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fix link and special characters #17

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Dec 21, 2013
Merged

fix link and special characters #17

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6 changes: 3 additions & 3 deletions imagenet_pretrained.md
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Expand Up @@ -3,8 +3,8 @@ layout: default
title: Caffe
---

Running Pretrained ImageNet: the Easy Way
=========================================
Running Pretrained ImageNet
===========================

[View this page as an IPython Notebook](http://nbviewer.ipython.org/url/daggerfs.com/caffe/imagenet_pretrained_files/imagenet_pretrained.ipynb)

Expand All @@ -18,7 +18,7 @@ We assume that you have successfully compiled Caffe and set the correct
`PYTHONPATH`. If not, please refer to the [installation
instructions](installation.html). You will use our pre-trained imagenet model,
which you can [download
here](https://www.dropbox.com/sh/854ufsssbqadhvr/t7zgt1meni) (232.57MB).
here](https://www.dropbox.com/s/n3jups0gr7uj0dv/caffe_reference_imagenet_model) (232.57MB).

Ready? Let's start.

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2 changes: 1 addition & 1 deletion imagenet_pretrained_files/imagenet_pretrained.ipynb
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Expand Up @@ -16,7 +16,7 @@
"\n",
"For easier use of pretrained models, we provide a wrapper specifically written for the case of ImageNet, so one can take an image and directly compute features or predictions from them. Both Python and Matlab wrappers are provided. We will describe the use of the Python wrapper here, and the Matlab wrapper usage is very similar.\n",
"\n",
"We assume that you have successfully compiled Caffe and set the correct `PYTHONPATH`. If not, please refer to the [installation instructions](installation.html). You will use our pre-trained imagenet model, which you can [download here](https://www.dropbox.com/sh/854ufsssbqadhvr/t7zgt1meni) (232.57MB).\n",
"We assume that you have successfully compiled Caffe and set the correct `PYTHONPATH`. If not, please refer to the [installation instructions](installation.html). You will use our pre-trained imagenet model, which you can [download here](https://www.dropbox.com/s/n3jups0gr7uj0dv/caffe_reference_imagenet_model) (232.57MB).\n",
"\n",
"Ready? Let's start."
]
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6 changes: 3 additions & 3 deletions mnist_prototxt.md
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Expand Up @@ -3,8 +3,8 @@ layout: default
title: Caffe
---

MNIST: Define the Network
===========================
Define the MNIST Network
=========================

This page explains the prototxt file used in the MNIST demo. We assume that you are familiar with [Google Protobuf](https://developers.google.com/protocol-buffers/docs/overview), and assume that you have read the protobuf definitions used by Caffe, which can be found at [src/caffe/proto/caffe.proto](https://github.com/Yangqing/caffe/blob/master/src/caffe/proto/caffe.proto).

Expand Down Expand Up @@ -154,4 +154,4 @@ Finally, we will write the loss!

The `softmax_loss` layer implements both the softmax and the multinomial logistic loss (that saves time and improves numerical stability). It takes two blobs, the first one being the prediction and the second one being the `label` provided by the data layer (remember it?). It does not produce any outputs - all it does is to compute the loss function value, report it when backpropagation starts, and initiates the gradient with respect to `ip2`. This is where all magic starts.

Now that we have demonstrated how to write the MNIST layer definition prototxt, maybe check out [how we write a solver prototxt](mnist_solver_prototxt.html)?
Now that we have demonstrated how to write the MNIST layer definition prototxt, maybe check out [how we write a solver prototxt](mnist_solver_prototxt.html)?
4 changes: 2 additions & 2 deletions mnist_solver_prototxt.md
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title: Caffe
---

MNIST: Define the Solver
===========================
Define the MNIST Solver
=======================

The page is under construction. For now, check out the comments in the solver prototxt file, which explains each line in the prototxt:

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