def submission():
"""
Generate submission file for the trained models.
"""
logging.info('Loading and compiling models...')
model_systole = get_model()
model_diastole = get_model()
logging.info('Loading models weights...')
model_systole.load_weights('../models/weights/weights_systole_best.hdf5')
model_diastole.load_weights('../models/weights/weights_diastole_best.hdf5')
logging.info('Loading validation data...')
X, ids = load_validation_data()
logging.info('Pre-processing images...')
X = preprocess(X)
batch_size = 32
logging.info('Predicting on validation data...')
pred_systole = model_systole.predict(X, batch_size=batch_size, verbose=1)
pred_diastole = model_diastole.predict(X, batch_size=batch_size, verbose=1)
# real predictions to CDF
cdf_pred_systole = correct_cdf(pred_systole)
cdf_pred_diastole = correct_cdf(pred_diastole)
logging.info('Accumulating results...')
sub_systole = accumulate_study_results(ids, cdf_pred_systole)
sub_diastole = accumulate_study_results(ids, cdf_pred_diastole)
# write to submission file
logging.info('Writing submission to file...')
fi = csv.reader(open('../input/sample_submission_validate.csv'))
f = open('../submissions/submission_13.csv', 'w')
fo = csv.writer(f, lineterminator='\n')
fo.writerow(next(fi))
for line in fi:
idx = line[0]
key, target = idx.split('_')
key = int(key)
out = [idx]
if key in sub_systole:
if target == 'Diastole':
out.extend(list(sub_diastole[key][0]))
else:
out.extend(list(sub_systole[key][0]))
else:
logging.info('Miss {0}'.format(idx))
fo.writerow(out)
f.close()
logging.info('Done.')
def train():
"""
Training systole and diastole models.
"""
logging.info('Loading and compiling models...')
model_systole = get_model()
model_diastole = get_model()
logging.info('Loading training data...')
X, y = load_train_data()
logging.info('Pre-processing images...')
X = preprocess(X)
# split to training and test
X_train, y_train, X_test, y_test = split_data(X, y, split_ratio=0.15)
nb_iter = 500
epochs_per_iter = 1
batch_size = 32
calc_crps = 1 # calculate CRPS every n-th iteration (set to 0 if CRPS estimation is not needed)
# remember min val. losses (best iterations), used as sigmas for submission
min_val_loss_systole = sys.float_info.max
min_val_loss_diastole = sys.float_info.max
logging.info('-' * 50)
logging.info('Training...')
logging.info('-' * 50)
for i in range(nb_iter):
logging.info('-' * 50)
logging.info('Iteration {0}/{1}'.format(i + 1, nb_iter))
logging.info('-' * 50)
logging.info('Augmenting images - rotations')
X_train_aug = rotation_augmentation(X_train, 20)
logging.info('Augmenting images - shifts')
X_train_aug = shift_augmentation(X_train_aug, 0.1, 0.1)
logging.info('Fitting systole model...')
hist_systole = model_systole.fit(
X_train_aug,
real_to_cdf(y_train[:, 0]),
shuffle=True,
nb_epoch=epochs_per_iter,
batch_size=batch_size,
validation_data=(X_test, real_to_cdf(y_test[:, 0])))
logging.info('Fitting diastole model...')
hist_diastole = model_diastole.fit(
X_train_aug,
real_to_cdf(y_train[:, 1]),
shuffle=True,
nb_epoch=epochs_per_iter,
batch_size=batch_size,
validation_data=(X_test, real_to_cdf(y_test[:, 1])))
# sigmas for predicted data, actually loss function values (RMSE)
loss_systole = hist_systole.history['loss'][-1]
loss_diastole = hist_diastole.history['loss'][-1]
val_loss_systole = hist_systole.history['val_loss'][-1]
val_loss_diastole = hist_diastole.history['val_loss'][-1]
if calc_crps > 0 and i % calc_crps == 0:
logging.info('Evaluating CRPS...')
pred_systole = model_systole.predict(X_train,
batch_size=batch_size,
verbose=1)
pred_diastole = model_diastole.predict(X_train,
batch_size=batch_size,
verbose=1)
val_pred_systole = model_systole.predict(X_test,
batch_size=batch_size,
verbose=1)
val_pred_diastole = model_diastole.predict(X_test,
batch_size=batch_size,
verbose=1)
# CDF for train and test data (actually a step function)
cdf_train = real_to_cdf(
np.concatenate((y_train[:, 0], y_train[:, 1])))
cdf_test = real_to_cdf(np.concatenate((y_test[:, 0], y_test[:,
1])))
# CDF for predicted data
cdf_pred_systole = correct_cdf(pred_systole)
cdf_pred_diastole = correct_cdf(pred_diastole)
cdf_val_pred_systole = correct_cdf(val_pred_systole)
cdf_val_pred_diastole = correct_cdf(val_pred_diastole)
# evaluate CRPS on training data
crps_train = crps(
cdf_train, np.concatenate(
(cdf_pred_systole, cdf_pred_diastole)))
logging.info('CRPS(train) = {0}'.format(crps_train))
# evaluate CRPS on test data
crps_test = crps(
cdf_test,
np.concatenate((cdf_val_pred_systole, cdf_val_pred_diastole)))
logging.info('CRPS(test) = {0}'.format(crps_test))
logging.info('Saving weights...')
# save weights so they can be loaded later
model_systole.save_weights('../models/weights/weights_systole.hdf5',
overwrite=True)
model_diastole.save_weights('../models/weights/weights_diastole.hdf5',
overwrite=True)
# for best (lowest) val losses, save weights
if val_loss_systole < min_val_loss_systole:
min_val_loss_systole = val_loss_systole
model_systole.save_weights(
'../models/weights/weights_systole_best.hdf5', overwrite=True)
if val_loss_diastole < min_val_loss_diastole:
min_val_loss_diastole = val_loss_diastole
model_diastole.save_weights(
'../models/weights/weights_diastole_best.hdf5', overwrite=True)
# save best (lowest) val losses in file (to be later used for generating submission)
with open('./logs/val_loss.txt', mode='w+') as f:
f.write(str(min_val_loss_systole))
f.write('\n')
f.write(str(min_val_loss_diastole))
def train():
"""
Training systole and diastole models.
"""
logging.info('Loading and compiling models...')
model_systole = get_model()
model_diastole = get_model()
logging.info('Loading training data...')
X, y = load_train_data()
logging.info('Pre-processing images...')
X = preprocess(X)
# split to training and test
X_train, y_train, X_test, y_test = split_data(X, y, split_ratio=0.15)
nb_iter = 500
epochs_per_iter = 1
batch_size = 32
calc_crps = 1 # calculate CRPS every n-th iteration (set to 0 if CRPS estimation is not needed)
# remember min val. losses (best iterations), used as sigmas for submission
min_val_loss_systole = sys.float_info.max
min_val_loss_diastole = sys.float_info.max
logging.info('-'*50)
logging.info('Training...')
logging.info('-'*50)
for i in range(nb_iter):
logging.info('-'*50)
logging.info('Iteration {0}/{1}'.format(i + 1, nb_iter))
logging.info('-'*50)
logging.info('Augmenting images - rotations')
X_train_aug = rotation_augmentation(X_train, 20)
logging.info('Augmenting images - shifts')
X_train_aug = shift_augmentation(X_train_aug, 0.1, 0.1)
logging.info('Fitting systole model...')
hist_systole = model_systole.fit(X_train_aug, real_to_ans(y_train[:, 0]), shuffle=True, nb_epoch=epochs_per_iter,
batch_size=batch_size, validation_data=(X_test, real_to_ans(y_test[:, 0])))
logging.info('Fitting diastole model...')
hist_diastole = model_diastole.fit(X_train_aug, real_to_ans(y_train[:, 1]), shuffle=True, nb_epoch=epochs_per_iter,
batch_size=batch_size, validation_data=(X_test, real_to_ans(y_test[:, 1])))
# sigmas for predicted data, actually loss function values (RMSE)
loss_systole = hist_systole.history['loss'][-1]
loss_diastole = hist_diastole.history['loss'][-1]
val_loss_systole = hist_systole.history['val_loss'][-1]
val_loss_diastole = hist_diastole.history['val_loss'][-1]
if calc_crps > 0 and i % calc_crps == 0:
logging.info('Evaluating CRPS...')
pred_systole = model_systole.predict(X_train, batch_size=batch_size, verbose=1)
pred_diastole = model_diastole.predict(X_train, batch_size=batch_size, verbose=1)
val_pred_systole = model_systole.predict(X_test, batch_size=batch_size, verbose=1)
val_pred_diastole = model_diastole.predict(X_test, batch_size=batch_size, verbose=1)
# CDF for train and test data (actually a step function)
cdf_train = real_to_cdf(np.concatenate((y_train[:, 0], y_train[:, 1])))
cdf_test = real_to_cdf(np.concatenate((y_test[:, 0], y_test[:, 1])))
# CDF for predicted data
cdf_pred_systole = correct_cdf(pred_systole)
cdf_pred_diastole = correct_cdf(pred_diastole)
cdf_val_pred_systole = correct_cdf(val_pred_systole)
cdf_val_pred_diastole = correct_cdf(val_pred_diastole)
# evaluate CRPS on training data
crps_train = crps(cdf_train, np.concatenate((cdf_pred_systole, cdf_pred_diastole)))
logging.info('CRPS(train) = {0}'.format(crps_train))
# evaluate CRPS on test data
crps_test = crps(cdf_test, np.concatenate((cdf_val_pred_systole, cdf_val_pred_diastole)))
logging.info('CRPS(test) = {0}'.format(crps_test))
logging.info('Saving weights...')
# save weights so they can be loaded later
model_systole.save_weights('../models/weights/weights_systole_1.hdf5', overwrite=True)
model_diastole.save_weights('../models/weights/weights_diastole_1.hdf5', overwrite=True)
# for best (lowest) val losses, save weights
if val_loss_systole < min_val_loss_systole:
min_val_loss_systole = val_loss_systole
model_systole.save_weights('../models/weights/weights_systole_best_1.hdf5', overwrite=True)
if val_loss_diastole < min_val_loss_diastole:
min_val_loss_diastole = val_loss_diastole
model_diastole.save_weights('../models/weights/weights_diastole_best_1.hdf5', overwrite=True)
# save best (lowest) val losses in file (to be later used for generating submission)
with open('./logs/val_loss.txt_1', mode='w+') as f:
f.write(str(min_val_loss_systole))
f.write('\n')
f.write(str(min_val_loss_diastole))