comments and typo

app/readout.py

@@ -2,7 +2,7 @@ import sys
 import numpy as np
 from tqdm import tqdm
 from spikenn.snn import Fc
-from spikenn.train import S2STDPOptimizer, RSTDPOptimizer, S4NNPOptimizer, AdditiveSTDP, MultiplicativeSTDP, BaseRegularizer, CompetitionRegularizerTwo, CompetitionRegularizerOne
+from spikenn.train import S2STDPOptimizer, RSTDPOptimizer, S4NNOptimizer, AdditiveSTDP, MultiplicativeSTDP, BaseRegularizer, CompetitionRegularizerTwo, CompetitionRegularizerOne
 from spikenn.utils import DecisionMap, Logger, EarlyStopper
 from spikenn._impl import spike_sort
 
@@ -249,7 +249,7 @@ class Readout:
         config_optim = config["optimizer"]
         # BP-based rule
         if config_optim["method"] == "s4nn":
-            optim = S4NNPOptimizer(
+            optim = S4NNOptimizer(
                 network=network, 
                 t_gap=config_optim["t_gap"],
                 class_inhib=config_optim.get('class_inhib', False), # intra-class WTA, use when multiple neurons per class (e.g. with NCGs),

spikenn/_impl.py

@@ -126,7 +126,6 @@ def class_inhibition(spks, pots, decision_map, max_time):
 
 
 # SSTDP and S2-STDP weight update
-# NOTE: Do not handle well dropout on the output neurons (contrary to R-STDP)
 @njit
 def s2stdp(outputs, network_weights, y, decision_map, t_gap, class_inhib, use_time_ranges, max_time, ap, am, anti_ap, anti_am, stdp_func, stdp_args):
     n_layers = len(outputs)
@@ -205,8 +204,7 @@ def s2stdp(outputs, network_weights, y, decision_map, t_gap, class_inhib, use_ti
 
 
 # S4NN backward pass
-# Almost similar to sstdp function, but in another function for the sake of clarity
-# NOTE: Do not handle well dropout on the output neurons (contrary to R-STDP)
+# Almost similar to sstdp code, but in another function for the sake of clarity
 @njit
 def s4nn(outputs, network_weights, y, decision_map, t_gap, class_inhib, use_time_ranges, max_time, lr):
     n_layers = len(outputs)

spikenn/snn.py

@@ -116,9 +116,9 @@ class Fc(SpikingLayer):
         self.input_size = input_size
         self.n_neurons = n_neurons
 
-    # NOTE: Implementation is in a function augmented with Numba to speed up computations on CPU
+    # NOTE: The implementation is in a function optimized with Numba to accelerate CPU computations.
     def __call__(self, sample):
-        # Convert sample to SpikingDataset format (needed for multi-layer networks only)
+        # Convert dense sample to SpikingDataset format (needed for multi-layer networks only)
         sample = self.convert_input(sample)
         # Select the employed thresholds
         thresholds = self.thresholds_train if self.train_mode else self.thresholds

spikenn/train.py

@@ -40,9 +40,8 @@ class S2STDPOptimizer(STDPOptimizer):
 
 
     # NOTE: 
-    # Implementation is in a function augmented with Numba to speed up computations on CPU
-    # 
-    # code is not very clean, should be reworked.
+    # The implementation is in a function optimized with Numba to accelerate CPU computations.
+    # Hence, code is not very clean, should be reworked...
     # However, Numba is not easily implementable in class methods.
     def __call__(self, outputs, target_ind, decision_map):
         n_layers = len(self.network)
@@ -138,7 +137,7 @@ class RSTDPOptimizer(STDPOptimizer):
 
 # S4NN optimizer (BP-based)
 # NOTE: Can only train the output layer of the network 
-class S4NNPOptimizer:
+class S4NNOptimizer:
     
     __slots__ = ('network', 'lr', 't_gap', 'class_inhib', 'use_time_ranges', 'annealing', 'max_time')
 
@@ -157,9 +156,8 @@ class S4NNPOptimizer:
         
 
     # NOTE: 
-    # Implementation is in a function augmented with Numba to speed up computations on CPU
-    # 
-    # code is not very clean, should be reworked.
+    # The implementation is in a function optimized with Numba to accelerate CPU computations.
+    # Hence, code is not very clean, should be reworked...
     # However, Numba is not easily implementable in class methods.
     def __call__(self, outputs, target_ind, decision_map):
         n_layers = len(self.network)
@@ -296,7 +294,6 @@ class CompetitionRegularizerOne(BaseRegularizer):
 
 
 # STDP inferface to store parameters and callable to core function
-# TODO: Implementation not very clean, to rework
 class MultiplicativeSTDP:
     __slots__ = ('beta', 'w_min', 'w_max')
 
@@ -312,7 +309,6 @@ class MultiplicativeSTDP:
 
 
 # STDP inferface to store parameters and callable to core function
-# TODO: Implementation not very clean, to rework
 class AdditiveSTDP:
     __slots__ = ('max_time')