Merge pull request fastai#576 from sck/master

Fix typo: classifer -> classifier.  Also keep around an alias for get_…

courses/dl2/imdb_scripts/train_clas.py

@@ -70,7 +70,7 @@ def train_clas(dir_path, cuda_id, lm_id='', clas_id=None, bs=64, cl=1, backwards
     #dps = np.array([0.65,0.48,0.039,0.335,0.34])*dropmult
     #dps = np.array([0.6,0.5,0.04,0.3,0.4])*dropmult
 
-    m = get_rnn_classifer(bptt, 20*70, c, vs, emb_sz=em_sz, n_hid=nh, n_layers=nl, pad_token=1,
+    m = get_rnn_classifier(bptt, 20*70, c, vs, emb_sz=em_sz, n_hid=nh, n_layers=nl, pad_token=1,
               layers=[em_sz*3, 50, c], drops=[dps[4], 0.1],
               dropouti=dps[0], wdrop=dps[1], dropoute=dps[2], dropouth=dps[3])
 

fastai/lm_rnn.py

@@ -243,3 +243,4 @@ def get_rnn_classifier(bptt, max_seq, n_class, n_tok, emb_sz, n_hid, n_layers, p
                       dropouth=dropouth, dropouti=dropouti, dropoute=dropoute, wdrop=wdrop, qrnn=qrnn)
     return SequentialRNN(rnn_enc, PoolingLinearClassifier(layers, drops))
 
+get_rnn_classifer=get_rnn_classifier

fastai/models/nasnet.py

@@ -486,9 +486,9 @@ def forward(self, x, x_prev):
 
 class NASNetALarge(nn.Module):
 
-    def __init__(self, use_classifer=False, num_classes=1001):
+    def __init__(self, use_classifier=False, num_classes=1001):
         super(NASNetALarge, self).__init__()
-        self.use_classifer,self.num_classes = use_classifer,num_classes
+        self.use_classifier,self.num_classes = use_classifier,num_classes
 
         self.conv0 = nn.Sequential()
         self.conv0.add_module('conv', nn.Conv2d(in_channels=3, out_channels=96, kernel_size=3, padding=0, stride=2,
@@ -586,7 +586,7 @@ def classifier(self, x):
 
     def forward(self, x):
         x = self.features(x)
-        if self.use_classifer: x = self.classifier(x)
+        if self.use_classifier: x = self.classifier(x)
         return x
 
 

fastai/nlp.py

@@ -360,7 +360,7 @@ def to_model(self, m, opt_fn):
         return RNN_Learner(self, model, opt_fn=opt_fn)
 
     def get_model(self, opt_fn, max_sl, bptt, emb_sz, n_hid, n_layers, dropout, **kwargs):
-        m = get_rnn_classifer(bptt, max_sl, self.c, self.nt,
+        m = get_rnn_classifier(bptt, max_sl, self.c, self.nt,
               layers=[emb_sz*3, self.c], drops=[dropout],
               emb_sz=emb_sz, n_hid=n_hid, n_layers=n_layers, pad_token=self.pad_idx, **kwargs)
         return self.to_model(m, opt_fn)