model.load_weights(checkpoint_path)
summary_path = os.path.join(SUMMARY_PATH, 'model_{}'.format(num_folds))
mkdirp(summary_path)
callbacks = [EarlyStopping(monitor='val_loss', patience=1, verbose=0, mode='auto'),
ModelCheckpoint(checkpoint_path, monitor='val_loss', verbose=0, save_best_only=True, mode='auto'),
TensorBoard(log_dir=summary_path, histogram_freq=0)]
model.fit(X_train, y_train, batch_size=BATCH_SIZE, nb_epoch=NB_EPOCHS,
shuffle=True,
verbose=1,
validation_data=(X_valid, y_valid),
callbacks=callbacks)
predictions_valid = model.predict(X_valid, batch_size=100, verbose=1)
score_valid = log_loss(y_valid, predictions_valid)
scores_total.append(score_valid)
print('Score: {}'.format(score_valid))
predictions_test = model.predict(X_test, batch_size=100, verbose=1)
predictions_total.append(predictions_test)
num_folds += 1
score_geom = calc_geom(scores_total, MAX_FOLDS)
predictions_geom = calc_geom_arr(predictions_total, MAX_FOLDS)
submission_path = os.path.join(SUMMARY_PATH, 'submission_{}_{:.2}.csv'.format(int(time.time()), score_geom))
write_submission(predictions_geom, X_test_ids, submission_path)
wait += 1
print(
'Validation loss did not improve for {}/{} epochs.'.format(
wait, patience))
if wait == 2:
print(
'Stopping early. Validation loss did not improve for {}/{} epochs.'
.format(wait, patience))
break
model.summary_writer.close()
scores_total.append(score)
print('Begin evaluation...')
predictions = model.evaluate(X_test)
predictions_total.append(predictions)
num_folds += 1
score_geom = calc_geom(scores_total, num_folds)
predictions_geom = calc_geom_arr(predictions_total, num_folds)
print('Writing submission for {} folds, score: {}...'.format(
num_folds, score_geom))
submission_dest = os.path.join(
SUMMARY_PATH, 'submission_{}_{}.csv'.format(int(time.time()), score_geom))
write_submission(predictions_geom, X_test_ids, submission_dest)
print('Done.')
if loss < best:
print('New best validation loss! Was: {}, Now: {}'.format(best, loss))
best = loss
wait = 0
else:
wait += 1
print('Validation loss did not improve for {}/{} epochs.'.format(wait, patience))
if wait == 2:
print('Stopping early. Validation loss did not improve for {}/{} epochs.'.format(wait, patience))
break
model.summary_writer.close()
scores_total.append(score)
print('Begin evaluation...')
predictions = model.evaluate(X_test)
predictions_total.append(predictions)
num_folds += 1
score_geom = calc_geom(scores_total, num_folds)
predictions_geom = calc_geom_arr(predictions_total, num_folds)
print('Writing submission for {} folds, score: {}...'.format(num_folds, score_geom))
submission_dest = os.path.join(SUMMARY_PATH, 'submission_{}_{}.csv'.format(int(time.time()), score_geom))
write_submission(predictions_geom, X_test_ids, submission_dest)
print('Done.')
mkdirp(summary_path)
callbacks = [
EarlyStopping(monitor='val_loss', patience=2, verbose=0, mode='auto'),
ModelCheckpoint(checkpoint_path, monitor='val_loss', verbose=0, save_best_only=True, mode='auto'),
TensorBoard(log_dir=summary_path, histogram_freq=0)
]
model.fit(X_train, y_train, \
batch_size=BATCH_SIZE, nb_epoch=NB_EPOCHS, \
shuffle=True, \
verbose=1, \
validation_data=(X_valid, y_valid), \
callbacks=callbacks)
predictions_valid = model.predict(X_valid, batch_size=100, verbose=1)
score_valid = log_loss(y_valid, predictions_valid)
scores_total.append(score_valid)
print('Score: {}'.format(score_valid))
predictions_test = model.predict(X_test, batch_size=100, verbose=1)
predictions_total.append(predictions_test)
num_folds += 1
score_geom = calc_geom(scores_total, MAX_FOLDS)
predictions_geom = calc_geom_arr(predictions_total, MAX_FOLDS)
submission_path = os.path.join(SUMMARY_PATH, 'submission_{}_{:.2}.csv'.format(int(time.time()), score_geom))
write_submission(predictions_geom, X_test_ids, submission_path)