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"cells": [
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"cell_type": "markdown",
"metadata": {},
"source": [
"# Tune a CNN on MNIST\n",
"\n",
"This tutorial walks through using Ax to tune two hyperparameters (learning rate and momentum) for a PyTorch CNN on the MNIST dataset trained using SGD with momentum.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
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"[INFO 04-17 18:31:21] ipy_plotting: Injecting Plotly library into cell. Do not overwrite or delete cell.\n"
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"data": {
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],
"source": [
"import torch\n",
"import numpy as np\n",
"\n",
"from ax.plot.contour import plot_contour\n",
"from ax.plot.trace import optimization_trace_single_method\n",
"from ax.service.managed_loop import optimize\n",
"from ax.utils.notebook.plotting import render, init_notebook_plotting\n",
"from ax.utils.tutorials.cnn_utils import load_mnist, train, evaluate, CNN\n",
"\n",
"init_notebook_plotting()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"torch.manual_seed(12345)\n",
"dtype = torch.float\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Load MNIST data\n",
"First, we need to load the MNIST data and partition it into training, validation, and test sets.\n",
"\n",
"Note: this will download the dataset if necessary."
]
},
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"text": [
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]
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{
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"text": [
"Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
"Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n",
"Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
"Processing...\n",
"Done!\n"
]
}
],
"source": [
"BATCH_SIZE = 512\n",
"train_loader, valid_loader, test_loader = load_mnist(batch_size=BATCH_SIZE)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Define function to optimize\n",
"In this tutorial, we want to optimize classification accuracy on the validation set as a function of the learning rate and momentum. The function takes in a parameterization (set of parameter values), computes the classification accuracy, and returns a dictionary of metric name ('accuracy') to a tuple with the mean and standard error."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def train_evaluate(parameterization):\n",
" net = CNN()\n",
" net = train(net=net, train_loader=train_loader, parameters=parameterization, dtype=dtype, device=device)\n",
" return evaluate(\n",
" net=net,\n",
" data_loader=valid_loader,\n",
" dtype=dtype,\n",
" device=device,\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Run the optimization loop\n",
"Here, we set the bounds on the learning rate and momentum and set the parameter space for the learning rate to be on a log scale. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:31:24] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:31:24] ax.service.managed_loop: Started full optimization with 20 steps.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:31:24] ax.service.managed_loop: Running optimization trial 1...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:31:34] ax.service.managed_loop: Running optimization trial 2...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:31:43] ax.service.managed_loop: Running optimization trial 3...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:31:52] ax.service.managed_loop: Running optimization trial 4...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:32:02] ax.service.managed_loop: Running optimization trial 5...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:32:11] ax.service.managed_loop: Running optimization trial 6...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:32:22] ax.service.managed_loop: Running optimization trial 7...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:32:32] ax.service.managed_loop: Running optimization trial 8...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:32:42] ax.service.managed_loop: Running optimization trial 9...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:32:52] ax.service.managed_loop: Running optimization trial 10...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:33:02] ax.service.managed_loop: Running optimization trial 11...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:33:13] ax.service.managed_loop: Running optimization trial 12...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:33:23] ax.service.managed_loop: Running optimization trial 13...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:33:34] ax.service.managed_loop: Running optimization trial 14...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:33:44] ax.service.managed_loop: Running optimization trial 15...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:33:54] ax.service.managed_loop: Running optimization trial 16...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:34:05] ax.service.managed_loop: Running optimization trial 17...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:34:16] ax.service.managed_loop: Running optimization trial 18...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:34:26] ax.service.managed_loop: Running optimization trial 19...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:34:36] ax.service.managed_loop: Running optimization trial 20...\n"
]
}
],
"source": [
"best_parameters, values, experiment, model = optimize(\n",
" parameters=[\n",
" {\"name\": \"lr\", \"type\": \"range\", \"bounds\": [1e-6, 0.4], \"log_scale\": True},\n",
" {\"name\": \"momentum\", \"type\": \"range\", \"bounds\": [0.0, 1.0]},\n",
" ],\n",
" evaluation_function=train_evaluate,\n",
" objective_name='accuracy',\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can introspect the optimal parameters and their outcomes:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'lr': 0.00019658556070670008, 'momentum': 0.7462332268108819}"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"best_parameters"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"({'accuracy': 0.9364998460436452},\n",
" {'accuracy': {'accuracy': 1.013823582119347e-08}})"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"means, covariances = values\n",
"means, covariances"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Plot response surface\n",
"\n",
"Contour plot showing classification accuracy as a function of the two hyperparameters.\n",
"\n",
"The black squares show points that we have actually run, notice how they are clustered in the optimal region."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
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