layer_lambda | R Documentation |
Layer
object.The layer_lambda()
layer exists so that arbitrary expressions can be used
as a Layer
when constructing Sequential
and Functional API models. Lambda
layers are best suited for simple
operations or quick experimentation. For more advanced use cases,
prefer writing new subclasses of Layer
using new_layer_class()
.
layer_lambda(
object,
f,
output_shape = NULL,
mask = NULL,
arguments = NULL,
...
)
object |
Object to compose the layer with. A tensor, array, or sequential model. |
f |
The function to be evaluated. Takes input tensor as first argument. |
output_shape |
Expected output shape from function. This argument
can usually be inferred if not explicitly provided.
Can be a list or function. If a list, it only specifies
the first dimension onward; sample dimension is assumed
either the same as the input:
|
mask |
Either |
arguments |
Optional named list of arguments to be passed to the function. |
... |
For forward/backward compatability. |
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.
# add a x -> x^2 layer model <- keras_model_sequential() model |> layer_lambda(\(x) x^2)
Other core layers:
layer_dense()
layer_einsum_dense()
layer_embedding()
layer_identity()
layer_masking()
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_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_tfsm()
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()
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.