fixing spelling errors, slight change to # of iterations to generate a better confusion matrix

Author: mgs28

en-wordlist.txt

@@ -147,6 +147,8 @@ Minifier
 MobileNet
 ModelABC
 Mypy
+NameData
+NamesDataset
 NAS
 NCCL
 NCHW
@@ -359,6 +361,7 @@ enum
 eq
 equalities
 et
+eval
 evaluateInput
 extensibility
 fastai
@@ -513,6 +516,7 @@ resnet
 restride
 rewinded
 rgb
+rnn
 rollout
 rollouts
 romanized
@@ -580,12 +584,14 @@ traceback
 tradeoff
 tradeoffs
 triton
+txt
 uint
 umap
 uncomment
 uncommented
 underflowing
 unfused
+unicode
 unimodal
 unnormalized
 unoptimized

intermediate_source/char_rnn_classification_tutorial.py

@@ -66,8 +66,8 @@
 There are two key pieces of this that we will flesh out over the course of this tutorial. First is the basic data 
 object which a label and some text. In this instance, label = the country of origin and text = the name. 
 
-However, our data has some issues that we will need to clean up. First off, we need to convert unicode to plain ASCII to 
-limit the RNN input layers. This is accomplished by converting unicode strings to ASCII and allowing a samll set of allowed characters (allowed_characters)
+However, our data has some issues that we will need to clean up. First off, we need to convert Unicode to plain ASCII to 
+limit the RNN input layers. This is accomplished by converting Unicode strings to ASCII and allowing a small set of allowed characters (allowed_characters)
 """
 
 import torch
@@ -218,7 +218,7 @@ def __getitem__(self, idx):
 print(f"example = {alldata[0]}")
 
 #########################
-#Using the dataset object allows us to easily split the data into train and test sets. Here we create na 80/20 
+#Using the dataset object allows us to easily split the data into train and test sets. Here we create a 80/20 
 #split but the torch.utils.data has more useful utilities.
 
 train_set, test_set = torch.utils.data.random_split(alldata, [.8, .2])
@@ -227,7 +227,7 @@ def __getitem__(self, idx):
 
 #########################
 #Now we have a basic dataset containing 20074 examples where each example is a pairing of label and name. We have also 
-#split the datset into training and testing so we can validate the model that we build. 
+#split the dataset into training and testing so we can validate the model that we build. 
 
 
 ######################################################################
@@ -397,7 +397,7 @@ def label_from_output(self, output):
         label_i = top_i[0].item()
         return self.output_labels[label_i], label_i
 
-    def learn(self, training_data, n_epoch = 1000, n_batch_size = 64, report_every = 50, learning_rate = 0.005, criterion = nn.NLLLoss()):
+    def learn(self, training_data, n_epoch = 250, n_batch_size = 64, report_every = 50, learning_rate = 0.005, criterion = nn.NLLLoss()):
         """
         Learn on a batch of training_data for a specified number of iterations and reporting thresholds
         """
@@ -480,7 +480,7 @@ def evaluate(rnn, testing_data):
     confusion = torch.zeros(len(rnn.output_labels), len(rnn.output_labels))
 
     rnn.eval() #set to eval mode
-    with torch.no_grad(): # do not record the gradiants during eval phase
+    with torch.no_grad(): # do not record the gradients during eval phase
         for i in range(len(testing_data)):
             (label_tensor, text_tensor, label, text) = testing_data[i]
             output = rnn.forward(text_tensor)