mirror of
https://github.com/wassname/keras-js.git
synced 2026-09-23 13:20:45 +08:00
254 lines
8.7 KiB
Plaintext
254 lines
8.7 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.core import Highway\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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"### Highway"
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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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"**[core.Highway.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": 6,
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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: (6, 6)\n",
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"W: [0.176262, 0.795427, 0.783061, 0.631675, -0.928221, 0.383515, -0.242638, 0.037022, 0.315903, -0.6123, -0.455367, 0.437212, 0.566007, 0.700655, 0.55049, -0.926671, -0.766613, 0.502561, -0.521564, -0.490388, 0.715251, 0.899558, 0.123374, -0.642439, 0.540504, -0.015238, 0.262506, 0.678996, -0.077921, -0.00412, 0.358822, 0.301572, -0.46241, -0.865351, 0.54289, -0.038032]\n",
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"b shape: (6,)\n",
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"b: [-0.90255, -0.421781, 0.441933, -0.956768, -0.588154, -0.898453]\n",
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"W_carry shape: (6, 6)\n",
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"W_carry: [-0.583079, -0.036638, -0.158924, 0.718364, -0.657677, -0.322272, -0.458934, 0.382083, -0.559191, 0.623902, -0.978946, 0.122407, 0.627452, 0.490201, -0.621777, -0.987718, 0.544088, 0.915664, 0.403876, -0.404843, 0.535985, 0.376437, -0.225633, 0.230412, -0.14489, 0.168579, 0.405271, -0.77621, 0.84654, 0.977773, 0.354822, 0.59033, -0.941847, -0.644482, 0.749855, 0.489864]\n",
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"b_carry shape: (6,)\n",
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"b_carry: [0.034596, 0.893925, 0.53092, -0.435208, -0.557909, 0.372444]\n",
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"\n",
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"in shape: (6,)\n",
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"in: [-0.665722, -0.215115, 0.236105, -0.17614, -0.99507, 0.768064]\n",
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"out shape: (6,)\n",
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"out: [-0.914347, -0.408456, -0.114281, -0.888056, -0.290505, -0.199544]\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 = (6,)\n",
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"layer_0 = Input(shape=data_in_shape)\n",
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"layer_1 = Highway(transform_bias=-2, activation='linear', bias=True)(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(20+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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"print('b shape:', weights[1].shape)\n",
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"print('b:', format_decimal(weights[1].ravel().tolist()))\n",
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"print('W_carry shape:', weights[2].shape)\n",
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"print('W_carry:', format_decimal(weights[2].ravel().tolist()))\n",
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"print('b_carry shape:', weights[3].shape)\n",
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"print('b_carry:', format_decimal(weights[3].ravel().tolist()))\n",
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"\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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"**[core.Highway.1]**"
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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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"W shape: (5, 5)\n",
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"W: [0.288287, -0.238503, 0.326096, -0.672699, 0.925216, -0.306676, 0.983502, -0.529884, 0.171389, -0.18662, -0.727531, 0.088273, 0.036353, 0.53371, 0.8677, -0.820593, -0.608457, 0.988387, -0.529639, -0.522027, 0.2582, 0.469905, 0.376689, -0.937739, 0.805028]\n",
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"b shape: (5,)\n",
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"b: [-0.427892, 0.916211, 0.540626, 0.97374, -0.583669]\n",
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"W_carry shape: (5, 5)\n",
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"W_carry: [0.717779, -0.254578, 0.110258, 0.911313, 0.473339, 0.63241, -0.797827, 0.856976, 0.218218, 0.193107, -0.816432, -0.309628, 0.325505, -0.116573, 0.102976, 0.407425, 0.178802, -0.900134, 0.123584, 0.532717, 0.821817, -0.81418, 0.805043, -0.078079, -0.095963]\n",
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"b_carry shape: (5,)\n",
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"b_carry: [-0.50298, -0.100049, -0.178118, -0.479401, 0.740791]\n",
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"\n",
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"in shape: (5,)\n",
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"in: [-0.62992, -0.960677, 0.906504, 0.360902, -0.026824]\n",
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"out shape: (5,)\n",
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"out: [-0.652907, -0.353259, 0.890362, 0.477292, -0.256096]\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 = (5,)\n",
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"layer_0 = Input(shape=data_in_shape)\n",
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"layer_1 = Highway(transform_bias=-5, activation='tanh', bias=True)(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(30+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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"print('b shape:', weights[1].shape)\n",
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"print('b:', format_decimal(weights[1].ravel().tolist()))\n",
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"print('W_carry shape:', weights[2].shape)\n",
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"print('W_carry:', format_decimal(weights[2].ravel().tolist()))\n",
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"print('b_carry shape:', weights[3].shape)\n",
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"print('b_carry:', format_decimal(weights[3].ravel().tolist()))\n",
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"\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": 10,
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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: (4, 4)\n",
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"W: [-0.184626, -0.889268, 0.57707, -0.42539, -0.099299, -0.392175, 0.052799, 0.247624, 0.553551, 0.372483, 0.961878, 0.201632, 0.627937, 0.41729, -0.944931, 0.808534]\n",
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"W_carry shape: (4, 4)\n",
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"W_carry: [-0.498153, -0.907808, 0.353632, -0.913061, -0.767153, 0.207731, -0.618139, 0.337031, 0.834896, -0.16244, -0.33548, -0.433933, -0.627435, -0.365779, -0.037663, -0.860959]\n",
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"\n",
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"in shape: (4,)\n",
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"in: [0.409965, -0.370646, 0.490565, -0.203574]\n",
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"out shape: (4,)\n",
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"out: [0.482075, -0.04199, 0.593448, 0.031503]\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,)\n",
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"layer_0 = Input(shape=data_in_shape)\n",
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"layer_1 = Highway(transform_bias=1, activation='hard_sigmoid', bias=False)(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(40+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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"print('W_carry shape:', weights[1].shape)\n",
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"print('W_carry:', format_decimal(weights[1].ravel().tolist()))\n",
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"\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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