],
labels=['has_diabetes'],
overwrite={
'has_diabetes': {
'transform': [{
'method': 'no_transform',
'parameters': {}
}]
}
}))
# Add a trainer
training_pipeline.add_trainer(
TFFeedForwardTrainer(loss='binary_crossentropy',
last_activation='sigmoid',
output_units=1,
metrics=['accuracy'],
epochs=20))
# Add an evaluator
training_pipeline.add_evaluator(
TFMAEvaluator(
slices=[['has_diabetes']],
metrics={'has_diabetes': ['binary_crossentropy', 'binary_accuracy']}))
# Run the pipeline locally
training_pipeline.run()
# See schema of data
training_pipeline.view_schema()
# Add a split
training_pipeline.add_split(CategoricalDomainSplit(
categorical_column="name",
split_map={'train': ["arnold", "nicholas"], 'eval': ["lülük"]}))
# Add a preprocessing unit
training_pipeline.add_preprocesser(
StandardPreprocesser(
features=["name", "age"],
labels=['gpa'],
overwrite={'gpa': {
'transform': [
{'method': 'no_transform', 'parameters': {}}]}}
))
# Add a trainer
training_pipeline.add_trainer(TFFeedForwardTrainer(
batch_size=1,
loss='binary_crossentropy',
last_activation='sigmoid',
output_units=1,
metrics=['accuracy'],
epochs=i))
# Run the pipeline locally
training_pipeline.run()
except Exception as e:
logger.error(e)