tft.TransformFeaturesLayer

A Keras layer for applying a tf.Transform output to input layers.

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 SeedGenerators.

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 SeedGenerators.

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

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

call

View source

compute_mask

count_params

Count the total number of scalars composing the weights.

Returns
An integer count.

__call__

Call self as a function.