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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
)

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").

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.

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:
##   139941849067216: TensorSpec(shape=(), dtype=tf.resource, name=None)
##   139941849069904: 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.

See also

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_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_feature_space()
layer_flatten()
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_lambda()
layer_layer_normalization()
layer_lstm()
layer_masking()
layer_max_pooling_1d()
layer_max_pooling_2d()
layer_max_pooling_3d()
layer_maximum()
layer_minimum()
layer_multi_head_attention()
layer_multiply()
layer_normalization()
layer_permute()
layer_random_brightness()
layer_random_contrast()
layer_random_crop()
layer_random_flip()
layer_random_rotation()
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_spatial_dropout_1d()
layer_spatial_dropout_2d()
layer_spatial_dropout_3d()
layer_spectral_normalization()
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()