'parameters': {}
}]
}
}))
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={
transformed_label_name('has_diabetes'):
['binary_crossentropy', 'binary_accuracy']
}))
# Add the deployer
training_pipeline.add_deployment(
GCAIPDeployer(
project_id=GCP_PROJECT,
model_name=MODEL_NAME,
))
# Run the pipeline
training_pipeline.run()
# Another way to do is is to create a DeploymentPipeline.
# Uncomment to create the model via this pipeline
'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()
# See statistics of train and eval
training_pipeline.view_statistics()
# Creates a notebook for evaluation
training_pipeline.evaluate()
],
labels=['has_diabetes'],
overwrite={
'has_diabetes': {
'transform': [{
'method': 'no_transform',
'parameters': {}
}]
}
}))
# Add a trainer
training_pipeline.add_trainer(MyScikitTrainer(
C=0.8,
kernel='rbf',
))
# Add an evaluator
label_name = naming_utils.transformed_label_name('has_diabetes')
training_pipeline.add_evaluator(
AgnosticEvaluator(prediction_key=naming_utils.output_name(label_name),
label_key=label_name,
slices=[['has_diabetes']],
metrics=['mean_squared_error']))
# Run the pipeline locally
training_pipeline.run()
# Evaluate
training_pipeline.evaluate()
