LSTMCell¶
- class paddle.fluid.layers. LSTMCell ( hidden_size, param_attr=None, bias_attr=None, gate_activation=None, activation=None, forget_bias=1.0, dtype='float32', name='LSTMCell' ) [source]
-
- api_attr
-
Static Graph
Long-Short Term Memory cell. It is a wrapper for fluid.contrib.layers.rnn_impl.BasicLSTMUnit to make it adapt to RNNCell.
The formula used is as follow:
\[ \begin{align}\begin{aligned}i_{t} & = act_g(W_{x_{i}}x_{t} + W_{h_{i}}h_{t-1} + b_{i})\\\begin{split}f_{t} & = act_g(W_{x_{f}}x_{t} + W_{h_{f}}h_{t-1} + b_{f} + forget\\_bias)\end{split}\\c_{t} & = f_{t}c_{t-1} + i_{t} act_c (W_{x_{c}}x_{t} + W_{h_{c}}h_{t-1} + b_{c})\\o_{t} & = act_g(W_{x_{o}}x_{t} + W_{h_{o}}h_{t-1} + b_{o})\\h_{t} & = o_{t} act_c (c_{t})\end{aligned}\end{align} \]For more details, please refer to RECURRENT NEURAL NETWORK REGULARIZATION
Examples
import paddle.fluid.layers as layers cell = layers.LSTMCell(hidden_size=256)-
call
(
inputs,
states
)
call¶
-
Perform calculations of LSTM.
- Parameters
-
inputs (Variable) – A tensor with shape [batch_size, input_size], corresponding to \(x_t\) in the formula. The data type should be float32 or float64.
states (Variable) – A list of containing two tensors, each shaped [batch_size, hidden_size], corresponding to \(h_{t-1}, c_{t-1}\) in the formula. The data type should be float32 or float64.
- Returns
-
-
A tuple(
(outputs, new_states)), where outputs is -
a tensor with shape [batch_size, hidden_size], corresponding to \(h_{t}\) in the formula; new_states is a list containing two tenser variables shaped [batch_size, hidden_size], corresponding to \(h_{t}, c_{t}\) in the formula. The data type of these tensors all is same as that of states.
-
A tuple(
- Return type
-
tuple
- property state_shape
-
[[hidden_size], [hidden_size]] (-1 for batch size would be automatically inserted into shape). These two shapes correspond to \(h_{t-1}\) and \(c_{t-1}\) separately.
- Type
-
The state_shape of LSTMCell is a list with two shapes
-
get_initial_states
(
batch_ref,
shape=None,
dtype='float32',
init_value=0,
batch_dim_idx=0
)
get_initial_states¶
-
Generate initialized states according to provided shape, data type and value.
- Parameters
-
batch_ref – A (possibly nested structure of) tensor variable[s]. The first dimension of the tensor will be used as batch size to initialize states.
shape – A (possibly nested structure of) shape[s], where a shape is represented as a list/tuple of integer). -1(for batch size) will beautomatically inserted if shape is not started with it. If None, property state_shape will be used. The default value is None.
dtype – A (possibly nested structure of) data type[s]. The structure must be same as that of shape, except when all tensors’ in states has the same data type, a single data type can be used. If property cell.state_shape is not available, float32 will be used as the data type. The default value is float32.
init_value – A float value used to initialize states.
batch_dim_idx – An integer indicating which dimension of the tensor in inputs represents batch size. The default value is 0.
- Returns
-
- tensor variable[s] packed in the same structure provided
-
by shape, representing the initialized states.
- Return type
-
Variable
- property state_dtype
-
Abstract method (property). Used to initialize states. A (possibly nested structure of) data types[s]. The structure must be same as that of shape, except when all tensors’ in states has the same data type, a single data type can be used. Not necessary to be implemented if states are not initialized by get_initial_states or the dtype argument is provided when using get_initial_states.