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updated docs

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Davis King 2017-02-20 20:47:32 -05:00
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33
docs/docs/algorithms.xml
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@ -58,6 +58,8 @@
<item>rand</item>
<item>median</item>
<item>running_stats</item>
<item>running_stats_decayed</item>
<item>running_scalar_covariance_decayed</item>
<item>running_gradient</item>
<item>running_scalar_covariance</item>
<item>mean_sign_agreement</item>
@ -480,6 +482,37 @@
</component>
<!-- ************************************************************************* -->
<component>
<name>running_stats_decayed</name>
<file>dlib/statistics.h</file>
<spec_file link="true">dlib/statistics/statistics_abstract.h</spec_file>
<description>
This object represents something that can compute the running mean and
variance of a stream of real numbers. It is similar to <a href="#running_stats">running_stats</a>
except that it forgets about data it has seen after a certain period of
time. It does this by exponentially decaying old statistics.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>running_scalar_covariance_decayed</name>
<file>dlib/statistics.h</file>
<spec_file link="true">dlib/statistics/statistics_abstract.h</spec_file>
<description>
This object represents something that can compute the running covariance of
a stream of real number pairs. It is essentially the same as
<a href="#running_scalar_covariance">running_scalar_covariance</a> except that it forgets about data it has seen
after a certain period of time. It does this by exponentially decaying old
statistics.
</description>
</component>
<!-- ************************************************************************* -->
<component>

1
docs/docs/index.xml
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@ -144,6 +144,7 @@
<li>Tools for <a href="imaging.html#object_detector">detecting objects</a> in images including
<a href="imaging.html#get_frontal_face_detector">frontal face detection</a> and
<a href="imaging.html#shape_predictor">object pose estimation</a>.</li>
<li>High quality <a href="dnn_face_recognition_ex.cpp.html">face recognition</a></li>
</ul>
</li>

10
docs/docs/main_menu.xml
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@ -152,7 +152,11 @@
<link>face_landmark_detection.py.html</link>
</item>
<item>
<name>find candidate object locations</name>
<name>Face Recognition</name>
<link>face_recognition.py.html</link>
</item>
<item>
<name>Find Candidate Object Locations</name>
<link>find_candidate_object_locations.py.html</link>
</item>
<item>
@ -216,6 +220,10 @@
<name>Deep Metric Learning on Images</name>
<link>dnn_metric_learning_on_images_ex.cpp.html</link>
</item>
<item>
<name>Deep Face Recognition</name>
<link>dnn_face_recognition_ex.cpp.html</link>
</item>
<item>
<name>Deep Learning Face Detection</name>
<link>dnn_mmod_face_detection_ex.cpp.html</link>

34
docs/docs/ml.xml
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@ -238,6 +238,10 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
<name>loss_mean_squared_</name>
<link>#loss_mean_squared_</link>
</item>
<item>
<name>loss_mean_squared_multioutput_</name>
<link>#loss_mean_squared_multioutput_</link>
</item>
</sub>
</item>
<item nolink="true">
@ -329,6 +333,7 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
<item>test_shape_predictor</item>
<item>average_precision</item>
<item>equal_error_rate</item>
<item>compute_roc_curve</item>
</section>
<section>
@ -461,6 +466,7 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
<example>dnn_mmod_dog_hipsterizer.cpp.html</example>
<example>dnn_metric_learning_ex.cpp.html</example>
<example>dnn_metric_learning_on_images_ex.cpp.html</example>
<example>dnn_face_recognition_ex.cpp.html</example>
</examples>
</component>
@ -509,6 +515,7 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
<example>dnn_mmod_ex.cpp.html</example>
<example>dnn_metric_learning_ex.cpp.html</example>
<example>dnn_metric_learning_on_images_ex.cpp.html</example>
<example>dnn_face_recognition_ex.cpp.html</example>
<example>dnn_mmod_face_detection_ex.cpp.html</example>
<example>dnn_mmod_dog_hipsterizer.cpp.html</example>
</examples>
@ -700,6 +707,8 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
<examples>
<example>dnn_metric_learning_ex.cpp.html</example>
<example>dnn_metric_learning_on_images_ex.cpp.html</example>
<example>dnn_face_recognition_ex.cpp.html</example>
<example>face_recognition.py.html</example>
</examples>
</component>
@ -716,6 +725,20 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>loss_mean_squared_multioutput_</name>
<file>dlib/dnn.h</file>
<spec_file link="true">dlib/dnn/loss_abstract.h</spec_file>
<description>
This object is a <a href="dlib/dnn/loss_abstract.h.html#EXAMPLE_LOSS_LAYER_">loss layer</a>
for a deep neural network. In particular, it implements the mean squared loss, which is
appropriate for regression problems. It is identical to the <a href="#loss_mean_squared_">loss_mean_squared_</a>
loss except this version supports multiple output values.
</description>
</component>
<!-- ************************************************************************* -->
<component>
@ -3359,6 +3382,17 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>compute_roc_curve</name>
<file>dlib/statistics.h</file>
<spec_file link="true">dlib/statistics/lda_abstract.h</spec_file>
<description>
This function computes a ROC curve (receiver operating characteristic curve).
</description>
</component>
<!-- ************************************************************************* -->
<component>

23
docs/docs/optimization.xml
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@ -42,6 +42,7 @@
<section>
<name>Special Purpose Optimizers</name>
<item>find_gap_between_convex_hulls</item>
<item>solve_qp_box_constrained</item>
<item>solve_qp_using_smo</item>
<item>solve_qp2_using_smo</item>
@ -412,6 +413,28 @@ subject to the following constraint:
</component>
<!-- ************************************************************************* -->
<component>
<name>find_gap_between_convex_hulls</name>
<file>dlib/optimization.h</file>
<spec_file link="true">dlib/optimization/optimization_solve_qp_using_smo_abstract.h</spec_file>
<description>
This function measures the position and size of the gap between two convex
polytopes. In particular, it solves the following quadratic program:
<pre>
Minimize: f(cA,cB) == length_squared(A*cA - B*cB)
subject to the following constraints on cA and cB:
- is_col_vector(cA) == true &amp;&amp; cA.size() == A.nc()
- is_col_vector(cB) == true &amp;&amp; cB.size() == B.nc()
- sum(cA) == 1 &amp;&amp; min(cA) >= 0
- sum(cB) == 1 &amp;&amp; min(cB) >= 0
</pre>
</description>
</component>
<!-- ************************************************************************* -->
<component>

49
docs/docs/release_notes.xml
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@ -11,6 +11,53 @@
<!-- ************************************************************************************** -->
<current>
New Features:
- Dlib's simd classes will now use PowerPC VSX instructions. So this makes the HOG based object detector faster on PowerPC machines.
- Added a python version of the DNN face recognition example program.
- Added a python interface to the face recognition DNN model.
- Added compute_roc_curve()
- Added find_gap_between_convex_hulls()
- Added serialization support for std::array.
- Added running_scalar_covariance_decayed
- Added running_stats_decayed
- Added min_pointwise() and max_pointwise().
- Added 1D clustering routine, segment_number_line().
- Add MKL DFTI FFT bindings.
- Added matlab_object to the mex wrapper. Now you can have parameters that are arbitrary matlab objects.
- add support for loading of RGBA JPEG images
- DNN stuff
- Added test_one_step() to the dnn_trainer. This allows you to do automatic early stopping based on observing the loss on held out data.
- Added loss_metric_
- Added loss_mean_squared_
- Added loss_mean_squared_multioutput_
- Made the dnn_trainer automatically reload from the last good state if a loss of NaN is encountered.
- Added l2normalize_ layer
- Added is_vector() for tensor objects.
- Made alias_tensor usable when it is const.
Non-Backwards Compatible Changes:
- Changed the loss layer interface to use two typedefs, output_label_type and training_label_type instead of a single label_type. This way, the label type used for training can be distinct from the type output by the network. This change breaks backwards compatibility with the previous API.
Bug fixes:
- Fixed compiler warnings and errors on newer compilers.
- Fixed a bug in the repeat layer that caused it to throw exceptions in some cases.
- Fixed matlab crashing when an error message from a mex file included the % character, since that is interpreted by matlab as part of an eventual printf() code.
- Fixed compile time error in random_subset_selector::swap()
- Fixed missing implementation of map_input_to_output() and map_output_to_input() in the concat_ layer.
Other:
- Minor usability improvements to DNN API.
- Wrote replacements for set_tensor() and scale_tensor() since the previous versions were calling into cuDNN, however, the cuDNN functions for doing this are horrifically slow, well over 100x slower than they should be, which is surprising since these functions are so trivial.
- Made the dnn_trainer's detection and backtracking from situations with increasing loss more robust. Now it will never get into a situation where it backtracks over and over. Instead, it will only backtrack a few times in a row before just letting SGD run unimpeded.
- Improved C++11 detection and enabling, especially on OS X.
- Made dlib::thread_pool use std::thread and join on the threads in thread_pool's destructor. The previous implementation used dlib's global thread pooling to allocate threads to dlib::thread_pool, however, this sometimes caused annoying behavior when used as part of a MATLAB mex file.
</current>
<!-- ************************************************************************************** -->
<old name="19.2" date="Oct 10, 2016">
New Features:
- Updates to the deep learning API:
- Added tools for making convolutional neural network based object detectors. See
@ -95,7 +142,7 @@ Other:
This way, if we hit a really bad mini-batch during training which negatively effects
the model in a significant way, the dnn_trainer will automatically revert back to an
earlier good state.
</current>
</old>
<!-- ************************************************************************************** -->

14
docs/docs/term_index.xml
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@ -127,6 +127,7 @@
<term file="dlib/dnn/loss_abstract.h.html" name="loss_multiclass_log_" include="dlib/dnn.h"/>
<term file="ml.html" name="loss_metric_" include="dlib/dnn.h"/>
<term file="ml.html" name="loss_mean_squared_" include="dlib/dnn.h"/>
<term file="ml.html" name="loss_mean_squared_multioutput_" include="dlib/dnn.h"/>
<term file="ml.html" name="loss_mmod_" include="dlib/dnn.h"/>
<term file="dlib/dnn/loss_abstract.h.html" name="mmod_options" include="dlib/dnn.h"/>
@ -286,6 +287,7 @@
<term file="optimization.html" name="elastic_net" include="dlib/optimization/elastic_net.h"/>
<term file="optimization.html" name="solve_qp_box_constrained" include="dlib/optimization.h"/>
<term file="optimization.html" name="solve_qp_using_smo" include="dlib/optimization.h"/>
<term file="optimization.html" name="find_gap_between_convex_hulls" include="dlib/optimization.h"/>
<term file="optimization.html" name="solve_qp2_using_smo" include="dlib/optimization.h"/>
<term file="optimization.html" name="solve_qp3_using_smo" include="dlib/optimization.h"/>
<term file="optimization.html" name="solve_qp4_using_smo" include="dlib/optimization.h"/>
@ -390,6 +392,8 @@
<term file="linear_algebra.html" name="find_projective_transform" include="dlib/geometry.h"/>
<term file="linear_algebra.html" name="rotation_matrix" include="dlib/geometry.h"/>
<term file="algorithms.html" name="running_stats" include="dlib/statistics.h"/>
<term file="algorithms.html" name="running_stats_decayed" include="dlib/statistics.h"/>
<term file="algorithms.html" name="running_scalar_covariance_decayed" include="dlib/statistics.h"/>
<term file="algorithms.html" name="running_gradient" include="dlib/statistics/running_gradient.h"/>
<term file="algorithms.html" name="find_upper_quantile" include="dlib/statistics/running_gradient.h"/>
<term file="dlib/statistics/running_gradient_abstract.h.html" name="probability_gradient_greater_than" include="dlib/statistics/running_gradient.h"/>
@ -509,6 +513,8 @@
<term file="ml.html" name="count_ranking_inversions" include="dlib/svm.h"/>
<term file="ml.html" name="average_precision" include="dlib/statistics.h"/>
<term file="ml.html" name="equal_error_rate" include="dlib/statistics.h"/>
<term file="ml.html" name="compute_roc_curve" include="dlib/statistics.h"/>
<term file="dlib/statistics/lda.h.html" name="roc_point" include="dlib/statistics.h"/>
<term file="ml.html" name="test_ranking_function" include="dlib/svm.h"/>
<term file="ml.html" name="cross_validate_ranking_trainer" include="dlib/svm.h"/>
<term file="ml.html" name="svm_c_ekm_trainer" include="dlib/svm.h"/>
@ -711,7 +717,11 @@
<term file="dlib/matrix/matrix_utilities_abstract.h.html" name="reshape" include="dlib/matrix.h"/>
<term file="dlib/matrix/matrix_math_functions_abstract.h.html" name="squared" include="dlib/matrix.h"/>
<term file="dlib/matrix/matrix_math_functions_abstract.h.html" name="cubed" include="dlib/matrix.h"/>
<term file="dlib/matrix/matrix_utilities_abstract.h.html" name="is_vector" include="dlib/matrix.h"/>
<term name="is_vector">
<term link="dlib/matrix/matrix_utilities_abstract.h.html#is_vector" name="for matrix objects" include="dlib/matrix.h"/>
<term link="dlib/dnn/tensor_abstract.h.html#is_vector" name="for tensor objects" include="dlib/dnn.h"/>
</term>
<term file="dlib/matrix/matrix_utilities_abstract.h.html" name="is_row_vector" include="dlib/matrix.h"/>
<term file="dlib/matrix/matrix_utilities_abstract.h.html" name="is_col_vector" include="dlib/matrix.h"/>
<term name="is_finite">
@ -757,6 +767,8 @@
<term file="dlib/matrix/matrix_subexp_abstract.h.html" name="set_ptrm" include="dlib/matrix.h"/>
<term file="dlib/matrix/matrix_utilities_abstract.h.html" name="min_pointwise" include="dlib/matrix.h"/>
<term file="dlib/matrix/matrix_utilities_abstract.h.html" name="max_pointwise" include="dlib/matrix.h"/>
<term file="dlib/matrix/matrix_utilities_abstract.h.html" name="pointwise_multiply" include="dlib/matrix.h"/>
<term file="dlib/matrix/matrix_utilities_abstract.h.html" name="join_rows" include="dlib/matrix.h"/>
<term file="dlib/matrix/matrix_utilities_abstract.h.html" name="join_cols" include="dlib/matrix.h"/>