DL之LSTM：tf.contrib.rnn.BasicLSTMCell(rnn_unit)函数的解读
tf.contrib.rnn.BasicLSTMCell(rnn_unit)函数的解读
函数功能解读
| """Basic LSTM recurrent network cell. The implementation is based on: http:///abs/1409.2329. We add forget_bias (default: 1) to the biases of the forget gate in order to reduce the scale of forgetting in the beginning of the training. It does not allow cell clipping, a projection layer, and does not use peep-hole connections: it is the basic baseline. For advanced models, please use the full @{tf.nn.rnn_cell.LSTMCell}
that follows. """ def __init__(self,
num_units,
forget_bias=1.0,
state_is_tuple=True,
activation=None,
reuse=None,
name=None,
dtype=None):
"""Initialize the basic LSTM cell. | 基本LSTM递归网络单元。 实现基于:http:///abs/1409.2329。 我们在遗忘门的偏见中加入了遗忘偏见(默认值:1)，以减少训练开始时的遗忘程度。 它不允许细胞剪切(一个投影层)，也不使用窥孔连接:它是基本的基线。对于高级模型，请使用完整的@{tf.n .rnn_cell. lstmcell}遵循。 | | Args:
num_units: int, The number of units in the LSTM cell.
forget_bias: float, The bias added to forget gates (see above).
Must set to `0.0` manually 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. The latter behavior will soon be deprecated.
activation: Activation function of the inner states. Default: `tanh`.
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`. When restoring from CudnnLSTM-trained checkpoints, must use `CudnnCompatibleLSTMCell` instead.
""" | 参数:
num_units: int类型, LSTM单元中的单元数。
forget_bias: float类型，偏见添加到忘记门(见上面)。
从cudnnlstm训练的检查点恢复时，必须手动设置为“0.0”。
state_is_tuple: 如果为真，则接受状态和返回状态是' c_state '和' m_state '的二元组。如果为假，则沿着列轴连接它们。后一种行为很快就会被摒弃。
activation: 内部状态的激活功能。默认值tanh激活函数。
reuse: (可选)Python布尔值，描述是否在现有范围内重用变量。如果不是“True”，并且现有范围已经有给定的变量，则会引发错误。
name:字符串，层的名称。具有相同名称的层将共享权重，但是为了避免错误，我们需要在这种情况下重用=True。
dtype:该层的默认dtype(默认为'None’意味着使用第一个输入的类型)。当' build '在' call '之前被调用时是必需的。 从经过cudnnlstm训练的检查点恢复时，必须使用“CudnnCompatibleLSTMCell”。
”“” |
函数代码实现
@tf_export("nn.rnn_cell.BasicLSTMCell")
class BasicLSTMCell(LayerRNNCell):
"""Basic LSTM recurrent network cell.
The implementation is based on: http:///abs/1409.2329.
We add forget_bias (default: 1) to the biases of the forget gate in order to
reduce the scale of forgetting in the beginning of the training.
It does not allow cell clipping, a projection layer, and does not
use peep-hole connections: it is the basic baseline.
For advanced models, please use the full @{tf.nn.rnn_cell.LSTMCell}
that follows.
"""
def __init__(self,
num_units,
forget_bias=1.0,
state_is_tuple=True,
activation=None,
reuse=None,
name=None,
dtype=None):
"""Initialize the basic LSTM cell.
Args:
num_units: int, The number of units in the LSTM cell.
forget_bias: float, The bias added to forget gates (see above).
Must set to `0.0` manually 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. The latter behavior will soon be deprecated.
activation: Activation function of the inner states. Default: `tanh`.
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`.
When restoring from CudnnLSTM-trained checkpoints, must use
`CudnnCompatibleLSTMCell` instead.
"""
super(BasicLSTMCell, self).__init__(_reuse=reuse, name=name, dtype=dtype)
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)
# Inputs must be 2-dimensional.
self.input_spec = base_layer.InputSpec(ndim=2)
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 build(self, inputs_shape):
if inputs_shape[1].value is None:
raise ValueError("Expected inputs.shape[-1] to be known, saw shape: %s"
% inputs_shape)
input_depth = inputs_shape[1].value
h_depth = self._num_units
self._kernel = self.add_variable(
_WEIGHTS_VARIABLE_NAME,
shape=[input_depth + h_depth, 4 * self._num_units])
self._bias = self.add_variable(
_BIAS_VARIABLE_NAME,
shape=[4 * self._num_units],
initializer=init_ops.zeros_initializer(dtype=self.dtype))
self.built = True
def call(self, inputs, state):
"""Long short-term memory cell (LSTM).
Args:
inputs: `2-D` tensor with shape `[batch_size, input_size]`.
state: An `LSTMStateTuple` of state tensors, each shaped
`[batch_size, num_units]`, if `state_is_tuple` has been set to
`True`. Otherwise, a `Tensor` shaped
`[batch_size, 2 * num_units]`.
Returns:
A pair containing the new hidden state, and the new state (either a
`LSTMStateTuple` or a concatenated state, depending on
`state_is_tuple`).
"""
sigmoid = math_ops.sigmoid
one = constant_op.constant(1, dtype=dtypes.int32)
# 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=one)
gate_inputs = math_ops.matmul(
array_ops.concat([inputs, h], 1), self._kernel)
gate_inputs = nn_ops.bias_add(gate_inputs, self._bias)
# i = input_gate, j = new_input, f = forget_gate, o = output_gate
i, j, f, o = array_ops.split(
value=gate_inputs, num_or_size_splits=4, axis=one)
forget_bias_tensor = constant_op.constant(self._forget_bias, dtype=f.dtype)
# Note that using `add` and `multiply` instead of `+` and `*` gives a
# performance improvement. So using those at the cost of readability.
add = math_ops.add
multiply = math_ops.multiply
new_c = add(multiply(c, sigmoid(add(f, forget_bias_tensor))),
multiply(sigmoid(i), self._activation(j)))
new_h = multiply(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
|
