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