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---
title: Example Experiment
keywords: fastai
sidebar: home_sidebar
summary: "Experiment using Repeated MNIST and BatchBALD vs BALD vs random sampling"
description: "Experiment using Repeated MNIST and BatchBALD vs BALD vs random sampling"
nb_path: "06_example_experiment.ipynb"
---
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<p>This notebook ties everything together and runs an AL loop.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">blackhc.project.script</span>
<span class="kn">from</span> <span class="nn">tqdm.auto</span> <span class="kn">import</span> <span class="n">tqdm</span>
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<pre>Appended /home/blackhc/PycharmProjects/blackhc.batchbald/src to paths
Switched to directory /home/blackhc/PycharmProjects/blackhc.batchbald
%load_ext autoreload
%autoreload 2
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">math</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span> <span class="k">as</span> <span class="n">nn</span>
<span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">functional</span> <span class="k">as</span> <span class="n">F</span>
<span class="kn">from</span> <span class="nn">batchbald_redux</span> <span class="kn">import</span> <span class="p">(</span>
<span class="n">active_learning</span><span class="p">,</span>
<span class="n">batchbald</span><span class="p">,</span>
<span class="n">consistent_mc_dropout</span><span class="p">,</span>
<span class="n">joint_entropy</span><span class="p">,</span>
<span class="n">repeated_mnist</span><span class="p">,</span>
<span class="p">)</span>
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<p>Let's define our Bayesian CNN model that we will use to train MNIST.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">class</span> <span class="nc">BayesianCNN</span><span class="p">(</span><span class="n">consistent_mc_dropout</span><span class="o">.</span><span class="n">BayesianModule</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="mi">10</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv1_drop</span> <span class="o">=</span> <span class="n">consistent_mc_dropout</span><span class="o">.</span><span class="n">ConsistentMCDropout2d</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv2_drop</span> <span class="o">=</span> <span class="n">consistent_mc_dropout</span><span class="o">.</span><span class="n">ConsistentMCDropout2d</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fc1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">1024</span><span class="p">,</span> <span class="mi">128</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fc1_drop</span> <span class="o">=</span> <span class="n">consistent_mc_dropout</span><span class="o">.</span><span class="n">ConsistentMCDropout</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fc2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">mc_forward_impl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
<span class="nb">input</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">max_pool2d</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv1_drop</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="nb">input</span><span class="p">)),</span> <span class="mi">2</span><span class="p">))</span>
<span class="nb">input</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">max_pool2d</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv2_drop</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="nb">input</span><span class="p">)),</span> <span class="mi">2</span><span class="p">))</span>
<span class="nb">input</span> <span class="o">=</span> <span class="nb">input</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1024</span><span class="p">)</span>
<span class="nb">input</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc1_drop</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc1</span><span class="p">(</span><span class="nb">input</span><span class="p">)))</span>
<span class="nb">input</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc2</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="nb">input</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">log_softmax</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="k">return</span> <span class="nb">input</span>
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<p>Grab our dataset, we'll use Repeated-MNIST. We will acquire to samples for each class for our initial training set.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">train_dataset</span><span class="p">,</span> <span class="n">test_dataset</span> <span class="o">=</span> <span class="n">repeated_mnist</span><span class="o">.</span><span class="n">create_repeated_MNIST_dataset</span><span class="p">(</span><span class="n">num_repetitions</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">add_noise</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">num_initial_samples</span> <span class="o">=</span> <span class="mi">20</span>
<span class="n">num_classes</span> <span class="o">=</span> <span class="mi">10</span>
<span class="n">initial_samples</span> <span class="o">=</span> <span class="n">active_learning</span><span class="o">.</span><span class="n">get_balanced_sample_indices</span><span class="p">(</span>
<span class="n">repeated_mnist</span><span class="o">.</span><span class="n">get_targets</span><span class="p">(</span><span class="n">train_dataset</span><span class="p">),</span> <span class="n">num_classes</span><span class="o">=</span><span class="n">num_classes</span><span class="p">,</span> <span class="n">n_per_digit</span><span class="o">=</span><span class="n">num_initial_samples</span> <span class="o">/</span> <span class="n">num_classes</span>
<span class="p">)</span>
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<p>For this example, we are going to take two shortcuts that will reduce the performance:</p>
<ul>
<li>we discard most of the training set and only keep 20k samples; and</li>
<li>we don't implement early stopping or epoch-wise training.</li>
</ul>
<p>Instead, we always train by drawing 24576 many samples from the training set. This will overfit in the beginning and underfit later, but it still is sufficient to achieve 90% accuracy with 105 samples in the training set.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">max_training_samples</span> <span class="o">=</span> <span class="mi">150</span>
<span class="n">acquisition_batch_size</span> <span class="o">=</span> <span class="mi">5</span>
<span class="n">num_inference_samples</span> <span class="o">=</span> <span class="mi">100</span>
<span class="n">num_test_inference_samples</span> <span class="o">=</span> <span class="mi">5</span>
<span class="n">num_samples</span> <span class="o">=</span> <span class="mi">100000</span>
<span class="n">test_batch_size</span> <span class="o">=</span> <span class="mi">512</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">64</span>
<span class="n">scoring_batch_size</span> <span class="o">=</span> <span class="mi">128</span>
<span class="n">training_iterations</span> <span class="o">=</span> <span class="mi">4096</span> <span class="o">*</span> <span class="mi">6</span>
<span class="n">use_cuda</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"use_cuda: </span><span class="si">{</span><span class="n">use_cuda</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="n">device</span> <span class="o">=</span> <span class="s2">"cuda"</span> <span class="k">if</span> <span class="n">use_cuda</span> <span class="k">else</span> <span class="s2">"cpu"</span>
<span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"num_workers"</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">"pin_memory"</span><span class="p">:</span> <span class="kc">True</span><span class="p">}</span> <span class="k">if</span> <span class="n">use_cuda</span> <span class="k">else</span> <span class="p">{}</span>
<span class="n">test_loader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">test_dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">test_batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="n">active_learning_data</span> <span class="o">=</span> <span class="n">active_learning</span><span class="o">.</span><span class="n">ActiveLearningData</span><span class="p">(</span><span class="n">train_dataset</span><span class="p">)</span>
<span class="c1"># Split off the initial samples first.</span>
<span class="n">active_learning_data</span><span class="o">.</span><span class="n">acquire</span><span class="p">(</span><span class="n">initial_samples</span><span class="p">)</span>
<span class="c1"># THIS REMOVES MOST OF THE POOL DATA. UNCOMMENT THIS TO TAKE ALL UNLABELLED DATA INTO ACCOUNT!</span>
<span class="n">active_learning_data</span><span class="o">.</span><span class="n">extract_dataset_from_pool</span><span class="p">(</span><span class="mi">40000</span><span class="p">)</span>
<span class="n">train_loader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span>
<span class="n">active_learning_data</span><span class="o">.</span><span class="n">training_dataset</span><span class="p">,</span>
<span class="n">sampler</span><span class="o">=</span><span class="n">active_learning</span><span class="o">.</span><span class="n">RandomFixedLengthSampler</span><span class="p">(</span><span class="n">active_learning_data</span><span class="o">.</span><span class="n">training_dataset</span><span class="p">,</span> <span class="n">training_iterations</span><span class="p">),</span>
<span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
<span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">pool_loader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span>
<span class="n">active_learning_data</span><span class="o">.</span><span class="n">pool_dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">scoring_batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
<span class="p">)</span>
<span class="c1"># Run experiment</span>
<span class="n">test_accs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">test_loss</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">added_indices</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">pbar</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">initial</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">active_learning_data</span><span class="o">.</span><span class="n">training_dataset</span><span class="p">),</span> <span class="n">total</span><span class="o">=</span><span class="n">max_training_samples</span><span class="p">,</span> <span class="n">desc</span><span class="o">=</span><span class="s2">"Training Set Size"</span><span class="p">)</span>
<span class="k">while</span> <span class="kc">True</span><span class="p">:</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">BayesianCNN</span><span class="p">(</span><span class="n">num_classes</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span>
<span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
<span class="c1"># Train</span>
<span class="k">for</span> <span class="n">data</span><span class="p">,</span> <span class="n">target</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">train_loader</span><span class="p">,</span> <span class="n">desc</span><span class="o">=</span><span class="s2">"Training"</span><span class="p">,</span> <span class="n">leave</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
<span class="n">target</span> <span class="o">=</span> <span class="n">target</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
<span class="n">prediction</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">nll_loss</span><span class="p">(</span><span class="n">prediction</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
<span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
<span class="c1"># Test</span>
<span class="n">loss</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">correct</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
<span class="k">for</span> <span class="n">data</span><span class="p">,</span> <span class="n">target</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">test_loader</span><span class="p">,</span> <span class="n">desc</span><span class="o">=</span><span class="s2">"Testing"</span><span class="p">,</span> <span class="n">leave</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
<span class="n">target</span> <span class="o">=</span> <span class="n">target</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
<span class="n">prediction</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">logsumexp</span><span class="p">(</span><span class="n">model</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">num_test_inference_samples</span><span class="p">),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="n">math</span><span class="o">.</span><span class="n">log</span><span class="p">(</span>
<span class="n">num_test_inference_samples</span>
<span class="p">)</span>
<span class="n">loss</span> <span class="o">+=</span> <span class="n">F</span><span class="o">.</span><span class="n">nll_loss</span><span class="p">(</span><span class="n">prediction</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">reduction</span><span class="o">=</span><span class="s2">"sum"</span><span class="p">)</span>
<span class="n">prediction</span> <span class="o">=</span> <span class="n">prediction</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="mi">1</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">correct</span> <span class="o">+=</span> <span class="n">prediction</span><span class="o">.</span><span class="n">eq</span><span class="p">(</span><span class="n">target</span><span class="o">.</span><span class="n">view_as</span><span class="p">(</span><span class="n">prediction</span><span class="p">))</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="n">loss</span> <span class="o">/=</span> <span class="nb">len</span><span class="p">(</span><span class="n">test_loader</span><span class="o">.</span><span class="n">dataset</span><span class="p">)</span>
<span class="n">test_loss</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span>
<span class="n">percentage_correct</span> <span class="o">=</span> <span class="mf">100.0</span> <span class="o">*</span> <span class="n">correct</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">test_loader</span><span class="o">.</span><span class="n">dataset</span><span class="p">)</span>
<span class="n">test_accs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">percentage_correct</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span>
<span class="s2">"Test set: Average loss: </span><span class="si">{:.4f}</span><span class="s2">, Accuracy: </span><span class="si">{}</span><span class="s2">/</span><span class="si">{}</span><span class="s2"> (</span><span class="si">{:.2f}</span><span class="s2">%)"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="n">loss</span><span class="p">,</span> <span class="n">correct</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">test_loader</span><span class="o">.</span><span class="n">dataset</span><span class="p">),</span> <span class="n">percentage_correct</span>
<span class="p">)</span>
<span class="p">)</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">active_learning_data</span><span class="o">.</span><span class="n">training_dataset</span><span class="p">)</span> <span class="o">>=</span> <span class="n">max_training_samples</span><span class="p">:</span>
<span class="k">break</span>
<span class="c1"># Acquire pool predictions</span>
<span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">active_learning_data</span><span class="o">.</span><span class="n">pool_dataset</span><span class="p">)</span>
<span class="n">logits_N_K_C</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">N</span><span class="p">,</span> <span class="n">num_inference_samples</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">double</span><span class="p">,</span> <span class="n">pin_memory</span><span class="o">=</span><span class="n">use_cuda</span><span class="p">)</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
<span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">_</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">tqdm</span><span class="p">(</span><span class="n">pool_loader</span><span class="p">,</span> <span class="n">desc</span><span class="o">=</span><span class="s2">"Evaluating Acquisition Set"</span><span class="p">,</span> <span class="n">leave</span><span class="o">=</span><span class="kc">False</span><span class="p">)):</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
<span class="n">lower</span> <span class="o">=</span> <span class="n">i</span> <span class="o">*</span> <span class="n">pool_loader</span><span class="o">.</span><span class="n">batch_size</span>
<span class="n">upper</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">lower</span> <span class="o">+</span> <span class="n">pool_loader</span><span class="o">.</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">N</span><span class="p">)</span>
<span class="n">logits_N_K_C</span><span class="p">[</span><span class="n">lower</span><span class="p">:</span><span class="n">upper</span><span class="p">]</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">model</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">num_inference_samples</span><span class="p">)</span><span class="o">.</span><span class="n">double</span><span class="p">(),</span> <span class="n">non_blocking</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
<span class="n">candidate_batch</span> <span class="o">=</span> <span class="n">batchbald</span><span class="o">.</span><span class="n">get_batchbald_batch</span><span class="p">(</span>
<span class="n">logits_N_K_C</span><span class="p">,</span> <span class="n">acquisition_batch_size</span><span class="p">,</span> <span class="n">num_samples</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">double</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span>
<span class="p">)</span>
<span class="n">targets</span> <span class="o">=</span> <span class="n">repeated_mnist</span><span class="o">.</span><span class="n">get_targets</span><span class="p">(</span><span class="n">active_learning_data</span><span class="o">.</span><span class="n">pool_dataset</span><span class="p">)</span>
<span class="n">dataset_indices</span> <span class="o">=</span> <span class="n">active_learning_data</span><span class="o">.</span><span class="n">get_dataset_indices</span><span class="p">(</span><span class="n">candidate_batch</span><span class="o">.</span><span class="n">indices</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Dataset indices: "</span><span class="p">,</span> <span class="n">dataset_indices</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Scores: "</span><span class="p">,</span> <span class="n">candidate_batch</span><span class="o">.</span><span class="n">scores</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Labels: "</span><span class="p">,</span> <span class="n">targets</span><span class="p">[</span><span class="n">candidate_batch</span><span class="o">.</span><span class="n">indices</span><span class="p">])</span>
<span class="n">active_learning_data</span><span class="o">.</span><span class="n">acquire</span><span class="p">(</span><span class="n">candidate_batch</span><span class="o">.</span><span class="n">indices</span><span class="p">)</span>
<span class="n">added_indices</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dataset_indices</span><span class="p">)</span>
<span class="n">pbar</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dataset_indices</span><span class="p">))</span>
</pre></div>
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{% raw %}
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<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">num_initial_samples</span><span class="p">,</span> <span class="n">max_training_samples</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">acquisition_batch_size</span><span class="p">),</span> <span class="n">test_accs</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"# training samples"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"Accuracy"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">hlines</span><span class="p">(</span><span class="mi">90</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">150</span><span class="p">,</span> <span class="n">linestyles</span><span class="o">=</span><span class="s2">"dashed"</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">"r"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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<img 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
"
>
</div>
</div>
</div>
</div>
</div>
{% endraw %}
</div>