fix galleryitem paths

chsasank · chsasank · commit 1ca4001f21c0 · 2017-04-22T10:45:42.000+05:30
diff --git a/README.md b/README.md
@@ -15,7 +15,7 @@ Here's how to create a new tutorial:
 1. Create a notebook styled python file. If you want it executed while inserted into documentation, save the file with suffix `tutorial` so that file name is `your_tutorial.py`.
 2. Put it in one of the beginner_source, intermediate_source, advanced_source based on the level.
 2. Include it in the right TOC tree at index.rst
-3. Create a thumbnail in the index file using a command like `.. galleryitem:: beginner/your_tutorial.py`. (This is a custom directive. Argument should be a full path to the python file without a slash at the beginning.) 
+3. Create a thumbnail in the index file using a command like `.. galleryitem:: beginner/your_tutorial.py`. (This is a custom directive. See `custom_directives.py` for more info.) 
 
 In case you prefer to write your tutorial in jupyter, you can use [this script](https://gist.github.com/chsasank/7218ca16f8d022e02a9c0deb94a310fe) to convert the notebook to python file. After conversion and addition to the project, please make sure the sections headings etc are in logical order.
 
diff --git a/beginner_source/deep_learning_60min_blitz.rst b/beginner_source/deep_learning_60min_blitz.rst
@@ -25,16 +25,16 @@ Goal of this tutorial:
    /beginner/blitz/neural_networks_tutorial
    /beginner/blitz/cifar10_tutorial
 
-.. galleryitem:: beginner/blitz/tensor_tutorial.py
+.. galleryitem:: /beginner/blitz/tensor_tutorial.py
     :figure: /_static/img/tensor_illustration_flat.png
 
-.. galleryitem:: beginner/blitz/autograd_tutorial.py
+.. galleryitem:: /beginner/blitz/autograd_tutorial.py
     :figure: /_static/img/Variable.png
 
-.. galleryitem:: beginner/blitz/neural_networks_tutorial.py
+.. galleryitem:: /beginner/blitz/neural_networks_tutorial.py
     :figure: /_static/img/mnist.png
 
-.. galleryitem:: beginner/blitz/cifar10_tutorial.py
+.. galleryitem:: /beginner/blitz/cifar10_tutorial.py
     :figure: /_static/img/cifar10.png
 
 .. raw:: html
diff --git a/beginner_source/deep_learning_nlp_tutorial.rst b/beginner_source/deep_learning_nlp_tutorial.rst
@@ -35,19 +35,19 @@ and use them on it.
     /beginner/nlp/advanced_tutorial
 
 
-.. galleryitem:: beginner/nlp/pytorch_tutorial.py
+.. galleryitem:: /beginner/nlp/pytorch_tutorial.py
     :intro: All of deep learning is computations on tensors, which are generalizations of a matrix that can be 
 
-.. galleryitem:: beginner/nlp/deep_learning_tutorial.py
+.. galleryitem:: /beginner/nlp/deep_learning_tutorial.py
     :intro: Deep learning consists of composing linearities with non-linearities in clever ways. The introduction of non-linearities allows
 
-.. galleryitem:: beginner/nlp/word_embeddings_tutorial.py
+.. galleryitem:: /beginner/nlp/word_embeddings_tutorial.py
     :intro: Word embeddings are dense vectors of real numbers, one per word in your vocabulary. In NLP, it is almost always the case that your features are
 
-.. galleryitem:: beginner/nlp/sequence_models_tutorial.py
+.. galleryitem:: /beginner/nlp/sequence_models_tutorial.py
     :intro: At this point, we have seen various feed-forward networks. That is, there is no state maintained by the network at all. 
 
-.. galleryitem:: beginner/nlp/advanced_tutorial.py
+.. galleryitem:: /beginner/nlp/advanced_tutorial.py
     :intro: Dyanmic versus Static Deep Learning Toolkits. Pytorch is a *dynamic* neural network kit. 
 
 
diff --git a/beginner_source/former_torchies_tutorial.rst b/beginner_source/former_torchies_tutorial.rst
@@ -20,16 +20,16 @@ In this tutorial, you will learn the following:
    /beginner/former_torchies/nn_tutorial
    /beginner/former_torchies/parallelism_tutorial
 
-.. galleryitem:: beginner/former_torchies/tensor_tutorial.py
+.. galleryitem:: /beginner/former_torchies/tensor_tutorial.py
     :figure: /_static/img/tensor_illustration_flat.png
 
-.. galleryitem:: beginner/former_torchies/autograd_tutorial.py
+.. galleryitem:: /beginner/former_torchies/autograd_tutorial.py
     :figure: /_static/img/Variable.png
 
-.. galleryitem:: beginner/former_torchies/nn_tutorial.py
+.. galleryitem:: /beginner/former_torchies/nn_tutorial.py
     :figure: /_static/img/torch-nn-vs-pytorch-nn.png
 
-.. galleryitem:: beginner/former_torchies/parallelism_tutorial.py
+.. galleryitem:: /beginner/former_torchies/parallelism_tutorial.py
 
 .. raw:: html
 
diff --git a/beginner_source/pytorch_with_examples.rst b/beginner_source/pytorch_with_examples.rst
@@ -262,9 +262,9 @@ You can browse the above examples here.
 Tensors
 -------
 
-.. galleryitem:: beginner/examples_tensor/two_layer_net_numpy.py
+.. galleryitem:: /beginner/examples_tensor/two_layer_net_numpy.py
 
-.. galleryitem:: beginner/examples_tensor/two_layer_net_tensor.py
+.. galleryitem:: /beginner/examples_tensor/two_layer_net_tensor.py
 
 .. raw:: html
 
@@ -273,11 +273,11 @@ Tensors
 Autograd
 --------
 
-.. galleryitem:: beginner/examples_autograd/two_layer_net_autograd.py
+.. galleryitem:: /beginner/examples_autograd/two_layer_net_autograd.py
 
-.. galleryitem:: beginner/examples_autograd/two_layer_net_custom_function.py
+.. galleryitem:: /beginner/examples_autograd/two_layer_net_custom_function.py
 
-.. galleryitem:: beginner/examples_autograd/tf_two_layer_net.py
+.. galleryitem:: /beginner/examples_autograd/tf_two_layer_net.py
 
 .. raw:: html
 
@@ -286,13 +286,13 @@ Autograd
 `nn` module
 -----------
 
-.. galleryitem:: beginner/examples_nn/two_layer_net_nn.py
+.. galleryitem:: /beginner/examples_nn/two_layer_net_nn.py
 
-.. galleryitem:: beginner/examples_nn/two_layer_net_optim.py
+.. galleryitem:: /beginner/examples_nn/two_layer_net_optim.py
 
-.. galleryitem:: beginner/examples_nn/two_layer_net_module.py
+.. galleryitem:: /beginner/examples_nn/two_layer_net_module.py
 
-.. galleryitem:: beginner/examples_nn/dynamic_net.py
+.. galleryitem:: /beginner/examples_nn/dynamic_net.py
 
 .. raw:: html
 
