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| <li> |
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| <a href="/docs/2.9.0/distributed-computing/distributed-computing" |
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| class='' |
| >Distributed Computing API</a> |
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| </li> |
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| <li> |
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| <a href="/docs/2.9.0/distributed-computing/cluster-groups" |
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| class='' |
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| <li> |
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| <a href="/docs/2.9.0/distributed-computing/executor-service" |
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| <li> |
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| <a href="/docs/2.9.0/distributed-computing/map-reduce" |
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| class='' |
| >MapReduce API</a> |
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| <li> |
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| <a href="/docs/2.9.0/distributed-computing/load-balancing" |
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| class='' |
| >Load Balancing</a> |
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| <li> |
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| <a href="/docs/2.9.0/distributed-computing/fault-tolerance" |
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| class='' |
| >Fault Tolerance</a> |
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| </li> |
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| <li> |
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| <a href="/docs/2.9.0/distributed-computing/job-scheduling" |
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| class='' |
| >Job Scheduling</a> |
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| </nav> |
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| <a href="/docs/2.9.0/collocated-computations" class='' >Colocating Computations with Data</a> |
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| <button type='button' data-guide-url="" class='group-toggle collapsed '>Working with Events<img class="state-indicator" src="/assets/images/left-nav-arrow.svg"></button> |
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| <a href="/docs/2.9.0/events/listening-to-events" |
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| >Enabling and Listenting to Events</a> |
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| <a href="/docs/2.9.0/events/events" |
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| >Events</a> |
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| </nav> |
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| <a href="/docs/2.9.0/near-cache" class='' >Near Caches</a> |
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| <a href="/docs/2.9.0/platform-cache" class='' >.NET Platform Cache</a> |
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| <a href="/docs/2.9.0/services/services" class='' >Services</a> |
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| >Queue and Set</a> |
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| >Atomic Types</a> |
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| <a href="/docs/2.9.0/data-structures/countdownlatch" |
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| class='' |
| >CountDownLatch</a> |
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| <a href="/docs/2.9.0/data-structures/atomic-sequence" |
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| class='' |
| >Atomic Sequence</a> |
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| <a href="/docs/2.9.0/data-structures/semaphore" |
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| class='' |
| >Semaphore</a> |
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| <a href="/docs/2.9.0/machine-learning/partition-based-dataset" |
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| <a href="/docs/2.9.0/machine-learning/updating-trained-models" |
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| <li><a href="/docs/2.9.0//machine-learning/binary-classification/introduction" class=''>Introduction</a></li> |
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| <li><a href="/docs/2.9.0//machine-learning/binary-classification/linear-svm" class=''>Linear SVM (Support Vector Machine)</a></li> |
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| <li><a href="/docs/2.9.0//machine-learning/binary-classification/decision-trees" class=''>Decision Trees</a></li> |
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| <li><a href="/docs/2.9.0//machine-learning/binary-classification/multilayer-perceptron" class='active'>Multilayer Perceptron</a></li> |
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| <li><a href="/docs/2.9.0//machine-learning/binary-classification/logistic-regression" class=''>Logistic Regression</a></li> |
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| <li><a href="/docs/2.9.0//machine-learning/binary-classification/ann" class=''>ANN (Approximate Nearest Neighbor)</a></li> |
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| <li><a href="/docs/2.9.0//machine-learning/binary-classification/naive-bayes" class=''>Naive Bayes</a></li> |
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| <li><a href="/docs/2.9.0//machine-learning/regression/introduction" class=''>Introduction</a></li> |
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| <li><a href="/docs/2.9.0//machine-learning/regression/linear-regression" class=''>Linear Regression</a></li> |
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| <li><a href="/docs/2.9.0//machine-learning/regression/decision-trees-regression" class=''>Decision Trees Regression</a></li> |
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| <li><a href="/docs/2.9.0//machine-learning/regression/knn-regression" class=''>k-NN Regression</a></li> |
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| <li><a href="/docs/2.9.0//machine-learning/clustering/introduction" class=''>Introduction</a></li> |
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| <li><a href="/docs/2.9.0//machine-learning/clustering/k-means-clustering" class=''>K-Means Clustering</a></li> |
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| <li><a href="/docs/2.9.0//machine-learning/clustering/gaussian-mixture" class=''>Gaussian mixture (GMM)</a></li> |
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| </nav> |
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| <li> |
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| <a href="/docs/2.9.0/machine-learning/preprocessing" |
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| class='' |
| >Preprocessing</a> |
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| </li> |
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| <li> |
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| class='collapsed '>Model Selection<img class="state-indicator" src="/assets/images/left-nav-arrow.svg"></button> |
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| <li><a href="/docs/2.9.0//machine-learning/model-selection/introduction" class=''>Introduction</a></li> |
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| <li><a href="/docs/2.9.0//machine-learning/model-selection/evaluator" class=''>Evaluator</a></li> |
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| <li><a href="/docs/2.9.0//machine-learning/model-selection/split-the-dataset-on-test-and-train-datasets" class=''>Split the dataset on test and train datasets</a></li> |
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| <li><a href="/docs/2.9.0//machine-learning/model-selection/hyper-parameter-tuning" class=''>Hyper-parameter tuning</a></li> |
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| <li> |
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| <a href="/docs/2.9.0/machine-learning/multiclass-classification" |
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| class='' |
| >Multiclass Classification</a> |
| |
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| <li><a href="/docs/2.9.0//machine-learning/ensemble-methods/introduction" class=''></a></li> |
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| <li><a href="/docs/2.9.0//machine-learning/ensemble-methods/stacking" class=''>Stacking</a></li> |
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| <li><a href="/docs/2.9.0//machine-learning/ensemble-methods/baggin" class=''>Bagging</a></li> |
| |
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| <li><a href="/docs/2.9.0//machine-learning/ensemble-methods/random-forest" class=''>Random Forest</a></li> |
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| <li><a href="/docs/2.9.0//machine-learning/ensemble-methods/gradient-boosting" class=''>Gradient Boosting</a></li> |
| |
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| <li> |
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| <a href="/docs/2.9.0/machine-learning/recommendation-systems" |
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| class='' |
| >Recommendation Systems</a> |
| |
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| |
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| <li> |
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| <li><a href="/docs/2.9.0//machine-learning/importing-model/introduction" class=''>Introduction</a></li> |
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| <li><a href="/docs/2.9.0//machine-learning/importing-model/model-import-from-gxboost" class=''>Import Model from XGBoost</a></li> |
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| <li><a href="/docs/2.9.0//machine-learning/importing-model/model-import-from-apache-spark" class=''>Import Model from Apache Spark</a></li> |
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| <a href="/docs/2.9.0/monitoring-metrics/intro" |
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| class='' |
| >Introduction</a> |
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| </li> |
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| <li> |
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| <a href="/docs/2.9.0/monitoring-metrics/cluster-id" |
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| class='' |
| >Cluster ID and Tag</a> |
| |
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| <li><a href="/docs/2.9.0//monitoring-metrics/configuring-metrics" class=''>Configuring Metrics</a></li> |
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| <li><a href="/docs/2.9.0//monitoring-metrics/metrics" class=''>JMX Metrics</a></li> |
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| <a href="/docs/2.9.0/security/authentication" |
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| <li><a href="/docs/2.9.0//security/tde" class=''>Introduction</a></li> |
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| <li><a href="/docs/2.9.0//security/master-key-rotation" class=''>Master key rotation</a></li> |
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| <a href="/docs/2.9.0/thin-clients/getting-started-with-thin-clients" |
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| class='' |
| >Thin Clients Overview</a> |
| |
| </li> |
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| <li> |
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| <a href="/docs/2.9.0/thin-clients/java-thin-client" |
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| class='' |
| >Java Thin Client</a> |
| |
| </li> |
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| <li> |
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| <a href="/docs/2.9.0/thin-clients/dotnet-thin-client" |
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| class='' |
| >.NET Thin Client</a> |
| |
| </li> |
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| <li> |
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| <a href="/docs/2.9.0/thin-clients/cpp-thin-client" |
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| class='' |
| >C++ Thin Client</a> |
| |
| </li> |
| |
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| |
| |
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| <li> |
| |
| <a href="/docs/2.9.0/thin-clients/python-thin-client" |
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| class='' |
| >Python Thin Client</a> |
| |
| </li> |
| |
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| |
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| <li> |
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| <a href="/docs/2.9.0/thin-clients/php-thin-client" |
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| class='' |
| >PHP Thin Client</a> |
| |
| </li> |
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| <li> |
| |
| <a href="/docs/2.9.0/thin-clients/nodejs-thin-client" |
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| class='' |
| >Node.js Thin Client</a> |
| |
| </li> |
| |
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| <li> |
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| <button |
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| |
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| <li><a href="/docs/2.9.0//binary-client-protocol/binary-client-protocol" class=''>Binary Client Protocol</a></li> |
| |
| |
| |
| <li><a href="/docs/2.9.0//binary-client-protocol/data-format" class=''>Data Format</a></li> |
| |
| |
| |
| <li><a href="/docs/2.9.0//binary-client-protocol/key-value-queries" class=''>Key-Value Queries</a></li> |
| |
| |
| |
| <li><a href="/docs/2.9.0//binary-client-protocol/sql-and-scan-queries" class=''>SQL and Scan Queries</a></li> |
| |
| |
| |
| <li><a href="/docs/2.9.0//binary-client-protocol/binary-type-metadata" class=''>Binary Types Metadata</a></li> |
| |
| |
| |
| <li><a href="/docs/2.9.0//binary-client-protocol/cache-configuration" class=''>Cache Configuration</a></li> |
| |
| </nav> |
| |
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| |
| </nav> |
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| <li> |
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| <button type='button' data-guide-url="" class='group-toggle collapsed '>ODBC Driver<img class="state-indicator" src="/assets/images/left-nav-arrow.svg"></button> |
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| <li> |
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| <a href="/docs/2.9.0/SQL/ODBC/odbc-driver" |
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| class='' |
| >ODBC Driver</a> |
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| </li> |
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| <li> |
| |
| <a href="/docs/2.9.0/SQL/ODBC/connection-string-dsn" |
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| class='' |
| >Connection String and DSN</a> |
| |
| </li> |
| |
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| |
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| <li> |
| |
| <a href="/docs/2.9.0/SQL/ODBC/querying-modifying-data" |
| |
| class='' |
| >Querying and Modifying Data</a> |
| |
| </li> |
| |
| |
| |
| |
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| <li> |
| |
| <a href="/docs/2.9.0/SQL/ODBC/specification" |
| |
| class='' |
| >Specification</a> |
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| </li> |
| |
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| <li> |
| |
| <a href="/docs/2.9.0/SQL/ODBC/data-types" |
| |
| class='' |
| >Data Types</a> |
| |
| </li> |
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| <li> |
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| <article data-swiftype-index='true'> |
| <a class='edit-link' href="https://github.com/apache/ignite/tree/IGNITE-7595/docs/_docs/machine-learning/binary-classification/multilayer-perceptron.adoc" target="_blank">Edit</a> |
| |
| <h1>Multilayer Perceptron</h1> |
| |
| <div id="preamble"> |
| <div class="sectionbody"> |
| <div class="paragraph"> |
| <p>Multiplayer Perceptron (MLP) is the basic form of neural network. It consists of one input layer and 0 or more transformation layers. Each transformation layer depends on the previous layer in the following way:</p> |
| </div> |
| <div class="imageblock"> |
| <div class="content"> |
| <img src="/docs/2.9.0/images/333.gif" alt="333"> |
| </div> |
| </div> |
| <div class="paragraph"> |
| <p>In the above equation, the dot operator is the dot product of two vectors, functions denoted by <code>sigma</code> are called activators, vectors denoted by <code>w</code> are called weights, and vectors denoted by <code>b</code> are called biases. Each transformation layer has associated weights, activator, and optionally biases. The set of all weights and biases of MLP is the set of MLP parameters.</p> |
| </div> |
| </div> |
| </div> |
| <div class="sect1"> |
| <h2 id="model">Model</h2> |
| <div class="sectionbody"> |
| <div class="paragraph"> |
| <p>Model in case of neural network is represented by class <code>MultilayerPerceptron</code>. It allows you to make a prediction for a given vector of features in the following way:</p> |
| </div> |
| <div class="listingblock"> |
| <div class="content"> |
| <pre class="rouge highlight"><code data-lang="java"><span class="nc">MultilayerPerceptron</span> <span class="n">mlp</span> <span class="o">=</span> <span class="o">...</span> |
| |
| <span class="nc">Matrix</span> <span class="n">prediction</span> <span class="o">=</span> <span class="n">mlp</span><span class="o">.</span><span class="na">apply</span><span class="o">(</span><span class="n">observation</span><span class="o">);</span></code></pre> |
| </div> |
| </div> |
| <div class="paragraph"> |
| <p>The model is a fully independent object and after the training it can be saved, serialized and restored.</p> |
| </div> |
| </div> |
| </div> |
| <div class="sect1"> |
| <h2 id="trainer">Trainer</h2> |
| <div class="sectionbody"> |
| <div class="paragraph"> |
| <p>One of the popular ways for supervised model training is batch training. In this approach, training is done in iterations; during each iteration we extract a <code>subpart(batch)</code> of labeled data (data consisting of input of approximated function and corresponding values of this function which are often called 'ground truth') on which we train and update model parameters using this subpart. Updates are made to minimize loss function on batches.</p> |
| </div> |
| <div class="paragraph"> |
| <p>Apache Ignite <code>MLPTrainer</code> is used for distributed batch training, which works in a map-reduce way. Each iteration (let’s call it global iteration) consists of several parallel iterations which in turn consists of several local steps. Each local iteration is executed by its own worker and performs the specified number of local steps (called synchronization period) to compute its update of model parameters. Then all updates are accumulated on the node that started the training, and are transformed to global update which is sent back to all workers. This process continues until stop criteria is reached.</p> |
| </div> |
| <div class="paragraph"> |
| <p><code>MLPTrainer</code> can be parameterized by neural network architecture, loss function, update strategy (<code>SGD</code>, <code>RProp</code> or <code>Nesterov</code>), max number of iterations, batch size, number of local iterations and seed.</p> |
| </div> |
| <div class="listingblock"> |
| <div class="content"> |
| <pre class="rouge highlight"><code data-lang="java"><span class="c1">// Define a layered architecture.</span> |
| <span class="nc">MLPArchitecture</span> <span class="n">arch</span> <span class="o">=</span> <span class="k">new</span> <span class="nc">MLPArchitecture</span><span class="o">(</span><span class="mi">2</span><span class="o">).</span> |
| <span class="n">withAddedLayer</span><span class="o">(</span><span class="mi">10</span><span class="o">,</span> <span class="kc">true</span><span class="o">,</span> <span class="nc">Activators</span><span class="o">.</span><span class="na">RELU</span><span class="o">).</span> |
| <span class="n">withAddedLayer</span><span class="o">(</span><span class="mi">1</span><span class="o">,</span> <span class="kc">false</span><span class="o">,</span> <span class="nc">Activators</span><span class="o">.</span><span class="na">SIGMOID</span><span class="o">);</span> |
| |
| <span class="c1">// Define a neural network trainer.</span> |
| <span class="nc">MLPTrainer</span><span class="o"><</span><span class="nc">SimpleGDParameterUpdate</span><span class="o">></span> <span class="n">trainer</span> <span class="o">=</span> <span class="k">new</span> <span class="nc">MLPTrainer</span><span class="o"><>(</span> |
| <span class="n">arch</span><span class="o">,</span> |
| <span class="nc">LossFunctions</span><span class="o">.</span><span class="na">MSE</span><span class="o">,</span> |
| <span class="k">new</span> <span class="nc">UpdatesStrategy</span><span class="o"><>(</span> |
| <span class="k">new</span> <span class="nf">SimpleGDUpdateCalculator</span><span class="o">(</span><span class="mf">0.1</span><span class="o">),</span> |
| <span class="nl">SimpleGDParameterUpdate:</span><span class="o">:</span><span class="n">sumLocal</span><span class="o">,</span> |
| <span class="nl">SimpleGDParameterUpdate:</span><span class="o">:</span><span class="n">avg</span> |
| <span class="o">),</span> |
| <span class="mi">3000</span><span class="o">,</span> <span class="c1">// Max iterations.</span> |
| <span class="mi">4</span><span class="o">,</span> <span class="c1">// Batch size.</span> |
| <span class="mi">50</span><span class="o">,</span> <span class="c1">// Local iterations.</span> |
| <span class="mi">123L</span> <span class="c1">// Random seed.</span> |
| <span class="o">);</span> |
| |
| <span class="c1">// Train model.</span> |
| <span class="nc">MultilayerPerceptron</span> <span class="n">mlp</span> <span class="o">=</span> <span class="n">trainer</span><span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">ignite</span><span class="o">,</span> <span class="n">dataCache</span><span class="o">,</span> <span class="n">vectorizer</span><span class="o">);</span></code></pre> |
| </div> |
| </div> |
| </div> |
| </div> |
| <div class="sect1"> |
| <h2 id="example">Example</h2> |
| <div class="sectionbody"> |
| <div class="paragraph"> |
| <p>To see how Deep Learning can be used in practice, try <a href="https://github.com/apache/ignite/blob/master/examples/src/main/java/org/apache/ignite/examples/ml/nn/MLPTrainerExample.java" target="_blank" rel="noopener">this example</a>, available on GitHub and delivered with every Apache Ignite distribution.</p> |
| </div> |
| </div> |
| </div> |
| <div class="copyright"> |
| © 2020 The Apache Software Foundation.<br/> |
| Apache, Apache Ignite, the Apache feather and the Apache Ignite logo are either registered trademarks or trademarks of The Apache Software Foundation. |
| |
| </div> |
| |
| </article> |
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| <li><a href="#model">Model</a></li> |
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| <li><a href="#example">Example</a></li> |
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