Tensorflow：tf.contrib.rnn.DropoutWrapper函数(谷歌已经为Dropout申请了专利！)、MultiRNNCell函数的解读与理解

Args:

• cell: an RNNCell, a projection to output_size is added to it.
• input_keep_prob: unit Tensor or float between 0 and 1, input keep probability; if it is constant and 1, no input dropout will be added.
• output_keep_prob: unit Tensor or float between 0 and 1, output keep probability; if it is constant and 1, no output dropout will be added.
• state_keep_prob: unit Tensor or float between 0 and 1, output keep probability; if it is constant and 1, no output dropout will be added. State dropout is performed on the outgoing states of the cell. Note the state components to which dropout is applied when state_keep_prob is in (0, 1) are also determined by the argumentdropout_state_filter_visitor (e.g. by default dropout is never applied to the c component of an LSTMStateTuple).
• variational_recurrent: Python bool. If True, then the same dropout pattern is applied across all time steps per run call. If this parameter is set, input_sizemust be provided.
• input_size: (optional) (possibly nested tuple of) TensorShape objects containing the depth(s) of the input tensors expected to be passed in to the DropoutWrapper. Required and used iffvariational_recurrent = True and input_keep_prob < 1.
• dtype: (optional) The dtype of the input, state, and output tensors. Required and used iffvariational_recurrent = True.
• seed: (optional) integer, the randomness seed.
• dropout_state_filter_visitor: (optional), default: (see below). Function that takes any hierarchical level of the state and returns a scalar or depth=1 structure of Python booleans describing which terms in the state should be dropped out. In addition, if the function returns True, dropout is applied across this sublevel. If the function returns False, dropout is not applied across this entire sublevel. Default behavior: perform dropout on all terms except the memory (c) state of LSTMCellState objects, and don't try to apply dropout to TensorArray objects: def dropout_state_filter_visitor(s): if isinstance(s, LSTMCellState): # Never perform dropout on the c state. return LSTMCellState(c=False, h=True) elif isinstance(s, TensorArray): return False return True
• **kwargs: dict of keyword arguments for base layer.

• cell:一个RNNCell，向它添加一个到output_size的投影。
• input_keep_prob:单位张量或浮点数在0到1之间，输入保持概率;如果是常数和1，则不添加输入dropout。
• output_keep_prob:单位张量或浮动在0和1之间，输出保持概率;如果是常数和1，则不添加输出dropout。
• state_keep_prob:单位张量或浮点数在0到1之间，输出保持概率;如果是常数和1，则不添加输出dropout。状态退出是在计算单元的输出状态上执行的。注意，当state_keep_prob位于(0,1)中时，dropout应用到的状态组件也由argumentdropout_state_filter_visitor(例如。默认情况下，dropout从不应用于LSTMStateTuple的c组件)。
• variational_recurrent: Python布尔类型。如果为真，则在每次运行调用的所有时间步上应用相同的退出模式。如果设置了该参数，则必须提供input_size。
• input_size:(可选的)(可能嵌套的元组)TensorShape对象，包含期望传递给DropoutWrapper的输入张量的深度。需要和使用的iff variational_= True和input_keep_prob < 1。
• (可选)输入、状态和输出张量的dtype。需要和使用iffvariational_= True。
• 种子:(可选)整数，随机种子。
• dropout_state_filter_visitor:(可选)，默认:(见下)。函数，该函数接受状态的任何层次结构，并返回一个标量或深度=1的Python布尔值结构，该结构描述应该删除状态中的哪些项。此外，如果函数返回True，则在此子层上应用dropout。如果函数返回False，则不会在整个子层上应用dropout。默认行为:除了LSTMCellState对象的内存(c)状态外，在所有条件下执行dropout，并且不要试图将dropout应用到TensorArray对象:def dropout_state_filter_visitor(s): if isinstance(s, LSTMCellState): #永远不要在c状态下执行dropout。返回LSTMCellState(c=False, h=True) elif isinstance(s, TensorArray):返回False返回True
• **kwargs:基层关键字参数的字典。

Methods

get_initial_state

View source

1. get_initial_state(
2.     inputs=None, batch_size=None, dtype=None
3. )

zero_state

View source

1. zero_state(
2.     batch_size, dtype
3. )

Args:

• cells: list of RNNCells that will be composed in this order.
• state_is_tuple: If True, accepted and returned states are n-tuples, where n = len(cells). If False, the states are all concatenated along the column axis. This latter behavior will soon be deprecated.

参数:

单元格:按此顺序组成的RNNCells列表。

state_is_tuple:如果为真，则接受状态和返回状态为n元组，其中n = len(cell)。如果为假，则所有状态都沿着列轴连接。后一种行为很快就会被摒弃。

Methods

get_initial_state

View source

1. get_initial_state(
2.     inputs=None, batch_size=None, dtype=None
3. )

zero_state

View source

1. zero_state(
2.     batch_size, dtype
3. )
4.

Return zero-filled state tensor(s).

Args:

• batch_size: int, float, or unit Tensor representing the batch size.
• dtype: the data type to use for the state.

Returns:

If state_size is an int or TensorShape, then the return value is a N-D tensor of shape [batch_size, state_size]filled with zeros.

If state_size is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of 2-Dtensors with the shapes [batch_size, s] for each s in state_size.

返回

如果state_size是一个int或TensorShape，那么返回值就是一个包含0的shape [batch_size, state_size]的N-D张量。

如果state_size是一个嵌套列表或元组，那么返回值就是一个嵌套列表或元组(具有相同结构)的2-张量，其中每个s的形状[batch_size, s]为state_size中的每个s。