Python v1.Tensor方法代碼示例

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import Tensor [as 別名]
def compute_logits(self, token_ids: tf.Tensor) -> tf.Tensor:
        """
        Implements a language model, where each output is conditional on the current
        input and inputs processed so far.

        Args:
            token_ids: int32 tensor of shape [B, T], storing integer IDs of tokens.

        Returns:
            tf.float32 tensor of shape [B, T, V], storing the distribution over output symbols
            for each timestep for each batch element.
        """
        # TODO 5# 1) Embed tokens
        # TODO 5# 2) Run RNN on embedded tokens
        # TODO 5# 3) Project RNN outputs onto the vocabulary to obtain logits.
        return rnn_output_logits 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import Tensor [as 別名]
def _normal_distribution_cdf(x, stddev):
  """Evaluates the CDF of the normal distribution.

  Normal distribution with mean 0 and standard deviation stddev,
  evaluated at x=x.

  input and output `Tensor`s have matching shapes.

  Args:
    x: a `Tensor`
    stddev: a `Tensor` with the same shape as `x`.

  Returns:
    a `Tensor` with the same shape as `x`.

  """
  return 0.5 * (1.0 + tf.erf(x / (math.sqrt(2) * stddev + 1e-20))) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import Tensor [as 別名]
def cv_squared(x):
  """The squared coefficient of variation of a sample.

  Useful as a loss to encourage a positive distribution to be more uniform.
  Epsilons added for numerical stability.
  Returns 0 for an empty Tensor.

  Args:
    x: a `Tensor`.

  Returns:
    a `Scalar`.
  """
  epsilon = 1e-10
  float_size = tf.to_float(tf.size(x)) + epsilon
  mean = tf.reduce_sum(x) / float_size
  variance = tf.reduce_sum(tf.squared_difference(x, mean)) / float_size
  return variance / (tf.square(mean) + epsilon) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import Tensor [as 別名]
def __init__(self, pad_mask):
    """Compute and store the location of the padding.

    Args:
      pad_mask (tf.Tensor): Reference padding tensor of shape
        [batch_size,length] or [dim_origin] (dim_origin=batch_size*length)
        containing non-zeros positive values to indicate padding location.
    """
    self.nonpad_ids = None
    self.dim_origin = None

    with tf.name_scope("pad_reduce/get_ids"):
      pad_mask = tf.reshape(pad_mask, [-1])  # Flatten the batch
      # nonpad_ids contains coordinates of zeros rows (as pad_mask is
      # float32, checking zero equality is done with |x| < epsilon, with
      # epsilon=1e-9 as standard, here pad_mask only contains positive values
      # so tf.abs would be redundant)
      self.nonpad_ids = tf.to_int32(tf.where(pad_mask < 1e-9))
      self.dim_origin = tf.shape(pad_mask)[:1] 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import Tensor [as 別名]
def remove(self, x):
    """Remove padding from the given tensor.

    Args:
      x (tf.Tensor): of shape [dim_origin,...]

    Returns:
      a tensor of shape [dim_compressed,...] with dim_compressed <= dim_origin
    """
    with tf.name_scope("pad_reduce/remove"):
      x_shape = x.get_shape().as_list()
      x = tf.gather_nd(
          x,
          indices=self.nonpad_ids,
      )
      if not tf.executing_eagerly():
        # This is a hack but for some reason, gather_nd return a tensor of
        # undefined shape, so the shape is set up manually
        x.set_shape([None] + x_shape[1:])
    return x 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import Tensor [as 別名]
def restore(self, x):
    """Add padding back to the given tensor.

    Args:
      x (tf.Tensor): of shape [dim_compressed,...]

    Returns:
      a tensor of shape [dim_origin,...] with dim_compressed >= dim_origin. The
      dim is restored from the original reference tensor
    """
    with tf.name_scope("pad_reduce/restore"):
      x = tf.scatter_nd(
          indices=self.nonpad_ids,
          updates=x,
          shape=tf.concat([self.dim_origin, tf.shape(x)[1:]], axis=0),
      )
    return x 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import Tensor [as 別名]
def combine(self, expert_out, multiply_by_gates=True):
    """Sum together the expert output, weighted by the gates.

    The slice corresponding to a particular batch element `b` is computed
    as the sum over all experts `i` of the expert output, weighted by the
    corresponding gate values.  If `multiply_by_gates` is set to False, the
    gate values are ignored.

    Args:
      expert_out: a list of `num_experts` `Tensor`s, each with shape
        `[expert_batch_size_i, <extra_output_dims>]`.
      multiply_by_gates: a boolean

    Returns:
      a `Tensor` with shape `[batch_size, <extra_output_dims>]`.
    """
    # see comments on convert_gradient_to_tensor
    stitched = common_layers.convert_gradient_to_tensor(
        tf.concat(expert_out, 0))
    if multiply_by_gates:
      stitched *= tf.expand_dims(self._nonzero_gates, 1)
    combined = tf.unsorted_segment_sum(stitched, self._batch_index,
                                       tf.shape(self._gates)[0])
    return combined 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import Tensor [as 別名]
def dispatch(self, inp):
    """Create one input Tensor for each expert.

    Args:
      inp: a list of length num_datashards `Tensor`s with shapes
        `[batch_size[d], <extra_input_dims>]`.
    Returns:
      a list of `num_experts` `Tensor`s with shapes
        `[num_examples[i], <extra_input_dims>]`.
    """
    dispatched = self._dp(lambda a, b: a.dispatch(b), self._dispatchers, inp)
    ret = self._ep(tf.concat, transpose_list_of_lists(dispatched), 0)
    if ret[0].dtype == tf.float32:
      # see comments on common_layers.convert_gradient_to_tensor
      ret = self._ep(common_layers.convert_gradient_to_tensor, ret)
    return ret 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import Tensor [as 別名]
def body(self, features):
    """Computes the targets' pre-logit activations given transformed inputs.

    Most `T2TModel` subclasses will override this method.

    Args:
      features: dict of str to Tensor, where each Tensor has shape [batch_size,
        ..., hidden_size]. It typically contains keys `inputs` and `targets`.

    Returns:
      output: Tensor of pre-logit activations with shape [batch_size, ...,
              hidden_size].
      losses: Either single loss as a scalar, a list, a Tensor (to be averaged),
              or a dictionary of losses. If losses is a dictionary with the key
              "training", losses["training"] is considered the final training
              loss and output is considered logits; self.top and self.loss will
              be skipped.
    """
    raise NotImplementedError("Abstract Method") 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import Tensor [as 別名]
def eval_autoregressive(self, features=None, decode_length=50):
    """Autoregressive eval.

    Quadratic time in decode_length.

    Args:
      features: an map of string to `Tensor`
      decode_length: an integer.  How many additional timesteps to decode.

    Returns:
      logits: `Tensor`
      losses: a dictionary: {loss-name (string): floating point `Scalar`}.
          Contains a single key "training".
    """
    results = self._slow_greedy_infer(features, decode_length=decode_length)
    return results["logits"], results["losses"] 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import Tensor [as 別名]
def _beam_decode(self,
                   features,
                   decode_length,
                   beam_size,
                   top_beams,
                   alpha,
                   use_tpu=False):
    """Beam search decoding.

    Models should ideally implement a more efficient version of this function.

    Args:
      features: an map of string to `Tensor`
      decode_length: an integer.  How many additional timesteps to decode.
      beam_size: number of beams.
      top_beams: an integer. How many of the beams to return.
      alpha: Float that controls the length penalty. larger the alpha, stronger
        the preference for longer translations.
      use_tpu: A bool, whether to do beam decode on TPU.

    Returns:
       samples: an integer `Tensor`. Top samples from the beam search
    """
    return self._beam_decode_slow(features, decode_length, beam_size, top_beams,
                                  alpha, use_tpu) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import Tensor [as 別名]
def average_sharded_losses(sharded_losses):
  """Average losses across datashards.

  Args:
    sharded_losses: list<dict<str loss_name, Tensor loss>>. The loss
      can be a single Tensor or a 2-tuple (numerator and denominator).

  Returns:
    losses: dict<str loss_name, Tensor avg_loss>
  """
  losses = {}
  for loss_name in sorted(sharded_losses[0]):
    all_shards = [shard_losses[loss_name] for shard_losses in sharded_losses]
    if isinstance(all_shards[0], tuple):
      sharded_num, sharded_den = zip(*all_shards)
      mean_loss = (
          tf.add_n(sharded_num) / tf.maximum(
              tf.cast(1.0, sharded_den[0].dtype), tf.add_n(sharded_den)))
    else:
      mean_loss = tf.reduce_mean(all_shards)

    losses[loss_name] = mean_loss
  return losses 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import Tensor [as 別名]
def summarize_features(features, num_shards=1):
  """Generate summaries for features."""
  if not common_layers.should_generate_summaries():
    return

  with tf.name_scope("input_stats"):
    for (k, v) in sorted(six.iteritems(features)):
      if (isinstance(v, tf.Tensor) and (v.get_shape().ndims > 1) and
          (v.dtype != tf.string)):
        tf.summary.scalar("%s_batch" % k, tf.shape(v)[0] // num_shards)
        tf.summary.scalar("%s_length" % k, tf.shape(v)[1])
        nonpadding = tf.to_float(tf.not_equal(v, 0))
        nonpadding_tokens = tf.reduce_sum(nonpadding)
        tf.summary.scalar("%s_nonpadding_tokens" % k, nonpadding_tokens)
        tf.summary.scalar("%s_nonpadding_fraction" % k,
                          tf.reduce_mean(nonpadding)) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import Tensor [as 別名]
def ror(x, n, p=1):
  """Bitwise right rotation.

  Args:
    x: Input tensor
    n: Bit count to represent x
    p: Bit positions to shift

  Returns:
    tf.Tensor: x shifted by p positions in n bits
  """

  a = tf.bitwise.right_shift(x, p)
  b = tf.bitwise.left_shift(1, p) - 1
  c = tf.bitwise.bitwise_and(x, b)
  d = tf.bitwise.left_shift(c, n - p)

  return a + d 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import Tensor [as 別名]
def rol(x, n, p=1):
  """Bitwise left rotation.

  Args:
    x: Input tensor
    n: Bit count to represent x
    p: Bit positions to shift

  Returns:
    tf.Tensor: x shifted by p positions in n bits
  """
  a = tf.bitwise.left_shift(x, p)
  b = tf.bitwise.left_shift(1, n) - 1
  c = tf.bitwise.bitwise_and(a, b)
  d = tf.bitwise.right_shift(x, n - p)

  return tf.bitwise.bitwise_or(c, d)