Python tensorflow.python.ops.array_ops.ones_like() Examples

def _log_prob(self, event):
    event = self._maybe_assert_valid_sample(event)
    # TODO(jaana): The current sigmoid_cross_entropy_with_logits has
    # inconsistent  behavior for logits = inf/-inf.
    event = math_ops.cast(event, self.logits.dtype)
    logits = self.logits
    # sigmoid_cross_entropy_with_logits doesn't broadcast shape,
    # so we do this here.

    def _broadcast(logits, event):
      return (array_ops.ones_like(event) * logits,
              array_ops.ones_like(logits) * event)

    # First check static shape.
    if (event.get_shape().is_fully_defined() and
        logits.get_shape().is_fully_defined()):
      if event.get_shape() != logits.get_shape():
        logits, event = _broadcast(logits, event)
    else:
      logits, event = control_flow_ops.cond(
          distribution_util.same_dynamic_shape(logits, event),
          lambda: (logits, event),
          lambda: _broadcast(logits, event))
    return -nn.sigmoid_cross_entropy_with_logits(labels=event, logits=logits) 

def ones_like(x, dtype=None, name=None):
  """Instantiates an all-ones variable of the same shape as another tensor.

  Arguments:
      x: Keras variable or tensor.
      dtype: String, dtype of returned Keras variable.
           None uses the dtype of x.
      name: String, name for the variable to create.

  Returns:
      A Keras variable with the shape of x filled with ones.

  Example:
  ```python
      >>> from keras import backend as K
      >>> kvar = K.variable(np.random.random((2,3)))
      >>> kvar_ones = K.ones_like(kvar)
      >>> K.eval(kvar_ones)
      array([[ 1.,  1.,  1.],
             [ 1.,  1.,  1.]], dtype=float32)
  ```
  """
  return array_ops.ones_like(x, dtype=dtype, name=name) 

def _log_prob(self, x):
    x = ops.convert_to_tensor(x, name="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(logits)
    if logits.get_shape().ndims == 2:
      logits_2d = logits
      x_2d = x
    else:
      logits_2d = array_ops.reshape(logits, [-1, self.num_classes])
      x_2d = array_ops.reshape(x, [-1, self.num_classes])
    ret = -nn_ops.softmax_cross_entropy_with_logits(labels=x_2d,
                                                    logits=logits_2d)
    ret = array_ops.reshape(ret, logits_shape)
    return ret 

def _sample_n(self, n, seed=None):
    lower_cutoff = self._lower_cutoff
    upper_cutoff = self._upper_cutoff
    with ops.name_scope("transform"):
      n = ops.convert_to_tensor(n, name="n")
      x_samps = self.distribution.sample(n, seed=seed)
      ones = array_ops.ones_like(x_samps)

      # Snap values to the intervals (j - 1, j].
      result_so_far = math_ops.ceil(x_samps)

      if lower_cutoff is not None:
        result_so_far = array_ops.where(result_so_far < lower_cutoff,
                                        lower_cutoff * ones, result_so_far)

      if upper_cutoff is not None:
        result_so_far = array_ops.where(result_so_far > upper_cutoff,
                                        upper_cutoff * ones, result_so_far)

      return result_so_far 

def remove(self, ids):
    """Remove the ids (and their associated scores) from the TopN."""
    with ops.control_dependencies(self.last_ops):
      scatter_op = state_ops.scatter_update(
          self.id_to_score,
          ids,
          array_ops.ones_like(
              ids, dtype=dtypes.float32) * dtypes.float32.min)
      # We assume that removed ids are almost always in the shortlist,
      # so it makes no sense to hide the Op behind a tf.cond
      shortlist_ids_to_remove, new_length = tensor_forest_ops.top_n_remove(
          self.sl_ids, ids)
      u1 = state_ops.scatter_update(
          self.sl_ids,
          array_ops.concat([[0], shortlist_ids_to_remove], 0),
          array_ops.concat(
              [new_length, array_ops.ones_like(shortlist_ids_to_remove) * -1],
              0))
      u2 = state_ops.scatter_update(
          self.sl_scores,
          shortlist_ids_to_remove,
          dtypes.float32.min * array_ops.ones_like(
              shortlist_ids_to_remove, dtype=dtypes.float32))
      self.last_ops = [scatter_op, u1, u2] 

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, -1))
    logits_2d = array_ops.reshape(logits, [-1, self.event_size])
    x_2d = array_ops.reshape(x, [-1, self.event_size])
    ret = -nn_ops.softmax_cross_entropy_with_logits(labels=x_2d,
                                                    logits=logits_2d)
    # Reshape back to user-supplied batch and sample dims prior to 2D reshape.
    ret = array_ops.reshape(ret, logits_shape)
    return ret 

def average_impurity(self):
    """Constructs a TF graph for evaluating the average leaf impurity of a tree.

    If in regression mode, this is the leaf variance. If in classification mode,
    this is the gini impurity.

    Returns:
      The last op in the graph.
    """
    children = array_ops.squeeze(array_ops.slice(
        self.variables.tree, [0, 0], [-1, 1]), squeeze_dims=[1])
    is_leaf = math_ops.equal(constants.LEAF_NODE, children)
    leaves = math_ops.to_int32(array_ops.squeeze(array_ops.where(is_leaf),
                                                 squeeze_dims=[1]))
    counts = array_ops.gather(self.variables.node_sums, leaves)
    gini = self._weighted_gini(counts)
    # Guard against step 1, when there often are no leaves yet.
    def impurity():
      return gini
    # Since average impurity can be used for loss, when there's no data just
    # return a big number so that loss always decreases.
    def big():
      return array_ops.ones_like(gini, dtype=dtypes.float32) * 10000000.
    return control_flow_ops.cond(math_ops.greater(
        array_ops.shape(leaves)[0], 0), impurity, big) 

def _safe_div(numerator, denominator, name="value"):
  """Computes a safe divide which returns 0 if the denominator is zero.

  Note that the function contains an additional conditional check that is
  necessary for avoiding situations where the loss is zero causing NaNs to
  creep into the gradient computation.

  Args:
    numerator: An arbitrary `Tensor`.
    denominator: `Tensor` whose shape matches `numerator` and whose values are
      assumed to be non-negative.
    name: An optional name for the returned op.

  Returns:
    The element-wise value of the numerator divided by the denominator.
  """
  return array_ops.where(
      math_ops.greater(denominator, 0),
      math_ops.div(numerator, array_ops.where(
          math_ops.equal(denominator, 0),
          array_ops.ones_like(denominator), denominator)),
      array_ops.zeros_like(numerator),
      name=name) 

def _sample_n(self, n, seed=None):
    expanded_concentration1 = array_ops.ones_like(
        self.total_concentration, dtype=self.dtype) * self.concentration1
    expanded_concentration0 = array_ops.ones_like(
        self.total_concentration, dtype=self.dtype) * self.concentration0
    gamma1_sample = random_ops.random_gamma(
        shape=[n],
        alpha=expanded_concentration1,
        dtype=self.dtype,
        seed=seed)
    gamma2_sample = random_ops.random_gamma(
        shape=[n],
        alpha=expanded_concentration0,
        dtype=self.dtype,
        seed=distribution_util.gen_new_seed(seed, "beta"))
    beta_sample = gamma1_sample / (gamma1_sample + gamma2_sample)
    return beta_sample 

def _sample_n(self, n, seed=None):
    low = self._low
    high = self._high
    with ops.name_scope("transform"):
      n = ops.convert_to_tensor(n, name="n")
      x_samps = self.distribution.sample(n, seed=seed)
      ones = array_ops.ones_like(x_samps)

      # Snap values to the intervals (j - 1, j].
      result_so_far = math_ops.ceil(x_samps)

      if low is not None:
        result_so_far = array_ops.where(result_so_far < low,
                                        low * ones, result_so_far)

      if high is not None:
        result_so_far = array_ops.where(result_so_far > high,
                                        high * ones, result_so_far)

      return result_so_far 

def hinge_loss(logits, labels=None, scope=None):
  """Method that returns the loss tensor for hinge loss.

  Args:
    logits: The logits, a float tensor.
    labels: The ground truth output tensor. Its shape should match the shape of
      logits. The values of the tensor are expected to be 0.0 or 1.0.
    scope: The scope for the operations performed in computing the loss.

  Returns:
    A `Tensor` of same shape as `logits` and `labels` representing the loss
      values across the batch.

  Raises:
    ValueError: If the shapes of `logits` and `labels` don't match.
  """
  with ops.name_scope(scope, "hinge_loss", [logits, labels]) as scope:
    logits.get_shape().assert_is_compatible_with(labels.get_shape())
    # We first need to convert binary labels to -1/1 labels (as floats).
    labels = math_ops.to_float(labels)
    all_ones = array_ops.ones_like(labels)
    labels = math_ops.subtract(2 * labels, all_ones)
    return nn_ops.relu(
        math_ops.subtract(all_ones, math_ops.multiply(labels, logits))) 

def _safe_div(numerator, denominator, name="value"):
  """Computes a safe divide which returns 0 if the denominator is zero.

  Note that the function contains an additional conditional check that is
  necessary for avoiding situations where the loss is zero causing NaNs to
  creep into the gradient computation.

  Args:
    numerator: An arbitrary `Tensor`.
    denominator: A `Tensor` whose shape matches `numerator` and whose values are
      assumed to be non-negative.
    name: An optional name for the returned op.

  Returns:
    The element-wise value of the numerator divided by the denominator.
  """
  return array_ops.where(
      math_ops.greater(denominator, 0),
      math_ops.div(numerator, array_ops.where(
          math_ops.equal(denominator, 0),
          array_ops.ones_like(denominator), denominator)),
      array_ops.zeros_like(numerator),
      name=name) 

def _bdtr(k, n, p):
  """The binomial cumulative distribution function.

  Args:
    k: floating point `Tensor`.
    n: floating point `Tensor`.
    p: floating point `Tensor`.

  Returns:
    `sum_{j=0}^k p^j (1 - p)^(n - j)`.
  """
  # Trick for getting safe backprop/gradients into n, k when
  #   betainc(a = 0, ..) = nan
  # Write:
  #   where(unsafe, safe_output, betainc(where(unsafe, safe_input, input)))
  ones = array_ops.ones_like(n - k)
  k_eq_n = math_ops.equal(k, n)
  safe_dn = array_ops.where(k_eq_n, ones, n - k)
  dk = math_ops.betainc(a=safe_dn, b=k + 1, x=1 - p)
  return array_ops.where(k_eq_n, ones, dk) 

def _log_prob(self, x):
    x = ops.convert_to_tensor(x, name="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(logits)
    if logits.get_shape().ndims == 2:
      logits_2d = logits
      x_2d = x
    else:
      logits_2d = array_ops.reshape(logits, [-1, self.num_classes])
      x_2d = array_ops.reshape(x, [-1, self.num_classes])
    # compute the normalization constant
    log_norm_const = (math_ops.lgamma(self.num_classes)
                      + (self.num_classes - 1)
                      * math_ops.log(self.temperature))
    # compute the unnormalized density
    log_softmax = nn_ops.log_softmax(logits_2d - x_2d * self.temperature)
    log_unnorm_prob = math_ops.reduce_sum(log_softmax, [-1], keep_dims=False)
    # combine unnormalized density with normalization constant
    log_prob = log_norm_const + log_unnorm_prob
    ret = array_ops.reshape(log_prob, logits_shape)
    return ret 

def _cdf(self, x):
    broadcasted_x = x * array_ops.ones(self.batch_shape())
    zeros = array_ops.zeros_like(x + self.a + self.b, dtype=self.dtype)
    ones = array_ops.ones_like(x + self.a + self.b, dtype=self.dtype)
    result_if_not_big = array_ops.where(
        x < self.a, zeros, (broadcasted_x - self.a) / self.range())
    return array_ops.where(x >= self.b, ones, result_if_not_big) 

def _prob(self, x):
    broadcasted_x = x * array_ops.ones(self.batch_shape())
    return array_ops.where(
        math_ops.is_nan(broadcasted_x),
        broadcasted_x,
        array_ops.where(
            math_ops.logical_or(broadcasted_x < self.a,
                                broadcasted_x > self.b),
            array_ops.zeros_like(broadcasted_x),
            (1. / self.range()) * array_ops.ones_like(broadcasted_x))) 

def _log_prob(self, k):
    k = ops.convert_to_tensor(k, name="k")
    if self.logits.get_shape()[:-1] == k.get_shape():
      logits = self.logits
    else:
      logits = self.logits * array_ops.ones_like(
          array_ops.expand_dims(k, -1), dtype=self.logits.dtype)
      logits_shape = array_ops.shape(logits)[:-1]
      k *= array_ops.ones(logits_shape, dtype=k.dtype)
      k.set_shape(tensor_shape.TensorShape(logits.get_shape()[:-1]))
    return -nn_ops.sparse_softmax_cross_entropy_with_logits(labels=k,
                                                            logits=logits) 

def _mode(self):
    return self.loc * array_ops.ones_like(self.scale) 

def _std(self):
    return self.sigma * array_ops.ones_like(self.mu) 

def _entropy(self):
    # Use broadcasting rules to calculate the full broadcast sigma.
    sigma = self.sigma * array_ops.ones_like(self.mu)
    return 0.5 * math.log(2. * math.pi * math.e) + math_ops.log(sigma) 

def _entropy(self):
    # Use broadcasting rules to calculate the full broadcast sigma.
    scale = self.scale * array_ops.ones_like(self.loc)
    return 2 + math_ops.log(scale) 

def testAddToTensor(self):
    with self.test_session():
      for batch_shape in [(), (
          2,
          3,)]:
        for k in [1, 4]:
          operator, mat = self._build_operator_and_mat(batch_shape, k)
          tensor = array_ops.ones_like(mat)

          self._compare_results(
              expected=(mat + tensor).eval(),
              actual=operator.add_to_tensor(tensor)) 

def _strict_1d_cumsum(tensor, len_tensor):
  """Cumsum of a 1D tensor with defined shape by padding and convolving."""
  # Assumes tensor shape is fully defined.
  with ops.name_scope('strict_1d_cumsum', values=[tensor]):
    if len_tensor == 0:
      return constant_op.constant([])
    len_pad = len_tensor - 1
    x = array_ops.pad(tensor, [[len_pad, 0]])
    h = array_ops.ones_like(x)
    return _strict_conv1d(x, h)[:len_tensor]


# TODO(langmore) Remove once a faster cumsum (accumulate_sum) Op is available.
# See:  https://github.com/tensorflow/tensorflow/issues/813 

def accuracy(predictions, labels, weights=None):
  """Computes the percentage of times that predictions matches labels.

  Args:
    predictions: the predicted values, a `Tensor` whose dtype and shape
                 matches 'labels'.
    labels: the ground truth values, a `Tensor` of any shape and
            bool, integer, or string dtype.
    weights: None or `Tensor` of float values to reweight the accuracy.

  Returns:
    Accuracy `Tensor`.

  Raises:
    ValueError: if dtypes don't match or
                if dtype is not bool, integer, or string.
  """
  if not (labels.dtype.is_integer or
          labels.dtype in (dtypes.bool, dtypes.string)):
    raise ValueError(
        'Labels should have bool, integer, or string dtype, not %r' %
        labels.dtype)
  if not labels.dtype.is_compatible_with(predictions.dtype):
    raise ValueError('Dtypes of predictions and labels should match. '
                     'Given: predictions (%r) and labels (%r)' %
                     (predictions.dtype, labels.dtype))
  with ops.name_scope('accuracy', values=[predictions, labels]):
    is_correct = math_ops.cast(
        math_ops.equal(predictions, labels), dtypes.float32)
    if weights is not None:
      is_correct = math_ops.multiply(is_correct, weights)
      num_values = math_ops.multiply(weights, array_ops.ones_like(is_correct))
      return math_ops.div(math_ops.reduce_sum(is_correct),
                          math_ops.reduce_sum(num_values))
    return math_ops.reduce_mean(is_correct) 

def hinge_loss(labels, logits, weights=1.0, scope=None,
               loss_collection=ops.GraphKeys.LOSSES):
  """Adds a hinge loss to the training procedure.

  Args:
    labels: The ground truth output tensor. Its shape should match the shape of
      logits. The values of the tensor are expected to be 0.0 or 1.0.
    logits: The logits, a float tensor.
    weights: Optional `Tensor` whose rank is either 0, or the same rank as
      `labels`, and must be broadcastable to `labels` (i.e., all dimensions must
      be either `1`, or the same as the corresponding `losses` dimension).
    scope: The scope for the operations performed in computing the loss.
    loss_collection: collection to which the loss will be added.

  Returns:
    A scalar `Tensor` of the loss value.

  Raises:
    ValueError: If the shapes of `logits` and `labels` don't match.
  """
  with ops.name_scope(scope, "hinge_loss", (logits, labels)) as scope:
    logits = math_ops.to_float(logits)
    labels = math_ops.to_float(labels)
    logits.get_shape().assert_is_compatible_with(labels.get_shape())
    # We first need to convert binary labels to -1/1 labels (as floats).
    all_ones = array_ops.ones_like(labels)
    labels = math_ops.subtract(2 * labels, all_ones)
    losses = nn_ops.relu(
        math_ops.subtract(all_ones, math_ops.multiply(labels, logits)))
    return compute_weighted_loss(losses, weights, scope, loss_collection) 

def _BiasAddGradGrad(op, received_grad):
  """Gradient for the BiasAddGrad op.

  Args:
    op: BiasAddGrad op for which we are calculating gradients.
    received_grad: The gradients passed to the BiasAddGrad op.

  Returns:
    A single gradient Tensor for the input to BiasAddGrad (which
    is the gradient of the bias term in BiasAdd)
  """

  try:
    data_format = op.get_attr("data_format")
  except ValueError:
    data_format = None

  shape = array_ops.shape(op.inputs[0])
  rank = array_ops.rank(op.inputs[0])
  bias_shape = array_ops.shape(received_grad)

  if data_format == b"NCHW":
    expanded_shape = array_ops.concat([
        array_ops.ones_like(shape[:-3]), bias_shape,
        array_ops.ones_like(shape[-2:])
    ], 0)
    tile_mults = array_ops.concat([shape[:-3], [1], shape[-2:]], 0)
  else:
    expanded_shape = array_ops.concat(
        [array_ops.ones_like(shape[:-1]), bias_shape], 0)
    tile_mults = array_ops.concat([shape[:-1], [1]], 0)

  expanded_grad = array_ops.reshape(received_grad, expanded_shape)
  return array_ops.tile(expanded_grad, tile_mults) 

def broadcast_weights(weights, values):
  """Broadcast `weights` to the same shape as `values`.

  This returns a version of `weights` following the same broadcast rules as
  `mul(weights, values)`, but limited to the weights shapes allowed by
  `assert_broadcastable`. When computing a weighted average, use this function
  to broadcast `weights` before summing them; e.g.,
  `reduce_sum(w * v) / reduce_sum(_broadcast_weights(w, v))`.

  Args:
    weights: `Tensor` whose shape is broadcastable to `values` according to the
      rules of `assert_broadcastable`.
    values: `Tensor` of any shape.

  Returns:
    `weights` broadcast to `values` shape according to the rules of
      `assert_broadcastable`.
  """
  with ops.name_scope(None, "broadcast_weights", (weights, values)) as scope:
    values = ops.convert_to_tensor(values, name="values")
    weights = ops.convert_to_tensor(
        weights, dtype=values.dtype.base_dtype, name="weights")

    # Try static check for exact match.
    weights_shape = weights.get_shape()
    values_shape = values.get_shape()
    if (weights_shape.is_fully_defined() and
        values_shape.is_fully_defined() and
        weights_shape.is_compatible_with(values_shape)):
      return weights

    with ops.control_dependencies((assert_broadcastable(weights, values),)):
      return math_ops.multiply(
          weights, array_ops.ones_like(values), name=scope) 

def _has_valid_dims(weights_shape, values_shape):
  with ops.name_scope(
      None, "has_invalid_dims", (weights_shape, values_shape)) as scope:
    values_shape_2d = array_ops.expand_dims(values_shape, -1)
    valid_dims = array_ops.concat(
        (values_shape_2d, array_ops.ones_like(values_shape_2d)), axis=1)
    weights_shape_2d = array_ops.expand_dims(weights_shape, -1)
    invalid_dims = sets.set_difference(weights_shape_2d, valid_dims)
    num_invalid_dims = array_ops.size(
        invalid_dims.values, name="num_invalid_dims")
    return math_ops.equal(0, num_invalid_dims, name=scope) 

def ones_like(labeled_tensor, dtype=None, name=None):
  """Creates an identical tensor with all elements set to one.

  Args:
    labeled_tensor: The input tensor.
    dtype: The type of the returned tensor.
    name: Optional op name.

  Returns:
    The tensor with elements set to one.
  """
  with ops.name_scope(name, 'lt_ones_like', [labeled_tensor]) as scope:
    labeled_tensor = core.convert_to_labeled_tensor(labeled_tensor)
    op = array_ops.ones_like(labeled_tensor.tensor, dtype=dtype, name=scope)
    return core.LabeledTensor(op, labeled_tensor.axes) 

def _mode(self):
    return self.loc * array_ops.ones_like(self.scale)