[numpy] Fix users of NumPy APIs that are removed in NumPy 2.0.

This change migrates users of APIs removed in NumPy 2.0 to their recommended replacements (https://numpy.org/devdocs/numpy_2_0_migration_guide.html).

PiperOrigin-RevId: 655772286

tf_keras/callbacks.py

@@ -1423,20 +1423,20 @@ def __init__(
  if mode == "min":
  self.monitor_op = np.less
  if self.best is None:
- self.best = np.Inf
+ self.best = np.inf
  elif mode == "max":
  self.monitor_op = np.greater
  if self.best is None:
- self.best = -np.Inf
+ self.best = -np.inf
  else:
  if "acc" in self.monitor or self.monitor.startswith("fmeasure"):
  self.monitor_op = np.greater
  if self.best is None:
- self.best = -np.Inf
+ self.best = -np.inf
  else:
  self.monitor_op = np.less
  if self.best is None:
- self.best = np.Inf
+ self.best = np.inf
 
  if self.save_freq != "epoch" and not isinstance(self.save_freq, int):
  raise ValueError(
@@ -2112,7 +2112,7 @@ def on_train_begin(self, logs=None):
  # Allow instances to be re-used
  self.wait = 0
  self.stopped_epoch = 0
- self.best = np.Inf if self.monitor_op == np.less else -np.Inf
+ self.best = np.inf if self.monitor_op == np.less else -np.inf
  self.best_weights = None
  self.best_epoch = 0
 
@@ -3115,10 +3115,10 @@ def _reset(self):
  self.mode == "auto" and "acc" not in self.monitor
  ):
  self.monitor_op = lambda a, b: np.less(a, b - self.min_delta)
- self.best = np.Inf
+ self.best = np.inf
  else:
  self.monitor_op = lambda a, b: np.greater(a, b + self.min_delta)
- self.best = -np.Inf
+ self.best = -np.inf
  self.cooldown_counter = 0
  self.wait = 0
 

tf_keras/mixed_precision/autocast_variable_test.py

@@ -517,13 +517,13 @@ def test_assign_stays_in_true_dtype(self, distribution):
  # same float16 value.
  self.evaluate(x.assign(1.0 + small_tensor))
  self.assertEqual(1.0, self.evaluate(x.value()))
- self.assertEqual(1.0 + small_val, self.evaluate(x))
+ self.assertEqual(1.0 + np.float64(small_val), self.evaluate(x))
 
  self.evaluate(x.assign(1.0))
  with autocast_variable.enable_auto_cast_variables(tf.float16):
  self.evaluate(x.assign_add(small_tensor))
  self.assertEqual(1.0, self.evaluate(x.value()))
- self.assertEqual(1.0 + small_val, self.evaluate(x))
+ self.assertEqual(1.0 + np.float64(small_val), self.evaluate(x))
 
  def test_thread_local_autocast_dtype(self):
  x = get_var(1.0, tf.float32)

tf_keras/utils/data_utils.py

@@ -1108,7 +1108,7 @@ def pad_sequences(
  maxlen = np.max(lengths)
 
  is_dtype_str = np.issubdtype(dtype, np.str_) or np.issubdtype(
- dtype, np.unicode_
+ dtype, np.str_
  )
  if isinstance(value, str) and dtype != object and not is_dtype_str:
  raise ValueError(