Use int8 instead of float64 to represent pixel values: divides RAM footprint by 8
Aurélien Geron
parent
2a02668e5e
commit
7686839b36
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@ -31,9 +31,7 @@
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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": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"# To support both python 2 and python 3\n",
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@ -95,9 +93,7 @@
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import gym"
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@ -129,9 +125,7 @@
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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": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"obs = env.reset()"
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@ -163,9 +157,7 @@
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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": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"img = env.render(mode=\"rgb_array\")"
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@ -226,9 +218,7 @@
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"def plot_environment(env, figsize=(5,4)):\n",
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@ -273,9 +263,7 @@
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"env.reset()\n",
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@ -311,9 +299,7 @@
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"obs, reward, done, info = env.step(0)"
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@ -393,9 +379,7 @@
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"frames = []\n",
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@ -424,9 +408,7 @@
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"def update_scene(num, frames, patch):\n",
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@ -461,9 +443,7 @@
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"env.close()"
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@ -502,9 +482,7 @@
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"obs = env.reset()"
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@ -547,9 +525,7 @@
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"from PIL import Image, ImageDraw\n",
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@ -633,9 +609,7 @@
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{
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"cell_type": "code",
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"execution_count": 28,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"obs = env.reset()\n",
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@ -677,9 +651,7 @@
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{
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"cell_type": "code",
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"execution_count": 31,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"obs = env.reset()\n",
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@ -722,9 +694,7 @@
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{
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"cell_type": "code",
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"execution_count": 33,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"frames = []\n",
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@ -795,9 +765,7 @@
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{
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"cell_type": "code",
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"execution_count": 35,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"import tensorflow as tf\n",
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@ -846,9 +814,7 @@
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{
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"cell_type": "code",
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"execution_count": 36,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"n_max_steps = 1000\n",
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@ -895,9 +861,7 @@
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{
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"cell_type": "code",
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"execution_count": 38,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"import tensorflow as tf\n",
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@ -965,9 +929,7 @@
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{
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"cell_type": "code",
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"execution_count": 40,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"def render_policy_net(model_path, action, X, n_max_steps = 1000):\n",
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@ -1024,9 +986,7 @@
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{
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"cell_type": "code",
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"execution_count": 42,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"import tensorflow as tf\n",
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@ -1069,9 +1029,7 @@
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{
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"cell_type": "code",
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"execution_count": 43,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"def discount_rewards(rewards, discount_rate):\n",
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@ -1157,9 +1115,7 @@
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{
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"cell_type": "code",
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"execution_count": 47,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"env.close()"
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@ -1309,9 +1265,7 @@
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{
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"cell_type": "code",
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"execution_count": 51,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"n_states = 3\n",
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@ -1336,9 +1290,7 @@
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{
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"cell_type": "code",
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"execution_count": 52,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"def optimal_policy(state):\n",
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@ -1439,23 +1391,28 @@
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{
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"cell_type": "code",
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"execution_count": 57,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"mspacman_color = np.array([210, 164, 74]).mean()\n",
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"mspacman_color = 210 + 164 + 74\n",
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"\n",
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"def preprocess_observation(obs):\n",
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" img = obs[1:176:2, ::2] # crop and downsize\n",
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" img = img.mean(axis=2) # to greyscale\n",
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" img = img.sum(axis=2) # to greyscale\n",
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" img[img==mspacman_color] = 0 # Improve contrast\n",
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" img = (img - 128) / 128 - 1 # normalize from -1. to 1.\n",
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" img = (img // 3 - 128).astype(np.int8) # normalize from -128 to 127\n",
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" return img.reshape(88, 80, 1)\n",
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"\n",
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"img = preprocess_observation(obs)"
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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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"Note: the `preprocess_observation()` function is slightly different from the one in the book: instead of representing pixels as 64-bit floats from -1.0 to 1.0, it represents them as 8-bit integers from -128 to 127. The benefit is that the replay memory will take up about 6.5 GB of RAM instead of 52 GB. The reduced precision has no impact on training."
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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": 58,
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@ -1498,9 +1455,7 @@
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{
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"cell_type": "code",
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"execution_count": 59,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"reset_graph()\n",
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@ -1545,9 +1500,7 @@
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{
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"cell_type": "code",
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"execution_count": 60,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"X_state = tf.placeholder(tf.float32, shape=[None, input_height, input_width,\n",
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@ -1572,9 +1525,7 @@
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{
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"cell_type": "code",
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"execution_count": 62,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"learning_rate = 0.001\n",
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@ -1608,9 +1559,7 @@
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{
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"cell_type": "code",
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"execution_count": 63,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"from collections import deque\n",
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@ -1632,9 +1581,7 @@
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{
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"cell_type": "code",
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"execution_count": 64,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"eps_min = 0.1\n",
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@ -1678,9 +1625,7 @@
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{
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"cell_type": "code",
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"execution_count": 66,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"loss_val = np.infty\n",
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@ -1970,7 +1915,7 @@
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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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"version": "3.6.3"
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},
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"nav_menu": {},
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"toc": {
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