RNNCell、LSTMCell、tf.nn.static_rnn、tf.nn.static_bidirectional_rnn和tf.nn.bidirectional_dynamic_rnn

@deprecation.deprecated(None,
                        "Please use `keras.layers.RNN(cell, unroll=True)`, "
                        "which is equivalent to this API")
@tf_export(v1=["nn.static_rnn"])
def static_rnn(cell,
               inputs,
               initial_state=None,
               dtype=None,
               sequence_length=None,
               scope=None):
  """Creates a recurrent neural network specified by RNNCell `cell`.
利用指定的RNNCell创建一个RNN。

  The simplest form of RNN network generated is:
最简单的RNN形式如下:

  ``python
    state = cell.zero_state(...)  # Cell状态
    outputs = []
    for input_ in inputs:
      output, state = cell(input_, state)  # 根据input_和state更新cell。
      outputs.append(output)  
    return (outputs, state)  # outputs是每个时间步的output,state是最终的细胞状态。
  ``
  However, a few other options are available:
不过,还有一些可选配置。

  An initial state can be provided.
可以提供初始状态。
  If the sequence_length vector is provided, dynamic calculation is performed.
  This method of calculation does not compute the RNN steps past the maximum
  sequence length of the minibatch (thus saving computational time),
  and properly propagates the state at an example's sequence length
  to the final state output.
如果提供了sequence_length向量,就会执行动态计算。动态计算方法不会对超过minibatch中最大序列长度的RNN steps进行计算(所以可以节省计算时间),并且能够以样例序列长度正确地传播状态到最终状态输出。

  The dynamic calculation performed is, at time `t` for batch row `b`,
动态计算的逻辑如下,比如时间t,batchsize=b:
  ``python
    (output, state)(b, t) =
      (t >= sequence_length(b))
        ? (zeros(cell.output_size), states(b, sequence_length(b) - 1))  # zeros,states(剩余的长度)
        : cell(input(b, t), state(b, t - 1))
  ``

  Args:
    cell: An instance of RNNCell.
cell:一个RNNCell实例。

    inputs: A length T list of inputs, each a `Tensor` of shape `[batch_size,
      input_size]`, or a nested tuple of such elements.
inputs:长度为T的inputs。每个Tensor的形状是[batch_size, input_size],或者嵌套的这种元素。

    initial_state: (optional) An initial state for the RNN. If `cell.state_size`
      is an integer, this must be a `Tensor` of appropriate type and shape
      `[batch_size, cell.state_size]`. If `cell.state_size` is a tuple, this
      should be a tuple of tensors having shapes `[batch_size, s] for s in
      cell.state_size`.
initial_state:可选,RNN的初始状态。
如果cell.state_size是int,那这个初始状态必须是相应的类型且形状是[batch_size, cell.state_size]。
如果cell.state_size是tuple,那这个初始状态必须是形状为`[batch_size, s] for s in cell.state_size`的tensors tuple。

    dtype: (optional) The data type for the initial state and expected output.
      Required if initial_state is not provided or RNN state has a heterogeneous
      dtype.
dtype:可选,初始状态和期望输出的数据类型。如果初始状态没有提供或者RNN状态是合成类型,那必须提供。

    sequence_length: Specifies the length of each sequence in inputs. An int32
      or int64 vector (tensor) size `[batch_size]`, values in `[0, T)`.
sequence_length:指定每个inputs序列的长度。一个int32或int64的向量,size是[batch_size],值是[0, T)

    scope: VariableScope for the created subgraph; defaults to "rnn".
scope:用于创建子图的变量作用域,默认rnn。

  Returns:
    A pair (outputs, state) where:

    - outputs is a length T list of outputs (one for each input), or a nested
      tuple of such elements.
outputs:长度为T的outputs,每个output对应一个input。或者嵌套的这种元素。
    - state is the final state
state:最终状态。

  Raises:
    TypeError: If `cell` is not an instance of RNNCell.
    ValueError: If `inputs` is `None` or an empty list, or if the input depth
      (column size) cannot be inferred from inputs via shape inference.
  """
@deprecation.deprecated(None, "Please use `keras.layers.Bidirectional("
                        "keras.layers.RNN(cell, unroll=True))`, which is "
                        "equivalent to this API")
@tf_export(v1=["nn.static_bidirectional_rnn"])
def static_bidirectional_rnn(cell_fw,
                             cell_bw,
                             inputs,
                             initial_state_fw=None,
                             initial_state_bw=None,
                             dtype=None,
                             sequence_length=None,
                             scope=None):
  """Creates a bidirectional recurrent neural network.
创建一个双向RNN。

  Similar to the unidirectional case above (rnn) but takes input and builds
  independent forward and backward RNNs with the final forward and backward
  outputs depth-concatenated, such that the output will have the format
  [time][batch][cell_fw.output_size + cell_bw.output_size]. The input_size of
  forward and backward cell must match. The initial state for both directions
  is zero by default (but can be set optionally) and no intermediate states are
  ever returned -- the network is fully unrolled for the given (passed in)
  length(s) of the sequence(s) or completely unrolled if length(s) is not given.

  Args:
    cell_fw: An instance of RNNCell, to be used for forward direction.
    cell_bw: An instance of RNNCell, to be used for backward direction.
    inputs: A length T list of inputs, each a tensor of shape [batch_size,
      input_size], or a nested tuple of such elements.
    initial_state_fw: (optional) An initial state for the forward RNN. This must
      be a tensor of appropriate type and shape `[batch_size,
      cell_fw.state_size]`. If `cell_fw.state_size` is a tuple, this should be a
      tuple of tensors having shapes `[batch_size, s] for s in
      cell_fw.state_size`.
    initial_state_bw: (optional) Same as for `initial_state_fw`, but using the
      corresponding properties of `cell_bw`.
    dtype: (optional) The data type for the initial state.  Required if either
      of the initial states are not provided.
    sequence_length: (optional) An int32/int64 vector, size `[batch_size]`,
      containing the actual lengths for each of the sequences.
    scope: VariableScope for the created subgraph; defaults to
      "bidirectional_rnn"

  Returns:
    A tuple (outputs, output_state_fw, output_state_bw) where:
      outputs is a length `T` list of outputs (one for each input), which
        are depth-concatenated forward and backward outputs.
      output_state_fw is the final state of the forward rnn.
      output_state_bw is the final state of the backward rnn.

  Raises:
    TypeError: If `cell_fw` or `cell_bw` is not an instance of `RNNCell`.
    ValueError: If inputs is None or an empty list.
  """
@deprecation.deprecated(None, "Please use `keras.layers.Bidirectional("
                        "keras.layers.RNN(cell))`, which is equivalent to "
                        "this API")
@tf_export(v1=["nn.bidirectional_dynamic_rnn"])
def bidirectional_dynamic_rnn(cell_fw,
                              cell_bw,
                              inputs,
                              sequence_length=None,
                              initial_state_fw=None,
                              initial_state_bw=None,
                              dtype=None,
                              parallel_iterations=None,
                              swap_memory=False,
                              time_major=False,
                              scope=None):
  """Creates a dynamic version of bidirectional recurrent neural network.
创建一个动态版本的双向RNN。

  Takes input and builds independent forward and backward RNNs. The input_size
  of forward and backward cell must match. The initial state for both directions
  is zero by default (but can be set optionally) and no intermediate states are
  ever returned -- the network is fully unrolled for the given (passed in)
  length(s) of the sequence(s) or completely unrolled if length(s) is not
  given.

  Args:
    cell_fw: An instance of RNNCell, to be used for forward direction.
    cell_bw: An instance of RNNCell, to be used for backward direction.
    inputs: The RNN inputs.
      If time_major == False (default), this must be a tensor of shape:
        `[batch_size, max_time, ...]`, or a nested tuple of such elements.
      If time_major == True, this must be a tensor of shape: `[max_time,
        batch_size, ...]`, or a nested tuple of such elements.
    sequence_length: (optional) An int32/int64 vector, size `[batch_size]`,
      containing the actual lengths for each of the sequences in the batch. If
      not provided, all batch entries are assumed to be full sequences; and time
      reversal is applied from time `0` to `max_time` for each sequence.
sequence_length:可选的,一个int32/int64向量,size是`[batch_size]`,包含了一个batch中每个序列的真实长度,如果没有提供,就认为batch中的序列都是完整的,时间反转会将0-max_time应用到每个序列。

    initial_state_fw: (optional) An initial state for the forward RNN. This must
      be a tensor of appropriate type and shape `[batch_size,
      cell_fw.state_size]`. If `cell_fw.state_size` is a tuple, this should be a
      tuple of tensors having shapes `[batch_size, s] for s in
      cell_fw.state_size`.
    initial_state_bw: (optional) Same as for `initial_state_fw`, but using the
      corresponding properties of `cell_bw`.
    dtype: (optional) The data type for the initial states and expected output.
      Required if initial_states are not provided or RNN states have a
      heterogeneous dtype.
    parallel_iterations: (Default: 32).  The number of iterations to run in
      parallel.  Those operations which do not have any temporal dependency and
      can be run in parallel, will be.  This parameter trades off time for
      space.  Values >> 1 use more memory but take less time, while smaller
      values use less memory but computations take longer.
    swap_memory: Transparently swap the tensors produced in forward inference
      but needed for back prop from GPU to CPU.  This allows training RNNs which
      would typically not fit on a single GPU, with very minimal (or no)
      performance penalty.
    time_major: The shape format of the `inputs` and `outputs` Tensors. If true,
      these `Tensors` must be shaped `[max_time, batch_size, depth]`. If false,
      these `Tensors` must be shaped `[batch_size, max_time, depth]`. Using
      `time_major = True` is a bit more efficient because it avoids transposes
      at the beginning and end of the RNN calculation.  However, most TensorFlow
      data is batch-major, so by default this function accepts input and emits
      output in batch-major form.
    scope: VariableScope for the created subgraph; defaults to
      "bidirectional_rnn"

  Returns:
    A tuple (outputs, output_states) where:
      outputs: A tuple (output_fw, output_bw) containing the forward and
        the backward rnn output `Tensor`.
        If time_major == False (default),
          output_fw will be a `Tensor` shaped:
          `[batch_size, max_time, cell_fw.output_size]`
          and output_bw will be a `Tensor` shaped:
          `[batch_size, max_time, cell_bw.output_size]`.
        If time_major == True,
          output_fw will be a `Tensor` shaped:
          `[max_time, batch_size, cell_fw.output_size]`
          and output_bw will be a `Tensor` shaped:
          `[max_time, batch_size, cell_bw.output_size]`.
        It returns a tuple instead of a single concatenated `Tensor`, unlike
        in the `bidirectional_rnn`. If the concatenated one is preferred,
        the forward and backward outputs can be concatenated as
        `tf.concat(outputs, 2)`.
      output_states: A tuple (output_state_fw, output_state_bw) containing
        the forward and the backward final states of bidirectional rnn.

  Raises:
    TypeError: If `cell_fw` or `cell_bw` is not an instance of `RNNCell`.
  """
@tf_export("nn.rnn_cell.RNNCell")
class RNNCell(base_layer.Layer):
  """Abstract object representing an RNN cell.

  Every `RNNCell` must have the properties below and implement `call` with
  the signature `(output, next_state) = call(input, state)`.  The optional
  third input argument, `scope`, is allowed for backwards compatibility
  purposes; but should be left off for new subclasses.

  This definition of cell differs from the definition used in the literature.
  In the literature, 'cell' refers to an object with a single scalar output.
  This definition refers to a horizontal array of such units.

  An RNN cell, in the most abstract setting, is anything that has
  a state and performs some operation that takes a matrix of inputs.
  This operation results in an output matrix with `self.output_size` columns.
  If `self.state_size` is an integer, this operation also results in a new
  state matrix with `self.state_size` columns.  If `self.state_size` is a
  (possibly nested tuple of) TensorShape object(s), then it should return a
  matching structure of Tensors having shape `[batch_size].concatenate(s)`
  for each `s` in `self.batch_size`.
  """

  def __call__(self, inputs, state, scope=None):
    """Run this RNN cell on inputs, starting from the given state.

    Args:
      inputs: `2-D` tensor with shape `[batch_size, input_size]`.
      state: if `self.state_size` is an integer, this should be a `2-D Tensor`
        with shape `[batch_size, self.state_size]`.  Otherwise, if
        `self.state_size` is a tuple of integers, this should be a tuple
        with shapes `[batch_size, s] for s in self.state_size`.
      scope: VariableScope for the created subgraph; defaults to class name.

    Returns:
      A pair containing:

      - Output: A `2-D` tensor with shape `[batch_size, self.output_size]`.
      - New state: Either a single `2-D` tensor, or a tuple of tensors matching
        the arity and shapes of `state`.
    """
class LayerRNNCell(RNNCell):
  """Subclass of RNNCells that act like proper `tf.Layer` objects.

  For backwards compatibility purposes, most `RNNCell` instances allow their
  `call` methods to instantiate variables via `tf.get_variable`.  The underlying
  variable scope thus keeps track of any variables, and returning cached
  versions.  This is atypical of `tf.layer` objects, which separate this
  part of layer building into a `build` method that is only called once.

  Here we provide a subclass for `RNNCell` objects that act exactly as
  `Layer` objects do.  They must provide a `build` method and their
  `call` methods do not access Variables `tf.get_variable`.
  """
@tf_export(v1=["nn.rnn_cell.LSTMCell"])
class LSTMCell(LayerRNNCell):
  """Long short-term memory unit (LSTM) recurrent network cell.

  The default non-peephole implementation is based on:

    https://pdfs.semanticscholar.org/1154/0131eae85b2e11d53df7f1360eeb6476e7f4.pdf

  Felix Gers, Jurgen Schmidhuber, and Fred Cummins.
  "Learning to forget: Continual prediction with LSTM." IET, 850-855, 1999.

  The peephole implementation is based on:

    https://research.google.com/pubs/archive/43905.pdf

  Hasim Sak, Andrew Senior, and Francoise Beaufays.
  "Long short-term memory recurrent neural network architectures for
   large scale acoustic modeling." INTERSPEECH, 2014.

  The class uses optional peep-hole connections, optional cell clipping, and
  an optional projection layer.

  Note that this cell is not optimized for performance. Please use
  `tf.contrib.cudnn_rnn.CudnnLSTM` for better performance on GPU, or
  `tf.contrib.rnn.LSTMBlockCell` and `tf.contrib.rnn.LSTMBlockFusedCell` for
  better performance on CPU.
  """
  @deprecated(None, "This class is equivalent as tf.keras.layers.LSTMCell,"
                    " and will be replaced by that in Tensorflow 2.0.")
  def __init__(self, num_units,
               use_peepholes=False, cell_clip=None,
               initializer=None, num_proj=None, proj_clip=None,
               num_unit_shards=None, num_proj_shards=None,
               forget_bias=1.0, state_is_tuple=True,
               activation=None, reuse=None, name=None, dtype=None, **kwargs):
    """Initialize the parameters for an LSTM cell.

    Args:
      num_units: int, The number of units in the LSTM cell.
      use_peepholes: bool, set True to enable diagonal/peephole connections.
      cell_clip: (optional) A float value, if provided the cell state is clipped
        by this value prior to the cell output activation.
      initializer: (optional) The initializer to use for the weight and
        projection matrices.
      num_proj: (optional) int, The output dimensionality for the projection
        matrices.  If None, no projection is performed.
      proj_clip: (optional) A float value.  If `num_proj > 0` and `proj_clip` is
        provided, then the projected values are clipped elementwise to within
        `[-proj_clip, proj_clip]`.
      num_unit_shards: Deprecated, will be removed by Jan. 2017.
        Use a variable_scope partitioner instead.
      num_proj_shards: Deprecated, will be removed by Jan. 2017.
        Use a variable_scope partitioner instead.
      forget_bias: Biases of the forget gate are initialized by default to 1
        in order to reduce the scale of forgetting at the beginning of
        the training. Must set it manually to `0.0` when restoring from
        CudnnLSTM trained checkpoints.
      state_is_tuple: If True, accepted and returned states are 2-tuples of
        the `c_state` and `m_state`.  If False, they are concatenated
        along the column axis.  This latter behavior will soon be deprecated.
      activation: Activation function of the inner states.  Default: `tanh`. It
        could also be string that is within Keras activation function names.
      reuse: (optional) Python boolean describing whether to reuse variables
        in an existing scope.  If not `True`, and the existing scope already has
        the given variables, an error is raised.
      name: String, the name of the layer. Layers with the same name will
        share weights, but to avoid mistakes we require reuse=True in such
        cases.
      dtype: Default dtype of the layer (default of `None` means use the type
        of the first input). Required when `build` is called before `call`.
      **kwargs: Dict, keyword named properties for common layer attributes, like
        `trainable` etc when constructing the cell from configs of get_config().

      When restoring from CudnnLSTM-trained checkpoints, use
      `CudnnCompatibleLSTMCell` instead.
    """
posted @ 2020-07-31 20:30  ZH奶酪  阅读(108)  评论(0编辑  收藏