Fix typos (#7087)

keras/datasets/imdb.py

@@ -17,7 +17,7 @@ def load_data(path='imdb.npz', num_words=None, skip_top=0,
         num_words: max number of words to include. Words are ranked
             by how often they occur (in the training set) and only
             the most frequent words are kept
-        skip_top: skip the top N most frequently occuring words
+        skip_top: skip the top N most frequently occurring words
             (which may not be informative).
         maxlen: truncate sequences after this length.
         seed: random seed for sample shuffling.

keras/datasets/reuters.py

@@ -18,7 +18,7 @@ def load_data(path='reuters.npz', num_words=None, skip_top=0,
         num_words: max number of words to include. Words are ranked
             by how often they occur (in the training set) and only
             the most frequent words are kept
-        skip_top: skip the top N most frequently occuring words
+        skip_top: skip the top N most frequently occurring words
             (which may not be informative).
         maxlen: truncate sequences after this length.
         test_split: Fraction of the dataset to be used as test data.

keras/engine/topology.py

@@ -744,7 +744,7 @@ class Layer(object):
                                     str(mask))
             # masking not explicitly supported: return None as mask
             return None
-        # if masking is explictly supported, by default
+        # if masking is explicitly supported, by default
         # carry over the input mask
         return mask
 
@@ -1529,7 +1529,7 @@ class Container(Layer):
         # every time the Container is called on a set on input tensors,
         # we compute the output tensors,
         # output masks and output shapes in one pass,
-        # then cache them here. When of of these output is queried later,
+        # then cache them here. When one of these output is queried later,
         # we retrieve it from there instead of recomputing it.
         self._output_mask_cache = {}
         self._output_tensor_cache = {}

keras/layers/normalization.py

@@ -137,7 +137,7 @@ class BatchNormalization(Layer):
 
         def normalize_inference():
             if needs_broadcasting:
-                # In this case we must explictly broadcast all parameters.
+                # In this case we must explicitly broadcast all parameters.
                 broadcast_moving_mean = K.reshape(self.moving_mean,
                                                   broadcast_shape)
                 broadcast_moving_variance = K.reshape(self.moving_variance,

keras/models.py

@@ -207,7 +207,7 @@ def load_model(filepath, custom_objects=None, compile=True):
             obj: object, dict, or list.
 
         # Returns
-            The same structure, where occurences
+            The same structure, where occurrences
                 of a custom object name have been replaced
                 with the custom object.
         """

keras/preprocessing/sequence.py

@@ -127,7 +127,7 @@ def skipgrams(sequence, vocabulary_size,
             of word indices (integers). If using a `sampling_table`,
             word indices are expected to match the rank
             of the words in a reference dataset (e.g. 10 would encode
-            the 10-th most frequently occuring token).
+            the 10-th most frequently occurring token).
             Note that index 0 is expected to be a non-word and will be skipped.
         vocabulary_size: int. maximum possible word index + 1
         window_size: int. actually half-window.

tests/keras/backend/backend_test.py

@@ -886,7 +886,7 @@ class TestBackend(object):
         check_two_tensor_operation('binary_crossentropy', (4, 2), (4, 2), BACKENDS, from_logits=True)
         # cross_entropy call require the label is a valid probability distribution,
         # otherwise it is garbage in garbage out...
-        # due to the algo difference, we can't guranteen CNTK has the same result on the garbage input.
+        # due to the algo difference, we can't guarantee CNTK has the same result on the garbage input.
         # so create a seperate test case for valid lable input
         check_two_tensor_operation('categorical_crossentropy', (4, 2), (4, 2), [KTH, KTF], from_logits=True)
         check_cross_entropy_with_valid_probability_distribution()