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钟尚武
dlib
Commits
539b416c
Commit
539b416c
authored
Dec 17, 2016
by
Davis King
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updated docs
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3 changed files
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55 additions
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5 deletions
+55
-5
main_menu.xml
docs/docs/main_menu.xml
+8
-0
ml.xml
docs/docs/ml.xml
+45
-5
term_index.xml
docs/docs/term_index.xml
+2
-0
No files found.
docs/docs/main_menu.xml
View file @
539b416c
...
@@ -208,6 +208,14 @@
...
@@ -208,6 +208,14 @@
<name>
Deep Learning Inception
</name>
<name>
Deep Learning Inception
</name>
<link>
dnn_inception_ex.cpp.html
</link>
<link>
dnn_inception_ex.cpp.html
</link>
</item>
</item>
<item>
<name>
Deep Metric Learning Introduction
</name>
<link>
dnn_metric_learning_ex.cpp.html
</link>
</item>
<item>
<name>
Deep Metric Learning on Images
</name>
<link>
dnn_metric_learning_on_images_ex.cpp.html
</link>
</item>
<item>
<item>
<name>
Deep Learning Face Detection
</name>
<name>
Deep Learning Face Detection
</name>
<link>
dnn_mmod_face_detection_ex.cpp.html
</link>
<link>
dnn_mmod_face_detection_ex.cpp.html
</link>
...
...
docs/docs/ml.xml
View file @
539b416c
...
@@ -230,6 +230,14 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
...
@@ -230,6 +230,14 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
<name>
loss_mmod
</name>
<name>
loss_mmod
</name>
<link>
#loss_mmod_
</link>
<link>
#loss_mmod_
</link>
</item>
</item>
<item>
<name>
loss_metric
</name>
<link>
#loss_metric_
</link>
</item>
<item>
<name>
loss_mean_squared_
</name>
<link>
#loss_mean_squared_
</link>
</item>
</sub>
</sub>
</item>
</item>
<item
nolink=
"true"
>
<item
nolink=
"true"
>
...
@@ -451,6 +459,8 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
...
@@ -451,6 +459,8 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
<example>
dnn_mmod_ex.cpp.html
</example>
<example>
dnn_mmod_ex.cpp.html
</example>
<example>
dnn_mmod_face_detection_ex.cpp.html
</example>
<example>
dnn_mmod_face_detection_ex.cpp.html
</example>
<example>
dnn_mmod_dog_hipsterizer.cpp.html
</example>
<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>
</examples>
</examples>
</component>
</component>
...
@@ -475,6 +485,8 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
...
@@ -475,6 +485,8 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
<example>
dnn_imagenet_ex.cpp.html
</example>
<example>
dnn_imagenet_ex.cpp.html
</example>
<example>
dnn_imagenet_train_ex.cpp.html
</example>
<example>
dnn_imagenet_train_ex.cpp.html
</example>
<example>
dnn_mmod_ex.cpp.html
</example>
<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>
</examples>
</examples>
</component>
</component>
...
@@ -495,6 +507,8 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
...
@@ -495,6 +507,8 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
<example>
dnn_imagenet_ex.cpp.html
</example>
<example>
dnn_imagenet_ex.cpp.html
</example>
<example>
dnn_imagenet_train_ex.cpp.html
</example>
<example>
dnn_imagenet_train_ex.cpp.html
</example>
<example>
dnn_mmod_ex.cpp.html
</example>
<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_mmod_face_detection_ex.cpp.html
</example>
<example>
dnn_mmod_face_detection_ex.cpp.html
</example>
<example>
dnn_mmod_dog_hipsterizer.cpp.html
</example>
<example>
dnn_mmod_dog_hipsterizer.cpp.html
</example>
</examples>
</examples>
...
@@ -656,11 +670,6 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
...
@@ -656,11 +670,6 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
<file>
dlib/dnn.h
</file>
<file>
dlib/dnn.h
</file>
<spec_file
link=
"true"
>
dlib/dnn/loss_abstract.h
</spec_file>
<spec_file
link=
"true"
>
dlib/dnn/loss_abstract.h
</spec_file>
<description>
<description>
This input layer works with RGB images of type
<tt>
matrix
<
rgb_pixel
>
</tt>
. It is
identical to
<a
href=
"#input_rgb_image"
>
input_rgb_image
</a>
except that it
outputs a tensor containing a
<a
href=
"imaging.html#create_tiled_pyramid"
>
tiled image pyramid
</a>
of each input image rather than a simple copy of each image.
This object is a
<a
href=
"dlib/dnn/loss_abstract.h.html#EXAMPLE_LOSS_LAYER_"
>
loss layer
</a>
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 Max Margin Object Detection
for a deep neural network. In particular, it implements the Max Margin Object Detection
loss defined in the paper:
loss defined in the paper:
...
@@ -675,6 +684,37 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
...
@@ -675,6 +684,37 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
<example>
dnn_mmod_dog_hipsterizer.cpp.html
</example>
<example>
dnn_mmod_dog_hipsterizer.cpp.html
</example>
</examples>
</examples>
</component>
</component>
<!-- ************************************************************************* -->
<component>
<name>
loss_metric_
</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 allows you to learn to map objects
into a vector space where objects sharing the same class label are close to
each other, while objects with different labels are far apart.
</description>
<examples>
<example>
dnn_metric_learning_ex.cpp.html
</example>
<example>
dnn_metric_learning_on_images_ex.cpp.html
</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>
loss_mean_squared_
</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.
</description>
</component>
<!-- ************************************************************************* -->
<!-- ************************************************************************* -->
...
...
docs/docs/term_index.xml
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539b416c
...
@@ -125,6 +125,8 @@
...
@@ -125,6 +125,8 @@
<term
file=
"dlib/dnn/loss_abstract.h.html"
name=
"loss_binary_hinge_"
include=
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/>
<term
file=
"dlib/dnn/loss_abstract.h.html"
name=
"loss_binary_hinge_"
include=
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/>
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include=
"dlib/dnn.h"
/>
<term
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/>
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file=
"dlib/dnn/loss_abstract.h.html"
name=
"loss_multiclass_log_"
include=
"dlib/dnn.h"
/>
<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_mmod_"
include=
"dlib/dnn.h"
/>
<term
file=
"ml.html"
name=
"loss_mmod_"
include=
"dlib/dnn.h"
/>
<term
file=
"dlib/dnn/loss_abstract.h.html"
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"mmod_options"
include=
"dlib/dnn.h"
/>
<term
file=
"dlib/dnn/loss_abstract.h.html"
name=
"mmod_options"
include=
"dlib/dnn.h"
/>
...
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