Python v2.range方法代码示例

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import range [as 别名]
def geomspace(start, stop, num=50, endpoint=True, dtype=float):  # pylint: disable=missing-docstring
  if dtype:
    dtype = utils.result_type(dtype)
  if num < 0:
    raise ValueError('Number of samples {} must be non-negative.'.format(num))
  if not num:
    return empty([0])
  step = 1.
  if endpoint:
    if num > 1:
      step = tf.pow((stop / start), 1 / (num - 1))
  else:
    step = tf.pow((stop / start), 1 / num)
  result = tf.cast(tf.range(num), step.dtype)
  result = tf.pow(step, result)
  result = tf.multiply(result, start)
  if dtype:
    result = tf.cast(result, dtype=dtype)
  return arrays_lib.tensor_to_ndarray(result)


# Building matrices. 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import range [as 别名]
def rot90(m, k=1, axes=(0, 1)):  # pylint: disable=missing-docstring
  m_rank = tf.rank(m)
  ax1, ax2 = utils._canonicalize_axes(axes, m_rank)  # pylint: disable=protected-access

  k = k % 4
  if k == 0:
    return m
  elif k == 2:
    return flip(flip(m, ax1), ax2)
  else:
    perm = tf.range(m_rank)
    perm = tf.tensor_scatter_nd_update(perm, [[ax1], [ax2]], [ax2, ax1])

    if k == 1:
      return transpose(flip(m, ax2), perm)
    else:
      return flip(transpose(m, perm), ax2) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import range [as 别名]
def diff(a, n=1, axis=-1):
  def f(a):
    nd = a.shape.rank
    if (axis + nd if axis < 0 else axis) >= nd:
      raise ValueError("axis %s is out of bounds for array of dimension %s" %
                       (axis, nd))
    if n < 0:
      raise ValueError("order must be non-negative but got %s" % n)
    slice1 = [slice(None)] * nd
    slice2 = [slice(None)] * nd
    slice1[axis] = slice(1, None)
    slice2[axis] = slice(None, -1)
    slice1 = tuple(slice1)
    slice2 = tuple(slice2)
    op = tf.not_equal if a.dtype == tf.bool else tf.subtract
    for _ in range(n):
      a = op(a[slice1], a[slice2])
    return a
  return _scalar(f, a) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import range [as 别名]
def test_many_small_batches_same_as_one_big_batch(self):
    dim = 2
    num_results_per_batch = 1
    num_batches = 3
    seed = 1925
    sample1, _ = random.halton.sample(
        dim, num_results_per_batch * num_batches, seed=seed)
    batch_indices = (
        tf.range(i * num_results_per_batch, (i + 1) * num_results_per_batch)
        for i in range(num_batches))
    sample2 = (
        random.halton.sample(dim, sequence_indices=sequence_indices, seed=seed)
        for sequence_indices in batch_indices)
    result_set1 = set(tuple(row) for row in self.evaluate(sample1))
    result_set2 = set()
    for batch, _ in sample2:
      result_set2.update(tuple(row) for row in self.evaluate(batch))
    self.assertEqual(result_set1, result_set2) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import range [as 别名]
def testOutputIsPermutation(self):
    """Checks that stateless_random_shuffle outputs a permutation."""
    for dtype in (tf.int32, tf.int64, tf.float32, tf.float64):
      identity_permutation = tf.range(10, dtype=dtype)
      random_shuffle_seed_1 = tff_rnd.stateless_random_shuffle(
          identity_permutation, seed=tf.constant((1, 42), tf.int64))
      random_shuffle_seed_2 = tff_rnd.stateless_random_shuffle(
          identity_permutation, seed=tf.constant((2, 42), tf.int64))
      # Check that the shuffles are of the correct dtype
      for shuffle in (random_shuffle_seed_1, random_shuffle_seed_2):
        np.testing.assert_equal(shuffle.dtype, dtype.as_numpy_dtype)
      random_shuffle_seed_1 = self.evaluate(random_shuffle_seed_1)
      random_shuffle_seed_2 = self.evaluate(random_shuffle_seed_2)
      identity_permutation = self.evaluate(identity_permutation)
      # Check that the shuffles are different
      self.assertTrue(
          np.abs(random_shuffle_seed_1 - random_shuffle_seed_2).max())
      # Check that the shuffles are indeed permutations
      for shuffle in (random_shuffle_seed_1, random_shuffle_seed_2):
        self.assertAllEqual(set(shuffle), set(identity_permutation)) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import range [as 别名]
def testOutputIsIndependentOfInputValues(self):
    """stateless_random_shuffle output is independent of input_tensor values."""
    # Generate sorted array of random numbers to control that the result
    # is independent of `input_tesnor` values
    np.random.seed(25)
    random_input = np.random.normal(size=[10])
    random_input.sort()
    for dtype in (tf.int32, tf.int64, tf.float32, tf.float64):
      # Permutation of a sequence [0, 1, .., 9]
      random_permutation = tff_rnd.stateless_random_shuffle(
          tf.range(10, dtype=dtype), seed=(100, 42))
      random_permutation = self.evaluate(random_permutation)
      # Shuffle `random_input` with the same seed
      random_shuffle_control = tff_rnd.stateless_random_shuffle(
          random_input, seed=(100, 42))
      random_shuffle_control = self.evaluate(random_shuffle_control)
      # Checks that the generated permutation does not depend on the underlying
      # values
      np.testing.assert_array_equal(
          np.argsort(random_permutation), np.argsort(random_shuffle_control)) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import range [as 别名]
def test_forward_unconnected_gradient(self):
    t = tf.range(1, 3, dtype=tf.float32)  # Shape [2]
    zeros = tf.zeros([2], dtype=t.dtype)
    func = lambda t: tf.stack([zeros, zeros, zeros], axis=0)  # Shape [3, 2]
    expected_result = [[0.0, 0.0], [0.0, 0.0], [0.0, 0.0]]
    with self.subTest("EagerExecution"):
      fwd_grad = self.evaluate(tff.math.fwd_gradient(
          func, t, unconnected_gradients=tf.UnconnectedGradients.ZERO))
      self.assertEqual(fwd_grad.shape, (3, 2))
      np.testing.assert_allclose(fwd_grad, expected_result)
    with self.subTest("GraphExecution"):
      @tf.function
      def grad_computation():
        y = func(t)
        return tff.math.fwd_gradient(
            y, t, unconnected_gradients=tf.UnconnectedGradients.ZERO)
      fwd_grad = self.evaluate(grad_computation())
      self.assertEqual(fwd_grad.shape, (3, 2))
      np.testing.assert_allclose(fwd_grad, expected_result) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import range [as 别名]
def test_backward_unconnected_gradient(self):
    t = tf.range(1, 3, dtype=tf.float32)  # Shape [2]
    zeros = tf.zeros([2], dtype=t.dtype)
    expected_result = [0.0, 0.0]
    func = lambda t: tf.stack([zeros, zeros, zeros], axis=0)  # Shape [3, 2]
    with self.subTest("EagerExecution"):
      backward_grad = self.evaluate(tff.math.gradients(
          func, t, unconnected_gradients=tf.UnconnectedGradients.ZERO))
      self.assertEqual(backward_grad.shape, (2,))
      np.testing.assert_allclose(backward_grad, expected_result)
    with self.subTest("GraphExecution"):
      @tf.function
      def grad_computation():
        y = func(t)
        return tff.math.gradients(
            y, t, unconnected_gradients=tf.UnconnectedGradients.ZERO)
      backward_grad = self.evaluate(grad_computation())
      self.assertEqual(backward_grad.shape, (2,))
      np.testing.assert_allclose(backward_grad, expected_result) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import range [as 别名]
def test_expected_continuation(self):
    """Tests that expected continuation works in V=1 case.

    In particular this verifies that the regression done to get the expected
    continuation value is performed on those elements which have a positive
    exercise value.
    """
    for dtype in (np.float32, np.float64):
      a = tf.range(start=-2, limit=3, delta=1, dtype=dtype)
      design = tf.concat([a, a], axis=0)
      design = tf.concat([[tf.ones_like(design), design]], axis=1)

      # These values ensure that the expected continuation value is `(1,...,1).`
      exercise_now = tf.expand_dims(
          tf.concat([tf.ones_like(a), tf.zeros_like(a)], axis=0), -1)
      cashflow = tf.expand_dims(
          tf.concat([tf.ones_like(a), -tf.ones_like(a)], axis=0), -1)

      expected_exercise = lsm.expected_exercise_fn(
          design, cashflow, exercise_now)
      self.assertAllClose(expected_exercise, tf.ones_like(cashflow)) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import range [as 别名]
def labels_of_top_ranked_predictions_in_batch(labels, predictions):
  """Applying tf.metrics.mean to this gives precision at 1.

  Args:
    labels: minibatch of dense 0/1 labels, shape [batch_size rows, num_classes]
    predictions: minibatch of predictions of the same shape

  Returns:
    one-dimension tensor top_labels, where top_labels[i]=1.0 iff the
    top-scoring prediction for batch element i has label 1.0
  """
  indices_of_top_preds = tf.cast(tf.argmax(input=predictions, axis=1), tf.int32)
  batch_size = tf.reduce_sum(input_tensor=tf.ones_like(indices_of_top_preds))
  row_indices = tf.range(batch_size)
  thresholded_labels = tf.where(labels > 0.0, tf.ones_like(labels),
                                tf.zeros_like(labels))
  label_indices_to_gather = tf.transpose(
      a=tf.stack([row_indices, indices_of_top_preds]))
  return tf.gather_nd(thresholded_labels, label_indices_to_gather) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import range [as 别名]
def train(self,
            num_steps: Optional[tf.Tensor]) -> Optional[Dict[Text, tf.Tensor]]:
    """Implements model training with multiple steps.

    In training, it is common to break the total training steps into several
    training loops, so users can do checkpointing, write summaries and run some
    python callbacks. This is necessary for getting good performance in TPU
    training, as the overhead for launching a multi worker tf.function may be
    large in Eager mode. It is usually encouraged to create a host training loop
    (e.g. using a `tf.range` wrapping `strategy.run` inside a
    `tf.function`) in the TPU case. For the cases that don't require host
    training loop to acheive peak performance, users can just implement a simple
    python loop to drive each step.

    Args:
      num_steps: A guideline for how many training steps to run. Note that it is
        up to the model what constitutes a "step" (this may involve more than
        one update to model parameters, e.g. if training a GAN).

    Returns:
      The function may return a dictionary of `Tensors`, which will be
      written to logs and as TensorBoard summaries.
    """
    pass 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import range [as 别名]
def arange(start, stop=None, step=1, dtype=None):
  """Returns `step`-separated values in the range [start, stop).

  Args:
    start: Start of the interval. Included in the range.
    stop: End of the interval. If not specified, `start` is treated as 0 and
      `start` value is used as `stop`. If specified, it is not included in the
      range if `step` is integer. When `step` is floating point, it may or may
      not be included.
    step: The difference between 2 consecutive values in the output range. It is
      recommended to use `linspace` instead of using non-integer values for
      `step`.
    dtype: Optional. Type of the resulting ndarray. Could be a python type, a
      NumPy type or a TensorFlow `DType`. If not provided, the largest type of
      `start`, `stop`, `step` is used.

  Raises:
    ValueError: If step is zero.
  """
  if not step:
    raise ValueError('step must be non-zero.')
  if dtype:
    dtype = utils.result_type(dtype)
  else:
    if stop is None:
      dtype = utils.result_type(start, step)
    else:
      dtype = utils.result_type(start, step, stop)
  if step > 0 and ((stop is not None and start > stop) or
                   (stop is None and start < 0)):
    return array([], dtype=dtype)
  if step < 0 and ((stop is not None and start < stop) or
                   (stop is None and start > 0)):
    return array([], dtype=dtype)
  # TODO(srbs): There are some bugs when start or stop is float type and dtype
  # is integer type.
  return arrays_lib.tensor_to_ndarray(
      tf.cast(tf.range(start, limit=stop, delta=step), dtype=dtype)) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import range [as 别名]
def swapaxes(a, axis1, axis2):  # pylint: disable=missing-docstring
  a = asarray(a)

  a_rank = tf.rank(a)
  if axis1 < 0:
    axis1 += a_rank
  if axis2 < 0:
    axis2 += a_rank

  perm = tf.range(a_rank)
  perm = tf.tensor_scatter_nd_update(perm, [[axis1], [axis2]], [axis2, axis1])
  a = tf.transpose(a, perm)

  return utils.tensor_to_ndarray(a) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import range [as 别名]
def diag_indices(n, ndim=2):  # pylint: disable=missing-docstring,redefined-outer-name
  if n < 0:
    raise ValueError('n argument to diag_indices must be nonnegative, got {}'
                     .format(n))
  if ndim < 0:
    raise ValueError('ndim argument to diag_indices must be nonnegative, got {}'
                     .format(ndim))

  return (tf.range(n),) * ndim 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import range [as 别名]
def vander(x, N=None, increasing=False):  # pylint: disable=missing-docstring,invalid-name
  x = asarray(x).data

  x_shape = tf.shape(x)
  N = N or x_shape[0]

  N_temp = utils.get_static_value(N)  # pylint: disable=invalid-name
  if N_temp is not None:
    N = N_temp
    if N < 0:
      raise ValueError('N must be nonnegative')
  else:
    tf.debugging.Assert(N >= 0, [N])

  rank = tf.rank(x)
  rank_temp = utils.get_static_value(rank)
  if rank_temp is not None:
    rank = rank_temp
    if rank != 1:
      raise ValueError('x must be a one-dimensional array')
  else:
    tf.debugging.Assert(rank == 1, [rank])

  if increasing:
    start = 0
    limit = N
    delta = 1
  else:
    start = N - 1
    limit = -1
    delta = -1

  x = tf.expand_dims(x, -1)
  return utils.tensor_to_ndarray(
      tf.math.pow(x, tf.cast(tf.range(start, limit, delta), dtype=x.dtype)))