Python nn_ops.log_softmax函数代码示例

  def test_step(self):
    dummy_cell_state = array_ops.zeros([self.batch_size, self.beam_width])
    beam_state = beam_search_decoder.BeamSearchDecoderState(
        cell_state=dummy_cell_state,
        log_probs=nn_ops.log_softmax(
            array_ops.ones([self.batch_size, self.beam_width])),
        lengths=constant_op.constant(
            2, shape=[self.batch_size, self.beam_width], dtype=dtypes.int64),
        finished=array_ops.zeros(
            [self.batch_size, self.beam_width], dtype=dtypes.bool),
        accumulated_attention_probs=())

    logits_ = np.full([self.batch_size, self.beam_width, self.vocab_size],
                      0.0001)
    logits_[0, 0, 2] = 1.9
    logits_[0, 0, 3] = 2.1
    logits_[0, 1, 3] = 3.1
    logits_[0, 1, 4] = 0.9
    logits_[1, 0, 1] = 0.5
    logits_[1, 1, 2] = 2.7
    logits_[1, 2, 2] = 10.0
    logits_[1, 2, 3] = 0.2
    logits = ops.convert_to_tensor(logits_, dtype=dtypes.float32)
    log_probs = nn_ops.log_softmax(logits)

    outputs, next_beam_state = beam_search_decoder._beam_search_step(
        time=2,
        logits=logits,
        next_cell_state=dummy_cell_state,
        beam_state=beam_state,
        batch_size=ops.convert_to_tensor(self.batch_size),
        beam_width=self.beam_width,
        end_token=self.end_token,
        length_penalty_weight=self.length_penalty_weight,
        coverage_penalty_weight=self.coverage_penalty_weight)

    with self.cached_session() as sess:
      outputs_, next_state_, state_, log_probs_ = sess.run(
          [outputs, next_beam_state, beam_state, log_probs])

    self.assertAllEqual(outputs_.predicted_ids, [[3, 3, 2], [2, 2, 1]])
    self.assertAllEqual(outputs_.parent_ids, [[1, 0, 0], [2, 1, 0]])
    self.assertAllEqual(next_state_.lengths, [[3, 3, 3], [3, 3, 3]])
    self.assertAllEqual(next_state_.finished,
                        [[False, False, False], [False, False, False]])

    expected_log_probs = []
    expected_log_probs.append(state_.log_probs[0][[1, 0, 0]])
    expected_log_probs.append(state_.log_probs[1][[2, 1, 0]])  # 0 --> 1
    expected_log_probs[0][0] += log_probs_[0, 1, 3]
    expected_log_probs[0][1] += log_probs_[0, 0, 3]
    expected_log_probs[0][2] += log_probs_[0, 0, 2]
    expected_log_probs[1][0] += log_probs_[1, 2, 2]
    expected_log_probs[1][1] += log_probs_[1, 1, 2]
    expected_log_probs[1][2] += log_probs_[1, 0, 1]
    self.assertAllEqual(next_state_.log_probs, expected_log_probs)

 def testLogSoftmaxAxes(self):
   arr = np.linspace(0., 1, 12).reshape(3, 4)
   x_neg_axis = nn_ops.log_softmax(arr, axis=-2)
   y_pos_axis = nn_ops.log_softmax(arr, axis=0)
   z_gt_axis = nn_ops.log_softmax(arr, axis=4)
   x_neg_axis_tf = self.evaluate(x_neg_axis)
   y_pos_axis_tf = self.evaluate(y_pos_axis)
   z_gt_axis_tf = self.evaluate(z_gt_axis)
   eps = 1e-3
   self.assertAllClose(x_neg_axis_tf, y_pos_axis_tf, eps)
   self.assertAllClose(y_pos_axis_tf, z_gt_axis_tf, eps)

  def test_step_with_eos(self):
    dummy_cell_state = array_ops.zeros([self.batch_size, self.beam_width])
    beam_state = beam_search_decoder.BeamSearchDecoderState(
        cell_state=dummy_cell_state,
        log_probs=nn_ops.log_softmax(
            array_ops.ones([self.batch_size, self.beam_width])),
        lengths=ops.convert_to_tensor(
            [[2, 1, 2], [2, 2, 1]], dtype=dtypes.int32),
        finished=ops.convert_to_tensor(
            [[False, True, False], [False, False, True]], dtype=dtypes.bool))

    logits_ = np.full([self.batch_size, self.beam_width, self.vocab_size],
                      0.0001)
    logits_[0, 0, 2] = 1.9
    logits_[0, 0, 3] = 2.1
    logits_[0, 1, 3] = 3.1
    logits_[0, 1, 4] = 0.9
    logits_[1, 0, 1] = 0.5
    logits_[1, 1, 2] = 5.7  # why does this not work when it's 2.7?
    logits_[1, 2, 2] = 1.0
    logits_[1, 2, 3] = 0.2
    logits = ops.convert_to_tensor(logits_, dtype=dtypes.float32)
    log_probs = nn_ops.log_softmax(logits)

    outputs, next_beam_state = beam_search_decoder._beam_search_step(
        time=2,
        logits=logits,
        beam_state=beam_state,
        batch_size=ops.convert_to_tensor(self.batch_size),
        beam_width=self.beam_width,
        end_token=self.end_token,
        length_penalty_weight=self.length_penalty_weight)

    with self.test_session() as sess:
      outputs_, next_state_, state_, log_probs_ = sess.run(
          [outputs, next_beam_state, beam_state, log_probs])

    np.testing.assert_array_equal(outputs_.parent_ids, [[1, 0, 0], [1, 2, 0]])
    np.testing.assert_array_equal(outputs_.predicted_ids, [[0, 3, 2], [2, 0,
                                                                       1]])
    np.testing.assert_array_equal(next_state_.lengths, [[1, 3, 3], [3, 1, 3]])
    np.testing.assert_array_equal(next_state_.finished, [[True, False, False],
                                                         [False, True, False]])

    expected_log_probs = []
    expected_log_probs.append(state_.log_probs[0][[1, 0, 0]])
    expected_log_probs.append(state_.log_probs[1][[1, 2, 0]])
    expected_log_probs[0][1] += log_probs_[0, 0, 3]
    expected_log_probs[0][2] += log_probs_[0, 0, 2]
    expected_log_probs[1][0] += log_probs_[1, 1, 2]
    expected_log_probs[1][2] += log_probs_[1, 0, 1]
    np.testing.assert_array_equal(next_state_.log_probs, expected_log_probs)

  def _sample_n(self, n, seed=None):
    sample_shape = array_ops.concat(([n], array_ops.shape(self.logits)), 0)
    logits = self.logits * array_ops.ones(sample_shape)
    logits_2d = array_ops.reshape(logits, [-1, self.event_size])
    np_dtype = self.dtype.as_numpy_dtype

    # Uniform variates must be sampled from the interval (0,1] rather than
    # [0,1], as they are passed through log() to compute Gumbel variates.
    # We need to use np.finfo(np_dtype).tiny because it is the smallest,
    # positive, "normal" number.  A "normal" number is such that the mantissa
    # has an implicit leading 1.  Normal, positive numbers x, y have the
    # reasonable property that: x + y >= max(x, y).
    # minval=np.nextafter(np.float32(0),1)) can cause
    # tf.random_uniform(dtype=tf.float32) to sample 0.

    uniform = random_ops.random_uniform(shape=array_ops.shape(logits_2d),
                                        minval=np.finfo(np_dtype).tiny,
                                        maxval=1,
                                        dtype=self.dtype,
                                        seed=seed)
    gumbel = -math_ops.log(-math_ops.log(uniform))
    noisy_logits = math_ops.div(gumbel + logits_2d, self._temperature_2d)
    samples = nn_ops.log_softmax(noisy_logits)
    ret = array_ops.reshape(samples, sample_shape)
    return ret

  def testEntropyGradient(self):
    with self.cached_session() as sess:
      logits = constant_op.constant([[1., 2., 3.], [2., 5., 1.]])

      probabilities = nn_ops.softmax(logits)
      log_probabilities = nn_ops.log_softmax(logits)
      true_entropy = - math_ops.reduce_sum(
          probabilities * log_probabilities, axis=-1)

      categorical_distribution = categorical.Categorical(probs=probabilities)
      categorical_entropy = categorical_distribution.entropy()

      # works
      true_entropy_g = gradients_impl.gradients(true_entropy, [logits])
      categorical_entropy_g = gradients_impl.gradients(
          categorical_entropy, [logits])

      res = sess.run({"true_entropy": true_entropy,
                      "categorical_entropy": categorical_entropy,
                      "true_entropy_g": true_entropy_g,
                      "categorical_entropy_g": categorical_entropy_g})
      self.assertAllClose(res["true_entropy"],
                          res["categorical_entropy"])
      self.assertAllClose(res["true_entropy_g"],
                          res["categorical_entropy_g"])

def _kl_divergence(p, p_logits, q):
  """Computes the Kullback-Liebler divergence between p and q.

  This function uses p's logits in some places to improve numerical stability.

  Specifically:

  KL(p || q) = sum[ p * log(p / q) ]
    = sum[ p * ( log(p)                - log(q) ) ]
    = sum[ p * ( log_softmax(p_logits) - log(q) ) ]

  Args:
    p: A 2-D floating-point Tensor p_ij, where `i` corresponds to the minibatch
      example and `j` corresponds to the probability of being in class `j`.
    p_logits: A 2-D floating-point Tensor corresponding to logits for `p`.
    q: A 1-D floating-point Tensor, where q_j corresponds to the probability
      of class `j`.

  Returns:
    KL divergence between two distributions. Output dimension is 1D, one entry
    per distribution in `p`.

  Raises:
    ValueError: If any of the inputs aren't floating-point.
    ValueError: If p or p_logits aren't 2D.
    ValueError: If q isn't 1D.
  """
  for tensor in [p, p_logits, q]:
    if not tensor.dtype.is_floating:
      raise ValueError('Input %s must be floating type.', tensor.name)
  p.shape.assert_has_rank(2)
  p_logits.shape.assert_has_rank(2)
  q.shape.assert_has_rank(1)
  return math_ops.reduce_sum(
      p * (nn_ops.log_softmax(p_logits) - math_ops.log(q)), axis=1)

 def _log_cdf(self, x):
   x = self._pad_sample_dims(x)
   log_cdf_x = self.components_distribution.log_cdf(x)      # [S, B, k]
   log_mix_prob = nn_ops.log_softmax(
       self.mixture_distribution.logits, axis=-1)           # [B, k]
   return math_ops.reduce_logsumexp(
       log_cdf_x + log_mix_prob, axis=-1)                   # [S, B]

def _SoftmaxCrossEntropyWithLogitsGrad(op, grad_loss, grad_grad):
  """Gradient function for SoftmaxCrossEntropyWithLogits."""
  # grad_loss is the backprop for cost, and we multiply it with the gradients
  # (which is output[1])
  # grad_grad is the backprop for softmax gradient.
  #
  # Second derivative is just softmax derivative w.r.t. logits.
  softmax_grad = op.outputs[1]
  grad = _BroadcastMul(grad_loss, softmax_grad)

  def IsZero(g):
    # Some introspection to check if the gradient is feeding zeros
    if context.executing_eagerly():
      # TODO(apassos) add an efficient way to detect eager zeros here.
      return False
    if g.op.type in ("ZerosLike", "Zeros"):
      return True
    const_fill_value = tensor_util.constant_value(g)
    return const_fill_value is not None and (const_fill_value == 0).all()

  logits = op.inputs[0]
  if grad_grad is not None and not IsZero(grad_grad):
    softmax = nn_ops.softmax(logits)

    grad += ((grad_grad - array_ops.squeeze(
        math_ops.matmul(
            array_ops.expand_dims(grad_grad, 1),
            array_ops.expand_dims(softmax, 2)),
        axis=1)) * softmax)

  return grad, _BroadcastMul(grad_loss, -nn_ops.log_softmax(logits))

 def _log_prob(self, x):
   x = self._assert_valid_sample(x)
   # broadcast logits or x if need be.
   logits = self.logits
   if (not x.get_shape().is_fully_defined() or
       not logits.get_shape().is_fully_defined() or
       x.get_shape() != logits.get_shape()):
     logits = array_ops.ones_like(x, dtype=logits.dtype) * logits
     x = array_ops.ones_like(logits, dtype=x.dtype) * x
   logits_shape = array_ops.shape(math_ops.reduce_sum(logits, axis=[-1]))
   logits_2d = array_ops.reshape(logits, [-1, self.event_size])
   x_2d = array_ops.reshape(x, [-1, self.event_size])
   # compute the normalization constant
   k = math_ops.cast(self.event_size, x.dtype)
   log_norm_const = (math_ops.lgamma(k)
                     + (k - 1.)
                     * math_ops.log(self.temperature))
   # compute the unnormalized density
   log_softmax = nn_ops.log_softmax(logits_2d - x_2d * self._temperature_2d)
   log_unnorm_prob = math_ops.reduce_sum(log_softmax, [-1], keepdims=False)
   # combine unnormalized density with normalization constant
   log_prob = log_norm_const + log_unnorm_prob
   # Reshapes log_prob to be consistent with shape of user-supplied logits
   ret = array_ops.reshape(log_prob, logits_shape)
   return ret

 def _log_prob(self, x):
   with ops.control_dependencies(self._runtime_assertions):
     x = self._pad_sample_dims(x)
     log_prob_x = self.components_distribution.log_prob(x)  # [S, B, k]
     log_mix_prob = nn_ops.log_softmax(
         self.mixture_distribution.logits, axis=-1)         # [B, k]
     return math_ops.reduce_logsumexp(
         log_prob_x + log_mix_prob, axis=-1)                # [S, B]

 def testLogSoftmax(self):
   x_shape = [5, 10]
   x_np = np.random.randn(*x_shape).astype(np.float32)
   y_np = self._log_softmax(x_np)
   x_tf = constant_op.constant(x_np)
   y_tf = nn_ops.log_softmax(x_tf)
   y_tf_np = self.evaluate(y_tf)
   eps = 1e-3
   self.assertAllClose(y_tf_np, y_np, eps)

def _kl_categorical_categorical(a, b, name=None):
  """Calculate the batched KL divergence KL(a || b) with a, b OneHotCategorical.

  Args:
    a: instance of a OneHotCategorical distribution object.
    b: instance of a OneHotCategorical distribution object.
    name: (optional) Name to use for created operations.
      default is "kl_categorical_categorical".

  Returns:
    Batchwise KL(a || b)
  """
  with ops.name_scope(
      name, "kl_categorical_categorical", [a.logits, b.logits]):
    # sum(p*ln(p/q))
    return math_ops.reduce_sum(
        nn_ops.softmax(a.logits)*(nn_ops.log_softmax(a.logits)
            - nn_ops.log_softmax(b.logits)), reduction_indices=[-1])

 def testGradient(self, x_shape):
   x_np = np.random.randn(*x_shape).astype(np.float64)
   with self.cached_session():
     x_tf = constant_op.constant(x_np)
     y_tf = nn_ops.log_softmax(x_tf)
     err = gradient_checker.compute_gradient_error(x_tf, x_shape, y_tf,
                                                   x_shape)
   eps = 1e-7
   self.assertLess(err, eps)

def _kl_categorical_categorical(a, b, name=None):
  """Calculate the batched KL divergence KL(a || b) with a and b Categorical.

  Args:
    a: instance of a Categorical distribution object.
    b: instance of a Categorical distribution object.
    name: (optional) Name to use for created operations.
      default is "kl_categorical_categorical".

  Returns:
    Batchwise KL(a || b)
  """
  with ops.name_scope(name, "kl_categorical_categorical",
                      values=[a.logits, b.logits]):
    # sum(probs log(probs / (1 - probs)))
    delta_log_probs1 = (nn_ops.log_softmax(a.logits) -
                        nn_ops.log_softmax(b.logits))
    return math_ops.reduce_sum(nn_ops.softmax(a.logits) * delta_log_probs1,
                               axis=-1)

def ctc_loss_and_grad(logits, labels, label_length, logit_length, unique=None):
  """Computes the CTC loss and gradients.

  Most users will want fwd_bwd.ctc_loss

  This function returns the computed gradient, it does not have a gradient
  of its own defined.

  Args:
    logits: tensor of shape [frames, batch_size, num_labels]
    labels: tensor of shape [batch_size, max_label_seq_length]
    label_length: tensor of shape [batch_size]
      Length of reference label sequence in labels.
    logit_length: tensor of shape [batch_size]
      Length of input sequence in logits.
    unique: (optional) unique label indices as computed by unique(labels)
      If supplied, enables an implementation that is faster and more memory
      efficient on TPU.

  Returns:
    loss: tensor of shape [batch_size]
    gradient: tensor of shape [frames, batch_size, num_labels]
  """

  num_labels = _get_dim(logits, 2)
  max_label_seq_length = _get_dim(labels, 1)

  ilabel_log_probs = nn_ops.log_softmax(logits)
  state_log_probs = _ilabel_to_state(labels, num_labels, ilabel_log_probs)
  state_trans_probs = _ctc_state_trans(labels)
  initial_state_log_probs, final_state_log_probs = ctc_state_log_probs(
      label_length, max_label_seq_length)
  fwd_bwd_log_probs, log_likelihood = _forward_backward_log(
      state_trans_log_probs=math_ops.log(state_trans_probs),
      initial_state_log_probs=initial_state_log_probs,
      final_state_log_probs=final_state_log_probs,
      observed_log_probs=state_log_probs,
      sequence_length=logit_length)

  if unique:
    olabel_log_probs = _state_to_olabel_unique(
        labels, num_labels, fwd_bwd_log_probs, unique)
  else:
    olabel_log_probs = _state_to_olabel(labels, num_labels, fwd_bwd_log_probs)

  grad = math_ops.exp(ilabel_log_probs) - math_ops.exp(olabel_log_probs)
  loss = -log_likelihood
  return loss, grad