changed line continuation method

Author: PoojanSmart

Diff for: machine_learning/loss_functions/perplexity_loss.py

@@ -1,9 +1,9 @@
 """
-Compute the Perplexity which useful in predicting language model
+Compute the Perplexity which useful in predicting language model 
 accuracy in Natural Language Processing (NLP.)
 Perplexity is measure of how certain the model in its predictions.
 
-Formula :
+Formula : 
 
 Perplexity Loss = exp(-1/N (Σ ln(p(x)))
 
@@ -29,45 +29,45 @@ def perplexity_loss(y_true: np.ndarray, y_pred: np.ndarray) -> float:
         Perplexity loss between y_true and y_pred.
 
     >>> y_true = np.array([[1, 4], [2, 3]])
-    >>> y_pred = np.array(                 \
-        [[[0.28, 0.19, 0.21 , 0.15, 0.15], \
-          [0.24, 0.19, 0.09, 0.18, 0.27]], \
-          [[0.03, 0.26, 0.21, 0.18, 0.30], \
-           [0.28, 0.10, 0.33, 0.15, 0.12]]]\
-    )
+    >>> y_pred = np.array(
+    ...    [[[0.28, 0.19, 0.21 , 0.15, 0.15],
+    ...      [0.24, 0.19, 0.09, 0.18, 0.27]],
+    ...      [[0.03, 0.26, 0.21, 0.18, 0.30],
+    ...       [0.28, 0.10, 0.33, 0.15, 0.12]]]
+    ... )
     >>> perplexity_loss(y_true, y_pred)
     5.024732177979022
     >>> y_true = np.array([[1, 4], [2, 3]])
-    >>> y_pred = np.array(                   \
-        [[[0.28, 0.19, 0.21 , 0.15, 0.15],   \
-          [0.24, 0.19, 0.09, 0.18, 0.27],    \
-          [0.30, 0.10, 0.20, 0.15, 0.25]],   \
-          [[0.03, 0.26, 0.21, 0.18, 0.30],   \
-           [0.28, 0.10, 0.33, 0.15, 0.12],   \
-           [0.30, 0.10, 0.20, 0.15, 0.25]],] \
-    )
+    >>> y_pred = np.array(
+    ...    [[[0.28, 0.19, 0.21 , 0.15, 0.15],
+    ...      [0.24, 0.19, 0.09, 0.18, 0.27],
+    ...      [0.30, 0.10, 0.20, 0.15, 0.25]],
+    ...      [[0.03, 0.26, 0.21, 0.18, 0.30],
+    ...       [0.28, 0.10, 0.33, 0.15, 0.12],
+    ...       [0.30, 0.10, 0.20, 0.15, 0.25]],]
+    ... )
     >>> perplexity_loss(y_true, y_pred)
     Traceback (most recent call last):
     ...
     ValueError: Sentence length of y_true and y_pred must be equal.
     >>> y_true = np.array([[1, 4], [2, 11]])
-    >>> y_pred = np.array(                 \
-        [[[0.28, 0.19, 0.21 , 0.15, 0.15], \
-          [0.24, 0.19, 0.09, 0.18, 0.27]], \
-          [[0.03, 0.26, 0.21, 0.18, 0.30], \
-           [0.28, 0.10, 0.33, 0.15, 0.12]]]\
-    )
+    >>> y_pred = np.array(
+    ...    [[[0.28, 0.19, 0.21 , 0.15, 0.15],
+    ...      [0.24, 0.19, 0.09, 0.18, 0.27]],
+    ...      [[0.03, 0.26, 0.21, 0.18, 0.30],
+    ...       [0.28, 0.10, 0.33, 0.15, 0.12]]]
+    ... )
     >>> perplexity_loss(y_true, y_pred)
     Traceback (most recent call last):
     ...
     ValueError: Label value must not be greater than vocabulary size.
     >>> y_true = np.array([[1, 4]])
-    >>> y_pred = np.array(                 \
-        [[[0.28, 0.19, 0.21 , 0.15, 0.15], \
-          [0.24, 0.19, 0.09, 0.18, 0.27]], \
-          [[0.03, 0.26, 0.21, 0.18, 0.30], \
-           [0.28, 0.10, 0.33, 0.15, 0.12]]]\
-    )
+    >>> y_pred = np.array(
+    ...    [[[0.28, 0.19, 0.21 , 0.15, 0.15],
+    ...      [0.24, 0.19, 0.09, 0.18, 0.27]],
+    ...      [[0.03, 0.26, 0.21, 0.18, 0.30],
+    ...       [0.28, 0.10, 0.33, 0.15, 0.12]]]
+    ... )
     >>> perplexity_loss(y_true, y_pred)
     Traceback (most recent call last):
     ...