Functional api guide #18
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…ndles multi input and output
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| folds <- rsample::vfold_cv(iris, v = 2) | ||
| params <- extract_parameter_set_dials(tune_wf) |> | ||
| params <- extract_parameter_set_dials(tune_wf) |> |
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| params <- extract_parameter_set_dials(tune_wf) |> | |
| params <- extract_parameter_set_dials(tune_wf) |> |
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| input_block_1 <- function(input_shape) layer_input(shape = input_shape, name = "input_1") | ||
| input_block_2 <- function(input_shape) layer_input(shape = input_shape, name = "input_2") |
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| input_block_1 <- function(input_shape) layer_input(shape = input_shape, name = "input_1") | |
| input_block_2 <- function(input_shape) layer_input(shape = input_shape, name = "input_2") | |
| input_block_1 <- function(input_shape) { | |
| layer_input(shape = input_shape, name = "input_1") | |
| } | |
| input_block_2 <- function(input_shape) { | |
| layer_input(shape = input_shape, name = "input_2") | |
| } |
| flatten_b = inp_spec(flatten_block, "input_b"), | ||
| path_a = inp_spec(dense_path, "flatten_a"), | ||
| path_b = inp_spec(dense_path, "flatten_b"), | ||
| concatenated = inp_spec(concat_block, c(path_a = "in_1", path_b = "in_2")), |
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| concatenated = inp_spec(concat_block, c(path_a = "in_1", path_b = "in_2")), | |
| concatenated = inp_spec( | |
| concat_block, | |
| c(path_a = "in_1", path_b = "in_2") | |
| ), |
| expect_equal(names(preds), c(".pred_class")) | ||
| expect_equal(nrow(preds), 5) | ||
| expect_true(is.factor(preds$.pred_class)) | ||
| }) |
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| }) | |
| }) |
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kerasnip/R/register_fit_predict.R
Lines 195 to 200 in 8267e80
R/build_and_compile_model.R
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| block_name | ||
| ]]) && | ||
| length(y_processed$class_levels[[block_name]]) > 0, | ||
| num_classes = if (!is.null(y_processed$class_levels[[block_name]])) { |
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| num_classes = if (!is.null(y_processed$class_levels[[block_name]])) { | |
| num_classes = if ( | |
| !is.null(y_processed$class_levels[[block_name]]) | |
| ) { |
R/build_and_compile_model.R
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| block_hyperparams$num_classes <- current_y_info$num_classes | ||
| } | ||
| } | ||
| } else { # Single output case |
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| } else { # Single output case | |
| } else { | |
| # Single output case |
R/register_fit_predict.R
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| combined_preds <- purrr::map2_dfc(results, names(results), function(res, name) { | ||
| lvls <- object$fit$lvl[[name]] # Assuming object$fit$lvl is a named list of levels | ||
| if (is.null(lvls)) { | ||
| # Fallback if levels are not specifically named for this output | ||
| lvls <- paste0("class", 1:ncol(res)) |
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| combined_preds <- purrr::map2_dfc(results, names(results), function(res, name) { | |
| lvls <- object$fit$lvl[[name]] # Assuming object$fit$lvl is a named list of levels | |
| if (is.null(lvls)) { | |
| # Fallback if levels are not specifically named for this output | |
| lvls <- paste0("class", 1:ncol(res)) | |
| combined_preds <- purrr::map2_dfc( | |
| results, | |
| names(results), | |
| function(res, name) { | |
| lvls <- object$fit$lvl[[name]] # Assuming object$fit$lvl is a named list of levels | |
| if (is.null(lvls)) { | |
| # Fallback if levels are not specifically named for this output | |
| lvls <- paste0("class", 1:ncol(res)) | |
| } | |
| colnames(res) <- lvls | |
| tibble::as_tibble(res, .name_repair = "unique") %>% | |
| dplyr::rename_with(~ paste0(".pred_", name, "_", .x)) |
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| tibble::tibble(.pred_class = pred_class) %>% | ||
| dplyr::rename_with(~ paste0(".pred_class_", name)) | ||
| }) |
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Suggested change
| tibble::tibble(.pred_class = pred_class) %>% | |
| dplyr::rename_with(~ paste0(".pred_class_", name)) | |
| }) | |
| ) |
| set_engine("keras") | ||
| fit_3 <- fit(spec_3, mpg ~ ., data = mtcars) | ||
| model_3_layers <- fit_3 |> | ||
| extract_keras_model() |> |
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| extract_keras_model() |> | |
| extract_keras_model() |> |
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| output_block_1 <- function(tensor) layer_dense(tensor, units = 1, name = "output_1") | ||
| output_block_2 <- function(tensor) layer_dense(tensor, units = 1, name = "output_2") |
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| output_block_1 <- function(tensor) layer_dense(tensor, units = 1, name = "output_1") | |
| output_block_2 <- function(tensor) layer_dense(tensor, units = 1, name = "output_2") | |
| output_block_1 <- function(tensor) { | |
| layer_dense(tensor, units = 1, name = "output_1") | |
| } | |
| output_block_2 <- function(tensor) { | |
| layer_dense(tensor, units = 1, name = "output_2") | |
| } |
| input_b = input_block_2, | ||
| path_a = inp_spec(dense_path, "input_a"), | ||
| path_b = inp_spec(dense_path, "input_b"), | ||
| concatenated = inp_spec(concat_block, c(path_a = "in_1", path_b = "in_2")), |
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| concatenated = inp_spec(concat_block, c(path_a = "in_1", path_b = "in_2")), | |
| concatenated = inp_spec( | |
| concat_block, | |
| c(path_a = "in_1", path_b = "in_2") | |
| ), |
| expect_equal(nrow(preds), 5) | ||
| expect_true(is.numeric(preds$.pred_output_1)) | ||
| expect_true(is.numeric(preds$.pred_output_2)) | ||
| }) No newline at end of file |
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| }) | |
| }) |
R/build_and_compile_model.R
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| block_name | ||
| ]]) && | ||
| length(y_processed$class_levels[[block_name]]) > 0, | ||
| num_classes = if (!is.null(y_processed$class_levels[[block_name]])) { |
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Suggested change
| num_classes = if (!is.null(y_processed$class_levels[[block_name]])) { | |
| num_classes = if ( | |
| !is.null(y_processed$class_levels[[block_name]]) | |
| ) { |
R/build_and_compile_model.R
Outdated
| block_hyperparams$num_classes <- current_y_info$num_classes | ||
| } | ||
| } | ||
| } else { # Single output case |
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Suggested change
| } else { # Single output case | |
| } else { | |
| # Single output case |
| ] | ||
| merged_args | ||
| } | ||
| } |
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| } | |
| } |
R/register_fit_predict.R
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| combined_preds <- purrr::map2_dfc(results, names(results), function(res, name) { | ||
| lvls <- object$fit$lvl[[name]] # Assuming object$fit$lvl is a named list of levels | ||
| if (is.null(lvls)) { | ||
| # Fallback if levels are not specifically named for this output | ||
| lvls <- paste0("class", 1:ncol(res)) |
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Suggested change
| combined_preds <- purrr::map2_dfc(results, names(results), function(res, name) { | |
| lvls <- object$fit$lvl[[name]] # Assuming object$fit$lvl is a named list of levels | |
| if (is.null(lvls)) { | |
| # Fallback if levels are not specifically named for this output | |
| lvls <- paste0("class", 1:ncol(res)) | |
| combined_preds <- purrr::map2_dfc( | |
| results, | |
| names(results), | |
| function(res, name) { | |
| lvls <- object$fit$lvl[[name]] # Assuming object$fit$lvl is a named list of levels | |
| if (is.null(lvls)) { | |
| # Fallback if levels are not specifically named for this output | |
| lvls <- paste0("class", 1:ncol(res)) | |
| } | |
| colnames(res) <- lvls | |
| tibble::as_tibble(res, .name_repair = "unique") %>% | |
| dplyr::rename_with(~ paste0(".pred_", name, "_", .x)) |
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| colnames(res) <- lvls | ||
| tibble::as_tibble(res, .name_repair = "unique") %>% | ||
| dplyr::rename_with(~ paste0(".pred_", name, "_", .x)) | ||
| }) |
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Suggested change
| colnames(res) <- lvls | |
| tibble::as_tibble(res, .name_repair = "unique") %>% | |
| dplyr::rename_with(~ paste0(".pred_", name, "_", .x)) | |
| }) | |
| ) |
| set_engine("keras") | ||
| fit_3 <- fit(spec_3, mpg ~ ., data = mtcars) | ||
| model_3_layers <- fit_3 |> | ||
| extract_keras_model() |> |
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Suggested change
| extract_keras_model() |> | |
| extract_keras_model() |> |
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| input_block_1 <- function(input_shape) layer_input(shape = input_shape, name = "input_1") | ||
| input_block_2 <- function(input_shape) layer_input(shape = input_shape, name = "input_2") |
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Suggested change
| input_block_1 <- function(input_shape) layer_input(shape = input_shape, name = "input_1") | |
| input_block_2 <- function(input_shape) layer_input(shape = input_shape, name = "input_2") | |
| input_block_1 <- function(input_shape) { | |
| layer_input(shape = input_shape, name = "input_1") | |
| } | |
| input_block_2 <- function(input_shape) { | |
| layer_input(shape = input_shape, name = "input_2") | |
| } |
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| output_block_1 <- function(tensor) layer_dense(tensor, units = 1, name = "output_1") | ||
| output_block_2 <- function(tensor) layer_dense(tensor, units = 1, name = "output_2") |
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| output_block_1 <- function(tensor) layer_dense(tensor, units = 1, name = "output_1") | |
| output_block_2 <- function(tensor) layer_dense(tensor, units = 1, name = "output_2") | |
| output_block_1 <- function(tensor) { | |
| layer_dense(tensor, units = 1, name = "output_1") | |
| } | |
| output_block_2 <- function(tensor) { | |
| layer_dense(tensor, units = 1, name = "output_2") | |
| } |
| input_b = input_block_2, | ||
| path_a = inp_spec(dense_path, "input_a"), | ||
| path_b = inp_spec(dense_path, "input_b"), | ||
| concatenated = inp_spec(concat_block, c(path_a = "in_1", path_b = "in_2")), |
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| concatenated = inp_spec(concat_block, c(path_a = "in_1", path_b = "in_2")), | |
| concatenated = inp_spec( | |
| concat_block, | |
| c(path_a = "in_1", path_b = "in_2") | |
| ), |
| expect_equal(nrow(preds), 5) | ||
| expect_true(is.numeric(preds$.pred_output_1)) | ||
| expect_true(is.numeric(preds$.pred_output_2)) | ||
| }) No newline at end of file |
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| }) | |
| }) |
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Refactoring functional api to handle multi-input and multi-output correctly and updating guid to reflect so