mirror of
https://github.com/wassname/keras-js.git
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257 lines
11 KiB
Plaintext
257 lines
11 KiB
Plaintext
{
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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 UpSampling2D\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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"### UpSampling2D"
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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.UpSampling2D.0] size 2x2 upsampling on 3x3x3 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, 3, 3)\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]\n",
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"out shape: (6, 6, 3)\n",
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"out: [-0.570441, -0.454673, -0.285321, -0.570441, -0.454673, -0.285321, 0.237249, 0.282682, 0.428035, 0.237249, 0.282682, 0.428035, 0.160547, -0.332203, 0.546391, 0.160547, -0.332203, 0.546391, -0.570441, -0.454673, -0.285321, -0.570441, -0.454673, -0.285321, 0.237249, 0.282682, 0.428035, 0.237249, 0.282682, 0.428035, 0.160547, -0.332203, 0.546391, 0.160547, -0.332203, 0.546391, 0.272735, 0.010827, -0.763164, 0.272735, 0.010827, -0.763164, -0.442696, 0.381948, -0.676994, -0.442696, 0.381948, -0.676994, 0.753553, -0.031788, 0.915329, 0.753553, -0.031788, 0.915329, 0.272735, 0.010827, -0.763164, 0.272735, 0.010827, -0.763164, -0.442696, 0.381948, -0.676994, -0.442696, 0.381948, -0.676994, 0.753553, -0.031788, 0.915329, 0.753553, -0.031788, 0.915329, -0.738844, 0.269075, 0.434091, -0.738844, 0.269075, 0.434091, 0.991585, -0.944288, 0.258834, 0.991585, -0.944288, 0.258834, 0.162138, 0.565201, -0.492094, 0.162138, 0.565201, -0.492094, -0.738844, 0.269075, 0.434091, -0.738844, 0.269075, 0.434091, 0.991585, -0.944288, 0.258834, 0.991585, -0.944288, 0.258834, 0.162138, 0.565201, -0.492094, 0.162138, 0.565201, -0.492094]\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, 3, 3)\n",
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"L = UpSampling2D(size=(2, 2), 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.UpSampling2D.0] size 2x2 upsampling on 3x3x3 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, 3, 3)\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]\n",
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"out shape: (3, 6, 6)\n",
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"out: [-0.570441, -0.570441, -0.454673, -0.454673, -0.285321, -0.285321, -0.570441, -0.570441, -0.454673, -0.454673, -0.285321, -0.285321, 0.237249, 0.237249, 0.282682, 0.282682, 0.428035, 0.428035, 0.237249, 0.237249, 0.282682, 0.282682, 0.428035, 0.428035, 0.160547, 0.160547, -0.332203, -0.332203, 0.546391, 0.546391, 0.160547, 0.160547, -0.332203, -0.332203, 0.546391, 0.546391, 0.272735, 0.272735, 0.010827, 0.010827, -0.763164, -0.763164, 0.272735, 0.272735, 0.010827, 0.010827, -0.763164, -0.763164, -0.442696, -0.442696, 0.381948, 0.381948, -0.676994, -0.676994, -0.442696, -0.442696, 0.381948, 0.381948, -0.676994, -0.676994, 0.753553, 0.753553, -0.031788, -0.031788, 0.915329, 0.915329, 0.753553, 0.753553, -0.031788, -0.031788, 0.915329, 0.915329, -0.738844, -0.738844, 0.269075, 0.269075, 0.434091, 0.434091, -0.738844, -0.738844, 0.269075, 0.269075, 0.434091, 0.434091, 0.991585, 0.991585, -0.944288, -0.944288, 0.258834, 0.258834, 0.991585, 0.991585, -0.944288, -0.944288, 0.258834, 0.258834, 0.162138, 0.162138, 0.565201, 0.565201, -0.492094, -0.492094, 0.162138, 0.162138, 0.565201, 0.565201, -0.492094, -0.492094]\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, 3, 3)\n",
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"L = UpSampling2D(size=(2, 2), 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.UpSampling2D.2] size 3x2 upsampling on 4x2x2 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: (4, 2, 2)\n",
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"in: [0.275222, -0.793967, -0.468107, -0.841484, -0.295362, 0.78175, 0.068787, -0.261747, -0.625733, -0.042907, 0.861141, 0.85267, 0.956439, 0.717838, -0.99869, -0.963008]\n",
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"out shape: (12, 4, 2)\n",
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"out: [0.275222, -0.793967, 0.275222, -0.793967, -0.468107, -0.841484, -0.468107, -0.841484, 0.275222, -0.793967, 0.275222, -0.793967, -0.468107, -0.841484, -0.468107, -0.841484, 0.275222, -0.793967, 0.275222, -0.793967, -0.468107, -0.841484, -0.468107, -0.841484, -0.295362, 0.78175, -0.295362, 0.78175, 0.068787, -0.261747, 0.068787, -0.261747, -0.295362, 0.78175, -0.295362, 0.78175, 0.068787, -0.261747, 0.068787, -0.261747, -0.295362, 0.78175, -0.295362, 0.78175, 0.068787, -0.261747, 0.068787, -0.261747, -0.625733, -0.042907, -0.625733, -0.042907, 0.861141, 0.85267, 0.861141, 0.85267, -0.625733, -0.042907, -0.625733, -0.042907, 0.861141, 0.85267, 0.861141, 0.85267, -0.625733, -0.042907, -0.625733, -0.042907, 0.861141, 0.85267, 0.861141, 0.85267, 0.956439, 0.717838, 0.956439, 0.717838, -0.99869, -0.963008, -0.99869, -0.963008, 0.956439, 0.717838, 0.956439, 0.717838, -0.99869, -0.963008, -0.99869, -0.963008, 0.956439, 0.717838, 0.956439, 0.717838, -0.99869, -0.963008, -0.99869, -0.963008]\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, 2, 2)\n",
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"L = UpSampling2D(size=(3, 2), 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(251)\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.UpSampling2D.3] size 1x3 upsampling on 4x3x2 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": 7,
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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, 3, 2)\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]\n",
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"out shape: (4, 3, 6)\n",
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"out: [-0.989173, -0.989173, -0.989173, -0.133618, -0.133618, -0.133618, -0.505338, -0.505338, -0.505338, 0.023259, 0.023259, 0.023259, 0.503982, 0.503982, 0.503982, -0.303769, -0.303769, -0.303769, -0.436321, -0.436321, -0.436321, 0.793911, 0.793911, 0.793911, 0.416102, 0.416102, 0.416102, 0.806405, 0.806405, 0.806405, -0.098342, -0.098342, -0.098342, -0.738022, -0.738022, -0.738022, -0.982676, -0.982676, -0.982676, 0.805073, 0.805073, 0.805073, 0.741244, 0.741244, 0.741244, -0.941634, -0.941634, -0.941634, -0.253526, -0.253526, -0.253526, -0.136544, -0.136544, -0.136544, -0.295772, -0.295772, -0.295772, 0.207565, 0.207565, 0.207565, -0.517246, -0.517246, -0.517246, -0.686963, -0.686963, -0.686963, -0.176235, -0.176235, -0.176235, -0.354111, -0.354111, -0.354111]\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, 3, 2)\n",
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"L = UpSampling2D(size=(1, 3), 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(252)\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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