tf.keras.layers.LSTM

Long Short-Term Memory layer - Hochreiter 1997.

Inherits From: RNN, Layer, Operation

tf.keras.layers.LSTM(
    units,
    activation='tanh',
    recurrent_activation='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,
    seed=None,
    return_sequences=False,
    return_state=False,
    go_backwards=False,
    stateful=False,
    unroll=False,
    use_cudnn='auto',
    **kwargs
)

Used in the notebooks

Used in the guide	Used in the tutorials
Ragged tensors	Time series forecasting Generate music with an RNN Text classification with an RNN Load metrics from Prometheus server Graph regularization for sentiment classification using synthesized graphs

Based on available runtime hardware and constraints, this layer will choose different implementations (cuDNN-based or backend-native) to maximize the performance. If a GPU is available and all the arguments to the layer meet the requirement of the cuDNN kernel (see below for details), the layer will use a fast cuDNN implementation when using the TensorFlow backend. The requirements to use the cuDNN implementation are:

activation == tanh
recurrent_activation == sigmoid
dropout == 0 and recurrent_dropout == 0
unroll is False
use_bias is True
Inputs, if use masking, are strictly right-padded.
Eager execution is enabled in the outermost context.

For example:

inputs = np.random.random((32, 10, 8))
lstm = keras.layers.LSTM(4)
output = lstm(inputs)
output.shape
(32, 4)
lstm = keras.layers.LSTM(
    4, return_sequences=True, return_state=True)
whole_seq_output, final_memory_state, final_carry_state = lstm(inputs)
whole_seq_output.shape
(32, 10, 4)
final_memory_state.shape
(32, 4)
final_carry_state.shape
(32, 4)

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: sigmoid (`sigmoid`). If you pass `None`, no activation is applied (ie. "linear" activation: `a(x) = x`).
`use_bias`	Boolean, (default `True`), whether the layer should use a bias vector.
`kernel_initializer`	Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs. Default: `"glorot_uniform"`.
`recurrent_initializer`	Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state. Default: `"orthogonal"`.
`bias_initializer`	Initializer for the bias vector. Default: `"zeros"`.
`unit_forget_bias`	Boolean (default `True`). 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.
`kernel_regularizer`	Regularizer function applied to the `kernel` weights matrix. Default: `None`.
`recurrent_regularizer`	Regularizer function applied to the `recurrent_kernel` weights matrix. Default: `None`.
`bias_regularizer`	Regularizer function applied to the bias vector. Default: `None`.
`activity_regularizer`	Regularizer function applied to the output of the layer (its "activation"). Default: `None`.
`kernel_constraint`	Constraint function applied to the `kernel` weights matrix. Default: `None`.
`recurrent_constraint`	Constraint function applied to the `recurrent_kernel` weights matrix. Default: `None`.
`bias_constraint`	Constraint function applied to the bias vector. Default: `None`.
`dropout`	Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. Default: 0.
`recurrent_dropout`	Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. Default: 0.
`seed`	Random seed for dropout.
`return_sequences`	Boolean. Whether to return the last output in the output sequence, or the full sequence. Default: `False`.
`return_state`	Boolean. Whether to return the last state in addition to the output. Default: `False`.
`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.
`use_cudnn`	Whether to use a cuDNN-backed implementation. `"auto"` will attempt to use cuDNN when feasible, and will fallback to the default implementation if not.

Call arguments
`inputs`	A 3D tensor, with shape `(batch, timesteps, feature)`.
`mask`	Binary tensor of shape `(samples, timesteps)` indicating whether a given timestep should be masked (optional). An individual `True` entry indicates that the corresponding timestep should be utilized, while a `False` entry indicates that the corresponding timestep should be ignored. Defaults to `None`.
`training`	Python boolean indicating whether the layer should behave in training mode or in inference mode. This argument is passed to the cell when calling it. This is only relevant if `dropout` or `recurrent_dropout` is used (optional). Defaults to `None`.
`initial_state`	List of initial state tensors to be passed to the first call of the cell (optional, `None` causes creation of zero-filled initial state tensors). Defaults to `None`.

Attributes
`activation`
`bias_constraint`
`bias_initializer`
`bias_regularizer`
`dropout`
`input`	Retrieves the input tensor(s) of a symbolic operation. Only returns the tensor(s) corresponding to the first time the operation was called.
`kernel_constraint`
`kernel_initializer`
`kernel_regularizer`
`output`	Retrieves the output tensor(s) of a layer. Only returns the tensor(s) corresponding to the first time the operation was called.
`recurrent_activation`
`recurrent_constraint`
`recurrent_dropout`
`recurrent_initializer`
`recurrent_regularizer`
`unit_forget_bias`
`units`
`use_bias`

Methods

`from_config`

View source

@classmethod
from_config(
    config
)

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Args
`config`	A Python dictionary, typically the output of get_config.

Returns
A layer instance.

`get_initial_state`

View source

get_initial_state(
    batch_size
)

`inner_loop`

View source

inner_loop(
    sequences, initial_state, mask, training=False
)

`reset_state`

View source

reset_state()

`reset_states`

View source

reset_states()

`symbolic_call`

View source

symbolic_call(
    *args, **kwargs
)

tf.keras.layers.LSTM

Used in the notebooks

For example:

Args

Call arguments

Attributes

Methods

from_config

get_initial_state

inner_loop

reset_state

reset_states

symbolic_call

`from_config`

`get_initial_state`

`inner_loop`

`reset_state`

`reset_states`

`symbolic_call`