Update noisy_dense.py

Author: LeonShams

tensorflow_addons/layers/noisy_dense.py

@@ -28,13 +28,15 @@
 class NoisyDense(tf.keras.layers.Layer):
     """Like normal dense layer (https://github.com/tensorflow/tensorflow/blob/v2.3.0/tensorflow/python/keras/layers/core.py#L1067-L1233)
   but random noisy is added to the weights matrix. But as the network improves the random noise is decayed until it is insignificant.
+
   A `NoisyDense` layer implements the operation:
   `output = activation(dot(input, µ_kernel + (σ_kernel * ε_kernel)) + bias)`
   where `activation` is the element-wise activation function
   passed as the `activation` argument, `µ_kernel` is your average weights matrix
   created by the layer, σ_kernel is a weights matrix that controls the importance of
   the ε_kernel which is just random noise, and `bias` is a bias vector created by the layer
   (only applicable if `use_bias` is `True`).
+
   Example:
   >>> # Create a `Sequential` model and add a Dense layer as the first layer.
   >>> model = tf.keras.models.Sequential()
@@ -47,6 +49,7 @@ class NoisyDense(tf.keras.layers.Layer):
   >>> model.add(NoisyDense(32))
   >>> model.output_shape
   (None, 32)
+
   Arguments:
     units: Positive integer, dimensionality of the output space.
     activation: Activation function to use.
@@ -61,10 +64,12 @@ class NoisyDense(tf.keras.layers.Layer):
     kernel_constraint: Constraint function applied to
       the `kernel` weights matrix.
     bias_constraint: Constraint function applied to the bias vector.
+
   Input shape:
     N-D tensor with shape: `(batch_size, ..., input_dim)`.
     The most common situation would be
     a 2D input with shape `(batch_size, input_dim)`.
+
   Output shape:
     N-D tensor with shape: `(batch_size, ..., units)`.
     For instance, for a 2D input with shape `(batch_size, input_dim)`,