View source on GitHub |
Long Short-Term Memory layer - Hochreiter 1997.
Inherits From: RNN
, Layer
, Module
tf.compat.v1.keras.layers.LSTM(
units,
activation='tanh',
recurrent_activation='hard_sigmoid',
use_bias=True,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
unit_forget_bias=True,
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
dropout=0.0,
recurrent_dropout=0.0,
return_sequences=False,
return_state=False,
go_backwards=False,
stateful=False,
unroll=False,
**kwargs
)
Note that this cell is not optimized for performance on GPU. Please use
tf.compat.v1.keras.layers.CuDNNLSTM
for better performance on GPU.
Args | |
---|---|
units
|
Positive integer, dimensionality of the output space. |
activation
|
Activation function to use.
Default: hyperbolic tangent (tanh ).
If you pass None , no activation is applied
(ie. "linear" activation: a(x) = x ).
|
recurrent_activation
|
Activation function to use
for the recurrent step.
Default: hard sigmoid (hard_sigmoid ).
If you pass None , no activation is applied
(ie. "linear" activation: a(x) = x ).
|
use_bias
|
Boolean, whether the layer uses a bias vector. |
kernel_initializer
|
Initializer for the kernel weights matrix,
used for the linear transformation of the inputs..
|
recurrent_initializer
|
Initializer for the recurrent_kernel
weights matrix,
used for the linear transformation of the recurrent state.
|
bias_initializer
|
Initializer for the bias vector. |
unit_forget_bias
|
Boolean.
If True, add 1 to the bias of the forget gate at initialization.
Setting it to true will also force bias_initializer="zeros" .
This is recommended in Jozefowicz et al., 2015.
|
kernel_regularizer
|
Regularizer function applied to
the kernel weights matrix.
|
recurrent_regularizer
|
Regularizer function applied to
the recurrent_kernel weights matrix.
|
bias_regularizer
|
Regularizer function applied to the bias vector. |
activity_regularizer
|
Regularizer function applied to the output of the layer (its "activation"). |
kernel_constraint
|
Constraint function applied to
the kernel weights matrix.
|
recurrent_constraint
|
Constraint function applied to
the recurrent_kernel weights matrix.
|
bias_constraint
|
Constraint function applied to the bias vector. |
dropout
|
Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. |
recurrent_dropout
|
Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. |
return_sequences
|
Boolean. Whether to return the last output in the output sequence, or the full sequence. |
return_state
|
Boolean. Whether to return the last state in addition to the output. |
go_backwards
|
Boolean (default False). If True, process the input sequence backwards and return the reversed sequence. |
stateful
|
Boolean (default False). If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch. |
unroll
|
Boolean (default False). If True, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory-intensive. Unrolling is only suitable for short sequences. |
time_major
|
The shape format of the inputs and outputs tensors.
If True, the inputs and outputs will be in shape
(timesteps, batch, ...) , whereas in the False case, it will be
(batch, timesteps, ...) . Using time_major = True is a bit more
efficient because it avoids transposes at the beginning and end of the
RNN calculation. However, most TensorFlow data is batch-major, so by
default this function accepts input and emits output in batch-major
form.
|
Methods
reset_states
reset_states(
states=None
)
Reset the recorded states for the stateful RNN layer.
Can only be used when RNN layer is constructed with stateful
= True
.
Args:
states: Numpy arrays that contains the value for the initial state,
which will be feed to cell at the first time step. When the value is
None, zero filled numpy array will be created based on the cell
state size.
Raises | |
---|---|
AttributeError
|
When the RNN layer is not stateful. |
ValueError
|
When the batch size of the RNN layer is unknown. |
ValueError
|
When the input numpy array is not compatible with the RNN layer state, either size wise or dtype wise. |