, 0.237249, 0.282682, 0.428035, 0.160547, -0.332203, 0.546391, 0.272735, 0.0, 0.0, 0.0, 0.0, 0.010827, -0.763164, -0.442696, 0.381948, -0.676994, 0.753553, -0.031788, 0.915329, -0.738844, 0.269075, 0.0, 0.0, 0.0, 0.0, 0.434091, 0.991585, -0.944288, 0.258834, 0.162138, 0.565201, -0.492094, 0.170854, -0.139788, -0.710674, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
||||
shape: [5, 7, 2]
|
||||
}
|
||||
},
|
||||
'convolutional.ZeroPadding2D.1': {
|
||||
input: {
|
||||
data: [-0.570441, -0.454673, -0.285321, 0.237249, 0.282682, 0.428035, 0.160547, -0.332203, 0.546391, 0.272735, 0.010827, -0.763164, -0.442696, 0.381948, -0.676994, 0.753553, -0.031788, 0.915329, -0.738844, 0.269075, 0.434091, 0.991585, -0.944288, 0.258834, 0.162138, 0.565201, -0.492094, 0.170854, -0.139788, -0.710674],
|
||||
shape: [3, 5, 2]
|
||||
},
|
||||
expected: {
|
||||
data: [0.0, 0.0, 0.0, 0.0, 0.0, -0.570441, -0.454673, 0.0, 0.0, -0.285321, 0.237249, 0.0, 0.0, 0.282682, 0.428035, 0.0, 0.0, 0.160547, -0.332203, 0.0, 0.0, 0.546391, 0.272735, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.010827, -0.763164, 0.0, 0.0, -0.442696, 0.381948, 0.0, 0.0, -0.676994, 0.753553, 0.0, 0.0, -0.031788, 0.915329, 0.0, 0.0, -0.738844, 0.269075, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.434091, 0.991585, 0.0, 0.0, -0.944288, 0.258834, 0.0, 0.0, 0.162138, 0.565201, 0.0, 0.0, -0.492094, 0.170854, 0.0, 0.0, -0.139788, -0.710674, 0.0, 0.0, 0.0, 0.0, 0.0],
|
||||
shape: [3, 7, 4]
|
||||
}
|
||||
},
|
||||
'convolutional.ZeroPadding2D.2': {
|
||||
input: {
|
||||
data: [-0.989173, -0.133618, -0.505338, 0.023259, 0.503982, -0.303769, -0.436321, 0.793911, 0.416102, 0.806405, -0.098342, -0.738022, -0.982676, 0.805073, 0.741244, -0.941634, -0.253526, -0.136544, -0.295772, 0.207565, -0.517246, -0.686963, -0.176235, -0.354111, -0.862411, -0.969822, 0.200074, 0.290718, -0.038623, 0.294839, 0.247968, 0.557946, -0.455596, 0.6624, 0.879529, -0.466772, 0.40423, 0.213794, 0.645662, -0.044634, -0.552595, 0.771242, -0.131944, -0.172725, 0.700856, -0.001994, 0.606737, -0.593306],
|
||||
shape: [2, 6, 4]
|
||||
},
|
||||
expected: {
|
||||
data: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.989173, -0.133618, -0.505338, 0.023259, 0.503982, -0.303769, -0.436321, 0.793911, 0.416102, 0.806405, -0.098342, -0.738022, -0.982676, 0.805073, 0.741244, -0.941634, -0.253526, -0.136544, -0.295772, 0.207565, -0.517246, -0.686963, -0.176235, -0.354111, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.862411, -0.969822, 0.200074, 0.290718, -0.038623, 0.294839, 0.247968, 0.557946, -0.455596, 0.6624, 0.879529, -0.466772, 0.40423, 0.213794, 0.645662, -0.044634, -0.552595, 0.771242, -0.131944, -0.172725, 0.700856, -0.001994, 0.606737, -0.593306, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
||||
shape: [8, 10, 4]
|
||||
}
|
||||
},
|
||||
'convolutional.ZeroPadding2D.3': {
|
||||
input: {
|
||||
data: [-0.989173, -0.133618, -0.505338, 0.023259, 0.503982, -0.303769, -0.436321, 0.793911, 0.416102, 0.806405, -0.098342, -0.738022, -0.982676, 0.805073, 0.741244, -0.941634, -0.253526, -0.136544, -0.295772, 0.207565, -0.517246, -0.686963, -0.176235, -0.354111, -0.862411, -0.969822, 0.200074, 0.290718, -0.038623, 0.294839, 0.247968, 0.557946, -0.455596, 0.6624, 0.879529, -0.466772, 0.40423, 0.213794, 0.645662, -0.044634, -0.552595, 0.771242, -0.131944, -0.172725, 0.700856, -0.001994, 0.606737, -0.593306],
|
||||
shape: [2, 6, 4]
|
||||
},
|
||||
expected: {
|
||||
data: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.989173, -0.133618, -0.505338, 0.023259, 0.0, 0.0, 0.0, 0.0, 0.503982, -0.303769, -0.436321, 0.793911, 0.0, 0.0, 0.0, 0.0, 0.416102, 0.806405, -0.098342, -0.738022, 0.0, 0.0, 0.0, 0.0, -0.982676, 0.805073, 0.741244, -0.941634, 0.0, 0.0, 0.0, 0.0, -0.253526, -0.136544, -0.295772, 0.207565, 0.0, 0.0, 0.0, 0.0, -0.517246, -0.686963, -0.176235, -0.354111, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.862411, -0.969822, 0.200074, 0.290718, 0.0, 0.0, 0.0, 0.0, -0.038623, 0.294839, 0.247968, 0.557946, 0.0, 0.0, 0.0, 0.0, -0.455596, 0.6624, 0.879529, -0.466772, 0.0, 0.0, 0.0, 0.0, 0.40423, 0.213794, 0.645662, -0.044634, 0.0, 0.0, 0.0, 0.0, -0.552595, 0.771242, -0.131944, -0.172725, 0.0, 0.0, 0.0, 0.0, 0.700856, -0.001994, 0.606737, -0.593306, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
|
||||
shape: [2, 12, 8]
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
window.TEST_DATA = Object.assign({}, window.TEST_DATA, DATA)
|
||||
})()
|
||||
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user