DTypePolicy
classkeras.mixed_precision.DTypePolicy(name)
A dtype policy for a Keras layer.
A dtype policy determines a layer's computation and variable dtypes. Each
layer has a policy. Policies can be passed to the dtype
argument of layer
constructors, or a global policy can be set with
keras.mixed_precision.set_dtype_policy
.
Arguments
"float32"
or "float64"
,
which causes both the compute and variable dtypes
will be that dtype.
Can also be the string "mixed_float16"
or "mixed_bfloat16"
,
which causes the compute dtype to be float16
or bfloat16
and the variable dtype to be float32
.Typically you only need to interact with dtype policies when using mixed
precision, which is the use of float16 or bfloat16 for computations and
float32 for variables. This is why the term mixed_precision
appears in the
API name. Mixed precision can be enabled by passing "mixed_float16"
or
"mixed_bfloat16"
to keras.mixed_precision.set_dtype_policy()
.
>>> keras.mixed_precision.set_dtype_policy("mixed_float16")
>>> layer1 = keras.layers.Dense(10)
>>> layer1.dtype_policy # layer1 will automatically use mixed precision
<DTypePolicy "mixed_float16">
>>> # Can optionally override layer to use float32
>>> # instead of mixed precision.
>>> layer2 = keras.layers.Dense(10, dtype="float32")
>>> layer2.dtype_policy
<DTypePolicy "float32">
>>> # Set policy back to initial float32.
>>> keras.mixed_precision.set_dtype_policy('float32')
In the example above, passing dtype="float32"
to the layer is
equivalent to passing
dtype=keras.mixed_precision.DTypePolicy("float32")
.
In general, passing a dtype policy name to a layer is equivalent
to passing the corresponding policy, so it is never necessary
to explicitly construct a DTypePolicy
object.
dtype_policy
functionkeras.mixed_precision.dtype_policy()
Returns the current default dtype policy object.
set_dtype_policy
functionkeras.mixed_precision.set_dtype_policy(policy)
Sets the default dtype policy globally.
Example
>>> keras.mixed_precision.set_dtype_policy("mixed_float16")