Python tensorflow.python.ops.math_ops.reduce_logsumexp() Examples

def _forward_log_det_jacobian(self, x):
    if self._static_event_ndims == 0:
      return x - 2. * nn_ops.softplus(x)
    else:
      # This code is similar to nn_ops.log_softmax but different because we have
      # an implicit zero column to handle. I.e., instead of:
      #   reduce_sum(logits - reduce_sum(exp(logits), dim))
      # we must do:
      #   log_normalization = 1 + reduce_sum(exp(logits))
      #   -log_normalization + reduce_sum(logits - log_normalization)
      log_normalization = nn_ops.softplus(
          math_ops.reduce_logsumexp(x, reduction_indices=-1, keep_dims=True))
      fldj = (-log_normalization +
              math_ops.reduce_sum(x - log_normalization,
                                  reduction_indices=-1,
                                  keep_dims=True))
      return array_ops.squeeze(fldj, squeeze_dims=-1) 

def testOverflow(self):
    x = [1000, 1001, 1002, 1003]
    for dtype in [np.float16, np.float32, np.double]:
      x_np = np.array(x, dtype=dtype)
      max_np = np.max(x_np)
      with self.assertRaisesRegexp(RuntimeWarning,
                                   "overflow encountered in exp"):
        out = log(np.sum(exp(x_np)))
        if out == np.inf:
          raise RuntimeWarning("overflow encountered in exp")

      with self.test_session(use_gpu=True):
        x_tf = constant_op.constant(x_np, shape=x_np.shape)
        y_tf_np = math_ops.reduce_logsumexp(x_tf).eval()
        y_np = log(np.sum(exp(x_np - max_np))) + max_np
        self.assertAllClose(y_tf_np, y_np) 

def _forward_log_det_jacobian(self, x):
    if self._static_event_ndims == 0:
      return x - 2. * nn_ops.softplus(x)
    else:
      # This code is similar to nn_ops.log_softmax but different because we have
      # an implicit zero column to handle. I.e., instead of:
      #   reduce_sum(logits - reduce_sum(exp(logits), dim))
      # we must do:
      #   log_normalization = 1 + reduce_sum(exp(logits))
      #   -log_normalization + reduce_sum(logits - log_normalization)
      log_normalization = nn_ops.softplus(
          math_ops.reduce_logsumexp(x, axis=-1, keep_dims=True))
      fldj = (-log_normalization +
              math_ops.reduce_sum(x - log_normalization,
                                  axis=-1,
                                  keep_dims=True))
      return array_ops.squeeze(fldj, squeeze_dims=-1) 

def logsumexp(x, axis=None, keepdims=False):
  """Computes log(sum(exp(elements across dimensions of a tensor))).

  This function is more numerically stable than log(sum(exp(x))).
  It avoids overflows caused by taking the exp of large inputs and
  underflows caused by taking the log of small inputs.

  Arguments:
      x: A tensor or variable.
      axis: An integer, the axis to reduce over.
      keepdims: A boolean, whether to keep the dimensions or not.
          If `keepdims` is `False`, the rank of the tensor is reduced
          by 1. If `keepdims` is `True`, the reduced dimension is
          retained with length 1.

  Returns:
      The reduced tensor.
  """
  return math_ops.reduce_logsumexp(x, axis=axis, keep_dims=keepdims) 

def _forward_log_det_jacobian(self, x):
    if self._static_event_ndims == 0:
      return x - 2. * nn_ops.softplus(x)
    else:
      # This code is similar to nn_ops.log_softmax but different because we have
      # an implicit zero column to handle. I.e., instead of:
      #   reduce_sum(logits - reduce_sum(exp(logits), dim))
      # we must do:
      #   log_normalization = 1 + reduce_sum(exp(logits))
      #   -log_normalization + reduce_sum(logits - log_normalization)
      log_normalization = nn_ops.softplus(
          math_ops.reduce_logsumexp(x, reduction_indices=-1, keep_dims=True))
      fldj = (-log_normalization +
              math_ops.reduce_sum(x - log_normalization,
                                  reduction_indices=-1,
                                  keep_dims=True))
      return array_ops.squeeze(fldj, squeeze_dims=-1) 

def _forward_log_det_jacobian(self, x):
    if self._static_event_ndims == 0:
      return x - 2. * nn_ops.softplus(x)
    else:
      # This code is similar to nn_ops.log_softmax but different because we have
      # an implicit zero column to handle. I.e., instead of:
      #   reduce_sum(logits - reduce_sum(exp(logits), dim))
      # we must do:
      #   log_normalization = 1 + reduce_sum(exp(logits))
      #   -log_normalization + reduce_sum(logits - log_normalization)
      log_normalization = nn_ops.softplus(
          math_ops.reduce_logsumexp(x, reduction_indices=-1, keep_dims=True))
      fldj = (-log_normalization +
              math_ops.reduce_sum(x - log_normalization,
                                  reduction_indices=-1,
                                  keep_dims=True))
      return array_ops.squeeze(fldj, squeeze_dims=-1) 

def testKeepDims(self):
    for dtype in [np.float16, np.float32, np.double]:
      x_np = np.random.rand(5, 5).astype(dtype)
      with self.test_session(use_gpu=True):
        y_tf_np = math_ops.reduce_logsumexp(x_np, keep_dims=True).eval()
        self.assertEqual(y_tf_np.ndim, x_np.ndim)
        y_np = log(np.sum(exp(x_np), keepdims=True))
        self.assertAllClose(y_tf_np, y_np) 

def testReductionIndices(self):
    for dtype in [np.float16, np.float32, np.double]:
      x_np = np.random.rand(5, 5).astype(dtype)
      with self.test_session(use_gpu=True):
        y_tf = math_ops.reduce_logsumexp(x_np, reduction_indices=[0])
        y_np = log(np.sum(exp(x_np), axis=0))
        self.assertShapeEqual(y_np, y_tf)
        y_tf_np = y_tf.eval()
        self.assertAllClose(y_tf_np, y_np) 

def crf_log_norm(inputs, sequence_lengths, transition_params):
  """Computes the normalization for a CRF.

  Args:
    inputs: A [batch_size, max_seq_len, num_tags] tensor of unary potentials
        to use as input to the CRF layer.
    sequence_lengths: A [batch_size] vector of true sequence lengths.
    transition_params: A [num_tags, num_tags] transition matrix.
  Returns:
    log_norm: A [batch_size] vector of normalizers for a CRF.
  """
  # Split up the first and rest of the inputs in preparation for the forward
  # algorithm.
  first_input = array_ops.slice(inputs, [0, 0, 0], [-1, 1, -1])
  first_input = array_ops.squeeze(first_input, [1])
  rest_of_input = array_ops.slice(inputs, [0, 1, 0], [-1, -1, -1])

  # Compute the alpha values in the forward algorithm in order to get the
  # partition function.
  forward_cell = CrfForwardRnnCell(transition_params)
  _, alphas = rnn.dynamic_rnn(
      cell=forward_cell,
      inputs=rest_of_input,
      sequence_length=sequence_lengths - 1,
      initial_state=first_input,
      dtype=dtypes.float32)
  log_norm = math_ops.reduce_logsumexp(alphas, [1])
  return log_norm 

def __call__(self, inputs, state, scope=None):
    """Build the CrfForwardRnnCell.

    Args:
      inputs: A [batch_size, num_tags] matrix of unary potentials.
      state: A [batch_size, num_tags] matrix containing the previous alpha
          values.
      scope: Unused variable scope of this cell.

    Returns:
      new_alphas, new_alphas: A pair of [batch_size, num_tags] matrices
          values containing the new alpha values.
    """
    state = array_ops.expand_dims(state, 2)

    # This addition op broadcasts self._transitions_params along the zeroth
    # dimension and state along the second dimension. This performs the
    # multiplication of previous alpha values and the current binary potentials
    # in log space.
    transition_scores = state + self._transition_params
    new_alphas = inputs + math_ops.reduce_logsumexp(transition_scores, [1])

    # Both the state and the output of this RNN cell contain the alphas values.
    # The output value is currently unused and simply satisfies the RNN API.
    # This could be useful in the future if we need to compute marginal
    # probabilities, which would require the accumulated alpha values at every
    # time step.
    return new_alphas, new_alphas 

def _log_prob(self, x):
    with ops.control_dependencies(self._assertions):
      x = ops.convert_to_tensor(x, name="x")
      distribution_log_probs = [d.log_prob(x) for d in self.components]
      cat_log_probs = self._cat_probs(log_probs=True)
      final_log_probs = [
          cat_lp + d_lp
          for (cat_lp, d_lp) in zip(cat_log_probs, distribution_log_probs)
      ]
      concat_log_probs = array_ops.pack(final_log_probs, 0)
      log_sum_exp = math_ops.reduce_logsumexp(concat_log_probs, [0])
      return log_sum_exp 

def __sample_w3(self, n, seed):
        shape = array_ops.concat(([n], self.batch_shape_tensor()[:-1], [1]), 0)
        u = random_ops.random_uniform(shape, dtype=self.dtype, seed=seed)
        self.__w = 1 + math_ops.reduce_logsumexp([math_ops.log(u), math_ops.log(1 - u) - 2 * self.scale], axis=0) / self.scale
        return self.__w 

def _log_prob(self, y):
    # For caching to work, it is imperative that the bijector is the first to
    # modify the input.
    x = self.bijector.inverse(y)
    ildj = self.bijector.inverse_log_det_jacobian(y)
    if self.bijector._is_injective:  # pylint: disable=protected-access
      return self._finish_log_prob_for_one_fiber(y, x, ildj)

    lp_on_fibers = [
        self._finish_log_prob_for_one_fiber(y, x_i, ildj_i)
        for x_i, ildj_i in zip(x, ildj)]
    return math_ops.reduce_logsumexp(array_ops.stack(lp_on_fibers), axis=0) 

def crf_log_norm(inputs, sequence_lengths, transition_params):
  """Computes the normalization for a CRF.

  Args:
    inputs: A [batch_size, max_seq_len, num_tags] tensor of unary potentials
        to use as input to the CRF layer.
    sequence_lengths: A [batch_size] vector of true sequence lengths.
    transition_params: A [num_tags, num_tags] transition matrix.
  Returns:
    log_norm: A [batch_size] vector of normalizers for a CRF.
  """
  # Split up the first and rest of the inputs in preparation for the forward
  # algorithm.
  first_input = array_ops.slice(inputs, [0, 0, 0], [-1, 1, -1])
  first_input = array_ops.squeeze(first_input, [1])
  rest_of_input = array_ops.slice(inputs, [0, 1, 0], [-1, -1, -1])

  # Compute the alpha values in the forward algorithm in order to get the
  # partition function.
  forward_cell = CrfForwardRnnCell(transition_params)
  _, alphas = rnn.dynamic_rnn(
      cell=forward_cell,
      inputs=rest_of_input,
      sequence_length=sequence_lengths - 1,
      initial_state=first_input,
      dtype=dtypes.float32)
  log_norm = math_ops.reduce_logsumexp(alphas, [1])
  return log_norm 

def __call__(self, inputs, state, scope=None):
    """Build the CrfForwardRnnCell.

    Args:
      inputs: A [batch_size, num_tags] matrix of unary potentials.
      state: A [batch_size, num_tags] matrix containing the previous alpha
          values.
      scope: Unused variable scope of this cell.

    Returns:
      new_alphas, new_alphas: A pair of [batch_size, num_tags] matrices
          values containing the new alpha values.
    """
    state = array_ops.expand_dims(state, 2)

    # This addition op broadcasts self._transitions_params along the zeroth
    # dimension and state along the second dimension. This performs the
    # multiplication of previous alpha values and the current binary potentials
    # in log space.
    transition_scores = state + self._transition_params
    new_alphas = inputs + math_ops.reduce_logsumexp(transition_scores, [1])

    # Both the state and the output of this RNN cell contain the alphas values.
    # The output value is currently unused and simply satisfies the RNN API.
    # This could be useful in the future if we need to compute marginal
    # probabilities, which would require the accumulated alpha values at every
    # time step.
    return new_alphas, new_alphas 

def _log_prob(self, x):
    with ops.control_dependencies(self._assertions):
      x = ops.convert_to_tensor(x, name="x")
      distribution_log_probs = [d.log_prob(x) for d in self.components]
      cat_log_probs = self._cat_probs(log_probs=True)
      final_log_probs = [
          cat_lp + d_lp
          for (cat_lp, d_lp) in zip(cat_log_probs, distribution_log_probs)
      ]
      concat_log_probs = array_ops.stack(final_log_probs, 0)
      log_sum_exp = math_ops.reduce_logsumexp(concat_log_probs, [0])
      return log_sum_exp 

def _assert_valid_sample(self, x):
    if not self.validate_args: return x
    return control_flow_ops.with_dependencies([
        check_ops.assert_non_positive(x),
        distribution_util.assert_close(
            array_ops.zeros((), dtype=self.dtype),
            math_ops.reduce_logsumexp(x, reduction_indices=[-1])),
    ], x) 

def testReductionIndices2(self):
    for dtype in [np.float16, np.float32, np.double]:
      x_np = np.random.rand(5, 5).astype(dtype)
      with self.test_session(use_gpu=True):
        y_tf = math_ops.reduce_logsumexp(x_np, reduction_indices=0)
        y_np = log(np.sum(exp(x_np), axis=0))
        self.assertShapeEqual(y_np, y_tf)
        y_tf_np = y_tf.eval()
        self.assertAllClose(y_tf_np, y_np) 

def _define_prior_log_prob_operation(self, shard_id):
    """Computes the prior probability of all samples.

    Updates a vector where each item is the prior probabibility of an
    input example.

    Args:
      shard_id: id of current shard_id.
    """
    self._prior_probs[shard_id] = math_ops.reduce_logsumexp(
        self._probs[shard_id], axis=1, keep_dims=True) 

def testReduceLogSumExp(self):
    for dtype in [np.float16, np.float32, np.double]:
      x_np = np.random.rand(5, 5).astype(dtype)
      with self.test_session(use_gpu=True):
        y_tf_np = math_ops.reduce_logsumexp(x_np).eval()
        y_np = log(np.sum(exp(x_np)))
        self.assertAllClose(y_tf_np, y_np) 

def __call__(self, inputs, state, scope=None):
    """Build the CrfForwardRnnCell.

    Args:
      inputs: A [batch_size, num_tags] matrix of unary potentials.
      state: A [batch_size, num_tags] matrix containing the previous alpha
          values.
      scope: Unused variable scope of this cell.

    Returns:
      new_alphas, new_alphas: A pair of [batch_size, num_tags] matrices
          values containing the new alpha values.
    """
    state = array_ops.expand_dims(state, 2)

    # This addition op broadcasts self._transitions_params along the zeroth
    # dimension and state along the second dimension. This performs the
    # multiplication of previous alpha values and the current binary potentials
    # in log space.
    transition_scores = state + self._transition_params
    new_alphas = inputs + math_ops.reduce_logsumexp(transition_scores, [1])

    # Both the state and the output of this RNN cell contain the alphas values.
    # The output value is currently unused and simply satisfies the RNN API.
    # This could be useful in the future if we need to compute marginal
    # probabilities, which would require the accumulated alpha values at every
    # time step.
    return new_alphas, new_alphas 

def crf_log_norm(inputs, sequence_lengths, transition_params):
  """Computes the normalization for a CRF.

  Args:
    inputs: A [batch_size, max_seq_len, num_tags] tensor of unary potentials
        to use as input to the CRF layer.
    sequence_lengths: A [batch_size] vector of true sequence lengths.
    transition_params: A [num_tags, num_tags] transition matrix.
  Returns:
    log_norm: A [batch_size] vector of normalizers for a CRF.
  """
  # Split up the first and rest of the inputs in preparation for the forward
  # algorithm.
  first_input = array_ops.slice(inputs, [0, 0, 0], [-1, 1, -1])
  first_input = array_ops.squeeze(first_input, [1])
  rest_of_input = array_ops.slice(inputs, [0, 1, 0], [-1, -1, -1])

  # Compute the alpha values in the forward algorithm in order to get the
  # partition function.
  forward_cell = CrfForwardRnnCell(transition_params)
  _, alphas = rnn.dynamic_rnn(
      cell=forward_cell,
      inputs=rest_of_input,
      sequence_length=sequence_lengths - 1,
      initial_state=first_input,
      dtype=dtypes.float32)
  log_norm = math_ops.reduce_logsumexp(alphas, [1])
  return log_norm

# 对数似然 

def _assert_valid_sample(self, x):
    if not self.validate_args: return x
    return control_flow_ops.with_dependencies([
        check_ops.assert_non_positive(x),
        distribution_util.assert_close(
            array_ops.zeros((), dtype=self.dtype),
            math_ops.reduce_logsumexp(x, reduction_indices=[-1])),
    ], x) 

def _log_prob(self, x):
    with ops.control_dependencies(self._assertions):
      x = ops.convert_to_tensor(x, name="x")
      distribution_log_probs = [d.log_prob(x) for d in self.components]
      cat_log_probs = self._cat_probs(log_probs=True)
      final_log_probs = [
          cat_lp + d_lp
          for (cat_lp, d_lp) in zip(cat_log_probs, distribution_log_probs)
      ]
      concat_log_probs = array_ops.stack(final_log_probs, 0)
      log_sum_exp = math_ops.reduce_logsumexp(concat_log_probs, [0])
      return log_sum_exp 

def __call__(self, inputs, state, scope=None):
    """Build the CrfForwardRnnCell.

    Args:
      inputs: A [batch_size, num_tags] matrix of unary potentials.
      state: A [batch_size, num_tags] matrix containing the previous alpha
          values.
      scope: Unused variable scope of this cell.

    Returns:
      new_alphas, new_alphas: A pair of [batch_size, num_tags] matrices
          values containing the new alpha values.
    """
    state = array_ops.expand_dims(state, 2)

    # This addition op broadcasts self._transitions_params along the zeroth
    # dimension and state along the second dimension. This performs the
    # multiplication of previous alpha values and the current binary potentials
    # in log space.
    transition_scores = state + self._transition_params
    new_alphas = inputs + math_ops.reduce_logsumexp(transition_scores, [1])

    # Both the state and the output of this RNN cell contain the alphas values.
    # The output value is currently unused and simply satisfies the RNN API.
    # This could be useful in the future if we need to compute marginal
    # probabilities, which would require the accumulated alpha values at every
    # time step.
    return new_alphas, new_alphas 

def crf_log_norm(inputs, sequence_lengths, transition_params):
  """Computes the normalization for a CRF.

  Args:
    inputs: A [batch_size, max_seq_len, num_tags] tensor of unary potentials
        to use as input to the CRF layer.
    sequence_lengths: A [batch_size] vector of true sequence lengths.
    transition_params: A [num_tags, num_tags] transition matrix.
  Returns:
    log_norm: A [batch_size] vector of normalizers for a CRF.
  """
  # Split up the first and rest of the inputs in preparation for the forward
  # algorithm.
  first_input = array_ops.slice(inputs, [0, 0, 0], [-1, 1, -1])
  first_input = array_ops.squeeze(first_input, [1])
  rest_of_input = array_ops.slice(inputs, [0, 1, 0], [-1, -1, -1])

  # Compute the alpha values in the forward algorithm in order to get the
  # partition function.
  forward_cell = CrfForwardRnnCell(transition_params)
  _, alphas = rnn.dynamic_rnn(
      cell=forward_cell,
      inputs=rest_of_input,
      sequence_length=sequence_lengths - 1,
      initial_state=first_input,
      dtype=dtypes.float32)
  log_norm = math_ops.reduce_logsumexp(alphas, [1])
  return log_norm 

def _assert_valid_sample(self, x):
    if not self.validate_args:
      return x
    return control_flow_ops.with_dependencies([
        check_ops.assert_non_positive(x),
        distribution_util.assert_close(
            array_ops.zeros([], dtype=self.dtype),
            math_ops.reduce_logsumexp(x, axis=[-1])),
    ], x) 

def _log_prob(self, x):
    with ops.control_dependencies(self._assertions):
      x = ops.convert_to_tensor(x, name="x")
      distribution_log_probs = [d.log_prob(x) for d in self.components]
      cat_log_probs = self._cat_probs(log_probs=True)
      final_log_probs = [
          cat_lp + d_lp
          for (cat_lp, d_lp) in zip(cat_log_probs, distribution_log_probs)
      ]
      concat_log_probs = array_ops.stack(final_log_probs, 0)
      log_sum_exp = math_ops.reduce_logsumexp(concat_log_probs, [0])
      return log_sum_exp 

def _assert_valid_sample(self, x):
    if not self.validate_args:
      return x
    return control_flow_ops.with_dependencies([
        check_ops.assert_non_positive(x),
        distribution_util.assert_close(
            array_ops.zeros([], dtype=self.dtype),
            math_ops.reduce_logsumexp(x, axis=[-1])),
    ], x) 

def __call__(self, inputs, state, scope=None):
    """Build the CrfForwardRnnCell.

    Args:
      inputs: A [batch_size, num_tags] matrix of unary potentials.
      state: A [batch_size, num_tags] matrix containing the previous alpha
          values.
      scope: Unused variable scope of this cell.

    Returns:
      new_alphas, new_alphas: A pair of [batch_size, num_tags] matrices
          values containing the new alpha values.
    """
    state = array_ops.expand_dims(state, 2)

    # This addition op broadcasts self._transitions_params along the zeroth
    # dimension and state along the second dimension. This performs the
    # multiplication of previous alpha values and the current binary potentials
    # in log space.
    transition_scores = state + self._transition_params
    new_alphas = inputs + math_ops.reduce_logsumexp(transition_scores, [1])

    # Both the state and the output of this RNN cell contain the alphas values.
    # The output value is currently unused and simply satisfies the RNN API.
    # This could be useful in the future if we need to compute marginal
    # probabilities, which would require the accumulated alpha values at every
    # time step.
    return new_alphas, new_alphas