a4bbadeb61
* Add nbsphinx extension for docs * Add specific ipynb files to readthedocs folder and add links to Nano Tutorials index page * Disable execution of ipynb notebook * Remove outputs of two ipynb files * Rename titles and add navigation texts to seq_and_func.ipynb under readthedocs folder * Rename titles and add navigation texts to custom.ipynb file under readthedocs folder
393 lines
18 KiB
Text
393 lines
18 KiB
Text
{
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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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"id": "yzsDlbsUBsuF"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/intel-analytics/BigDL/blob/main/python/nano/notebooks/hpo/custom.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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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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"id": "zcHLkcVjB7Jg"
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},
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"source": [
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""
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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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"id": "rICdZgjQBfUl"
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},
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"source": [
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"# BigDL-Nano Hyperparameter Tuning (Tensorflow Subclassing Model) Quickstart\n",
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"In this notebook we demonstrates how to use Nano HPO to tune the hyperparameters in tensorflow training. The model is built by subclassing tensorflow.keras.Model.\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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"id": "XZHU5E3ABfUm"
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},
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"source": [
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"## Step 0: Prepare Environment\n",
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"You can install the latest pre-release version with nano support using below commands.\n",
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"\n",
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"We recommend to run below commands, especially `source bigdl-nano-init` before jupyter kernel is started, or some of the optimizations may not take effect."
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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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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "3UyXJWA4Cjx2",
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"outputId": "4518ce08-cfc8-4f6c-c40c-01006ffabc81"
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},
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"outputs": [],
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"source": [
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"# Install latest pre-release version of bigdl-nano\n",
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"!pip install --pre bigdl-nano[tensorflow]\n",
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"!pip install setuptools==58.0.4\n",
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"!pip install protobuf==3.20.1\n",
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"!source bigdl-nano-init"
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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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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "phw-whHaCnpR",
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"outputId": "b517e45c-c98a-4b12-820b-a1a8009fc5aa"
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},
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"outputs": [],
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"source": [
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"# Install other dependecies for Nano HPO\n",
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"!pip install ConfigSpace\n",
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"!pip install optuna"
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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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"id": "xKKCHTU9BfUn"
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},
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"source": [
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"## Step 1: Init Nano AutoML\n",
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"We need to enable Nano HPO before we use it for tensorflow 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": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "gF1XGVtuBfUn",
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"outputId": "c610c446-328d-43e4-e701-51fe180aef1f"
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},
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"outputs": [],
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"source": [
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"import bigdl.nano.automl as automl\n",
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"import bigdl.nano.automl.hpo as hpo\n",
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"automl.hpo_config.enable_hpo_tf()"
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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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"id": "3InpJGOuBfUp"
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},
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"source": [
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"## Step 2: Prepare data\n",
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"We use fashion MNIST dataset for demonstration."
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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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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "A4AjqFBTBfUp",
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"outputId": "d80596f1-98e4-4358-a583-706e24c3da0c"
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},
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"outputs": [],
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"source": [
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"from tensorflow import keras\n",
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"(x_train, y_train), (x_test, y_test) = keras.datasets.fashion_mnist.load_data()\n",
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"\n",
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"CLASSES = 10\n",
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"\n",
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"img_x, img_y = x_train.shape[1], x_train.shape[2]\n",
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"x_train = x_train.reshape(-1, img_x, img_y,1).astype(\"float32\") / 255\n",
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"x_test = x_test.reshape(-1, img_x, img_y,1).astype(\"float32\") / 255"
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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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"id": "hwrtVD9yBfUq"
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},
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"source": [
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"## Step 3: Build model and specify search spaces\n",
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"We now create our model. \n",
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"\n",
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"Decorate the model class with hpo.tfmodel, and you will be able to specify search spaces in init arguments when creating the model, as shown below. For more details, refer to [user doc](https://bigdl.readthedocs.io/en/latest/doc/Nano/QuickStart/hpo.html).\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": null,
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"metadata": {
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"id": "uRFIRllGr0xu"
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},
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"outputs": [],
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"source": [
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"import tensorflow as tf\n",
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"from tensorflow.keras.datasets import mnist\n",
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"from tensorflow.keras.layers import Conv2D, Dropout, MaxPooling2D\n",
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"from tensorflow.keras.layers import Dense\n",
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"from tensorflow.keras.layers import Flatten"
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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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"id": "SNmZHFMMBfUr"
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},
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"outputs": [],
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"source": [
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"import tensorflow as tf\n",
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"@hpo.tfmodel()\n",
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"class MyModel(tf.keras.Model):\n",
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"\n",
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" def __init__(self, filters, kernel_size, strides, activation):\n",
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" super().__init__()\n",
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" self.conv1 = Conv2D(\n",
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" filters=filters,\n",
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" kernel_size=kernel_size,\n",
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" strides=strides,\n",
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" activation=activation)\n",
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" self.pool1 = MaxPooling2D(pool_size=2)\n",
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" self.drop1 = Dropout(0.3)\n",
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" self.flat = Flatten()\n",
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" self.dense1 = Dense(256, activation='relu')\n",
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" self.drop3 = Dropout(0.5)\n",
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" self.dense2 = Dense(CLASSES, activation=\"softmax\")\n",
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"\n",
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" def call(self, inputs):\n",
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" x = self.conv1(inputs)\n",
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" x = self.pool1(x)\n",
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" x = self.drop1(x)\n",
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" x = self.flat(x)\n",
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" x = self.dense1(x)\n",
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" x = self.drop3(x)\n",
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" x = self.dense2(x)\n",
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" return x\n",
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"model = MyModel(\n",
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" filters=hpo.space.Categorical(32, 64),\n",
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" kernel_size=hpo.space.Categorical(2, 4),\n",
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" strides=hpo.space.Categorical(1, 2),\n",
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" activation=hpo.space.Categorical(\"relu\", \"linear\")\n",
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")"
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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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"id": "msSa9fyeGHTU"
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},
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"source": [
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"## Step 4: Compile model\n",
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"We now compile our model with loss function, optimizer and metrics. If you want to tune learning rate and batch size, refer to [user guide](https://bigdl.readthedocs.io/en/latest/doc/Nano/QuickStart/hpo.html#search-the-learning-rate)."
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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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"id": "PfeBIEmoGHxA"
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},
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"outputs": [],
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"source": [
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"from tensorflow.keras.optimizers import RMSprop\n",
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"model.compile(\n",
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" loss=\"sparse_categorical_crossentropy\",\n",
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" optimizer=RMSprop(learning_rate=0.001),\n",
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" metrics=[\"accuracy\"],\n",
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")"
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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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"id": "tuZXTIjXBfUs"
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},
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"source": [
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"## Step 5: Run hyperparameter search\n",
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"Run hyperparameter search by calling `model.search`. Set the `target_metric` and `direction` so that HPO optimizes the `target_metric` in the specified `direction`. Each trial will use a different set of hyperparameters in the search space range. Use `n_parallels` to set the nubmer of parallel processes to run trials. After search completes, you can use `search_summary` to retrive the search results for analysis. For more details, refer to [user doc](https://bigdl.readthedocs.io/en/latest/doc/Nano/QuickStart/hpo.html)."
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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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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "hfBS_1JLBfUs",
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"outputId": "897ac4d1-5766-44f2-d6fb-ed7b2a437b9e"
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},
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"outputs": [],
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"source": [
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"%%time\n",
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"from bigdl.nano.automl.hpo.backend import PrunerType\n",
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"model.search(\n",
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" n_trials=5,\n",
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" target_metric='val_accuracy',\n",
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" direction=\"maximize\",\n",
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" pruner=PrunerType.HyperBand,\n",
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" pruner_kwargs={'min_resource':1, 'max_resource':100, 'reduction_factor':3},\n",
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" x=x_train,\n",
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" y=y_train,\n",
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" batch_size=128,\n",
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" epochs=5,\n",
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" validation_split=0.2,\n",
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" verbose=False,\n",
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")"
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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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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "jeK1hT42HCD5",
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"outputId": "204acc43-94db-4cb8-f058-46c02e43c6ab"
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},
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"outputs": [],
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"source": [
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"print(model.search_summary())"
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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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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 345
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},
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"id": "n_vwMwJVLaCc",
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"outputId": "8996e8ac-5ced-4cfb-c454-de4df5860012"
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},
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"outputs": [],
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"source": [
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"study = model.search_summary()\n",
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"study.trials_dataframe(attrs=(\"number\", \"value\", \"params\", \"state\"))"
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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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"id": "NVDG1hl-BfUt"
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},
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"source": [
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"## Step 6: fit with the best hyperparameters\n",
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"After search, `model.fit` will autotmatically use the best hyperparmeters found in search to fit the model."
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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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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "aAet3uX5BfUt",
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"outputId": "2401cd38-09a6-4251-b459-c97e64d89cc9"
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},
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"outputs": [],
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"source": [
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"history = model.fit(x_train, y_train,\n",
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" batch_size=128, epochs=5, validation_split=0.2)\n",
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"\n",
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"test_scores = model.evaluate(x_test, y_test, verbose=2)\n",
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"print(\"Test loss:\", test_scores[0])\n",
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"print(\"Test accuracy:\", test_scores[1])"
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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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"id": "9JKTKAGQBfUt"
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},
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"source": [
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"Check out the summary of the model. The model has already been built with the best hyperparameters found by nano hpo."
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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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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "d8O_cz4DBfUu",
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"outputId": "dd7d6add-a32c-4618-dc88-d19bec8e4c3e"
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},
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"outputs": [],
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"source": [
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"print(model.summary())"
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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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"You can find the running output from [here](https://github.com/intel-analytics/BigDL/blob/main/python/nano/notebooks/hpo/custom.ipynb), or run the notebook by yourself in [Google Colab](https://colab.research.google.com/github/intel-analytics/BigDL/blob/main/python/nano/notebooks/hpo/custom.ipynb)."
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]
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}
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],
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"metadata": {
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"colab": {
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"collapsed_sections": [],
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"name": "custom.ipynb",
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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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": "3.7.13"
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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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