未验证 提交 00d6c90f 编写于 作者: C Cao Ying 提交者: GitHub

Merge pull request #5962 from ranqiu92/doc

Refine the doc of layers.py
......@@ -2996,7 +2996,7 @@ def spp_layer(input,
Reference:
`Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
https://arxiv.org/abs/1406.4729`_
<https://arxiv.org/abs/1406.4729>`_
The example usage is:
......@@ -3098,7 +3098,7 @@ def img_cmrnorm_layer(input,
Reference:
`ImageNet Classification with Deep Convolutional Neural Networks
http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf`_
<http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf>`_
The example usage is:
......@@ -3166,7 +3166,7 @@ def batch_norm_layer(input,
Reference:
`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:
......@@ -5424,9 +5424,9 @@ def maxout_layer(input, groups, num_channels=None, name=None, layer_attr=None):
Reference:
`Maxout Networks
http://www.jmlr.org/proceedings/papers/v28/goodfellow13.pdf`_
<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`_
<https://arxiv.org/pdf/1312.6082v4.pdf>`_
.. math::
......@@ -5495,7 +5495,7 @@ def ctc_layer(input,
Reference:
`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)
......@@ -5569,7 +5569,7 @@ def warp_ctc_layer(input,
Reference:
`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'
......@@ -5790,7 +5790,7 @@ def nce_layer(input,
Reference:
`A fast and simple algorithm for training neural probabilistic language
models. https://www.cs.toronto.edu/~amnih/papers/ncelm.pdf`_
models. <https://www.cs.toronto.edu/~amnih/papers/ncelm.pdf>`_
The example usage is:
......@@ -5906,7 +5906,7 @@ def rank_cost(left,
Reference:
`Learning to Rank using Gradient Descent
http://research.microsoft.com/en-us/um/people/cburges/papers/ICML_ranking.pdf`_
<http://research.microsoft.com/en-us/um/people/cburges/papers/ICML_ranking.pdf>`_
.. math::
......@@ -6442,7 +6442,7 @@ def smooth_l1_cost(input, label, name=None, coeff=1.0, layer_attr=None):
Reference:
`Fast R-CNN
https://arxiv.org/pdf/1504.08083v2.pdf`_
<https://arxiv.org/pdf/1504.08083v2.pdf>`_
The example usage is:
......@@ -6649,7 +6649,7 @@ def prelu_layer(input,
Reference:
`Delving Deep into Rectifiers: Surpassing Human-Level Performance on
ImageNet Classification http://arxiv.org/pdf/1502.01852v1.pdf`_
ImageNet Classification <http://arxiv.org/pdf/1502.01852v1.pdf>`_
.. math::
z_i &\\quad if \\quad z_i > 0 \\\\
......@@ -6746,7 +6746,7 @@ def gated_unit_layer(input,
Reference:
`Language Modeling with Gated Convolutional Networks
https://arxiv.org/abs/1612.08083`_
<https://arxiv.org/abs/1612.08083>`_
.. math::
y=\\text{act}(X \cdot W + b)\otimes \sigma(X \cdot V + c)
......
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