Enum defining the optimizations to apply when generating a tflite model.
DEFAULT The default optimization strategy that enables post-training quantization. The type of post-training quantization that will be used is dependent on the other converter options supplied. Refer to the documentation for further information on the types available and how to use them.
OPTIMIZE_FOR_SIZE Deprecated. Does the same as DEFAULT.
OPTIMIZE_FOR_LATENCY Deprecated. Does the same as DEFAULT.
EXPERIMENTAL_SPARSITY Experimental flag, subject to change.
Enable
optimization
by
taking
advantage
of
the
sparse
model
weights
trained
with
pruning
.
The
converter
will
inspect
the
sparsity
pattern
of
the
model
weights
and
do
its
best
to
improve
size
and
latency
.
The
flag
can
be
used
alone
to
optimize
float32
models
with
sparse
weights
.
It
can
also
be
used
together
with
the
DEFAULT
optimization
mode
to
optimize
quantized
models
with
sparse
weights
.
