提交 d4c2f2f2 编写于 作者: R ranqiu

Refine the doc of layers.py

上级 e4c8de9e
......@@ -2985,8 +2985,8 @@ def spp_layer(input,
A layer performs spatial pyramid pooling.
Reference:
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
https://arxiv.org/abs/1406.4729
`Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
https://arxiv.org/abs/1406.4729`_
The example usage is:
......@@ -3087,8 +3087,8 @@ def img_cmrnorm_layer(input,
Response normalization across feature maps.
Reference:
ImageNet Classification with Deep Convolutional Neural Networks
http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf
`ImageNet Classification with Deep Convolutional Neural Networks
http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf`_
The example usage is:
......@@ -3154,9 +3154,9 @@ def batch_norm_layer(input,
y_i &\\gets \\gamma \\hat{x_i} + \\beta \\qquad &//\ scale\ and\ shift
Reference:
Batch Normalization: Accelerating Deep Network Training by Reducing
`Batch Normalization: Accelerating Deep Network Training by Reducing
Internal Covariate Shift
http://arxiv.org/abs/1502.03167
http://arxiv.org/abs/1502.03167`_
The example usage is:
......@@ -5413,10 +5413,10 @@ def maxout_layer(input, groups, num_channels=None, name=None, layer_attr=None):
to be devided by groups.
Reference:
Maxout Networks
http://www.jmlr.org/proceedings/papers/v28/goodfellow13.pdf
Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks
https://arxiv.org/pdf/1312.6082v4.pdf
`Maxout Networks
http://www.jmlr.org/proceedings/papers/v28/goodfellow13.pdf`_
`Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks
https://arxiv.org/pdf/1312.6082v4.pdf`_
.. math::
y_{si+j} = \max_k x_{gsi + sk + j}
......@@ -5481,9 +5481,9 @@ def ctc_layer(input,
alignment between the inputs and the target labels is unknown.
Reference:
Connectionist Temporal Classification: Labelling Unsegmented Sequence Data
`Connectionist Temporal Classification: Labelling Unsegmented Sequence Data
with Recurrent Neural Networks
http://machinelearning.wustl.edu/mlpapers/paper_files/icml2006_GravesFGS06.pdf
http://machinelearning.wustl.edu/mlpapers/paper_files/icml2006_GravesFGS06.pdf`_
Note:
Considering the 'blank' label needed by CTC, you need to use (num_classes + 1)
......@@ -5555,9 +5555,9 @@ def warp_ctc_layer(input,
install it to :code:`third_party/install/warpctc` directory.
Reference:
Connectionist Temporal Classification: Labelling Unsegmented Sequence Data
`Connectionist Temporal Classification: Labelling Unsegmented Sequence Data
with Recurrent Neural Networks
http://machinelearning.wustl.edu/mlpapers/paper_files/icml2006_GravesFGS06.pdf
http://machinelearning.wustl.edu/mlpapers/paper_files/icml2006_GravesFGS06.pdf`_
Note:
- Let num_classes represents the category number. Considering the 'blank'
......@@ -5777,8 +5777,8 @@ def nce_layer(input,
Noise-contrastive estimation.
Reference:
A fast and simple algorithm for training neural probabilistic language
models. https://www.cs.toronto.edu/~amnih/papers/ncelm.pdf
`A fast and simple algorithm for training neural probabilistic language
models. https://www.cs.toronto.edu/~amnih/papers/ncelm.pdf`_
The example usage is:
......@@ -5893,8 +5893,8 @@ def rank_cost(left,
A cost Layer for learning to rank using gradient descent.
Reference:
Learning to Rank using Gradient Descent
http://research.microsoft.com/en-us/um/people/cburges/papers/ICML_ranking.pdf
`Learning to Rank using Gradient Descent
http://research.microsoft.com/en-us/um/people/cburges/papers/ICML_ranking.pdf`_
.. math::
......@@ -6429,8 +6429,8 @@ def smooth_l1_cost(input, label, name=None, coeff=1.0, layer_attr=None):
smooth_{L1}(x) = \\begin{cases} 0.5x^2& \\text{if} \\ |x| < 1 \\\\ |x|-0.5& \\text{otherwise} \end{cases}
Reference:
Fast R-CNN
https://arxiv.org/pdf/1504.08083v2.pdf
`Fast R-CNN
https://arxiv.org/pdf/1504.08083v2.pdf`_
The example usage is:
......@@ -6636,8 +6636,8 @@ def prelu_layer(input,
The Parametric Relu activation that actives outputs with a learnable weight.
Reference:
Delving Deep into Rectifiers: Surpassing Human-Level Performance on
ImageNet Classification http://arxiv.org/pdf/1502.01852v1.pdf
`Delving Deep into Rectifiers: Surpassing Human-Level Performance on
ImageNet Classification http://arxiv.org/pdf/1502.01852v1.pdf`_
.. math::
z_i &\\quad if \\quad z_i > 0 \\\\
......@@ -6733,8 +6733,8 @@ def gated_unit_layer(input,
product between :match:`X'` and :math:`\sigma` is finally returned.
Reference:
Language Modeling with Gated Convolutional Networks
https://arxiv.org/abs/1612.08083
`Language Modeling with Gated Convolutional Networks
https://arxiv.org/abs/1612.08083`_
.. math::
y=\\text{act}(X \cdot W + b)\otimes \sigma(X \cdot V + c)
......
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