Fixed the documentation for the readme and core layers. (#375)

README.md

@@ -9,7 +9,8 @@ As the community contributions in Keras-Contrib are tested, used, validated, and
 ---
 ## Installation
 
-For instructions on how to install Keras, see https://keras.io/#installation
+For instructions on how to install Keras, 
+see [the Keras installation page](https://keras.io/#installation).
 
 ```shell
 git clone https://www.github.com/keras-team/keras-contrib.git
@@ -23,7 +24,7 @@ Alternatively, using pip:
 sudo pip install git+https://www.github.com/keras-team/keras-contrib.git
 ```
 
-For contributor guidelines see [CONTRIBUTING.md](CONTRIBUTING.md)
+For contributor guidelines see [CONTRIBUTING.md](https://github.com/keras-team/keras-contrib/blob/master/CONTRIBUTING.md)
 
 ---
 ## Example Usage

keras_contrib/layers/core.py

@@ -14,9 +14,6 @@
 
 class CosineDense(Layer):
     """A cosine normalized densely-connected NN layer
-    Cosine Normalization: Using Cosine Similarity Instead
-    of Dot Product in Neural Networks
-    https://arxiv.org/pdf/1702.05870.pdf
 
     # Example
 
@@ -35,7 +32,7 @@ class CosineDense(Layer):
         # the size of the input anymore:
         model.add(CosineDense(32))
 
-        **Note that a regular Dense layer may work better as the final layer
+        # Note that a regular Dense layer may work better as the final layer
     ```
 
     # Arguments
@@ -46,23 +43,25 @@ class CosineDense(Layer):
             initialization. This parameter is only relevant
             if you don't pass a `weights` argument.
         activation: name of activation function to use
-            (see [activations](../activations.md)),
-            or alternatively, elementwise Theano function.
+            (see [activations](https://keras.io/activations)),
+            or alternatively, elementwise Python function.
             If you don't specify anything, no activation is applied
             (ie. "linear" activation: a(x) = x).
         weights: list of Numpy arrays to set as initial weights.
             The list should have 2 elements, of shape `(input_dim, units)`
             and (units,) for weights and biases respectively.
-        kernel_regularizer: instance of [WeightRegularizer](../regularizers.md)
+        kernel_regularizer: instance of [WeightRegularizer](
+            https://keras.io/regularizers)
             (eg. L1 or L2 regularization), applied to the main weights matrix.
-        bias_regularizer: instance of [WeightRegularizer](../regularizers.md),
-            applied to the bias.
-        activity_regularizer: instance of [ActivityRegularizer](../regularizers.md),
-            applied to the network output.
-        kernel_constraint: instance of the [constraints](../constraints.md) module
+        bias_regularizer: instance of [WeightRegularizer](
+            https://keras.io/regularizers), applied to the bias.
+        activity_regularizer: instance of [ActivityRegularizer](
+            https://keras.io/regularizers), applied to the network output.
+        kernel_constraint: instance of the [constraints](
+            https://keras.io/constraints/) module
             (eg. maxnorm, nonneg), applied to the main weights matrix.
-        bias_constraint: instance of the [constraints](../constraints.md) module,
-            applied to the bias.
+        bias_constraint: instance of the [constraints](
+            https://keras.io/constraints/) module, applied to the bias.
         use_bias: whether to include a bias
             (i.e. make the layer affine rather than linear).
         input_dim: dimensionality of the input (integer). This argument
@@ -78,6 +77,10 @@ class CosineDense(Layer):
         nD tensor with shape: `(nb_samples, ..., units)`.
         For instance, for a 2D input with shape `(nb_samples, input_dim)`,
         the output would have shape `(nb_samples, units)`.
+
+    # References
+        - [Cosine Normalization: Using Cosine Similarity Instead
+           of Dot Product in Neural Networks](https://arxiv.org/pdf/1702.05870.pdf)
     """
 
     def __init__(self, units, kernel_initializer='glorot_uniform',