A Keras layer for applying a tf.Transform output to input layers.
tft.TransformFeaturesLayer(
tft_output: tft.TFTransformOutput
,
exported_as_v1: Optional[bool] = None
)
Attributes |
compiled_metrics
|
|
compute_dtype
|
The dtype of the computations performed by the layer.
|
distribute_reduction_method
|
|
distribute_strategy
|
|
dtype
|
Alias of layer.variable_dtype .
|
input
|
Retrieves the input tensor(s) of a symbolic operation.
Only returns the tensor(s) corresponding to the first time
the operation was called.
|
input_dtype
|
The dtype layer inputs should be converted to.
|
input_spec
|
|
jit_compile
|
|
layers
|
|
losses
|
List of scalar losses from add_loss , regularizers and sublayers.
|
metrics
|
|
metrics_names
|
|
metrics_variables
|
|
non_trainable_variables
|
List of all non-trainable layer state.
This extends layer.non_trainable_weights to include all state used by
the layer including state for metrics and SeedGenerator s.
|
non_trainable_weights
|
List of all non-trainable weight variables of the layer.
These are the weights that should not be updated by the optimizer during
training. Unlike, layer.non_trainable_variables this excludes metric
state and random seeds.
|
output
|
Retrieves the output tensor(s) of a layer.
Only returns the tensor(s) corresponding to the first time
the operation was called.
|
run_eagerly
|
|
supports_masking
|
Whether this layer supports computing a mask using compute_mask .
|
trainable
|
Settable boolean, whether this layer should be trainable or not.
|
trainable_variables
|
List of all trainable layer state.
This is equivalent to layer.trainable_weights .
|
trainable_weights
|
List of all trainable weight variables of the layer.
These are the weights that get updated by the optimizer during training.
|
variable_dtype
|
The dtype of the state (weights) of the layer.
|
variables
|
List of all layer state, including random seeds.
This extends layer.weights to include all state used by the layer
including SeedGenerator s.
Note that metrics variables are not included here, use
metrics_variables to visit all the metric variables.
|
weights
|
List of all weight variables of the layer.
Unlike, layer.variables this excludes metric state and random seeds.
|
Methods
add_loss
add_loss(
loss
)
Can be called inside of the call()
method to add a scalar loss.
Example:
class MyLayer(Layer):
...
def call(self, x):
self.add_loss(ops.sum(x))
return x
build
build(
input_shape
)
call
View source
call(
inputs: Mapping[str, common_types.TensorType]
) -> Dict[str, common_types.TensorType]
compute_mask
compute_mask(
inputs, previous_mask
)
count_params
count_params()
Count the total number of scalars composing the weights.
Returns |
An integer count.
|
__call__
__call__(
*args, **kwargs
)
Call self as a function.