Fix typo

slim/nets/inception_v1.py

@@ -270,7 +270,7 @@ def inception_v1(inputs,
     is_training: whether is training or not.
     dropout_keep_prob: the percentage of activation values that are retained.
     prediction_fn: a function to get predictions out of logits.
-    spatial_squeeze: if True, logits is of shape is [B, C], if false logits is
+    spatial_squeeze: if True, logits is of shape [B, C], if false logits is
         of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
     reuse: whether or not the network and its variables should be reused. To be
       able to reuse 'scope' must be given.

slim/nets/inception_v2.py

@@ -443,7 +443,7 @@ def inception_v2(inputs,
       usage will be to set this value in (0, 1) to reduce the number of
       parameters or computation cost of the model.
     prediction_fn: a function to get predictions out of logits.
-    spatial_squeeze: if True, logits is of shape is [B, C], if false logits is
+    spatial_squeeze: if True, logits is of shape [B, C], if false logits is
         of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
     reuse: whether or not the network and its variables should be reused. To be
       able to reuse 'scope' must be given.

slim/nets/inception_v3.py

@@ -453,7 +453,7 @@ def inception_v3(inputs,
       usage will be to set this value in (0, 1) to reduce the number of
       parameters or computation cost of the model.
     prediction_fn: a function to get predictions out of logits.
-    spatial_squeeze: if True, logits is of shape is [B, C], if false logits is
+    spatial_squeeze: if True, logits is of shape [B, C], if false logits is
         of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
     reuse: whether or not the network and its variables should be reused. To be
       able to reuse 'scope' must be given.

slim/nets/resnet_v1.py

@@ -159,7 +159,7 @@ def resnet_v1(inputs,
       ratio of input to output spatial resolution.
     include_root_block: If True, include the initial convolution followed by
       max-pooling, if False excludes it.
-    spatial_squeeze: if True, logits is of shape is [B, C], if false logits is
+    spatial_squeeze: if True, logits is of shape [B, C], if false logits is
         of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
     reuse: whether or not the network and its variables should be reused. To be
       able to reuse 'scope' must be given.

slim/nets/resnet_v2.py

@@ -158,7 +158,7 @@ def resnet_v2(inputs,
     include_root_block: If True, include the initial convolution followed by
       max-pooling, if False excludes it. If excluded, `inputs` should be the
       results of an activation-less convolution.
-    spatial_squeeze: if True, logits is of shape is [B, C], if false logits is
+    spatial_squeeze: if True, logits is of shape [B, C], if false logits is
         of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
     reuse: whether or not the network and its variables should be reused. To be
       able to reuse 'scope' must be given.