RNNCell本地文件的路径：~/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/ops/rnn_cell_impl.py
Github地址：https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/rnn_cell_impl.py
有兴趣的可以直接去查看实现源码。
RNNCell是所有cell的父类，LayerRNNCell的设计是为了方便参数的管理，其他的cell和wrapper等结构均是根据具体论文和相关应用场景设计实现的。
rnn_cell的继承关系：
LayerRNNCell(RNNCell)
BasicRNNCell(LayerRNNCell)
BasicLSTMCell(LayerRNNCell)
LSTMCell(LayerRNNCell)
GRUCell(LayerRNNCell)
rnn_cell wrapper 用于装载rnncell，warpper均继承自RNNCell，因为它们的实现是在rnncell的信息的输入、输出和信息的自环的过程中进行操作，该过程和rnn_cell有相同的自环操作过程。主要包含：
- MultiRNNCell
- DropoutWrapper
- DeviceWrapper
- ResidualWrapper
RNNCell
class RNNCell(base_layer.Layer):
def __call__(self, inputs, state, scope=None):
if scope is not None:
with vs.variable_scope(scope,
custom_getter=self._rnn_get_variable) as scope:
return super(RNNCell, self).__call__(inputs, state, scope=scope)
else:
with vs.variable_scope(vs.get_variable_scope(),
custom_getter=self._rnn_get_variable):
return super(RNNCell, self).__call__(inputs, state)
def _rnn_get_variable(self, getter, *args, **kwargs):
variable = getter(*args, **kwargs)
trainable = (variable in tf_variables.trainable_variables() or
(isinstance(variable, tf_variables.PartitionedVariable) and
list(variable)[0] in tf_variables.trainable_variables()))
if trainable and variable not in self._trainable_weights:
self._trainable_weights.append(variable)
elif not trainable and variable not in self._non_trainable_weights:
self._non_trainable_weights.append(variable)
return variable
@property
def state_size(self):
raise NotImplementedError("Abstract method")
@property
def output_size(self):
raise NotImplementedError("Abstract method")
def build(self, _):
pass
def zero_state(self, batch_size, dtype):
with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
state_size = self.state_size
return _zero_state_tensors(state_size, batch_size, dtype)
RNNCell是一个抽象的父类，其他的RNNcell都会继承该方法，然后具体实现其中的call()函数。从上面的定义中我们发现其主要有state_size和output_size两个属性，分别代表了隐藏层和输出层的维度。然后就是zero_state()和call()两个函数，分别用于初始化初始状态h0为全零向量和定义实际的RNNCell的操作（比如RNN就是一个激活，GRU的两个门，LSTM的三个门控等，不同的RNN的区别主要体现在这个函数）。有了这个抽象类，我们接下来看看tf中BasicRNNCell、GRUCell、BasicLSTMCell三个cell的定义方法，了解不同变种RNN模型的定义方式的区别和实现方法。
BasicRNNCell
首先是BasicRNNCell，就是我们常说的RNN
最简单的RNN结构如上图所示，可以看出BasicRNNCell中把state_size和output_size定义成相同，而且ht和output也是相同的（看call函数的输出是两个output，也就是其并未定义输出部分）。再看一下其主要功能实现就是call函数的第一行，就是input和前一时刻状态state经过一个线性函数在经过一个激活函数即可，也就是最普通的RNN定义方式。也就是说output = new_state = f(W * input + U * state + B)。
class BasicRNNCell(RNNCell):
def __init__(self, num_units, activation=None, reuse=None):
super(BasicRNNCell, self).__init__(_reuse=reuse)
self._num_units = num_units
self._activation = activation or math_ops.tanh
@property
def state_size(self):
return self._num_units
@property
def output_size(self):
return self._num_units
def call(self, inputs, state):
output = self._activation(_linear([inputs, state], self._num_units, True))
return output, output
GRUCell
接下来我们看一下GRU的定义，相比BasicRNNCell只改变了call函数部分，增加了重置门和更新门两部分，分别由r和u表示。然后c表示要更新的状态值。其对应的图及公式如下所示：
r = f(W1 * input + U1 * state + B1)
u = f(W2 * input + U2 * state + B2)
c = f(W3 * input + U3 * r * state + B3)
h_new = u * h + (1 - u) * c
GRUCell的实现代码如下：
class GRUCell(RNNCell):
def __init__(self,
num_units,
activation=None,
reuse=None,
kernel_initializer=None,
bias_initializer=None):
super(GRUCell, self).__init__(_reuse=reuse)
self._num_units = num_units
self._activation = activation or math_ops.tanh
self._kernel_initializer = kernel_initializer
self._bias_initializer = bias_initializer
@property
def state_size(self):
return self._num_units
@property
def output_size(self):
return self._num_units
def call(self, inputs, state):
with vs.variable_scope("gates"): # Reset gate and update gate.
# We start with bias of 1.0 to not reset and not update.
bias_ones = self._bias_initializer
if self._bias_initializer is None:
dtype = [a.dtype for a in [inputs, state]][0]
bias_ones = init_ops.constant_initializer(1.0, dtype=dtype)
value = math_ops.sigmoid(
_linear([inputs, state], 2 * self._num_units, True, bias_ones,
self._kernel_initializer))
r, u = array_ops.split(value=value, num_or_size_splits=2, axis=1)
with vs.variable_scope("candidate"):
c = self._activation(
_linear([inputs, r * state], self._num_units, True,
self._bias_initializer, self._kernel_initializer))
new_h = u * state + (1 - u) * c
return new_h, new_h
BasicLSTMCell
接下来再看一下BasicLSTMCell的实现方法，相比GRU，LSTM又多了一个输出门，而且又新增添了一个C表示其内部状态，然后将h和c以tuple的形式返回作为LSTM内部的状态变量。其内部结构和公式表示如下图所示：
具体实现公式如下：
BasicLSTMCell的实现代码如下：
class BasicLSTMCell(RNNCell):
def __init__(self, num_units, forget_bias=1.0,
state_is_tuple=True, activation=None, reuse=None):
super(BasicLSTMCell, self).__init__(_reuse=reuse)
if not state_is_tuple:
logging.warn("%s: Using a concatenated state is slower and will soon be "
"deprecated. Use state_is_tuple=True.", self)
self._num_units = num_units
self._forget_bias = forget_bias
self._state_is_tuple = state_is_tuple
self._activation = activation or math_ops.tanh
@property
def state_size(self):
return (LSTMStateTuple(self._num_units, self._num_units)
if self._state_is_tuple else 2 * self._num_units)
@property
def output_size(self):
return self._num_units
def call(self, inputs, state):
sigmoid = math_ops.sigmoid
# Parameters of gates are concatenated into one multiply for efficiency.
if self._state_is_tuple:
c, h = state
else:
c, h = array_ops.split(value=state, num_or_size_splits=2, axis=1)
concat = _linear([inputs, h], 4 * self._num_units, True)
# i = input_gate, j = new_input, f = forget_gate, o = output_gate
i, j, f, o = array_ops.split(value=concat, num_or_size_splits=4, axis=1)
new_c = (
c * sigmoid(f + self._forget_bias) + sigmoid(i) * self._activation(j))
new_h = self._activation(new_c) * sigmoid(o)
if self._state_is_tuple:
new_state = LSTMStateTuple(new_c, new_h)
else:
new_state = array_ops.concat([new_c, new_h], 1)
return new_h, new_state
从上面的代码可以发现，其与BasicRNNCell和GRUCell的区别也主要在call()函数上，不同的功能实现也都在call里面进行。不难发现，还有一个在三个累里面都频繁使用到的函数_linear()，这个函数的作用是什么呢，我们先来看一下其源码：
_linear函数
def _linear(args,
output_size,
bias,
bias_initializer=None,
kernel_initializer=None):
if args is None or (nest.is_sequence(args) and not args):
raise ValueError("`args` must be specified")
if not nest.is_sequence(args):
args = [args]
# Calculate the total size of arguments on dimension 1.
total_arg_size = 0
shapes = [a.get_shape() for a in args]
for shape in shapes:
if shape.ndims != 2:
raise ValueError("linear is expecting 2D arguments: %s" % shapes)
if shape[1].value is None:
raise ValueError("linear expects shape[1] to be provided for shape %s, "
"but saw %s" % (shape, shape[1]))
else:
total_arg_size += shape[1].value
dtype = [a.dtype for a in args][0]
# Now the computation.
scope = vs.get_variable_scope()
with vs.variable_scope(scope) as outer_scope:
weights = vs.get_variable(
_WEIGHTS_VARIABLE_NAME, [total_arg_size, output_size],
dtype=dtype,
initializer=kernel_initializer)
if len(args) == 1:
res = math_ops.matmul(args[0], weights)
else:
res = math_ops.matmul(array_ops.concat(args, 1), weights)
if not bias:
return res
with vs.variable_scope(outer_scope) as inner_scope:
inner_scope.set_partitioner(None)
if bias_initializer is None:
bias_initializer = init_ops.constant_initializer(0.0, dtype=dtype)
biases = vs.get_variable(
_BIAS_VARIABLE_NAME, [output_size],
dtype=dtype,
initializer=bias_initializer)
return nn_ops.bias_add(res, biases)
这个函数的输入args就是[input, state]，而output_size是输出层的大小，我们可以看到BasicRNNCell中，output_size就是_num_units，GRUCell中是2*_num_units，BasicLSTMCell中是4*_num_units，这是因为_linear中执行的是RNN中的几个等式的Wx+Uh+B的功能，但是不同的RNN中数量不同，比如LSTM中需要计算四次，然后直接把output_size定义为4*_num_units，再把输出进行拆分成四个变量即可~~
自定义RNNCell
看完BasicRNNCell 、GRUCell和BasicLSTMCell的实现方案，应该不难想象出自定义RNNCell的方法，那就是继承_LayerRNNCell这个抽象类，然后一定要实现__init__、build、__call__这三个函数就行了，其中在call函数中实现自己需要的功能即可。（注意：build只调用一次，在build中进行变量实例化，在call中实现具体的rnncell操作）。
参考博客：
tensorflow中RNNcell源码分析以及自定义RNNCell的方法
Tensorflow rnn_cell api 阅读笔记
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