Python v2.constant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 constant [as 别名]
def _key2seed(a):
  """Converts an RNG key to an RNG seed.

  Args:
    a: an RNG key, an ndarray of shape [] and dtype `np.int64`.

  Returns:
    an RNG seed, a tensor of shape [2] and dtype `tf.int32`.
  """

  def int64_to_int32s(a):
    """Converts an int64 tensor of shape [] to an int32 tensor of shape [2]."""
    a = tf.cast(a, tf.uint64)
    fst = tf.cast(a, tf.uint32)
    snd = tf.cast(
        tf.bitwise.right_shift(a, tf.constant(32, tf.uint64)), tf.uint32)
    a = [fst, snd]
    a = tf.nest.map_structure(lambda x: tf.cast(x, tf.int32), a)
    a = tf.stack(a)
    return a

  return int64_to_int32s(a.data) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import constant [as 别名]
def _seed2key(a):
  """Converts an RNG seed to an RNG key.

  Args:
    a: an RNG seed, a tensor of shape [2] and dtype `tf.int32`.

  Returns:
    an RNG key, an ndarray of shape [] and dtype `np.int64`.
  """

  def int32s_to_int64(a):
    """Converts an int32 tensor of shape [2] to an int64 tensor of shape []."""
    a = tf.bitwise.bitwise_or(
        tf.cast(a[0], tf.uint64),
        tf.bitwise.left_shift(
            tf.cast(a[1], tf.uint64), tf.constant(32, tf.uint64)))
    a = tf.cast(a, tf.int64)
    return a

  return tf_np.asarray(int32s_to_int64(a)) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import constant [as 别名]
def tril(m, k=0):  # pylint: disable=missing-docstring
  m = asarray(m).data
  m_shape = m.shape.as_list()

  if len(m_shape) < 2:
    raise ValueError('Argument to tril must have rank at least 2')

  if m_shape[-1] is None or m_shape[-2] is None:
    raise ValueError('Currently, the last two dimensions of the input array '
                     'need to be known.')

  z = tf.constant(0, m.dtype)

  mask = tri(*m_shape[-2:], k=k, dtype=bool)
  return utils.tensor_to_ndarray(
      tf.where(tf.broadcast_to(mask, tf.shape(m)), m, z)) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import constant [as 别名]
def triu(m, k=0):  # pylint: disable=missing-docstring
  m = asarray(m).data
  m_shape = m.shape.as_list()

  if len(m_shape) < 2:
    raise ValueError('Argument to triu must have rank at least 2')

  if m_shape[-1] is None or m_shape[-2] is None:
    raise ValueError('Currently, the last two dimensions of the input array '
                     'need to be known.')

  z = tf.constant(0, m.dtype)

  mask = tri(*m_shape[-2:], k=k - 1, dtype=bool)
  return utils.tensor_to_ndarray(
      tf.where(tf.broadcast_to(mask, tf.shape(m)), z, m)) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import constant [as 别名]
def _tf_gcd(x1, x2):
  def _gcd_cond_fn(x1, x2):
    return tf.reduce_any(x2 != 0)
  def _gcd_body_fn(x1, x2):
    # tf.math.mod will raise an error when any element of x2 is 0. To avoid
    # that, we change those zeros to ones. Their values don't matter because
    # they won't be used.
    x2_safe = tf.where(x2 != 0, x2, tf.constant(1, x2.dtype))
    x1, x2 = (tf.where(x2 != 0, x2, x1),
              tf.where(x2 != 0, tf.math.mod(x1, x2_safe),
                       tf.constant(0, x2.dtype)))
    return (tf.where(x1 < x2, x2, x1), tf.where(x1 < x2, x1, x2))
  if (not np.issubdtype(x1.dtype.as_numpy_dtype, np.integer) or
      not np.issubdtype(x2.dtype.as_numpy_dtype, np.integer)):
    raise ValueError("Arguments to gcd must be integers.")
  shape = tf.broadcast_static_shape(x1.shape, x2.shape)
  x1 = tf.broadcast_to(x1, shape)
  x2 = tf.broadcast_to(x2, shape)
  gcd, _ = tf.while_loop(_gcd_cond_fn, _gcd_body_fn,
                         (tf.math.abs(x1), tf.math.abs(x2)))
  return gcd 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import constant [as 别名]
def argsort(a, axis=-1, kind='quicksort', order=None):  # pylint: disable=missing-docstring
  # TODO(nareshmodi): make string tensors also work.
  if kind not in ('quicksort', 'stable'):
    raise ValueError("Only 'quicksort' and 'stable' arguments are supported.")
  if order is not None:
    raise ValueError("'order' argument to sort is not supported.")
  stable = (kind == 'stable')

  a = array_ops.array(a).data

  def _argsort(a, axis, stable):
    if axis is None:
      a = tf.reshape(a, [-1])
      axis = 0

    return tf.argsort(a, axis, stable=stable)

  tf_ans = tf.cond(
      tf.rank(a) == 0, lambda: tf.constant([0]),
      lambda: _argsort(a, axis, stable))

  return array_ops.array(tf_ans, dtype=np.intp) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import constant [as 别名]
def testPad(self):
    t = [[1, 2, 3], [4, 5, 6]]
    paddings = [[1, 1,], [2, 2]]
    self.assertAllEqual(
        array_ops.pad(t, paddings, 'constant'),
        [[0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 2, 3, 0, 0], [0, 0, 4, 5, 6, 0, 0],
         [0, 0, 0, 0, 0, 0, 0]])

    self.assertAllEqual(
        array_ops.pad(t, paddings, 'reflect'),
        [[6, 5, 4, 5, 6, 5, 4], [3, 2, 1, 2, 3, 2, 1], [6, 5, 4, 5, 6, 5, 4],
         [3, 2, 1, 2, 3, 2, 1]])

    self.assertAllEqual(
        array_ops.pad(t, paddings, 'symmetric'),
        [[2, 1, 1, 2, 3, 3, 2], [2, 1, 1, 2, 3, 3, 2], [5, 4, 4, 5, 6, 6, 5],
         [5, 4, 4, 5, 6, 6, 5]]) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import constant [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 constant [as 别名]
def testMultiDimensionalShape(self):
    """Check that stateless_random_shuffle works with multi-dim shapes."""
    for dtype in (tf.int32, tf.int64, tf.float32, tf.float64):
      input_permutation = tf.constant([[[1], [2], [3]], [[4], [5], [6]]],
                                      dtype=dtype)
      random_shuffle = tff_rnd.stateless_random_shuffle(
          input_permutation, seed=(1, 42))
      random_permutation_first_call = self.evaluate(random_shuffle)
      random_permutation_next_call = self.evaluate(random_shuffle)
      input_permutation = self.evaluate(input_permutation)
      # Check that the dtype is correct
      np.testing.assert_equal(random_permutation_first_call.dtype,
                              dtype.as_numpy_dtype)
      # Check that the shuffles are the same
      np.testing.assert_array_equal(random_permutation_first_call,
                                    random_permutation_next_call)
      # Check that the output shape is correct
      np.testing.assert_equal(random_permutation_first_call.shape,
                              input_permutation.shape) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import constant [as 别名]
def test_batching(self):
    x = tf.constant([[3.0, 4.0], [30.0, 40.0]])
    y = tf.constant([[5.0, 6.0], [50.0, 60.0]])
    z = tf.constant([[7.0, 8.0], [70.0, 80.0]])
    alpha = tf.constant(2.0)
    beta = tf.constant(1.0)

    with tf.GradientTape(persistent=True) as tape:
      tape.watch([alpha, beta])
      def body(i, state):
        x, y, z = state
        k = tf.cast(i + 1, tf.float32)
        return [x * alpha - beta, y * k * alpha * beta, z * beta + x]
      out = for_loop(body, [x, y, z], [alpha, beta], 3)
    with self.subTest("independent_vars"):
      grad = tape.gradient(out[1], alpha)
      self.assertAllEqual(8712, grad)
    with self.subTest("dependent_vars"):
      grad = tape.gradient(out[2], beta)
      self.assertAllEqual(783, grad) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import constant [as 别名]
def test_with_xla(self):
    @tf.function
    def fn():
      x = tf.constant([[3.0, 4.0], [30.0, 40.0]])
      y = tf.constant([[7.0, 8.0], [70.0, 80.0]])
      alpha = tf.constant(2.0)
      beta = tf.constant(1.0)
      with tf.GradientTape(persistent=True) as tape:
        tape.watch([alpha, beta])
        def body(i, state):
          del i
          x, y = state
          return [x * alpha - beta, y * beta + x]
        out = for_loop(body, [x, y], [alpha, beta], 3)
      return tape.gradient(out[1], beta)

    grad = self.evaluate(tf.xla.experimental.compile(fn))[0]
    self.assertAllEqual(783, grad) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import constant [as 别名]
def _test_batches_and_types(self, integrate_function, args):
    """Checks handling batches and dtypes."""
    dtypes = [np.float32, np.float64, np.complex64, np.complex128]
    a = [[0.0, 0.0], [0.0, 0.0]]
    b = [[np.pi / 2, np.pi], [1.5 * np.pi, 2 * np.pi]]
    a = [a, a]
    b = [b, b]
    k = tf.constant([[[[1.0]]], [[[2.0]]]])
    func = lambda x: tf.cast(k, dtype=x.dtype) * tf.sin(x)
    ans = [[[1.0, 2.0], [1.0, 0.0]], [[2.0, 4.0], [2.0, 0.0]]]

    results = []
    for dtype in dtypes:
      lower = tf.constant(a, dtype=dtype)
      upper = tf.constant(b, dtype=dtype)
      results.append(integrate_function(func, lower, upper, **args))

    results = self.evaluate(results)

    for i in range(len(results)):
      assert results[i].dtype == dtypes[i]
      assert np.allclose(results[i], ans, atol=1e-3) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import constant [as 别名]
def testHomogeneousBackwards(self, scheme, accuracy_order):
    # Tests solving du/dt = At for a backward time step.
    # Compares with exact solution u(0) = exp(-At) u(t).
    time_step = 0.0001
    u = tf.constant([1, 2, -1, -2], dtype=tf.float64)
    matrix = tf.constant(
        [[1, -1, 0, 0], [3, 1, 2, 0], [0, -2, 1, 4], [0, 0, 3, 1]],
        dtype=tf.float64)

    tridiag_form = self._convert_to_tridiagonal_format(matrix)
    actual = self.evaluate(
        scheme(u, time_step, 0, lambda t: (tridiag_form, None)))

    expected = self.evaluate(
        tf.squeeze(
            tf.matmul(
                tf.linalg.expm(-matrix * time_step), tf.expand_dims(u, 1))))

    error_tolerance = 30 * time_step**(accuracy_order + 1)
    self.assertLess(np.max(np.abs(actual - expected)), error_tolerance) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import constant [as 别名]
def testInhomogeneous(self, scheme, accuracy_order):
    # Tests solving du/dt = At + b for a time step.
    # Compares with exact solution u(t) = exp(At) u(0) + (exp(At) - 1) A^(-1) b.
    time_step = 0.0001
    u = tf.constant([1, 2, -1, -2], dtype=tf.float64)
    matrix = tf.constant(
        [[1, -1, 0, 0], [3, 1, 2, 0], [0, -2, 1, 4], [0, 0, 3, 1]],
        dtype=tf.float64)
    b = tf.constant([1, -1, -2, 2], dtype=tf.float64)

    tridiag_form = self._convert_to_tridiagonal_format(matrix)
    actual = self.evaluate(scheme(u, 0, time_step, lambda t: (tridiag_form, b)))

    exponent = tf.linalg.expm(matrix * time_step)
    eye = tf.eye(4, 4, dtype=tf.float64)
    u = tf.expand_dims(u, 1)
    b = tf.expand_dims(b, 1)
    expected = (
        tf.matmul(exponent, u) +
        tf.matmul(exponent - eye, tf.matmul(tf.linalg.inv(matrix), b)))
    expected = self.evaluate(tf.squeeze(expected))

    error_tolerance = 30 * time_step**(accuracy_order + 1)
    self.assertLess(np.max(np.abs(actual - expected)), error_tolerance) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import constant [as 别名]
def testInhomogeneousBackwards(self, scheme, accuracy_order):
    # Tests solving du/dt = At + b for a backward time step.
    # Compares with exact solution u(0) = exp(-At) u(t)
    # + (exp(-At) - 1) A^(-1) b.
    time_step = 0.0001
    u = tf.constant([1, 2, -1, -2], dtype=tf.float64)
    matrix = tf.constant(
        [[1, -1, 0, 0], [3, 1, 2, 0], [0, -2, 1, 4], [0, 0, 3, 1]],
        dtype=tf.float64)
    b = tf.constant([1, -1, -2, 2], dtype=tf.float64)

    tridiag_form = self._convert_to_tridiagonal_format(matrix)
    actual = self.evaluate(scheme(u, time_step, 0, lambda t: (tridiag_form, b)))

    exponent = tf.linalg.expm(-matrix * time_step)
    eye = tf.eye(4, 4, dtype=tf.float64)
    u = tf.expand_dims(u, 1)
    b = tf.expand_dims(b, 1)
    expected = (
        tf.matmul(exponent, u) +
        tf.matmul(exponent - eye, tf.matmul(tf.linalg.inv(matrix), b)))
    expected = self.evaluate(tf.squeeze(expected))

    error_tolerance = 30 * time_step**(accuracy_order + 1)
    self.assertLess(np.max(np.abs(actual - expected)), error_tolerance)