Fix typo "constrastive"

examples/vision/siamese_contrastive.py

@@ -37,7 +37,7 @@
 
 epochs = 10
 batch_size = 16
-margin = 1  # Margin for constrastive loss.
+margin = 1  # Margin for contrastive loss.
 
 """
 ## Load the MNIST dataset
@@ -301,33 +301,33 @@ def euclidean_distance(vects):
 
 
 """
-## Define the constrastive Loss
+## Define the contrastive Loss
 """
 
 
 def loss(margin=1):
-    """Provides 'constrastive_loss' an enclosing scope with variable 'margin'.
+    """Provides 'contrastive_loss' an enclosing scope with variable 'margin'.
 
     Arguments:
         margin: Integer, defines the baseline for distance for which pairs
                 should be classified as dissimilar. - (default is 1).
 
     Returns:
-        'constrastive_loss' function with data ('margin') attached.
+        'contrastive_loss' function with data ('margin') attached.
     """
 
     # Contrastive loss = mean( (1-true_value) * square(prediction) +
     #                         true_value * square( max(margin-prediction, 0) ))
     def contrastive_loss(y_true, y_pred):
-        """Calculates the constrastive loss.
+        """Calculates the contrastive loss.
 
         Arguments:
             y_true: List of labels, each label is of type float32.
             y_pred: List of predictions of same length as of y_true,
                     each label is of type float32.
 
         Returns:
-            A tensor containing constrastive loss as floating point value.
+            A tensor containing contrastive loss as floating point value.
         """
 
         square_pred = tf.math.square(y_pred)
@@ -389,8 +389,8 @@ def plt_metric(history, metric, title, has_valid=True):
 # Plot the accuracy
 plt_metric(history=history.history, metric="accuracy", title="Model accuracy")
 
-# Plot the constrastive loss
-plt_metric(history=history.history, metric="loss", title="Constrastive Loss")
+# Plot the contrastive loss
+plt_metric(history=history.history, metric="loss", title="Contrastive Loss")
 
 """
 ## Evaluate the model