tf.keras.layers.LocallyConnected1D

Long Short-Term Memory layer - Hochreiter 1997.

Inherits From: LSTM, RNN, Layer, Module

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,
    return_sequences=False, return_state=False, go_backwards=False, stateful=False,
    time_major=False, unroll=False, **kwargs
)

Used in the notebooks

Used in the guide	Used in the tutorials
Recurrent Neural Networks (RNN) with Keras Masking and padding with Keras The Functional API Working with preprocessing layers Ragged tensors	Text classification with an RNN Time series forecasting TensorFlow Addons Networks : Sequence-to-Sequence NMT with Attention Mechanism Load metrics from Prometheus server

See the Keras RNN API guide for details about the usage of RNN API.

Based on available runtime hardware and constraints, this layer will choose different implementations (cuDNN-based or pure-TensorFlow) 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.

The requirements to use the cuDNN implementation are:

activation == tanh
recurrent_activation == sigmoid
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 = tf.random.normal([32, 10, 8])
lstm = tf.keras.layers.LSTM(4)
output = lstm(inputs)
print(output.shape)
(32, 4)
lstm = tf.keras.layers.LSTM(4, return_sequences=True, return_state=True)
whole_seq_output, final_memory_state, final_carry_state = lstm(inputs)
print(whole_seq_output.shape)
(32, 10, 4)
print(final_memory_state.shape)
(32, 4)
print(final_carry_state.shape)
(32, 4)

Arguments
`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 uses 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.
`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.
`time_major`	The shape format of the `inputs` and `outputs` tensors. If True, the inputs and outputs will be in shape `[timesteps, batch, feature]`, whereas in the False case, it will be `[batch, timesteps, feature]`. 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.
`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.

Call arguments:

inputs: A 3D tensor with shape [batch, timesteps, feature].
mask: Binary tensor of shape [batch, timesteps] indicating whether a given timestep should be masked (optional, 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, defaults to None which causes creation of zero-filled initial state tensors).

Attributes
`activation`
`bias_constraint`
`bias_initializer`
`bias_regularizer`
`dropout`
`implementation`
`kernel_constraint`
`kernel_initializer`
`kernel_regularizer`
`recurrent_activation`
`recurrent_constraint`
`recurrent_dropout`
`recurrent_initializer`
`recurrent_regularizer`
`states`
`unit_forget_bias`
`units`
`use_bias`

Methods

`get_dropout_mask_for_cell`

View source

get_dropout_mask_for_cell(
    inputs, training, count=1
)

Get the dropout mask for RNN cell's input.

It will create mask based on context if there isn't any existing cached mask. If a new mask is generated, it will update the cache in the cell.

Args
`inputs`	The input tensor whose shape will be used to generate dropout mask.
`training`	Boolean tensor, whether its in training mode, dropout will be ignored in non-training mode.
`count`	Int, how many dropout mask will be generated. It is useful for cell that has internal weights fused together.

Returns
List of mask tensor, generated or cached mask based on context.

`get_recurrent_dropout_mask_for_cell`

View source

get_recurrent_dropout_mask_for_cell(
    inputs, training, count=1
)

Get the recurrent dropout mask for RNN cell.

It will create mask based on context if there isn't any existing cached mask. If a new mask is generated, it will update the cache in the cell.

Args
`inputs`	The input tensor whose shape will be used to generate dropout mask.
`training`	Boolean tensor, whether its in training mode, dropout will be ignored in non-training mode.
`count`	Int, how many dropout mask will be generated. It is useful for cell that has internal weights fused together.

Returns
List of mask tensor, generated or cached mask based on context.

`reset_dropout_mask`

View source

reset_dropout_mask()

Reset the cached dropout masks if any.

This is important for the RNN layer to invoke this in it call() method so that the cached mask is cleared before calling the cell.call(). The mask should be cached across the timestep within the same batch, but shouldn't be cached between batches. Otherwise it will introduce unreasonable bias against certain index of data within the batch.

`reset_recurrent_dropout_mask`

View source

reset_recurrent_dropout_mask()

Reset the cached recurrent dropout masks if any.

This is important for the RNN layer to invoke this in it call() method so that the cached mask is cleared before calling the cell.call(). The mask should be cached across the timestep within the same batch, but shouldn't be cached between batches. Otherwise it will introduce unreasonable bias against certain index of data within the batch.

`reset_states`

View source

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.

TensorFlow

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tf.keras / layers / layers.LocallyConnected1D

Used in the notebooks

For example:

Arguments

Call arguments:

Attributes

Methods

get_dropout_mask_for_cell

get_recurrent_dropout_mask_for_cell

reset_dropout_mask

reset_recurrent_dropout_mask

reset_states

`get_dropout_mask_for_cell`

`get_recurrent_dropout_mask_for_cell`

`reset_dropout_mask`

`reset_recurrent_dropout_mask`

`reset_states`