Improve error messages in Keras activations / constraints / metrics.

PiperOrigin-RevId: 387713769

keras/activations.py

@@ -85,7 +85,7 @@ def softmax(x, axis=-1):
       output = e / s
   else:
     raise ValueError('Cannot apply softmax to a tensor that is 1D. '
-                     'Received input: %s' % (x,))
+                     f'Received input: {x}')
 
   # Cache the logits to use for crossentropy loss.
   output._keras_logits = x  # pylint: disable=protected-access
@@ -600,5 +600,4 @@ def get(identifier):
     return identifier
   else:
     raise TypeError(
-        'Could not interpret activation function identifier: {}'.format(
-            identifier))
+        f'Could not interpret activation function identifier: {identifier}')

keras/backend_config.py

@@ -103,8 +103,10 @@ def set_floatx(value):
       ValueError: In case of invalid value.
   """
   global _FLOATX
-  if value not in {'float16', 'float32', 'float64'}:
-    raise ValueError('Unknown floatx type: ' + str(value))
+  accepted_dtypes = {'float16', 'float32', 'float64'}
+  if value not in accepted_dtypes:
+    raise ValueError(
+        f'Unknown `floatx` value: {value}. Expected one of {accepted_dtypes}')
   _FLOATX = str(value)
 
 
@@ -142,6 +144,9 @@ def set_image_data_format(data_format):
       ValueError: In case of invalid `data_format` value.
   """
   global _IMAGE_DATA_FORMAT
-  if data_format not in {'channels_last', 'channels_first'}:
-    raise ValueError('Unknown data_format: ' + str(data_format))
+  accepted_formats = {'channels_last', 'channels_first'}
+  if data_format not in accepted_formats:
+    raise ValueError(
+        f'Unknown `data_format`: {data_format}. '
+        f'Expected one of {accepted_formats}')
   _IMAGE_DATA_FORMAT = str(data_format)

keras/constraints.py

@@ -257,7 +257,8 @@ def __call__(self, w):
     w_shape = w.shape
     if w_shape.rank is None or w_shape.rank != 4:
       raise ValueError(
-          'The weight tensor must be of rank 4, but is of shape: %s' % w_shape)
+          'The weight tensor must have rank 4. '
+          f'Received weight tensor with shape: {w_shape}')
 
     height, width, channels, kernels = w_shape
     w = backend.reshape(w, (height, width, channels * kernels))
@@ -332,6 +333,7 @@ def deserialize(config, custom_objects=None):
 
 @keras_export('keras.constraints.get')
 def get(identifier):
+  """Retrieves a Keras constraint function."""
   if identifier is None:
     return None
   if isinstance(identifier, dict):
@@ -342,5 +344,5 @@ def get(identifier):
   elif callable(identifier):
     return identifier
   else:
-    raise ValueError('Could not interpret constraint identifier: ' +
-                     str(identifier))
+    raise ValueError(
+        f'Could not interpret constraint function identifier: {identifier}')

keras/metrics.py

@@ -394,9 +394,9 @@ def update_state(self, values, sample_weight=None):
       values = tf.cast(values, self._dtype)
     except (ValueError, TypeError):
       msg = ('The output of a metric function can only be a single Tensor. '
-             'Got: %s' % (values,))
+             f'Received: {values}. ')
       if isinstance(values, dict):
-        msg += ('. To return a dict of values, implement a custom Metric '
+        msg += ('To return a dict of values, implement a custom Metric '
                 'subclass.')
       raise RuntimeError(msg)
     if sample_weight is not None:
@@ -438,7 +438,8 @@ def update_state(self, values, sample_weight=None):
         num_values = tf.reduce_sum(sample_weight)
     else:
       raise NotImplementedError(
-          'reduction [%s] not implemented' % self.reduction)
+          f'Reduction "{self.reduction}" not implemented. Expected '
+          '"sum", "weighted_mean", or "sum_over_batch_size".')
 
     with tf.control_dependencies([update_total_op]):
       return self.count.assign_add(num_values)
@@ -453,7 +454,8 @@ def result(self):
       return tf.math.divide_no_nan(self.total, self.count)
     else:
       raise NotImplementedError(
-          'reduction [%s] not implemented' % self.reduction)
+          f'Reduction "{self.reduction}" not implemented. Expected '
+          '"sum", "weighted_mean", or "sum_over_batch_size".')
 
 
 @keras_export('keras.metrics.Sum')
@@ -1530,7 +1532,9 @@ def __init__(self,
                dtype=None):
     super(SensitivitySpecificityBase, self).__init__(name=name, dtype=dtype)
     if num_thresholds <= 0:
-      raise ValueError('`num_thresholds` must be > 0.')
+      raise ValueError(
+          'Argument `num_thresholds` must be an integer > 0. '
+          f'Received: num_thresholds={num_thresholds}')
     self.value = value
     self.class_id = class_id
     self.true_positives = self.add_weight(
@@ -1689,7 +1693,9 @@ def __init__(self,
                name=None,
                dtype=None):
     if specificity < 0 or specificity > 1:
-      raise ValueError('`specificity` must be in the range [0, 1].')
+      raise ValueError(
+          'Argument `specificity` must be in the range [0, 1]. '
+          f'Received: specificity={specificity}')
     self.specificity = specificity
     self.num_thresholds = num_thresholds
     super(SensitivityAtSpecificity, self).__init__(
@@ -1781,7 +1787,9 @@ def __init__(self,
                name=None,
                dtype=None):
     if sensitivity < 0 or sensitivity > 1:
-      raise ValueError('`sensitivity` must be in the range [0, 1].')
+      raise ValueError(
+          'Argument `sensitivity` must be in the range [0, 1]. '
+          f'Received: sensitivity={sensitivity}')
     self.sensitivity = sensitivity
     self.num_thresholds = num_thresholds
     super(SpecificityAtSensitivity, self).__init__(
@@ -1865,7 +1873,9 @@ def __init__(self,
                name=None,
                dtype=None):
     if recall < 0 or recall > 1:
-      raise ValueError('`recall` must be in the range [0, 1].')
+      raise ValueError(
+          'Argument `recall` must be in the range [0, 1]. '
+          f'Received: recall={recall}')
     self.recall = recall
     self.num_thresholds = num_thresholds
     super(PrecisionAtRecall, self).__init__(
@@ -1949,7 +1959,9 @@ def __init__(self,
                name=None,
                dtype=None):
     if precision < 0 or precision > 1:
-      raise ValueError('`precision` must be in the range [0, 1].')
+      raise ValueError(
+          'Argument `precision` must be in the range [0, 1]. '
+          f'Received: precision={precision}')
     self.precision = precision
     self.num_thresholds = num_thresholds
     super(RecallAtPrecision, self).__init__(
@@ -2105,15 +2117,16 @@ def __init__(self,
     # Validate configurations.
     if isinstance(curve, metrics_utils.AUCCurve) and curve not in list(
         metrics_utils.AUCCurve):
-      raise ValueError('Invalid curve: "{}". Valid options are: "{}"'.format(
-          curve, list(metrics_utils.AUCCurve)))
+      raise ValueError(
+          f'Invalid `curve` argument value "{curve}". '
+          f'Expected one of: {list(metrics_utils.AUCCurve)}')
     if isinstance(
         summation_method,
         metrics_utils.AUCSummationMethod) and summation_method not in list(
             metrics_utils.AUCSummationMethod):
       raise ValueError(
-          'Invalid summation method: "{}". Valid options are: "{}"'.format(
-              summation_method, list(metrics_utils.AUCSummationMethod)))
+          f'Invalid `summation_method` argument value "{summation_method}". '
+          f'Expected one of: {list(metrics_utils.AUCSummationMethod)}')
 
     # Update properties.
     self._init_from_thresholds = thresholds is not None
@@ -2126,7 +2139,8 @@ def __init__(self,
               np.array([0.0] + thresholds + [1.0])))
     else:
       if num_thresholds <= 1:
-        raise ValueError('`num_thresholds` must be > 1.')
+        raise ValueError('Argument `num_thresholds` must be an integer > 1. '
+                         f'Received: num_thresholds={num_thresholds}')
 
       # Otherwise, linearly interpolate (num_thresholds - 2) thresholds in
       # (0, 1).
@@ -2188,8 +2202,10 @@ def _build(self, shape):
     """Initialize TP, FP, TN, and FN tensors, given the shape of the data."""
     if self.multi_label:
       if shape.ndims != 2:
-        raise ValueError('`y_true` must have rank=2 when `multi_label` is '
-                         'True. Found rank %s.' % shape.ndims)
+        raise ValueError(
+            '`y_true` must have rank 2 when `multi_label=True`. '
+            f'Found rank {shape.ndims}. '
+            f'Full shape received for `y_true`: {shape}')
       self._num_labels = shape[1]
       variable_shape = tf.TensorShape(
           [tf.compat.v1.Dimension(self.num_thresholds), self._num_labels])
@@ -3136,9 +3152,10 @@ def update_state(self, values, sample_weight=None):
     if not self._built:
       self._build(values.shape)
     elif values.shape != self._shape:
-      raise ValueError('MeanTensor input values must always have the same '
-                       'shape. Expected shape (set during the first call): {}. '
-                       'Got: {}'.format(self._shape, values.shape))
+      raise ValueError(
+          'MeanTensor input values must always have the same '
+          f'shape. Expected shape (set during the first call): {self._shape}. '
+          f'Got: {values.shape}.')
 
     num_values = tf.ones_like(values)
     if sample_weight is not None:
@@ -3168,7 +3185,7 @@ def update_state(self, values, sample_weight=None):
   def result(self):
     if not self._built:
       raise ValueError(
-          'MeanTensor does not have any result yet. Please call the MeanTensor '
+          'MeanTensor does not have any value yet. Please call the MeanTensor '
           'instance or use `.update_state(value)` before retrieving the result.'
           )
     return tf.math.divide_no_nan(self.total, self.count)
@@ -3715,7 +3732,7 @@ def get(identifier):
     return identifier
   else:
     raise ValueError(
-        'Could not interpret metric function identifier: {}'.format(identifier))
+        f'Could not interpret metric identifier: {identifier}')
 
 
 def is_built_in(cls):

keras/metrics_confusion_matrix_test.py

@@ -829,7 +829,8 @@ def test_invalid_specificity(self):
       metrics.SensitivityAtSpecificity(-1)
 
   def test_invalid_num_thresholds(self):
-    with self.assertRaisesRegex(ValueError, '`num_thresholds` must be > 0.'):
+    with self.assertRaisesRegex(
+        ValueError, 'Argument `num_thresholds` must be an integer > 0'):
       metrics.SensitivityAtSpecificity(0.4, num_thresholds=-1)
 
 
@@ -941,7 +942,8 @@ def test_invalid_sensitivity(self):
       metrics.SpecificityAtSensitivity(-1)
 
   def test_invalid_num_thresholds(self):
-    with self.assertRaisesRegex(ValueError, '`num_thresholds` must be > 0.'):
+    with self.assertRaisesRegex(
+        ValueError, 'Argument `num_thresholds` must be an integer > 0'):
       metrics.SpecificityAtSensitivity(0.4, num_thresholds=-1)
 
 
@@ -1054,7 +1056,8 @@ def test_invalid_sensitivity(self):
       metrics.PrecisionAtRecall(-1)
 
   def test_invalid_num_thresholds(self):
-    with self.assertRaisesRegex(ValueError, '`num_thresholds` must be > 0.'):
+    with self.assertRaisesRegex(
+        ValueError, 'Argument `num_thresholds` must be an integer > 0'):
       metrics.PrecisionAtRecall(0.4, num_thresholds=-1)
 
 
@@ -1186,7 +1189,8 @@ def test_invalid_sensitivity(self):
       metrics.RecallAtPrecision(-1)
 
   def test_invalid_num_thresholds(self):
-    with self.assertRaisesRegex(ValueError, '`num_thresholds` must be > 0.'):
+    with self.assertRaisesRegex(
+        ValueError, 'Argument `num_thresholds` must be an integer > 0'):
       metrics.RecallAtPrecision(0.4, num_thresholds=-1)
 
 
@@ -1453,10 +1457,12 @@ def test_weighted_pr_interpolation(self):
     self.assertAllClose(self.evaluate(result), expected_result, 1e-3)
 
   def test_invalid_num_thresholds(self):
-    with self.assertRaisesRegex(ValueError, '`num_thresholds` must be > 1.'):
+    with self.assertRaisesRegex(
+        ValueError, 'Argument `num_thresholds` must be an integer > 1'):
       metrics.AUC(num_thresholds=-1)
 
-    with self.assertRaisesRegex(ValueError, '`num_thresholds` must be > 1.'):
+    with self.assertRaisesRegex(
+        ValueError, 'Argument `num_thresholds` must be an integer > 1.'):
       metrics.AUC(num_thresholds=1)
 
   def test_invalid_curve(self):

keras/metrics_test.py

@@ -1379,7 +1379,7 @@ def test_config(self):
       self.assertEqual(m.dtype, tf.float32)
       self.assertEmpty(m.variables)
 
-      with self.assertRaisesRegex(ValueError, 'does not have any result yet'):
+      with self.assertRaisesRegex(ValueError, 'does not have any value yet'):
         m.result()
 
       self.evaluate(m([[3], [5], [3]]))