layer_tfsm: Reload a Keras model/layer that was saved via `export_savedmodel()`.

Description

Reload a Keras model/layer that was saved via export_savedmodel().

Usage

layer_tfsm(
  object,
  filepath,
  call_endpoint = "serve",
  call_training_endpoint = NULL,
  trainable = TRUE,
  name = NULL,
  dtype = NULL
)

Value

The return value depends on the value provided for the first argument. If object is:

a keras_model_sequential(), then the layer is added to the sequential model (which is modified in place). To enable piping, the sequential model is also returned, invisibly.
a keras_input(), then the output tensor from calling layer(input) is returned.
NULL or missing, then a Layer instance is returned.

Arguments

object: Object to compose the layer with. A tensor, array, or sequential model.
filepath: string, the path to the SavedModel.
call_endpoint: Name of the endpoint to use as the call() method of the reloaded layer. If the SavedModel was created via export_savedmodel(), then the default endpoint name is 'serve'. In other cases it may be named 'serving_default'.
call_training_endpoint: see description
trainable: see description
name: String, name for the object
dtype: datatype (e.g., "float32").

Examples

model <- keras_model_sequential(input_shape = c(784)) |> layer_dense(10)
model |> export_savedmodel("path/to/artifact")

## Saved artifact at 'path/to/artifact'. The following endpoints are available:
##
## * Endpoint 'serve'
##   args_0 (POSITIONAL_ONLY): TensorSpec(shape=(None, 784), dtype=tf.float32, name='keras_tensor')
## Output Type:
##   TensorSpec(shape=(None, 10), dtype=tf.float32, name=None)
## Captures:
##   135633806507664: TensorSpec(shape=(), dtype=tf.resource, name=None)
##   135634129975248: TensorSpec(shape=(), dtype=tf.resource, name=None)

reloaded_layer <- layer_tfsm(filepath = "path/to/artifact")
input <- random_normal(c(2, 784))
output <- reloaded_layer(input)
stopifnot(all.equal(as.array(output), as.array(model(input))))

The reloaded object can be used like a regular Keras layer, and supports training/fine-tuning of its trainable weights. Note that the reloaded object retains none of the internal structure or custom methods of the original object -- it's a brand new layer created around the saved function.

Limitations:

Only call endpoints with a single inputs tensor argument (which may optionally be a named list/list of tensors) are supported. For endpoints with multiple separate input tensor arguments, consider subclassing layer_tfsm and implementing a call() method with a custom signature.
If you need training-time behavior to differ from inference-time behavior (i.e. if you need the reloaded object to support a training=TRUE argument in __call__()), make sure that the training-time call function is saved as a standalone endpoint in the artifact, and provide its name to the layer_tfsm via the call_training_endpoint argument.

Other layers:
Layer()
layer_activation()
layer_activation_elu()
layer_activation_leaky_relu()
layer_activation_parametric_relu()
layer_activation_relu()
layer_activation_softmax()
layer_activity_regularization()
layer_add()
layer_additive_attention()
layer_alpha_dropout()
layer_attention()
layer_auto_contrast()
layer_average()
layer_average_pooling_1d()
layer_average_pooling_2d()
layer_average_pooling_3d()
layer_batch_normalization()
layer_bidirectional()
layer_category_encoding()
layer_center_crop()
layer_concatenate()
layer_conv_1d()
layer_conv_1d_transpose()
layer_conv_2d()
layer_conv_2d_transpose()
layer_conv_3d()
layer_conv_3d_transpose()
layer_conv_lstm_1d()
layer_conv_lstm_2d()
layer_conv_lstm_3d()
layer_cropping_1d()
layer_cropping_2d()
layer_cropping_3d()
layer_dense()
layer_depthwise_conv_1d()
layer_depthwise_conv_2d()
layer_discretization()
layer_dot()
layer_dropout()
layer_einsum_dense()
layer_embedding()
layer_equalization()
layer_feature_space()
layer_flatten()
layer_flax_module_wrapper()
layer_gaussian_dropout()
layer_gaussian_noise()
layer_global_average_pooling_1d()
layer_global_average_pooling_2d()
layer_global_average_pooling_3d()
layer_global_max_pooling_1d()
layer_global_max_pooling_2d()
layer_global_max_pooling_3d()
layer_group_normalization()
layer_group_query_attention()
layer_gru()
layer_hashed_crossing()
layer_hashing()
layer_identity()
layer_integer_lookup()
layer_jax_model_wrapper()
layer_lambda()
layer_layer_normalization()
layer_lstm()
layer_masking()
layer_max_num_bounding_boxes()
layer_max_pooling_1d()
layer_max_pooling_2d()
layer_max_pooling_3d()
layer_maximum()
layer_mel_spectrogram()
layer_minimum()
layer_mix_up()
layer_multi_head_attention()
layer_multiply()
layer_normalization()
layer_permute()
layer_rand_augment()
layer_random_brightness()
layer_random_color_degeneration()
layer_random_color_jitter()
layer_random_contrast()
layer_random_crop()
layer_random_flip()
layer_random_grayscale()
layer_random_hue()
layer_random_posterization()
layer_random_rotation()
layer_random_saturation()
layer_random_sharpness()
layer_random_shear()
layer_random_translation()
layer_random_zoom()
layer_repeat_vector()
layer_rescaling()
layer_reshape()
layer_resizing()
layer_rnn()
layer_separable_conv_1d()
layer_separable_conv_2d()
layer_simple_rnn()
layer_solarization()
layer_spatial_dropout_1d()
layer_spatial_dropout_2d()
layer_spatial_dropout_3d()
layer_spectral_normalization()
layer_stft_spectrogram()
layer_string_lookup()
layer_subtract()
layer_text_vectorization()
layer_time_distributed()
layer_torch_module_wrapper()
layer_unit_normalization()
layer_upsampling_1d()
layer_upsampling_2d()
layer_upsampling_3d()
layer_zero_padding_1d()
layer_zero_padding_2d()
layer_zero_padding_3d()
rnn_cell_gru()
rnn_cell_lstm()
rnn_cell_simple()
rnn_cells_stack()

Other saving and loading functions:
export_savedmodel.keras.src.models.model.Model()
load_model()
load_model_weights()
register_keras_serializable()
save_model()
save_model_config()
save_model_weights()
with_custom_object_scope()

Description

Usage

Value

Arguments

Examples

See Also