BasicLSTMCell 是最简单的LSTMCell，源码位于：/tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py。
BasicLSTMCell 继承了RNNCell，源码位于：/tensorflow/python/ops/rnn_cell_impl.py
注意事项：
1. input_size 这个参数不能使用，使用的是num_units
2. state_is_tuple 官方建议设置为True。此时，输入和输出的states为c(cell状态)和h（输出）的二元组
3. 输入、输出、cell的维度相同，都是 batch_size * num_units，
cell = tf.contrib.rnn.BasicLSTMCell(num_units, forget_bias=0.0, state_is_tuple=True) #指定num_units
_initial_state = cell.zero_state(batch_size, tf.float32) #指定batch_size,将c和h全部初始化为0，shape全是batch_size * num_units，
4.
class BasicLSTMCell(RNNCell):
"""Basic LSTM recurrent network cell. The implementation is based on: http://arxiv.org/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 LSTMCell that follows.
""" def __init__(self, num_units, forget_bias=1.0, input_size=None,
state_is_tuple=True, activation=tanh):
"""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).
input_size: Deprecated and unused.
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.
"""
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)
if input_size is not None:
logging.warn("%s: The input_size parameter is deprecated.", self)
self._num_units = num_units
self._forget_bias = forget_bias
self._state_is_tuple = state_is_tuple
self._activation = activation @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, scope=None):
"""Long short-term memory cell (LSTM)."""
with vs.variable_scope(scope or "basic_lstm_cell"):
# 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) # 线性计算 concat = [inputs, h]W + b
# 线性计算，分配W和b，W的shape为（2*num_units, 4*num_units）, b的shape为（4*num_units,）,共包含有四套参数，
# concat shape(batch_size, 4*num_units)
# 注意：只有cell 的input和output的size相等时才可以这样计算，否则要定义两套W,b.每套再包含四套参数
concat = _linear([inputs, h], 4 * self._num_units, True, scope=scope) # 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
5. lstm层，每一batch的运算
with tf.variable_scope("RNN"):
for time_step in range(num_steps):
if time_step > 0: tf.get_variable_scope().reuse_variables()
(cell_output, state) = cell(inputs[:, time_step, :], state)
outputs.append(cell_output)6. 每一epoch
7.全部运算