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updated docs
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Davis King
parent
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@ -65,6 +65,7 @@
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</li>
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<li><b>Machine Learning Algorithms</b>
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<ul>
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<li><a href="ml.html#add_layer">Deep Learning</a></li>
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<li>Conventional SMO based Support Vector Machines for <a href="ml.html#svm_nu_trainer">classification</a>
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and <a href="ml.html#svr_trainer">regression</a> </li>
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<li>Reduced-rank methods for large-scale <a href="ml.html#svm_c_ekm_trainer">classification</a>
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@ -186,6 +186,18 @@
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<item nolink="true">
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<name>Examples: C++</name>
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<sub>
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<item>
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<name>Deep Learning</name>
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<link>dnn_mnist_ex.cpp.html</link>
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</item>
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<item>
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<name>Deep Learning Advanced</name>
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<link>dnn_mnist_advanced_ex.cpp.html</link>
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</item>
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<item>
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<name>Deep Learning Inception</name>
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<link>dnn_inception_ex.cpp.html</link>
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</item>
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<item>
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<name>Linear Model Predictive Control</name>
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<link>mpc_ex.cpp.html</link>
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394
docs/docs/ml.xml
394
docs/docs/ml.xml
@ -9,22 +9,16 @@
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<body>
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<a href="ml_guide.svg"><img src="ml_guide.svg" width="100%"/></a>
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<p>
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A major design goal of this portion of the library is to provide a highly modular and
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simple architecture for dealing with kernel algorithms. Towards this end, dlib takes a generic
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programming approach using C++ templates. In particular, each algorithm is parameterized
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to allow a user to supply either one of the predefined dlib kernels (e.g. <a
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href="#radial_basis_kernel">RBF</a> operating
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on <a href="linear_algebra.html#matrix">column vectors</a>), or a new
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<a href="using_custom_kernels_ex.cpp.html">user defined kernel</a>.
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Moreover, the implementations of the algorithms are totally separated from the data on
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which they operate. This makes the dlib implementation generic enough to operate on
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any kind of data, be it column vectors, images, or some other form of structured data.
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All that is necessary is an appropriate kernel.
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</p>
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<h3>Paper Describing dlib Machine Learning</h3>
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<br/>
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<br/>
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<p><font style='font-size:1.4em;line-height:1.1em'>
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Dlib contains a wide range of machine learning algorithms. All
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designed to be highly modular, quick to execute, and simple to use
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via a clean and modern C++ API. It is used in a wide range of
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applications including robotics, embedded devices, mobile phones, and large
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high performance computing environments. If you use dlib in your
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research please cite:
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</font></p>
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<pre>
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Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09a.pdf">Dlib-ml: A Machine Learning Toolkit</a>.
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<i>Journal of Machine Learning Research</i>, 2009
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@ -105,6 +99,147 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
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<item>svm_rank_trainer</item>
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<item>shape_predictor_trainer</item>
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</section>
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<section>
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<name>Deep Learning</name>
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<item nolink="true">
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<name>Core Tools</name>
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<sub>
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<item>add_layer</item>
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<item>add_loss_layer</item>
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<item>repeat</item>
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<item>add_tag_layer</item>
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<item>add_skip_layer</item>
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<item>layer</item>
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<item>test_layer</item>
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<item>resizable_tensor</item>
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<item>alias_tensor</item>
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</sub>
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</item>
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<item nolink="true">
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<name>Input Layers</name>
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<sub>
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<item>input</item>
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<item>input_rgb_image</item>
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<item>
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<name>EXAMPLE_INPUT_LAYER</name>
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<link>dlib/dnn/input_abstract.h.html#EXAMPLE_INPUT_LAYER</link>
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</item>
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</sub>
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</item>
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<item nolink="true">
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<name>Computational Layers</name>
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<sub>
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<item>
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<name>EXAMPLE_COMPUTATIONAL_LAYER</name>
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<link>dlib/dnn/layers_abstract.h.html#EXAMPLE_COMPUTATIONAL_LAYER_</link>
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</item>
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<item>
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<name>fc</name>
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<link>dlib/dnn/layers_abstract.h.html#fc_</link>
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</item>
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<item>
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<name>con</name>
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<link>dlib/dnn/layers_abstract.h.html#con_</link>
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</item>
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<item>
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<name>dropout</name>
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<link>dlib/dnn/layers_abstract.h.html#dropout_</link>
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</item>
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<item>
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<name>multiply</name>
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<link>dlib/dnn/layers_abstract.h.html#multiply_</link>
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</item>
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<item>
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<name>bn</name>
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<link>dlib/dnn/layers_abstract.h.html#bn_</link>
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</item>
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<item>
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<name>affine</name>
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<link>dlib/dnn/layers_abstract.h.html#affine_</link>
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</item>
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<item>
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<name>max_pool</name>
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<link>dlib/dnn/layers_abstract.h.html#max_pool_</link>
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</item>
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<item>
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<name>avg_pool</name>
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<link>dlib/dnn/layers_abstract.h.html#avg_pool_</link>
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</item>
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<item>
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<name>relu</name>
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<link>dlib/dnn/layers_abstract.h.html#relu_</link>
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</item>
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<item>
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<name>concat</name>
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<link>dlib/dnn/layers_abstract.h.html#concat_</link>
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</item>
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<item>
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<name>prelu</name>
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<link>dlib/dnn/layers_abstract.h.html#prelu_</link>
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</item>
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<item>
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<name>sig</name>
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<link>dlib/dnn/layers_abstract.h.html#sig_</link>
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</item>
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<item>
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<name>htan</name>
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<link>dlib/dnn/layers_abstract.h.html#htan_</link>
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</item>
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<item>
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<name>softmax</name>
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<link>dlib/dnn/layers_abstract.h.html#softmax_</link>
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</item>
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<item>
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<name>add_prev</name>
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<link>dlib/dnn/layers_abstract.h.html#add_prev_</link>
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</item>
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<item>
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<name>inception</name>
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<link>dlib/dnn/layers_abstract.h.html#inception</link>
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</item>
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</sub>
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</item>
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<item nolink="true">
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<name>Loss Layers</name>
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<sub>
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<item>
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<name>EXAMPLE_LOSS_LAYER</name>
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<link>dlib/dnn/loss_abstract.h.html#EXAMPLE_LOSS_LAYER_</link>
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</item>
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<item>
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<name>loss_binary_hinge</name>
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<link>dlib/dnn/loss_abstract.h.html#loss_binary_hinge_</link>
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</item>
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<item>
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<name>loss_binary_log</name>
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<link>dlib/dnn/loss_abstract.h.html#loss_binary_log_</link>
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</item>
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<item>
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<name>loss_multiclass_log</name>
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<link>dlib/dnn/loss_abstract.h.html#loss_multiclass_log_</link>
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</item>
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</sub>
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</item>
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<item nolink="true">
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<name>Solvers</name>
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<sub>
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<item>
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<name>EXAMPLE_SOLVER</name>
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<link>dlib/dnn/solvers_abstract.h.html#EXAMPLE_SOLVER</link>
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</item>
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<item>
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<name>sgd</name>
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<link>dlib/dnn/solvers_abstract.h.html#sgd</link>
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</item>
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<item>
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<name>adam</name>
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<link>dlib/dnn/solvers_abstract.h.html#adam</link>
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</item>
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</sub>
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</item>
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</section>
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<section>
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<name>Clustering</name>
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<item>pick_initial_centers</item>
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@ -273,6 +408,233 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
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|
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<components>
|
||||
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component cpp11="true">
|
||||
<name>add_layer</name>
|
||||
<file>dlib/dnn.h</file>
|
||||
<spec_file link="true">dlib/dnn/core_abstract.h</spec_file>
|
||||
<description>
|
||||
In dlib, a deep neural network is composed of 3 main parts. An
|
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<a href="dlib/dnn/input_abstract.h.html#EXAMPLE_INPUT_LAYER">input layer</a>, a bunch of
|
||||
<a href="dlib/dnn/layers_abstract.h.html#EXAMPLE_COMPUTATIONAL_LAYER_">computational layers</a>,
|
||||
and optionally a
|
||||
<a href="dlib/dnn/loss_abstract.h.html#EXAMPLE_LOSS_LAYER_">loss layer</a>. The add_layer
|
||||
class is the central object which adds a computational layer onto an
|
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input layer or an entire network. Therefore, deep neural networks are created
|
||||
by stacking many layers on top of each other using the add_layer class.
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<p>
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||||
For a tutorial showing how this is accomplished see
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<a href="dnn_mnist_ex.cpp.html">this MNIST example</a>.
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||||
</p>
|
||||
</description>
|
||||
<examples>
|
||||
<example>dnn_mnist_ex.cpp.html</example>
|
||||
<example>dnn_mnist_advanced_ex.cpp.html</example>
|
||||
<example>dnn_inception_ex.cpp.html</example>
|
||||
</examples>
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component cpp11="true">
|
||||
<name>add_loss_layer</name>
|
||||
<file>dlib/dnn.h</file>
|
||||
<spec_file link="true">dlib/dnn/core_abstract.h</spec_file>
|
||||
<description>
|
||||
This object is a tool for stacking a <a href="dlib/dnn/loss_abstract.h.html#EXAMPLE_LOSS_LAYER_">loss layer</a>
|
||||
on the top of a deep neural network.
|
||||
</description>
|
||||
<examples>
|
||||
<example>dnn_mnist_ex.cpp.html</example>
|
||||
<example>dnn_mnist_advanced_ex.cpp.html</example>
|
||||
<example>dnn_inception_ex.cpp.html</example>
|
||||
</examples>
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component cpp11="true">
|
||||
<name>repeat</name>
|
||||
<file>dlib/dnn.h</file>
|
||||
<spec_file link="true">dlib/dnn/core_abstract.h</spec_file>
|
||||
<description>
|
||||
This object adds N copies of a computational layer onto a deep neural network.
|
||||
It is essentially the same as using <a href="#add_layer">add_layer</a> N times,
|
||||
except that it involves less typing, and for large N, will compile much faster.
|
||||
</description>
|
||||
<examples>
|
||||
<example>dnn_mnist_advanced_ex.cpp.html</example>
|
||||
</examples>
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component cpp11="true">
|
||||
<name>add_tag_layer</name>
|
||||
<file>dlib/dnn.h</file>
|
||||
<spec_file link="true">dlib/dnn/core_abstract.h</spec_file>
|
||||
<description>
|
||||
This object is a tool for tagging layers in a deep neural network. These tags make it
|
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easy to refer to the tagged layer in other parts of your code.
|
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Specifically, this object adds a new layer onto a deep neural network.
|
||||
However, this layer simply performs the identity transform.
|
||||
This means it is a no-op and its presence does not change the
|
||||
behavior of the network. It exists solely to be used by <a
|
||||
href="#add_skip_layer">add_skip_layer</a> or <a href="#layer">layer()</a> to reference a
|
||||
particular part of a network.
|
||||
|
||||
<p>
|
||||
For a tutorial showing how to use tagging see the
|
||||
<a href="dnn_mnist_advanced_ex.cpp.html">dnn_mnist_advanced_ex.cpp</a>
|
||||
example program.
|
||||
</p>
|
||||
</description>
|
||||
<examples>
|
||||
<example>dnn_mnist_advanced_ex.cpp.html</example>
|
||||
</examples>
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component cpp11="true">
|
||||
<name>add_skip_layer</name>
|
||||
<file>dlib/dnn.h</file>
|
||||
<spec_file link="true">dlib/dnn/core_abstract.h</spec_file>
|
||||
<description>
|
||||
This object adds a new layer to a deep neural network which draws its input
|
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from a <a href="#add_tag_layer">tagged layer</a> rather than from
|
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the immediate predecessor layer as is normally done.
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||||
|
||||
<p>
|
||||
For a tutorial showing how to use tagging see the
|
||||
<a href="dnn_mnist_advanced_ex.cpp.html">dnn_mnist_advanced_ex.cpp</a>
|
||||
example program.
|
||||
</p>
|
||||
</description>
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component cpp11="true">
|
||||
<name>layer</name>
|
||||
<file>dlib/dnn.h</file>
|
||||
<spec_file link="true">dlib/dnn/core_abstract.h</spec_file>
|
||||
<description>
|
||||
This global function references a <a href="#add_tag_layer">tagged layer</a>
|
||||
inside a deep neural network object.
|
||||
|
||||
<p>
|
||||
For a tutorial showing how to use tagging see the
|
||||
<a href="dnn_mnist_advanced_ex.cpp.html">dnn_mnist_advanced_ex.cpp</a>
|
||||
example program.
|
||||
</p>
|
||||
</description>
|
||||
<examples>
|
||||
<example>dnn_mnist_advanced_ex.cpp.html</example>
|
||||
</examples>
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component cpp11="true">
|
||||
<name>input</name>
|
||||
<file>dlib/dnn.h</file>
|
||||
<spec_file link="true">dlib/dnn/input_abstract.h</spec_file>
|
||||
<description>
|
||||
This is a simple input layer type for use in a deep neural network which
|
||||
takes some kind of image as input and loads it into a network.
|
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</description>
|
||||
<examples>
|
||||
<example>dnn_mnist_ex.cpp.html</example>
|
||||
<example>dnn_mnist_advanced_ex.cpp.html</example>
|
||||
<example>dnn_inception_ex.cpp.html</example>
|
||||
</examples>
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component cpp11="true">
|
||||
<name>input_rgb_image</name>
|
||||
<file>dlib/dnn.h</file>
|
||||
<spec_file link="true">dlib/dnn/input_abstract.h</spec_file>
|
||||
<description>
|
||||
This is a simple input layer type for use in a deep neural network
|
||||
which takes an RGB image as input and loads it into a network. It
|
||||
is very similar to the <a href="#input">input layer</a> except that
|
||||
it allows you to subtract the average color value from each color
|
||||
channel when converting an image to a tensor.
|
||||
</description>
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component cpp11="true">
|
||||
<name>test_layer</name>
|
||||
<file>dlib/dnn.h</file>
|
||||
<spec_file link="true">dlib/dnn/core_abstract.h</spec_file>
|
||||
<description>
|
||||
This is a function which tests if a layer object correctly implements
|
||||
the <a href="dlib/dnn/layers_abstract.h.html#EXAMPLE_COMPUTATIONAL_LAYER_">documented contract</a>
|
||||
for a computational layer in a deep neural network.
|
||||
</description>
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component cpp11="true">
|
||||
<name>resizable_tensor</name>
|
||||
<file>dlib/dnn.h</file>
|
||||
<spec_file link="true">dlib/dnn/tensor_abstract.h</spec_file>
|
||||
<description>
|
||||
This object represents a 4D array of float values, all stored contiguously
|
||||
in memory. Importantly, it keeps two copies of the floats, one on the host
|
||||
CPU side and another on the GPU device side. It automatically performs the
|
||||
necessary host/device transfers to keep these two copies of the data in
|
||||
sync.
|
||||
|
||||
<p>
|
||||
All transfers to the device happen asynchronously with respect to the
|
||||
default CUDA stream so that CUDA kernel computations can overlap with data
|
||||
transfers. However, any transfers from the device to the host happen
|
||||
synchronously in the default CUDA stream. Therefore, you should perform
|
||||
all your CUDA kernel launches on the default stream so that transfers back
|
||||
to the host do not happen before the relevant computations have completed.
|
||||
</p>
|
||||
|
||||
<p>
|
||||
If DLIB_USE_CUDA is not #defined then this object will not use CUDA at all.
|
||||
Instead, it will simply store one host side memory block of floats.
|
||||
</p>
|
||||
|
||||
<p>
|
||||
Finally, the convention in dlib code is to interpret the tensor as a set of
|
||||
num_samples() 3D arrays, each of dimension k() by nr() by nc(). Also,
|
||||
while this class does not specify a memory layout, the convention is to
|
||||
assume that indexing into an element at coordinates (sample,k,nr,nc) can be
|
||||
accomplished via:
|
||||
<tt>host()[((sample*t.k() + k)*t.nr() + nr)*t.nc() + nc]</tt>
|
||||
</p>
|
||||
|
||||
</description>
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component cpp11="true">
|
||||
<name>alias_tensor</name>
|
||||
<file>dlib/dnn.h</file>
|
||||
<spec_file link="true">dlib/dnn/tensor_abstract.h</spec_file>
|
||||
<description>
|
||||
This object is a <a href="#resizable_tensor">tensor</a> that
|
||||
aliases another tensor. That is, it doesn't have its own block of
|
||||
memory but instead simply holds pointers to the memory of another
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
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|
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|
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Loading…
Reference in New Issue
Block a user