polmiro
Machine Learning in Elixir: Error in Chapter 9, identifying cats and dogs again
the code provided I am running into an error when training the first model that uses transfer learning with ONNX model mobilenetv2-7. I am a bit stuck here, not really sure what may be wrong.
warning: Axon.Optimizers.adam/1 is deprecated. Use Polaris.Optimizers.adam/1 instead
machine-learning-in-elixir-src/StopReinventingTheWheel.livemd#cell:s4yqqx5n4j6oyja2b2c57iqes4fvcbny:2
12:22:35.339 [warning] found unexpected key in the initial parameters map: "mobilenetv20_output_pred_fwd"
Epoch: 0, Batch: 700, accuracy: 0.7747430 loss: 0.4431267
Batch: 6, accuracy: 0.9151786 loss: 0.1636844
** (ArgumentError) argument at position 1 is not compatible with compiled function template.
{
<<<<< Expected <<<<<
#Nx.Tensor<
f32[32][channels: 3][height: 160][width: 160]
>
==========
#Nx.Tensor<
f32[26][channels: 3][height: 160][width: 160]
>
>>>>> Argument >>>>>
,
<<<<< Expected <<<<<
#Nx.Tensor<
s64[32][1]
>
==========
#Nx.Tensor<
s64[26][1]
>
>>>>> Argument >>>>>
}
(nx 0.6.2) lib/nx/defn.ex:323: anonymous fn/7 in Nx.Defn.compile_flatten/5
(elixir 1.15.2) lib/enum.ex:1819: Enum."-map_reduce/3-lists^mapfoldl/2-0-"/3
(nx 0.6.2) lib/nx/lazy_container.ex:61: Nx.LazyContainer.Tuple.traverse/3
(nx 0.6.2) lib/nx/defn.ex:320: Nx.Defn.compile_flatten/5
(nx 0.6.2) lib/nx/defn.ex:312: anonymous fn/4 in Nx.Defn.compile/3
(stdlib 5.0.2) timer.erl:270: :timer.tc/2
(axon 0.6.0) lib/axon/loop.ex:1805: anonymous fn/4 in Axon.Loop.run_epoch/5
/data/machine-learning-in-elixir-src/StopReinventingTheWheel.livemd#cell:s4yqqx5n4j6oyja2b2c57iqes4fvcbny:10: (file)
Marked As Solved
joshprintsimple
The length of the val_paths has to be divisible by the batch number so that the tensors come out the right shape.
You can just add this line
val_paths = Enum.take(val_paths, 224)
after setting the val_paths. I also removed the Enum.take line at the end because the length of the train_paths is 24000 and that is also divisible by a batch size of 32.
so that cell would look like this now:
{test_paths, train_paths} =
Path.wildcard("train/*.jpg")
|> Enum.shuffle()
|> Enum.split(1000)
{test_paths, val_paths} = test_paths |> Enum.split(750)
val_paths = Enum.take(val_paths, 224)
batch_size = 32
target_height = 160
target_width = 160
train_pipeline =
CatsAndDogs.pipeline_with_augmentations(
train_paths,
batch_size,
target_height,
target_width
)
val_pipeline =
CatsAndDogs.pipeline(
val_paths,
batch_size,
target_height,
target_width
)
test_pipeline =
CatsAndDogs.pipeline(
test_paths,
batch_size,
target_height,
target_width
)
# Enum.take(train_pipeline, 1)
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