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new d008356 remove broken links (#20793)
d008356 is described below
commit d0083566ea36622086ef955dbbfe23e3284031b3
Author: bgawrych <[email protected]>
AuthorDate: Fri Dec 24 17:09:55 2021 +0100
remove broken links (#20793)
* remove broken links
* remove anchor from link
Co-authored-by: Bartlomiej Gawrych <[email protected]>
---
.../python/tutorials/getting-started/logistic_regression_explained.md | 2 +-
docs/python_docs/python/tutorials/packages/gluon/image/info_gan.md | 4 ++--
python/mxnet/gluon/trainer.py | 2 +-
3 files changed, 4 insertions(+), 4 deletions(-)
diff --git
a/docs/python_docs/python/tutorials/getting-started/logistic_regression_explained.md
b/docs/python_docs/python/tutorials/getting-started/logistic_regression_explained.md
index caa2975..d0056e1 100644
---
a/docs/python_docs/python/tutorials/getting-started/logistic_regression_explained.md
+++
b/docs/python_docs/python/tutorials/getting-started/logistic_regression_explained.md
@@ -92,7 +92,7 @@ After defining the model, we need to define a few more
things: our loss, our tra
Loss function is used to calculate how the output of the network differs from
the ground truth. Because classes of the logistic regression are either 0 or
1, we are using
[SigmoidBinaryCrossEntropyLoss](../../api/gluon/loss/index.rst#mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss).
Notice that we do not specify `from_sigmoid` attribute in the code, which
means that the output of the neuron doesn't need to go through sigmoid, but at
inference we'd have to pass it through sigmoid. You can [...]
-Trainer object allows to specify the method of training to be used. For our
tutorial we use [Stochastic Gradient Descent
(SGD)](../../api/optimizer/index.rst#mxnet.optimizer.SGD). For more information
on SGD refer to [the following
tutorial](https://gluon.mxnet.io/chapter06_optimization/gd-sgd-scratch.html).
We also need to parametrize it with learning rate value, which defines the
weight updates, and weight decay, which is used for regularization.
+Trainer object allows to specify the method of training to be used. For our
tutorial we use [Stochastic Gradient Descent
(SGD)](../../api/optimizer/index.rst#mxnet.optimizer.SGD). For more information
on SGD refer to [the following
tutorial](https://d2l.ai/chapter_optimization/sgd.html). We also need to
parametrize it with learning rate value, which defines the weight updates, and
weight decay, which is used for regularization.
Metric helps us to estimate how good our model is in terms of a problem we are
trying to solve. Where loss function has more importance for the training
process, a metric is usually the thing we are trying to improve and reach
maximum value. We also can use more than one metric, to measure various aspects
of our model. In our example, we are using
[Accuracy](../../api/gluon/metric/index.rst#mxnet.gluon.metric.Accuracy) and
[F1 score](../../api/gluon/metric/index.rst#mxnet.gluon.metric.F1 [...]
diff --git a/docs/python_docs/python/tutorials/packages/gluon/image/info_gan.md
b/docs/python_docs/python/tutorials/packages/gluon/image/info_gan.md
index 3a82855..5b86643 100644
--- a/docs/python_docs/python/tutorials/packages/gluon/image/info_gan.md
+++ b/docs/python_docs/python/tutorials/packages/gluon/image/info_gan.md
@@ -19,7 +19,7 @@
# Image similarity search with InfoGAN
This notebook shows how to implement an InfoGAN based on Gluon. InfoGAN is an
extension of GANs, where the generator input is split in 2 parts: random noise
and a latent code (see [InfoGAN Paper](https://arxiv.org/pdf/1606.03657.pdf)).
-The codes are made meaningful by maximizing the mutual information between
code and generator output. InfoGAN learns a disentangled representation in a
completely unsupervised manner. It can be used for many applications such as
image similarity search. This notebook uses the DCGAN example from the
[Straight Dope
Book](https://gluon.mxnet.io/chapter14_generative-adversarial-networks/dcgan.html)
and extends it to create an InfoGAN.
+The codes are made meaningful by maximizing the mutual information between
code and generator output. InfoGAN learns a disentangled representation in a
completely unsupervised manner. It can be used for many applications such as
image similarity search. This notebook uses the DCGAN example and extends it to
create an InfoGAN.
```{.python .input}
@@ -112,7 +112,7 @@ train_dataloader = gluon.data.DataLoader(train_data,
batch_size=batch_size, shuf
```
## Generator
-Define the Generator model. Architecture is taken from the DCGAN
implementation in [Straight Dope
Book](https://gluon.mxnet.io/chapter14_generative-adversarial-networks/dcgan.html).
The Generator consist of 4 layers where each layer involves a strided
convolution, batch normalization, and rectified nonlinearity. It takes as input
random noise and the latent code and produces an `(64,64,3)` output image.
+Define the Generator model. The Generator consist of 4 layers where each
layer involves a strided convolution, batch normalization, and rectified
nonlinearity. It takes as input random noise and the latent code and produces
an `(64,64,3)` output image.
```{.python .input}
diff --git a/python/mxnet/gluon/trainer.py b/python/mxnet/gluon/trainer.py
index 0566a73..afbe3e4 100644
--- a/python/mxnet/gluon/trainer.py
+++ b/python/mxnet/gluon/trainer.py
@@ -48,7 +48,7 @@ class Trainer(object):
The set of parameters to optimize.
optimizer : str or Optimizer
The optimizer to use. See
- `help
<https://mxnet.apache.org/api/python/docs/api/optimizer/index.html#mxnet.optimizer.Optimizer>`_
+ `help
<https://mxnet.apache.org/api/python/docs/api/optimizer/index.html>`_
on Optimizer for a list of available optimizers.
optimizer_params : dict
Key-word arguments to be passed to optimizer constructor. For example,
