Restored doc/convolutional_mlp.txt to doc/lenet.txt and doc/logistic_sgd.txt to doc/logreg.txt to respect existing bookmarks.

Author: mspandit

doc/DBN.txt

@@ -4,7 +4,7 @@ Deep Belief Networks
 ====================
 
 .. note::
-  This section assumes the reader has already read through :doc:`logistic_sgd`
+  This section assumes the reader has already read through :doc:`logreg`
   and :doc:`mlp` and :doc:`rbm`. Additionally it uses the following Theano
   functions and concepts : `T.tanh`_, `shared variables`_, `basic arithmetic
   ops`_, `T.grad`_, `Random numbers`_, `floatX`_. If you intend to run the
@@ -164,7 +164,7 @@ hidden bias with its corresponding sigmoid layer.
 
 All that is left is to stack one last logistic regression layer in order to
 form an MLP. We will use the ``LogisticRegression`` class introduced in
-:ref:`logistic_sgd`. 
+:ref:`logreg`. 
 
 .. literalinclude:: ../code/DBN.py
   :start-after: # We now need to add a logistic layer on top of the MLP

doc/SdA.txt

@@ -4,7 +4,7 @@ Stacked Denoising Autoencoders (SdA)
 ====================================
 
 .. note::
-  This section assumes the reader has already read through :doc:`logistic_sgd`
+  This section assumes the reader has already read through :doc:`logreg`
   and :doc:`mlp`. Additionally it uses the following Theano functions
   and concepts : `T.tanh`_, `shared variables`_, `basic arithmetic ops`_, `T.grad`_, `Random numbers`_, `floatX`_. If you intend to run the code on GPU also read `GPU`_.
 
@@ -95,7 +95,7 @@ bias of the encoding part with its corresponding sigmoid layer.
 
 All we need now is to add the logistic layer on top of the sigmoid
 layers such that we have an MLP. We will 
-use the ``LogisticRegression`` class introduced in :ref:`logistic_sgd`. 
+use the ``LogisticRegression`` class introduced in :ref:`logreg`. 
 
 .. literalinclude:: ../code/SdA.py
   :start-after: self.dA_layers.append(dA_layer)

doc/contents.txt

@@ -11,9 +11,9 @@ Contents
    LICENSE
    intro
    gettingstarted
-   logistic_sgd
+   logreg
    mlp
-   convolutional_mlp
+   lenet
    dA
    SdA
    rbm

doc/dA.txt

@@ -4,7 +4,7 @@ Denoising Autoencoders (dA)
 ===========================
 
 .. note::
-  This section assumes the reader has already read through :doc:`logistic_sgd`
+  This section assumes the reader has already read through :doc:`logreg`
   and :doc:`mlp`. Additionally it uses the following Theano functions
   and concepts : `T.tanh`_, `shared variables`_, `basic arithmetic ops`_, `T.grad`_, `Random numbers`_, `floatX`_. If you intend to run the code on GPU also read `GPU`_.
 

doc/intro.txt

@@ -29,9 +29,9 @@ read through our :ref:`gettingstarted` chapter -- it introduces the notation, an
 
 The purely supervised learning algorithms are meant to be read in order:
 
-  #. :ref:`Logistic Regression <logistic_sgd>` - using Theano for something simple
+  #. :ref:`Logistic Regression <logreg>` - using Theano for something simple
   #. :ref:`Multilayer perceptron <mlp>` - introduction to layers
-  #. :ref:`Deep Convolutional Network <convolutional_mlp>` - a simplified version of LeNet5
+  #. :ref:`Deep Convolutional Network <lenet>` - a simplified version of LeNet5
 
 The unsupervised and semi-supervised learning algorithms can be read in any
 order (the auto-encoders can be read independently of the RBM/DBN thread):

doc/convolutional_mlp.txt renamed to doc/lenet.txt

@@ -1,10 +1,10 @@
-.. _convolutional_mlp:
+.. _lenet:
 
 Convolutional Neural Networks (LeNet)
 =====================================
 
 .. note::
-    This section assumes the reader has already read through :doc:`logistic_sgd` and
+    This section assumes the reader has already read through :doc:`logreg` and
     :doc:`mlp`. Additionally, it uses the following new Theano functions and concepts:
     `T.tanh`_, `shared variables`_, `basic arithmetic ops`_, `T.grad`_,
     `floatX`_, `downsample`_ , `conv2d`_, `dimshuffle`_. If you intend to run the
@@ -406,7 +406,7 @@ layer.
 Notice that when initializing the weight values, the fan-in is determined by
 the size of the receptive fields and the number of input feature maps.
 
-Finally, using the LogisticRegression class defined in :doc:`logistic_sgd` and
+Finally, using the LogisticRegression class defined in :doc:`logreg` and
 the HiddenLayer class defined in :doc:`mlp` , we can
 instantiate the network as follows.
 

doc/logistic_sgd.txt renamed to doc/logreg.txt

@@ -1,6 +1,6 @@
 .. index:: Logistic Regression
 
-.. _logistic_sgd :
+.. _logreg :
 
 
 Classifying MNIST digits using Logistic Regression

doc/mlp.txt

@@ -7,7 +7,7 @@ Multilayer Perceptron
 =====================
 
 .. note::
-    This section assumes the reader has already read through :doc:`logistic_sgd`.
+    This section assumes the reader has already read through :doc:`logreg`.
     Additionally, it uses the following new Theano functions and concepts:
     `T.tanh`_, `shared variables`_, `basic arithmetic ops`_, `T.grad`_,
     :ref:`L1_L2_regularization`, `floatX`_. If you intend to run the
@@ -80,7 +80,7 @@ extension to vectors and tensors consists in applying them element-wise
 
 The output vector is then obtained as: :math:`o(x) = G(b^{(2)} + W^{(2)} h(x))`.
 The reader should recognize the form we already used for
-:doc:`logistic_sgd`. As before,
+:doc:`logreg`. As before,
 class-membership probabilities can be obtained by choosing :math:`G` as the
 :math:`softmax` function (in the case of multi-class classification).
 
@@ -129,7 +129,7 @@ to use something else.
 If you look into theory this class implements the graph that computes
 the hidden layer value :math:`h(x) = \Phi(x) = s(b^{(1)} + W^{(1)} x)`.
 If you give this as input to the ``LogisticRegression`` class,
-implemented in the previous tutorial :doc:`logistic_sgd`, you get the output
+implemented in the previous tutorial :doc:`logreg`, you get the output
 of the MLP. You can see this in the following short implementation of
 the ``MLP`` class.
 

doc/rbm.txt

@@ -5,7 +5,7 @@ Restricted Boltzmann Machines (RBM)
 
 
 .. note::
-  This section assumes the reader has already read through :doc:`logistic_sgd`
+  This section assumes the reader has already read through :doc:`logreg`
   and :doc:`mlp`. Additionally it uses the following Theano functions
   and concepts : `T.tanh`_, `shared variables`_, `basic arithmetic ops`_, `T.grad`_, `Random numbers`_, `floatX`_ and `scan`_. If you intend to run the code on GPU also read `GPU`_.