State of a tfa.seq2seq.AttentionWrapper
.
tfa.seq2seq.AttentionWrapperState(
cell_state, attention, alignments, alignment_history, attention_state
)
Attributes |
cell_state
|
The state of the wrapped RNN cell at the previous time
step.
|
attention
|
The attention emitted at the previous time step.
|
alignments
|
A single or tuple of Tensor (s) containing the
alignments emitted at the previous time step for each attention
mechanism.
|
alignment_history
|
(if enabled) a single or tuple of TensorArray (s)
containing alignment matrices from all time steps for each attention
mechanism. Call stack() on each to convert to a Tensor .
|
attention_state
|
A single or tuple of nested objects
containing attention mechanism state for each attention mechanism.
The objects may contain Tensors or TensorArrays.
|
Methods
clone
View source
clone(
**kwargs
)
Clone this object, overriding components provided by kwargs.
The new state fields' shape must match original state fields' shape.
This will be validated, and original fields' shape will be propagated
to new fields.
Example:
batch_size = 1
memory = tf.random.normal(shape=[batch_size, 3, 100])
encoder_state = [tf.zeros((batch_size, 100)), tf.zeros((batch_size, 100))]
attention_mechanism = tfa.seq2seq.LuongAttention(100, memory=memory, memory_sequence_length=[3] * batch_size)
attention_cell = tfa.seq2seq.AttentionWrapper(tf.keras.layers.LSTMCell(100), attention_mechanism, attention_layer_size=10)
decoder_initial_state = attention_cell.get_initial_state(batch_size=batch_size, dtype=tf.float32)
decoder_initial_state = decoder_initial_state.clone(cell_state=encoder_state)
Args |
**kwargs
|
Any properties of the state object to replace in the
returned AttentionWrapperState .
|
Returns |
A new AttentionWrapperState whose properties are the same as
this one, except any overridden properties as provided in kwargs .
|