Training settings
Please provide a valid training processor option
Neural network architecture
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, InputLayer
from tensorflow.keras.optimizers.legacy import Adam
from ei_tensorflow.velo import train_keras_model_with_velo
EPOCHS = args.epochs or 200 # Naik sedikit, masih masuk akal
ENSURE_DETERMINISM = args.ensure_determinism
BATCH_SIZE = args.batch_size or 32
if not ENSURE_DETERMINISM:
train_dataset = train_dataset.shuffle(buffer_size=BATCH_SIZE * 4)
train_dataset = train_dataset.batch(BATCH_SIZE, drop_remainder=False)
validation_dataset = validation_dataset.batch(BATCH_SIZE, drop_remainder=False)
# =========================
# Model Architecture (REKOMENDASI #3)
# =========================
model = Sequential([
InputLayer(input_shape=(6,)), # 6 input fitur
Dense(12, activation='relu'), # Hidden layer 1
Dense(8, activation='relu'), # Hidden layer 2
Dense(classes, activation='softmax', # 3 output kelas
name='y_pred')
])
callbacks.append(
BatchLoggerCallback(
BATCH_SIZE,
train_sample_count,
epochs=EPOCHS,
ensure_determinism=ENSURE_DETERMINISM
)
)
# Train model
train_keras_model_with_velo(
keras_model=model,
training_data=train_dataset,
validation_data=validation_dataset,
loss_fn=tf.keras.metrics.categorical_crossentropy,
num_epochs=EPOCHS,
callbacks=callbacks
)
# Quantization
disable_per_channel_quantization = False
Input layer (6 features)
Select a scoring function
Dense layer (10 neurons)
Dense layer (10 neurons)
Dense layer (10 neurons)
Output layer (3 classes)
Model
Model version:
