Merge pull request #326 from IkemOkoh/patch-1

Update leakyparallel.py

snntorch/_neurons/leakyparallel.py

@@ -24,13 +24,24 @@ class LeakyParallel(nn.Module):
 
     Several differences between `LeakyParallel` and `Leaky` include:
     
-    * Negative hidden states are clipped due to the forced ReLU operation in RNN
-    * Linear weights are included in addition to recurrent weights
-    * `beta` is clipped between [0,1] and cloned to `weight_hh_l` only upon layer initialization. It is unused otherwise
-    * There is no explicit reset mechanism
-    * Several functions such as `init_hidden`, `output`, `inhibition`, and `state_quant` are unavailable in `LeakyParallel`
-    * Only the output spike is returned. Membrane potential is not accessible by default
-    * RNN uses a hidden matrix of size (num_hidden, num_hidden) to transform the hidden state vector. This would 'leak' the membrane potential between LIF neurons, and so the hidden matrix is forced to a diagonal matrix by default. This can be disabled by setting `weight_hh_enable=True`.
+    * Negative hidden states are clipped due to the 
+        forced ReLU operation in RNN.
+    * Linear weights are included in addition to 
+        recurrent weights.
+    * `beta` is clipped between [0,1] and cloned to 
+        `weight_hh_l` only upon layer initialization. 
+        It is unused otherwise.
+    * There is no explicit reset mechanism.
+    * Several functions such as `init_hidden`, `output`, 
+        `inhibition`, and `state_quant` are unavailable 
+        in `LeakyParallel`.
+    * Only the output spike is returned. Membrane potential
+        is not accessible by default.
+    * RNN uses a hidden matrix of size (num_hidden, num_hidden)
+        to transform the hidden state vector. This would 'leak' 
+        the membrane potential between LIF neurons, and so the 
+        hidden matrix is forced to a diagonal matrix by default. 
+        This can be disabled by setting `weight_hh_enable=True`.
 
     Example::
 
@@ -117,22 +128,28 @@ def forward(self, x):
         
     where:
 
-    `L = sequence length`
+   * **`L** = sequence length`
     
-    `N = batch size`
+   * **`N** = batch size`
 
-    `H_{in} = input_size`
+   * **`H_{in}** = input_size`
 
-    `H_{out} = hidden_size`
+   * **`H_{out}** = hidden_size`
 
     Learnable Parameters:
-        - **rnn.weight_ih_l** (torch.Tensor) - the learnable input-hidden weights of shape (hidden_size, input_size)
-        - **rnn.weight_hh_l** (torch.Tensor) - the learnable hidden-hidden weights of the k-th layer which are sampled from `beta` of shape (hidden_size, hidden_size)
-        - **bias_ih_l** - the learnable input-hidden bias of the k-th layer, of shape (hidden_size)
-        - **bias_hh_l** - the learnable hidden-hidden bias of the k-th layer, of shape (hidden_size)
-        - **threshold** (torch.Tensor) - optional learnable thresholds
-            must be manually passed in, of shape `1` or`` (input_size).
-        - **graded_spikes_factor** (torch.Tensor) - optional learnable graded spike factor
+        - **rnn.weight_ih_l** (torch.Tensor) - the learnable input-hidden 
+            weights of shape (hidden_size, input_size).
+        - **rnn.weight_hh_l** (torch.Tensor) - the learnable hidden-hidden
+            weights of the k-th layer which are sampled from `beta` of shape 
+            (hidden_size, hidden_size).
+        - **bias_ih_l** - the learnable input-hidden bias of the k-th layer, 
+            of shape (hidden_size).
+        - **bias_hh_l** - the learnable hidden-hidden bias of the k-th layer, 
+            of shape (hidden_size).
+        - **threshold** (torch.Tensor) - optional learnable thresholds must be 
+            manually passed in, of shape `1` or`` (input_size).
+        - **graded_spikes_factor** (torch.Tensor) - optional learnable graded 
+            spike factor.
 
     """
 
@@ -303,4 +320,4 @@ def _threshold_buffer(self, threshold, learn_threshold):
         if learn_threshold:
             self.threshold = nn.Parameter(threshold)
         else:
-            self.register_buffer("threshold", threshold)
+            self.register_buffer("threshold", threshold)