Python linalg_ops.eye函数代码示例

 def Test(self):
   eye_np = np.eye(num_rows, M=num_columns, dtype=dtype.as_numpy_dtype)
   if batch_shape is not None:
     eye_np = np.tile(eye_np, batch_shape + [1, 1])
   for use_placeholder in False, True:
     if use_placeholder and (num_columns is None or batch_shape is None):
       return
     with self.test_session(use_gpu=True) as sess:
       if use_placeholder:
         num_rows_placeholder = array_ops.placeholder(
             dtypes.int32, name="num_rows")
         num_columns_placeholder = array_ops.placeholder(
             dtypes.int32, name="num_columns")
         batch_shape_placeholder = array_ops.placeholder(
             dtypes.int32, name="batch_shape")
         eye = linalg_ops.eye(
             num_rows_placeholder,
             num_columns=num_columns_placeholder,
             batch_shape=batch_shape_placeholder,
             dtype=dtype)
         eye_tf = sess.run(
             eye,
             feed_dict={
                 num_rows_placeholder: num_rows,
                 num_columns_placeholder: num_columns,
                 batch_shape_placeholder: batch_shape
             })
       else:
         eye_tf = linalg_ops.eye(
             num_rows,
             num_columns=num_columns,
             batch_shape=batch_shape,
             dtype=dtype).eval()
       self.assertAllEqual(eye_np, eye_tf)

  def get_observation_model(self, times):
    """Construct observation model matrix from VARMA parameters.

    Args:
      times: A [batch size] vector indicating the times observation models are
          requested for. Unused.
    Returns:
      the observation model matrix. It has shape
        [self.num_features, self.state_dimension].
    """
    del times  # StateSpaceModel will broadcast along the batch dimension
    if self.ar_order > self.ma_order or self.state_num_blocks < 2:
      return array_ops.pad(
          linalg_ops.eye(self.num_features, dtype=self.dtype),
          [[0, 0], [0, self.num_features * (self.state_num_blocks - 1)]],
          name="observation_model")
    else:
      # Add a second observed component which "catches" the accumulated moving
      # average errors as they reach the end of the state. If ar_order >
      # ma_order, this is unnecessary, since accumulated errors cycle naturally.
      return array_ops.concat(
          [
              array_ops.pad(
                  linalg_ops.eye(self.num_features, dtype=self.dtype),
                  [[0, 0], [0,
                            self.num_features * (self.state_num_blocks - 2)]]),
              linalg_ops.eye(self.num_features, dtype=self.dtype)
          ],
          axis=1,
          name="observation_model")

 def testShapeInferenceStaticBatch(self):
   batch_shape = (2, 3)
   self.assertEqual(
       (2, 3, 2, 2),
       linalg_ops.eye(num_rows=2, batch_shape=batch_shape).shape)
   self.assertEqual(
       (2, 3, 2, 3),
       linalg_ops.eye(
           num_rows=2, num_columns=3, batch_shape=batch_shape).shape)

  def _operator_and_mat_and_feed_dict(self, shape, dtype, use_placeholder):
    shape = list(shape)
    assert shape[-1] == shape[-2]

    batch_shape = shape[:-2]
    num_rows = shape[-1]

    # Uniform values that are at least length 1 from the origin.  Allows the
    # operator to be well conditioned.
    # Shape batch_shape
    multiplier = linear_operator_test_util.random_sign_uniform(
        shape=batch_shape, minval=1., maxval=2., dtype=dtype)

    operator = linalg_lib.LinearOperatorScaledIdentity(num_rows, multiplier)

    # Nothing to feed since LinearOperatorScaledIdentity takes no Tensor args.
    if use_placeholder:
      multiplier_ph = array_ops.placeholder(dtype=dtype)
      multiplier = multiplier.eval()
      operator = linalg_lib.LinearOperatorScaledIdentity(
          num_rows, multiplier_ph)
      feed_dict = {multiplier_ph: multiplier}
    else:
      feed_dict = None

    multiplier_matrix = array_ops.expand_dims(
        array_ops.expand_dims(multiplier, -1), -1)
    mat = multiplier_matrix * linalg_ops.eye(
        num_rows, batch_shape=batch_shape, dtype=dtype)

    return operator, mat, feed_dict

  def _compute_power_svd(self, var, mat_g, mat_g_size, alpha, mat_h_slot_name):
    """Computes mat_h = mat_g^alpha using svd. mat_g is a symmetric PSD matrix.

    Args:
      var: the variable we are updating.
      mat_g: the symmetric PSD matrix whose power it to be computed
      mat_g_size: size of mat_g
      alpha: a real number
      mat_h_slot_name: name of slot to store the power, if needed.

    Returns:
      mat_h = mat_g^alpha

    Stores mat_h in the appropriate slot, if it exists.
    Note that mat_g is PSD. So we could use linalg_ops.self_adjoint_eig.
    """
    if mat_g_size == 1:
      mat_h = math_ops.pow(mat_g + self._epsilon, alpha)
    else:
      damping = self._epsilon * linalg_ops.eye(math_ops.to_int32(mat_g_size))
      diag_d, mat_u, mat_v = linalg_ops.svd(mat_g + damping, full_matrices=True)
      mat_h = math_ops.matmul(
          mat_v * math_ops.pow(math_ops.maximum(diag_d, self._epsilon), alpha),
          array_ops.transpose(mat_u))
    if mat_h_slot_name is not None:
      return state_ops.assign(self.get_slot(var, mat_h_slot_name), mat_h)
    return mat_h

  def test_inv_update_thunks(self):
    """Ensures inverse update ops run once per global_step."""
    with self._graph.as_default(), self.test_session() as sess:
      fisher_estimator = estimator.FisherEstimator(
          damping_fn=lambda: 0.2,
          variables=[self.weights],
          layer_collection=self.layer_collection,
          cov_ema_decay=0.0)

      # Construct op that updates one inverse per global step.
      global_step = training_util.get_or_create_global_step()
      inv_matrices = [
          matrix
          for fisher_factor in self.layer_collection.get_factors()
          for matrix in fisher_factor._inverses_by_damping.values()
      ]
      inv_update_op_thunks = fisher_estimator.inv_update_thunks
      inv_update_op = control_flow_ops.case(
          [(math_ops.equal(global_step, i), thunk)
           for i, thunk in enumerate(inv_update_op_thunks)])
      increment_global_step = global_step.assign_add(1)

      sess.run(variables.global_variables_initializer())
      initial_inv_values = sess.run(inv_matrices)

      # Ensure there's one update per inverse matrix. This is true as long as
      # there's no fan-in/fan-out or parameter re-use.
      self.assertEqual(len(inv_matrices), len(inv_update_op_thunks))

      # Test is no-op if only 1 invariance matrix.
      assert len(inv_matrices) > 1

      # Assign each covariance matrix a value other than the identity. This
      # ensures that the inverse matrices are updated to something different as
      # well.
      cov_matrices = [
          fisher_factor.get_cov()
          for fisher_factor in self.layer_collection.get_factors()
      ]
      sess.run([
          cov_matrix.assign(2 * linalg_ops.eye(int(cov_matrix.shape[0])))
          for cov_matrix in cov_matrices
      ])

      for i in range(len(inv_matrices)):
        # Compare new and old inverse values
        new_inv_values = sess.run(inv_matrices)
        is_inv_equal = [
            np.allclose(initial_inv_value, new_inv_value)
            for (initial_inv_value,
                 new_inv_value) in zip(initial_inv_values, new_inv_values)
        ]
        num_inv_equal = sum(is_inv_equal)

        # Ensure exactly one inverse matrix changes per step.
        self.assertEqual(num_inv_equal, len(inv_matrices) - i)

        # Run all inverse update ops.
        sess.run(inv_update_op)
        sess.run(increment_global_step)

def transition_power_test_template(test_case, model, num_steps):
  """Tests the transition_to_powers function of a state space model."""
  transition_matrix = ops.convert_to_tensor(
      model.get_state_transition(), dtype=model.dtype)
  step_number = array_ops.placeholder(shape=[], dtype=dtypes.int64)
  state_dimension = transition_matrix.get_shape()[0].value
  previous_matrix = array_ops.placeholder(
      shape=[state_dimension, state_dimension], dtype=transition_matrix.dtype)
  true_single_step_update = math_ops.matmul(previous_matrix,
                                            transition_matrix)
  model_output_tensor = model.transition_to_powers(powers=array_ops.stack(
      [step_number, step_number]))
  with test_case.test_session():
    starting_matrix = linalg_ops.eye(
        state_dimension, batch_shape=array_ops.shape(num_steps)).eval()
    evaled_current_matrix = starting_matrix
    for iteration_number in range(num_steps):
      model_output = model_output_tensor.eval(
          feed_dict={step_number: iteration_number})
      test_case.assertAllClose(
          evaled_current_matrix,
          model_output[0],
          rtol=1e-8 if evaled_current_matrix.dtype == numpy.float64 else 1e-4)
      evaled_current_matrix = true_single_step_update.eval(
          feed_dict={previous_matrix: evaled_current_matrix})

def power_sums_tensor(array_size, power_matrix, multiplier):
  r"""Computes \sum_{i=0}^{N-1} A^i B (A^i)^T for N=0..(array_size + 1).

  Args:
    array_size: The number of non-trivial sums to pre-compute.
    power_matrix: The "A" matrix above.
    multiplier: The "B" matrix above
  Returns:
    A Tensor with S[N] = \sum_{i=0}^{N-1} A^i B (A^i)^T
      S[0] is the zero matrix
      S[1] is B
      S[2] is A B A^T + B
      ...and so on
  """
  array_size = math_ops.cast(array_size, dtypes.int32)
  power_matrix = ops.convert_to_tensor(power_matrix)
  identity_like_power_matrix = linalg_ops.eye(
      array_ops.shape(power_matrix)[0], dtype=power_matrix.dtype)
  identity_like_power_matrix.set_shape(
      ops.convert_to_tensor(power_matrix).get_shape())
  transition_powers = functional_ops.scan(
      lambda previous_power, _: math_ops.matmul(previous_power, power_matrix),
      math_ops.range(array_size - 1),
      initializer=identity_like_power_matrix)
  summed = math_ops.cumsum(
      array_ops.concat([
          array_ops.expand_dims(multiplier, 0), math_ops.matmul(
              batch_times_matrix(transition_powers, multiplier),
              transition_powers,
              adjoint_b=True)
      ], 0))
  return array_ops.concat(
      [array_ops.expand_dims(array_ops.zeros_like(multiplier), 0), summed], 0)

  def _underdetermined(op, grad):
    """Gradients for the underdetermined case of MatrixSolveLs.

    This is the backprop for the solution to the normal equations of the second
    kind:
      X = F(A, B) = A * (A*A^T + lambda*I)^{-1} * B
    that (for lambda=0) solve the least squares problem
      min ||X||_F subject to A*X = B.
    """
    a = op.inputs[0]
    b = op.inputs[1]
    l2_regularizer = math_ops.cast(op.inputs[2], a.dtype.base_dtype)
    a_shape = array_ops.shape(a)
    batch_shape = a_shape[:-2]
    m = a_shape[-2]

    identity = linalg_ops.eye(m, batch_shape=batch_shape, dtype=a.dtype)
    gramian = math_ops.matmul(a, a, adjoint_b=True) + l2_regularizer * identity
    chol = linalg_ops.cholesky(gramian)
    grad_b = linalg_ops.cholesky_solve(chol, math_ops.matmul(a, grad))
    # Temporary tmp = (A * A^T + lambda * I)^{-1} * B.
    tmp = linalg_ops.cholesky_solve(chol, b)
    a1 = math_ops.matmul(tmp, a, adjoint_a=True)
    a1 = -math_ops.matmul(grad_b, a1)
    a2 = grad - math_ops.matmul(a, grad_b, adjoint_a=True)
    a2 = math_ops.matmul(tmp, a2, adjoint_b=True)
    grad_a = a1 + a2
    return (grad_a, grad_b, None)

  def _overdetermined(op, grad):
    """Gradients for the overdetermined case of MatrixSolveLs.

    This is the backprop for the solution to the normal equations of the first
    kind:
       X = F(A, B) = (A^T * A + lambda * I)^{-1} * A^T * B
    which solve the least squares problem
       min ||A * X - B||_F^2 + lambda ||X||_F^2.
    """
    a = op.inputs[0]
    b = op.inputs[1]
    l2_regularizer = math_ops.cast(op.inputs[2], a.dtype.base_dtype)
    x = op.outputs[0]
    a_shape = array_ops.shape(a)
    batch_shape = a_shape[:-2]
    n = a_shape[-1]

    identity = linalg_ops.eye(n, batch_shape=batch_shape, dtype=a.dtype)
    gramian = math_ops.matmul(a, a, adjoint_a=True) + l2_regularizer * identity
    chol = linalg_ops.cholesky(gramian)
    # Temporary z = (A^T * A + lambda * I)^{-1} * grad.
    z = linalg_ops.cholesky_solve(chol, grad)
    xzt = math_ops.matmul(x, z, adjoint_b=True)
    zx_sym = xzt + array_ops.matrix_transpose(xzt)
    grad_a = -math_ops.matmul(a, zx_sym) + math_ops.matmul(b, z, adjoint_b=True)
    grad_b = math_ops.matmul(a, z)
    return (grad_a, grad_b, None)

  def _operator_and_matrix(self, build_info, dtype, use_placeholder):
    shape = list(build_info.shape)
    assert shape[-1] == shape[-2]

    batch_shape = shape[:-2]
    num_rows = shape[-1]

    # Uniform values that are at least length 1 from the origin.  Allows the
    # operator to be well conditioned.
    # Shape batch_shape
    multiplier = linear_operator_test_util.random_sign_uniform(
        shape=batch_shape, minval=1., maxval=2., dtype=dtype)


    # Nothing to feed since LinearOperatorScaledIdentity takes no Tensor args.
    lin_op_multiplier = multiplier

    if use_placeholder:
      lin_op_multiplier = array_ops.placeholder_with_default(
          multiplier, shape=None)

    operator = linalg_lib.LinearOperatorScaledIdentity(
        num_rows, lin_op_multiplier)

    multiplier_matrix = array_ops.expand_dims(
        array_ops.expand_dims(multiplier, -1), -1)
    matrix = multiplier_matrix * linalg_ops.eye(
        num_rows, batch_shape=batch_shape, dtype=dtype)

    return operator, matrix

 def test_non_batch_2x2(self):
   num_rows = 2
   dtype = np.float32
   np_eye = np.eye(num_rows).astype(dtype)
   with self.test_session():
     eye = linalg_ops.eye(num_rows, dtype=dtype)
     self.assertAllEqual((num_rows, num_rows), eye.get_shape())
     self.assertAllEqual(np_eye, eye.eval())

  def _create_slots(self, var_list):
    for v in var_list:
      with ops.colocate_with(v):
        _ = self._zeros_slot(v, "gbar", self._name)
        shape = np.array(v.get_shape())
        for i, d in enumerate(shape):
          d_tensor = ops.convert_to_tensor(d)
          if d <= self._max_matrix_size:
            mat_g_init = array_ops.zeros_like(linalg_ops.eye(d_tensor))
            if self._svd_interval > 1:
              _ = self._get_or_make_slot(v, linalg_ops.eye(d_tensor),
                                         "H_" + str(i), self._name)
          else:
            mat_g_init = array_ops.zeros([d_tensor])

          _ = self._get_or_make_slot(v, mat_g_init, "Gbar_" + str(i),
                                     self._name)

 def test_non_batch_0x0(self):
   num_rows = 0
   dtype = np.int64
   np_eye = np.eye(num_rows).astype(dtype)
   with self.test_session(use_gpu=True):
     eye = linalg_ops.eye(num_rows, dtype=dtype)
     self.assertAllEqual((num_rows, num_rows), eye.get_shape())
     self.assertAllEqual(np_eye, eye.eval())

  def _verifyLu(self, x, output_idx_type=dtypes.int64):
    # Verify that Px = LU.
    lu, perm = linalg_ops.lu(x, output_idx_type=output_idx_type)

    # Prepare the lower factor of shape num_rows x num_rows
    lu_shape = np.array(lu.shape.as_list())
    batch_shape = lu_shape[:-2]
    num_rows = lu_shape[-2]
    num_cols = lu_shape[-1]

    lower = array_ops.matrix_band_part(lu, -1, 0)

    if num_rows > num_cols:
      eye = linalg_ops.eye(
          num_rows, batch_shape=batch_shape, dtype=lower.dtype)
      lower = array_ops.concat([lower, eye[..., num_cols:]], axis=-1)
    elif num_rows < num_cols:
      lower = lower[..., :num_rows]

    # Fill the diagonal with ones.
    ones_diag = array_ops.ones(
        np.append(batch_shape, num_rows), dtype=lower.dtype)
    lower = array_ops.matrix_set_diag(lower, ones_diag)

    # Prepare the upper factor.
    upper = array_ops.matrix_band_part(lu, 0, -1)

    verification = math_ops.matmul(lower, upper)

    # Permute the rows of product of the Cholesky factors.
    if num_rows > 0:
      # Reshape the product of the triangular factors and permutation indices
      # to a single batch dimension. This makes it easy to apply
      # invert_permutation and gather_nd ops.
      perm_reshaped = array_ops.reshape(perm, [-1, num_rows])
      verification_reshaped = array_ops.reshape(verification,
                                                [-1, num_rows, num_cols])
      # Invert the permutation in each batch.
      inv_perm_reshaped = map_fn.map_fn(array_ops.invert_permutation,
                                        perm_reshaped)
      batch_size = perm_reshaped.shape.as_list()[0]
      # Prepare the batch indices with the same shape as the permutation.
      # The corresponding batch index is paired with each of the `num_rows`
      # permutation indices.
      batch_indices = math_ops.cast(
          array_ops.broadcast_to(
              math_ops.range(batch_size)[:, None], perm_reshaped.shape),
          dtype=output_idx_type)
      permuted_verification_reshaped = array_ops.gather_nd(
          verification_reshaped,
          array_ops.stack([batch_indices, inv_perm_reshaped], axis=-1))

      # Reshape the verification matrix back to the original shape.
      verification = array_ops.reshape(permuted_verification_reshaped,
                                       lu_shape)

    self._verifyLuBase(x, lower, upper, perm, verification,
                       output_idx_type)