Fixes a few warnings in the example scripts (#505)

* fixes Nx.random_uniform deprecation warning * fixes 'warning: Nx.to_batched_list/2 is deprecated. Use to_batched/3 instead' * Update examples/vision/cifar10.exs Co-authored-by: Sean Moriarity <[email protected]> * Update examples/vision/cifar10.exs Co-authored-by: Sean Moriarity <[email protected]> --------- Co-authored-by: Sean Moriarity <[email protected]>
elixir-nx · Jun 29, 2023 · 68d34cb · 68d34cb
1 parent 9a6efae
commit 68d34cb
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diff --git a/examples/basics/multi_output_example.exs b/examples/basics/multi_output_example.exs
@@ -31,11 +31,14 @@ defmodule Power do
     # model input and y is the target. Because we have multiple targets, we represent
     # y as a tuple. In the future, Axon will support any Nx container as an output
     data =
-      Stream.repeatedly(fn ->
-        # Batch size of 32
-        x = Nx.random_uniform({32, 1}, -10, 10, type: {:f, 32})
-        {x, {Nx.pow(x, 2), Nx.pow(x, 3)}}
-      end)
+      Stream.unfold(
+        Nx.Random.key(:erlang.system_time()),
+        fn key ->
+          # Batch size of 32
+          {x, next_key} = Nx.Random.uniform(key, -10, 10, shape: {32, 1}, type: {:f, 32})
+          {{x, {Nx.pow(x, 2), Nx.pow(x, 3)}}, next_key}
+        end
+      )
 
     # Create the training loop, notice we specify 2 MSE objectives, 1 for the first
     # output and 1 for the second output. This will create a loss function which is

diff --git a/examples/vision/cifar10.exs b/examples/vision/cifar10.exs
@@ -13,7 +13,7 @@ defmodule Cifar do
     |> Nx.from_binary(type)
     |> Nx.reshape({elem(shape, 0), 32, 32, 3})
     |> Nx.divide(255.0)
-    |> Nx.to_batched_list(32)
+    |> Nx.to_batched(32)
     |> Enum.split(1500)
   end
 
@@ -22,7 +22,7 @@ defmodule Cifar do
     |> Nx.from_binary(type)
     |> Nx.new_axis(-1)
     |> Nx.equal(Nx.tensor(Enum.to_list(0..9)))
-    |> Nx.to_batched_list(32)
+    |> Nx.to_batched(32)
     |> Enum.split(1500)
   end