edited comment lenguage for metric accuracy methods
@@ -3195,7 +3195,7 @@ def binary_accuracy(y_true, y_pred, threshold=0.5):
"""Calculates how often predictions match binary labels.
Note: calls metrics_utils.binary_matches with mean reduction. This maintains
- public facing binary_accuracy behavior and decouples it from the vital
+ public facing binary_accuracy behavior and seperates it from the vital
behavior of the binary_matches method needed in backend dependencies.
Standalone usage:
@@ -3224,7 +3224,7 @@ def binary_accuracy(y_true, y_pred, threshold=0.5):
def categorical_accuracy(y_true, y_pred):
"""Calculates how often predictions match one-hot labels.
- Note: wraps metrics_utils.categorical_matches. This decouples public facing
+ Note: wraps metrics_utils.categorical_matches. This seperates public facing
categorical_accuracy behavior from the vital behavior of the
categorical_matches method needed in backend dependencies.
@@ -3254,7 +3254,7 @@ def categorical_accuracy(y_true, y_pred):
def sparse_categorical_accuracy(y_true, y_pred):
"""Calculates how often predictions match integer labels.
- Note: wraps metrics_utils.sparse_categorical_matches. This decouples
+ Note: wraps metrics_utils.sparse_categorical_matches. This seperates
public facing sparse_categorical_accuracy behavior from the vital behavior
of the sparse_categorical_matches method needed in backend dependencies.
@@ -3284,7 +3284,7 @@ def sparse_categorical_accuracy(y_true, y_pred):
def top_k_categorical_accuracy(y_true, y_pred, k=5):
"""Computes how often targets are in the top `K` predictions.
- Note: wraps metrics_utils.top_k_categorical_matches. This decouples
+ Note: wraps metrics_utils.top_k_categorical_matches. This seperates
public facing top_k_categorical_accuracy behavior from the vital behavior
of the top_k_categorical_matches method needed in backend dependencies.
@@ -3313,7 +3313,7 @@ def top_k_categorical_accuracy(y_true, y_pred, k=5):
def sparse_top_k_categorical_accuracy(y_true, y_pred, k=5):
"""Computes how often integer targets are in the top `K` predictions.
- Note: wraps metrics_utils.sparse_top_k_categorical_matches. This decouples
+ Note: wraps metrics_utils.sparse_top_k_categorical_matches. This seperates
public facing sparse_top_k_categorical_accuracy behavior from the vital
behavior of the sparse_top_k_categorical_matches method needed in backend
dependencies.
