# ====== trainign ====== #
print('Start training ...')
task = training.MainLoop(batch_size=128, seed=1234, shuffle_level=2,
allow_rollback=True)
task.set_checkpoint(MODEL_PATH, model)
task.set_callbacks([
training.NaNDetector(),
training.EarlyStopGeneralizationLoss('valid', ce, threshold=5, patience=3)
])
task.set_train_task(func=f_train,
data=(X_train, y_train),
epoch=NB_EPOCH,
name='train')
task.set_valid_task(func=f_test,
data=(X_valid, y_valid),
freq=training.Timer(percentage=0.6),
name='valid')
task.set_eval_task(func=f_test,
data=(X_test, y_test),
name='eval')
task.run()
# ===========================================================================
# Exsternal validation
# Load model from file and perform validation
# ===========================================================================
script = r"""
import os
os.environ['ODIN'] = 'float32,gpu,seed=87654321'
import pickle
import numpy as np
task_name='train',
signal=training.TaskSignal.EpochEnd),
training.LambdaCallback(fn=lambda t: print(str(t)),
task_name='valid',
signal=training.TaskSignal.EpochEnd),
training.EpochSummary(task_name=('train', 'valid'),
output_name=loss,
print_plot=True,
save_path=os.path.join(model_save_path,
'summary.pdf'))
])
task.set_train_task(f_train, (ds['X_train'], ds['y_train']),
epoch=N_EPOCH,
name='train')
task.set_valid_task(f_test, (ds['X_test'], ds['y_test']),
freq=training.Timer(percentage=1.),
name='valid')
task.set_eval_task(f_test, (ds['X_test'], ds['y_test']), name='test')
task.run()
# ===========================================================================
# Evaluate
# ===========================================================================
ACC = []
CM = []
for X, y in zip(
F.as_data(ds['X_test']).set_batch(128, seed=None),
F.as_data(ds['y_test']).set_batch(128, seed=None)):
ce, acc, cm = f_test(X, y)
ACC.append(acc)
CM.append(cm)
print("Accuracy:", np.mean(ACC))
