initial commit

edgeml/pytorch/graph/rnn.py

@@ -60,7 +60,7 @@ class BaseRNN(nn.Module):
     '''
     Generic equivalent of static_rnn in tf
     Used to unroll all the cell written in this file
-    We assume data to be batch_first by default ie.,
+    We assume input to be batch_first by default ie.,
     [batchSize, timeSteps, inputDims] else
     [timeSteps, batchSize, inputDims]
     '''
@@ -436,7 +436,7 @@ class LSTMLRCell(nn.Module):
     LSTM architecture and compression techniques are found in
     LSTM paper
 
-    Basic architecture is like:
+    Basic architecture:
 
     f_t = gate_nl(W1x_t + U1h_{t-1} + B_f)
     i_t = gate_nl(W2x_t + U2h_{t-1} + B_i)

edgeml/pytorch/trainer/fastmodel.py

@@ -10,9 +10,13 @@ from edgeml.pytorch.graph.rnn import *
 
 def fastgrnnmodel(inheritance_class=nn.Module):
     class FastGRNNModel(inheritance_class):
-        """This class is a PyTorch Module that implements a 1, 2 or 3 layer GRU based audio classifier"""
+        """This class is a PyTorch Module that implements a 1, 2 or 3 layer
+           RNN-based classifier
+        """
 
-        def __init__(self, input_dim, num_layers, hidden_units_list, wRank_list, uRank_list, gate_nonlinearity, update_nonlinearity, num_classes=None, linear=True, batch_first=False, apply_softmax=True):
+        def __init__(self, input_dim, num_layers, hidden_units_list,
+                     wRank_list, uRank_list, gate_nonlinearity, update_nonlinearity,
+                     num_classes=None, linear=True, batch_first=False, apply_softmax=True):
             """
             Initialize the KeywordSpotter with the following parameters:
             input_dim - the size of the input audio frame in # samples.

examples/pytorch/FastCells/train_classifier.py

@@ -111,9 +111,9 @@ class KeywordSpotter(nn.Module):
 
     def fit(self, training_data, validation_data, options, model, device=None, detail=False, run=None):
         """
-        Perform the training.  This is not called "train" because the base class already defines
-        that method with a different meaning.  The base class "train" method puts the Module into
-        "training mode".
+        Perform the training.  This is not called "train" because
+		the base class already defines that method with a different meaning.
+		The base class "train" method puts the Module into "training mode".
         """
         print("Training {} using {} rows of featurized training input...".format(self.name(), training_data.num_rows))