reflect Python objects in keras_array()

closes: #1341

Author: t-kalinowski

NEWS.md

@@ -14,6 +14,9 @@
 
 - `model$get_config()` method now returns an R object that can be safely serialized 
   to rds.
+  
+- `keras_array()` now reflects unconverted Python objects. This enables passing
+  objects like `pandas.Series()` to `fit()` and `evaluate()` methods. (#1341)
 
 # keras 2.9.0
 

R/utils.R

@@ -278,6 +278,11 @@ keras_array <- function(x, dtype = NULL) {
     x <- as.list(x)
   }
 
+  # allow passing things like pandas.Series(), for workarounds like
+  # https://github.com/rstudio/keras/issues/1341
+  if(inherits(x, "python.builtin.object"))
+    return(x)
+
   # recurse for lists
   if (is.list(x))
     return(lapply(x, keras_array))

tests/testthat/test-model.R

@@ -362,3 +362,41 @@ test_succeeds("can use functional api with dicts", {
   chk(inputs, outputs, unname(x)[c(3,1,2)], unname(y), error = TRUE)
 
 })
+
+
+
+test_succeeds("can pass pandas.Series() to fit()", {
+  #https://github.com/rstudio/keras/issues/1341
+  n <- 30
+  p <- 10
+
+  w <- runif(n)
+  y <- runif(n)
+  X <- matrix(runif(n * p), ncol = p)
+
+  make_nn <- function() {
+    input <- layer_input(p)
+    output <- input %>%
+      layer_dense(2 * p, activation = "tanh") %>%
+      layer_dense(1)
+    keras_model(inputs = input, outputs = output)
+  }
+
+  nn <- make_nn()
+
+  pd <- reticulate::import("pandas", convert = FALSE)
+  w <- pd$Series(w)
+
+  nn %>%
+    compile(optimizer = optimizer_adam(0.02), loss = "mse",
+            weighted_metrics = list()) %>% # silence warning
+    fit(
+      x = X,
+      y = y,
+      sample_weight = w,
+      weighted_metrics = list(),
+      epochs = 2,
+      validation_split = 0.2,
+      verbose = 0
+    )
+})