Two speed test notebooks: MNIST training loop and Collatz.
PiperOrigin-RevId: 204757439
This commit is contained in:
Dan Moldovan
TensorFlower Gardener
parent
97ae13e08d
commit
b70a39b4e6
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "etTmZVFN8fYO"
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},
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"source": [
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"This notebook runs a basic speed test for a short training loop of a neural network training on the MNIST dataset."
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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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"colab_type": "text",
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"id": "eqOvRhOz8SWs"
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},
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"source": [
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"### Imports"
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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": 0,
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"metadata": {
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"colab": {
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"autoexec": {
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"startup": false,
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"wait_interval": 0
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}
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},
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"colab_type": "code",
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"id": "nHY0tntRizGb"
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},
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"outputs": [],
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"source": [
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"!pip install -U -q tf-nightly"
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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": 0,
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@ -15,18 +53,93 @@
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},
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"outputs": [],
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"source": [
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"import gzip\n",
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"import os\n",
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"import shutil\n",
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"import time\n",
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"\n",
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"import numpy as np\n",
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"import six\n",
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"from six.moves import urllib\n",
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"import tensorflow as tf\n",
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"\n",
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"from tensorflow.contrib import autograph\n",
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"from tensorflow.contrib import autograph as ag\n",
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"from tensorflow.contrib.eager.python import tfe\n",
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"from tensorflow.python.eager import context\n"
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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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"colab_type": "text",
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"id": "PZWxEJFM9A7b"
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},
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"source": [
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"### Testing boilerplate"
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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": 0,
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"metadata": {
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"colab": {
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"autoexec": {
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"startup": false,
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"wait_interval": 0
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}
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},
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"colab_type": "code",
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"id": "kfZk9EFZ5TeQ"
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},
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"outputs": [],
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"source": [
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"# Test-only parameters. Test checks successful completion not correctness. \n",
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"burn_ins = 1\n",
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"trials = 1\n",
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"max_steps = 2\n"
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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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"colab_type": "text",
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"id": "k0GKbZBJ9Gt9"
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},
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"source": [
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"### Speed test configuration"
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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": 0,
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"metadata": {
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"colab": {
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"autoexec": {
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"startup": false,
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"wait_interval": 0
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}
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},
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"colab_type": "code",
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"id": "gWXV8WHn43iZ"
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},
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"outputs": [],
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"source": [
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"#@test {\"skip\": true} \n",
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"burn_ins = 3\n",
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"trials = 10\n",
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"max_steps = 500\n"
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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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"colab_type": "text",
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"id": "kZV_3pGy8033"
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},
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"source": [
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"### Data source setup"
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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": 0,
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@ -42,12 +155,6 @@
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},
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"outputs": [],
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"source": [
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"import gzip\n",
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"import shutil\n",
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"\n",
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"from six.moves import urllib\n",
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"\n",
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"\n",
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"def download(directory, filename):\n",
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" filepath = os.path.join(directory, filename)\n",
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" if tf.gfile.Exists(filepath):\n",
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@ -107,6 +214,16 @@
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" return ds\n"
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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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"colab_type": "text",
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"id": "qzkZyZcS9THu"
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},
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"source": [
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"### Keras model definition"
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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": 0,
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@ -131,48 +248,6 @@
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" return model\n"
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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": 0,
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"metadata": {
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"colab": {
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"autoexec": {
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"startup": false,
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"wait_interval": 0
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}
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},
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"colab_type": "code",
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"id": "kfZk9EFZ5TeQ"
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},
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"outputs": [],
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"source": [
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"# Test-only parameters. Test checks successful completion not correctness. \n",
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"burn_ins = 1\n",
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"trials = 1\n",
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"max_steps = 2\n"
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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": 0,
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"metadata": {
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"colab": {
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"autoexec": {
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"startup": false,
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"wait_interval": 0
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}
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},
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"colab_type": "code",
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"id": "gWXV8WHn43iZ"
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},
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"outputs": [],
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"source": [
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"#@test {\"skip\": true} \n",
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"burn_ins = 3\n",
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"trials = 10\n",
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"max_steps = 500\n"
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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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@ -180,7 +255,7 @@
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"id": "DXt4GoTxtvn2"
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},
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"source": [
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"# Autograph"
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"# AutoGraph"
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]
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},
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{
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@ -274,10 +349,10 @@
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" test_losses = []\n",
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" train_accuracies = []\n",
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" test_accuracies = []\n",
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" autograph.set_element_type(train_losses, tf.float32)\n",
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" autograph.set_element_type(test_losses, tf.float32)\n",
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" autograph.set_element_type(train_accuracies, tf.float32)\n",
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" autograph.set_element_type(test_accuracies, tf.float32)\n",
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" ag.set_element_type(train_losses, tf.float32)\n",
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" ag.set_element_type(test_losses, tf.float32)\n",
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" ag.set_element_type(train_accuracies, tf.float32)\n",
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" ag.set_element_type(test_accuracies, tf.float32)\n",
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"\n",
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" i = tf.constant(0)\n",
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" while i \u003c hp.max_steps:\n",
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" test_accuracies.append(step_test_accuracy)\n",
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"\n",
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" i += 1\n",
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" return (autograph.stack(train_losses), autograph.stack(test_losses),\n",
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" autograph.stack(train_accuracies), autograph.stack(test_accuracies))\n"
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" return (ag.stack(train_losses), ag.stack(test_losses),\n",
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" ag.stack(train_accuracies), ag.stack(test_accuracies))\n"
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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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"execution_count": 0,
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"metadata": {
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"colab": {
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"autoexec": {
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"startup": false,
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"wait_interval": 0
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},
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"height": 220
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"height": 215
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},
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"colab_type": "code",
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"executionInfo": {
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"elapsed": 12896,
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"elapsed": 12156,
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"status": "ok",
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"timestamp": 1531534784996,
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"timestamp": 1531752050611,
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"user": {
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"displayName": "",
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"photoUrl": "",
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@ -320,24 +395,24 @@
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"user_tz": 240
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},
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"id": "K1m8TwOKjdNd",
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"outputId": "2ee3ff78-9aae-4fac-a1fd-32bf3b2f18f4"
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"outputId": "bd5746f2-bf91-44aa-9eff-38eb11ced33f"
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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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"('Duration:', 0.7540969848632812)\n",
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"('Duration:', 0.7829370498657227)\n",
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"('Duration:', 0.7111489772796631)\n",
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"('Duration:', 0.6425611972808838)\n",
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"('Duration:', 0.6188449859619141)\n",
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"('Duration:', 0.6388339996337891)\n",
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"('Duration:', 0.6235959529876709)\n",
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"('Mean duration:', 0.66165139675140383, '+/-', 0.060382254849383483)\n"
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"('Duration:', 0.6226680278778076)\n",
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"('Duration:', 0.6176400184631348)\n",
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"('Duration:', 0.6476900577545166)\n",
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"('Mean duration:', 0.62254641056060789, '+/-', 0.0099792188690656976)\n"
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]
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}
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],
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@ -350,7 +425,7 @@
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" )\n",
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" train_ds = setup_mnist_data(True, hp, 500)\n",
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" test_ds = setup_mnist_data(False, hp, 100)\n",
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" tf_train = autograph.to_graph(train)\n",
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" tf_train = ag.to_graph(train)\n",
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" losses = tf_train(train_ds, test_ds, hp)\n",
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"\n",
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" with tf.Session() as sess:\n",
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@ -458,20 +533,20 @@
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 0,
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"metadata": {
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"colab": {
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"autoexec": {
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"startup": false,
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"wait_interval": 0
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},
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"height": 220
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"height": 215
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},
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"colab_type": "code",
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"executionInfo": {
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"elapsed": 53945,
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"elapsed": 52499,
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"status": "ok",
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"timestamp": 1531534839296,
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"timestamp": 1531752103279,
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"user": {
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"displayName": "",
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"photoUrl": "",
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@ -480,24 +555,24 @@
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"user_tz": 240
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},
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"id": "plv_yrn_t8Dy",
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"outputId": "93f2f468-7191-430c-88d2-948b4ce1ea06"
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"outputId": "55d5ab3d-252d-48ba-8fb4-20ec3c3e6d00"
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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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"('Duration:', 4.095034837722778)\n",
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"('Duration:', 4.11162805557251)\n",
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"('Mean duration:', 4.1102658748626713, '+/-', 0.020919605607527668)\n"
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"('Duration:', 3.9973549842834473)\n",
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"('Duration:', 4.018772125244141)\n",
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"('Duration:', 4.012261867523193)\n",
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"('Duration:', 4.004716157913208)\n",
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"('Mean duration:', 3.9924648046493529, '+/-', 0.015681688635624851)\n"
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]
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}
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],
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@ -535,13 +610,13 @@
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],
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"metadata": {
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"colab": {
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"collapsed_sections": [],
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"collapsed_sections": [
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"eqOvRhOz8SWs",
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"PZWxEJFM9A7b",
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"kZV_3pGy8033"
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],
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"default_view": {},
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"last_runtime": {
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"build_target": "",
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"kind": "local"
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},
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"name": "Autograph vs. Eager MNIST benchmark",
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"name": "Autograph vs. Eager MNIST speed test",
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"provenance": [
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{
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"file_id": "1tAQW5tHUgAc8M4-iwwJm6Xs6dV9nEqtD",
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