Add docs for RNN implementation modes.
@@ -1174,6 +1174,11 @@ class GRUCell(Layer):
Fraction of the units to drop for
the linear transformation of the recurrent state.
implementation: Implementation mode, either 1 or 2.
+ Mode 1 will structure its operations as a larger number of
+ smaller dot products and additions, whereas mode 2 will
+ batch them into fewer, larger operations. These modes will
+ have different performance profiles on different hardware and
+ for different applications.
"""
def __init__(self, units,
@@ -1409,6 +1414,11 @@ class GRU(RNN):
Fraction of the units to drop for
the linear transformation of the recurrent state.
implementation: Implementation mode, either 1 or 2.
+ Mode 1 will structure its operations as a larger number of
+ smaller dot products and additions, whereas mode 2 will
+ batch them into fewer, larger operations. These modes will
+ have different performance profiles on different hardware and
+ for different applications.
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
@@ -1646,6 +1656,11 @@ class LSTMCell(Layer):
Fraction of the units to drop for
the linear transformation of the recurrent state.
implementation: Implementation mode, either 1 or 2.
+ Mode 1 will structure its operations as a larger number of
+ smaller dot products and additions, whereas mode 2 will
+ batch them into fewer, larger operations. These modes will
+ have different performance profiles on different hardware and
+ for different applications.
"""
def __init__(self, units,
@@ -1904,6 +1919,11 @@ class LSTM(RNN):
Fraction of the units to drop for
the linear transformation of the recurrent state.
implementation: Implementation mode, either 1 or 2.
+ Mode 1 will structure its operations as a larger number of
+ smaller dot products and additions, whereas mode 2 will
+ batch them into fewer, larger operations. These modes will
+ have different performance profiles on different hardware and
+ for different applications.
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
