[onert] Fix accuracy measurement

[ragmani]

What

I found that the accuracy measurement was not working, and I checked that obtaining output results during training does not works.

$ python3 runtime/onert/sample/minimal-python/experimental/src/train_with_dataset.py -m mobilenetv2 -i out/imagenet_a.test.input.100.bin -l out/imagenet_a.test.output.100.bin --data_length 100
Load data
Epoch 1/5
Batch 1: Loss=0.0012
Batch 2: Loss=0.0012
Batch 3: Loss=0.0011
Batch 4: Loss=0.0012
Batch 5: Loss=0.0011
Train Loss: 0.0012
Batch 1: Loss=0.0012
Batch 2: Loss=0.0013
Validation Loss: 0.0012
CategoricalAccuracy: 0.0000

$ ./Product/x86_64-linux.release/out/bin/onert_train mobilenetv2.circle --load_expected:raw test-models/imagenet_a/test.output.100.bin --load_input:raw test-models/imagenet_a/test.input.100.bin --loss 1 --loss_reduction_type 1 --optimizer 1 --learning_rate 0.01 --epoch 5 --batch_size 10 --num_of_trainable_ops -1 --validation_split 0.2 --metric 0
Model Filename mobilenetv2.circle
== training parameter ==
- learning_rate        = 0.01
- batch_size           = 10
- loss_info            = {loss = mean squared error, reduction = sum over batch size}
- optimizer            = sgd
- num_of_trainable_ops = -1
========================
Epoch 1/5 - time: 769.609ms/step - loss: [0] 0.0012 - categorical_accuracy: [0] 0.0000 - val_loss: [0] 0.0012 - val_categorical_accuracy: [0] 0.0000
Epoch 2/5 - time: 795.052ms/step - loss: [0] 0.0012 - categorical_accuracy: [0] 0.0000 - val_loss: [0] 0.0012 - val_categorical_accuracy: [0] 0.0000
Epoch 3/5 - time: 724.606ms/step - loss: [0] 0.0011 - categorical_accuracy: [0] 0.0000 - val_loss: [0] 0.0012 - val_categorical_accuracy: [0] 0.0000
Epoch 4/5 - time: 712.029ms/step - loss: [0] 0.0011 - categorical_accuracy: [0] 0.0000 - val_loss: [0] 0.0012 - val_categorical_accuracy: [0] 0.0000
Epoch 5/5 - time: 721.155ms/step - loss: [0] 0.0011 - categorical_accuracy: [0] 0.0000 - val_loss: [0] 0.0012 - val_categorical_accuracy: [0] 0.0000
===================================
MODEL_LOAD   takes 7.5380 ms
PREPARE      takes 268.6270 ms
EXECUTE      takes 30332.8390 ms
- Epoch 1      takes 6156.8730 ms
- Epoch 2      takes 6360.4160 ms
- Epoch 3      takes 5796.8470 ms
- Epoch 4      takes 5696.2360 ms
- Epoch 5      takes 5769.2410 ms
===================================

We need to fix the problem.

Originally posted by @ragmani in #14505 (comment)