Merge pull request #216 from mendesmiguel/typos/fix

Typos/fix

keras_contrib/datasets/coco.py

@@ -340,7 +340,7 @@ def coco_image_segmentation_stats(seg_mask_output_paths, annotation_paths, seg_m
             # print('\ntarget_shape:', target_shape)
             mask_one_hot = np.zeros(target_shape, dtype=np.uint8)
 
-            # Note to only count backgroung pixels once, we define a temporary
+            # Note to only count background pixels once, we define a temporary
             # null class of 0, and shift all class category ids up by 1
             mask_one_hot[:, :, 0] = 1  # every pixel begins as background
 

keras_contrib/datasets/conll2000.py

@@ -43,7 +43,7 @@ def _process_data(data, vocab, pos_tags, chunk_tags, maxlen=None, onehot=False):
 
     x = pad_sequences(x, maxlen)  # left padding
 
-    y_pos = pad_sequences(y_pos, maxlen, value=-1)  # lef padded with -1. Indeed, any interger works as it will be masked
+    y_pos = pad_sequences(y_pos, maxlen, value=-1)  # lef padded with -1. Indeed, any integer works as it will be masked
     y_chunk = pad_sequences(y_chunk, maxlen, value=-1)
 
     if onehot:

keras_contrib/initializers/convaware.py

@@ -9,7 +9,7 @@ class ConvolutionAware(Initializer):
     """
     Initializer that generates orthogonal convolution filters in the fourier
     space. If this initializer is passed a shape that is not 3D or 4D,
-    orthogonal intialization will be used.
+    orthogonal initialization will be used.
     # Arguments
         eps_std: Standard deviation for the random normal noise used to break
         symmetry in the inverse fourier transform.

keras_contrib/layers/crf.py

@@ -26,7 +26,7 @@ class CRF(Layer):
 
     This implementation has two modes for optimization:
     1. (`join mode`) optimized by maximizing join likelihood, which is optimal in theory of statistics.
-       Note that in this case, CRF mast be the output/last layer.
+       Note that in this case, CRF must be the output/last layer.
     2. (`marginal mode`) return marginal probabilities on each time step and optimized via composition
        likelihood (product of marginal likelihood), i.e., using `categorical_crossentropy` loss.
        Note that in this case, CRF can be either the last layer or an intermediate layer (though not explored).
@@ -72,9 +72,9 @@ class CRF(Layer):
             gives one-hot representation of the best path at test (prediction) time,
             while the latter is recommended and chosen as default when `learn_mode = 'marginal'`,
             which produces marginal probabilities for each time step.
-        sparse_target: Boolen (default False) indicating if provided labels are one-hot or
+        sparse_target: Boolean (default False) indicating if provided labels are one-hot or
             indices (with shape 1 at dim 3).
-        use_boundary: Boolen (default True) inidicating if trainable start-end chain energies
+        use_boundary: Boolean (default True) indicating if trainable start-end chain energies
             should be added to model.
         use_bias: Boolean, whether the layer uses a bias vector.
         kernel_initializer: Initializer for the `kernel` weights matrix,
@@ -372,7 +372,7 @@ def add_boundary_energy(self, energy, mask, start, end):
         return energy
 
     def get_log_normalization_constant(self, input_energy, mask, **kwargs):
-        """Compute logarithm of the normalization constance Z, where
+        """Compute logarithm of the normalization constant Z, where
         Z = sum exp(-E) -> logZ = log sum exp(-E) =: -nlogZ
         """
         # should have logZ[:, i] == logZ[:, j] for any i, j
@@ -436,7 +436,7 @@ def step(self, input_energy_t, states, return_logZ=True):
     def recursion(self, input_energy, mask=None, go_backwards=False, return_sequences=True, return_logZ=True, input_length=None):
         """Forward (alpha) or backward (beta) recursion
 
-        If `return_logZ = True`, compute the logZ, the normalization constance:
+        If `return_logZ = True`, compute the logZ, the normalization constant:
 
         \[ Z = \sum_{y1, y2, y3} exp(-E) # energy
           = \sum_{y1, y2, y3} exp(-(u1' y1 + y1' W y2 + u2' y2 + y2' W y3 + u3' y3))