def train():
"""
Training systole and diastole models.
"""
print('Loading and compiling models...')
model_systole = get_model()
model_diastole = get_model()
print('Loading training data...')
X, y = load_train_data()
print('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=0.2)
nb_iter = 200
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
print('-'*50)
print('Training...')
print('-'*50)
for i in range(nb_iter):
print('-'*50)
print('Iteration {0}/{1}'.format(i + 1, nb_iter))
print('-'*50)
print('Augmenting images - rotations')
X_train_aug = rotation_augmentation(X_train, 15)
print('Augmenting images - shifts')
X_train_aug = shift_augmentation(X_train_aug, 0.1, 0.1)
print('Fitting systole model...')
hist_systole = model_systole.fit(X_train_aug, y_train[:, 0], shuffle=True, nb_epoch=epochs_per_iter,
batch_size=batch_size, validation_data=(X_test, y_test[:, 0]))
print('Fitting diastole model...')
hist_diastole = model_diastole.fit(X_train_aug, y_train[:, 1], shuffle=True, nb_epoch=epochs_per_iter,
batch_size=batch_size, validation_data=(X_test, 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:
print('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 = real_to_cdf(pred_systole, loss_systole)
cdf_pred_diastole = real_to_cdf(pred_diastole, loss_diastole)
cdf_val_pred_systole = real_to_cdf(val_pred_systole, val_loss_systole)
cdf_val_pred_diastole = real_to_cdf(val_pred_diastole, val_loss_diastole)
# evaluate CRPS on training data
crps_train = crps(cdf_train, np.concatenate((cdf_pred_systole, cdf_pred_diastole)))
print('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)))
print('CRPS(test) = {0}'.format(crps_test))
print('Saving weights...')
# save weights so they can be loaded later
model_systole.save_weights('weights_systole.hdf5', overwrite=True)
model_diastole.save_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('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('weights_diastole_best.hdf5', overwrite=True)
# save best (lowest) val losses in file (to be later used for generating submission)
with open('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.
"""
# Compile training and testing functions
[model, train_fn, val_fn, predict_fn] = get_model()
# Load training data
print('Loading training data...')
X, y = load_train_data()
print('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=0.2)
y_train_systole = from_values_to_step_probability(y_train[:, 0], n_range=600) # ADDED
y_train_diastole = from_values_to_step_probability(y_train[:, 1], n_range=600) # ADDED
y_test_systole = from_values_to_step_probability(y_test[:, 0], n_range=600) # ADDED
y_test_diastole = from_values_to_step_probability(y_test[:, 1], n_range=600) # ADDED
# concatenate systole and diastole outputs
y_train = np.concatenate((y_train_systole, y_train_diastole), axis=1)
y_test = np.concatenate((y_test_systole, y_test_diastole), axis=1)
nb_epoch = 200
batch_size = 32
calc_crps = 0 # calculate CRPS every n-th iteration (set to 0 if CRPS estimation is not needed)
print('-'*50)
print('Training...')
print('-'*50)
min_val_err = sys.float_info.max
for i in range(nb_epoch):
print('-'*50)
print('Iteration {0}/{1}'.format(i + 1, nb_epoch))
print('-'*50)
print('Augmenting images - rotations')
X_train_aug = rotation_augmentation(X_train, 15)
print('Augmenting images - shifts')
X_train_aug = shift_augmentation(X_train_aug, 0.1, 0.1)
# In each epoch, we do a full pass over the training data:
print('Fitting model...')
train_err = 0
train_batches = 0
for batch in iterate_minibatches(X_train_aug, y_train, batch_size, shuffle=True):
inputs, targets = batch
train_err += train_fn(inputs, targets)
train_batches += 1
# And a full pass over the validation data:
val_err = 0
val_batches = 0
for batch in iterate_minibatches(X_test, y_test, batch_size, shuffle=False):
inputs, targets = batch
val_err += val_fn(inputs, targets)
val_batches += 1
assert(calc_crps == 0)
if calc_crps > 0 and i % calc_crps == 0:
print('Evaluating CRPS...')
train_pred = predict_fn(X_train)
val_pred = predict_fn(X_test)
pred_systole = train_pred[:600]
pred_diastole = train_pred[600:]
# CDF for train and test prediction
pred_systole = np.cumsum(pred_systole, axis=1)
pred_diastole = np.cumsum(pred_diastole, axis=1)
val_pred_systole = np.cumsum(val_pred_systole, axis=1)
val_pred_diastole = np.cumsum(val_pred_diastole, axis=1)
# evaluate CRPS on training data
crps_train = crps(y_train, np.concatenate((pred_systole)))
print('CRPS(train) = {0}'.format(crps_train))
# evaluate CRPS on test data
crps_test = crps(cdf_test, np.concatenate((val_pred_systole, val_pred_diastole)))
print('CRPS(test) = {0}'.format(crps_test))
print('Saving weights...')
# save weights so they can be loaded later
np.savez('weights.hdf5.npz', *get_all_param_values(model))
# for best (lowest) val losses, save weights
if val_err < min_val_err:
min_val_err = val_err
np.savez('weights_best.hdf5.npz', *get_all_param_values(model))
# save best (lowest) val losses in file (to be later used for generating submission)
with open('val_loss.txt', mode='w+') as f:
f.write(str(min_val_err))
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 = 600
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, y_train[:, 0], shuffle=True, nb_epoch=epochs_per_iter,
batch_size=batch_size, validation_data=(X_test, y_test[:, 0]))
logging.info('Fitting diastole model...')
hist_diastole = model_diastole.fit(X_train_aug, y_train[:, 1], shuffle=True, nb_epoch=epochs_per_iter,
batch_size=batch_size, validation_data=(X_test, 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 = real_to_cdf(pred_systole, loss_systole)
cdf_pred_diastole = real_to_cdf(pred_diastole, loss_diastole)
cdf_val_pred_systole = real_to_cdf(val_pred_systole, val_loss_systole)
cdf_val_pred_diastole = real_to_cdf(val_pred_diastole, val_loss_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.
"""
# Compile training and testing functions
[model_sys, train_fn_sys, val_fn_sys, predict_fn_sys] = get_model()
[model_dia, train_fn_dia, val_fn_dia, predict_fn_dia] = get_model()
# Load training data
print('Loading training data...')
X, y = load_train_data()
print('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=0.2)
y_train_systole = from_values_to_step_probability(y_train[:, 0], n_range=600) # ADDED
y_train_diastole = from_values_to_step_probability(y_train[:, 1], n_range=600) # ADDED
y_test_systole = from_values_to_step_probability(y_test[:, 0], n_range=600) # ADDED
y_test_diastole = from_values_to_step_probability(y_test[:, 1], n_range=600) # ADDED
nb_epoch = 200
batch_size = 32
calc_crps = 0 # calculate CRPS every n-th iteration (set to 0 if CRPS estimation is not needed)
print('-'*50)
print('Training...')
print('-'*50)
min_val_err_systole = sys.float_info.max
min_val_err_diastole = sys.float_info.max
for i in range(nb_epoch):
print('-'*50)
print('Iteration {0}/{1}'.format(i + 1, nb_epoch))
print('-'*50)
print('Augmenting images - rotations')
X_train_aug = rotation_augmentation(X_train, 15)
print('Augmenting images - shifts')
X_train_aug = shift_augmentation(X_train_aug, 0.1, 0.1)
# In each epoch, we do a full pass over the training data:
print('Fitting systole model...')
train_err_sys = 0
train_batches_sys = 0
for batch in iterate_minibatches(X_train_aug, y_train_systole, batch_size, shuffle=True):
inputs, targets = batch
train_err_sys += train_fn_sys(inputs, targets)
train_batches_sys += 1
# And a full pass over the validation data:
val_err_sys = 0
val_batches_sys = 0
for batch in iterate_minibatches(X_test, y_test_systole, batch_size, shuffle=False):
inputs, targets = batch
val_err_sys += val_fn_sys(inputs, targets)
val_batches_sys += 1
print('Systole model error evaluation : {0}'.format(val_err_sys))
print('Fitting diastole model...')
train_err_dia = 0
train_batches_dia = 0
for batch in iterate_minibatches(X_train_aug, y_train_diastole, batch_size, shuffle=True):
inputs, targets = batch
train_err_dia += train_fn_dia(inputs, targets)
train_batches_dia += 1
# And a full pass over the validation data:
val_err_dia = 0
val_batches_dia = 0
for batch in iterate_minibatches(X_test, y_test_diastole, batch_size, shuffle=False):
inputs, targets = batch
val_err_dia += val_fn_dia(inputs, targets)
val_batches_dia += 1
print('Diastole model error evaluation : {0}'.format(val_err_dia))
if calc_crps > 0 and i % calc_crps == 0:
print('Evaluating CRPS...')
pred_systole = predict_fn_sys(X_train)
pred_diastole = predict_fn_dia(X_train)
val_pred_systole = predict_fn_sys(X_test)
val_pred_diastole = predict_fn_dia(X_test)
# CDF for train and test data (actually a step function)
cdf_train = np.concatenate((y_train_systole, y_train_diastole))
cdf_test = np.concatenate((y_test_systole, y_test_diastole))
# CDF for train and test prediction
pred_systole = np.cumsum(pred_systole, axis=1)
pred_diastole = np.cumsum(pred_diastole, axis=1)
val_pred_systole = np.cumsum(val_pred_systole, axis=1)
val_pred_diastole = np.cumsum(val_pred_diastole, axis=1)
# evaluate CRPS on training data
crps_train = crps(cdf_train, np.concatenate((pred_systole, pred_diastole)))
print('CRPS(train) = {0}'.format(crps_train))
# evaluate CRPS on test data
crps_test = crps(cdf_test, np.concatenate((val_pred_systole, val_pred_diastole)))
print('CRPS(test) = {0}'.format(crps_test))
print('Saving weights...')
# save weights so they can be loaded later
np.savez('weights_systole.hdf5.npz', *get_all_param_values(model_sys))
np.savez('weights_diastole.hdf5.npz', *get_all_param_values(model_dia))
# for best (lowest) val losses, save weights
if val_err_sys < min_val_err_systole:
min_val_err_systole = val_err_sys
np.savez('weights_systole_best.hdf5.npz', *get_all_param_values(model_sys))
if val_err_dia < min_val_err_diastole:
min_val_err_diastole = val_err_dia
np.savez('weights_diastole_best.hdf5.npz', *get_all_param_values(model_dia))
# save best (lowest) val losses in file (to be later used for generating submission)
with open('val_loss.txt', mode='w+') as f:
f.write(str(min_val_err_systole))
f.write('\n')
f.write(str(min_val_err_diastole))
def train():
"""
Training model.
"""
# Compile training and testing functions
[model, train_fn, val_fn, predict_fn] = get_model()
# Load training data
print('Loading training data...')
X, y = load_train_data()
#print('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=0.2)
nb_epoch = 200
batch_size = 32
calc_crps = 0 # calculate CRPS every n-th iteration (set to 0 if CRPS estimation is not needed) NOT IMPLEMENTED YET
print('-'*50)
print('Training...')
print('-'*50)
min_val_err = sys.float_info.max
patience = 0
for i in range(nb_epoch):
print('-'*50)
print('Iteration {0}/{1}'.format(i + 1, nb_epoch))
print('-'*50)
print('Augmenting images - rotations')
X_train_aug = rotation_augmentation(X_train, 15)
print('Augmenting images - shifts')
X_train_aug = shift_augmentation(X_train_aug, 0.1, 0.1)
# In each epoch, we do a full pass over the training data:
print('Fitting model...')
train_err = 0
train_batches = 0
for batch in iterate_minibatches(X_train_aug, y_train, batch_size, shuffle=True):
inputs, targets = batch
train_err += train_fn(inputs, targets)
train_batches += 1
# And a full pass over the validation data:
val_err = 0
val_batches = 0
for batch in iterate_minibatches(X_test, y_test, batch_size, shuffle=False):
inputs, targets = batch
val_err += val_fn(inputs, targets)
val_batches += 1
print('Saving weights...')
# save weights so they can be loaded later
# np.savez('weights.npz', *get_all_param_values(model))
# for best (lowest) val losses, save weights
if val_err < min_val_err:
patience = 0
min_val_err = val_err
np.savez('weights_best.npz', *get_all_param_values(model))
else:
patience += 1
print('error on validation set: ' + str(val_err))
print('patience variable is: ' + str(patience))
print('\n')
# save best (lowest) val losses in file (to be later used for generating submission)
with open('val_loss.txt', mode='a') as f:
f.write(str(val_err))
f.write('\n')
if (patience == 8):
break
def train():
print('Loading and compiling models...')
model_systole = get_model()
model_diastole = get_model()
print('Loading training data...')
X, y = load_train_data()
print('Pre-processing images...')
X = preprocess(X)
X_train, y_train, X_test, y_test = split_data(X, y, split_ratio=0.2)
nb_iter = 200
epochs_per_iter = 1
batch_size = 32
calc_crps = 1
min_val_loss_systole = sys.float_info.max
min_val_loss_diastole = sys.float_info.max
print('-'*50)
print('Training...')
print('-'*50)
for i in range(nb_iter):
print('-'*50)
print('Iteration {0}/{1}'.format(i + 1, nb_iter))
print('-'*50)
print('Augmenting images - rotations')
X_train_aug = rotation_augmentation(X_train, 15)
print('Augmenting images - shifts')
X_train_aug = shift_augmentation(X_train_aug, 0.1, 0.1)
print('Fitting systole model...')
hist_systole = model_systole.fit(X_train_aug, y_train[:, 0], shuffle=True, nb_epoch=epochs_per_iter,
batch_size=batch_size, validation_data=(X_test, y_test[:, 0]))
print('Fitting diastole model...')
hist_diastole = model_diastole.fit(X_train_aug, y_train[:, 1], shuffle=True, nb_epoch=epochs_per_iter,
batch_size=batch_size, validation_data=(X_test, y_test[:, 1]))
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:
print('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_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_pred_systole = real_to_cdf(pred_systole, loss_systole)
cdf_pred_diastole = real_to_cdf(pred_diastole, loss_diastole)
cdf_val_pred_systole = real_to_cdf(val_pred_systole, val_loss_systole)
cdf_val_pred_diastole = real_to_cdf(val_pred_diastole, val_loss_diastole)
crps_train = crps(cdf_train, np.concatenate((cdf_pred_systole, cdf_pred_diastole)))
print('CRPS(train) = {0}'.format(crps_train))
crps_test = crps(cdf_test, np.concatenate((cdf_val_pred_systole, cdf_val_pred_diastole)))
print('CRPS(test) = {0}'.format(crps_test))
print('Saving weights...')
model_systole.save_weights('weights_systole.hdf5', overwrite=True)
model_diastole.save_weights('weights_diastole.hdf5', overwrite=True)
if val_loss_systole < min_val_loss_systole:
min_val_loss_systole = val_loss_systole
model_systole.save_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('weights_diastole_best.hdf5', overwrite=True)
with open('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 ONE model for systole and diastole "
print('Loading and compiling models...')
model_systole = get_model3()
print('Loading models weights...')
model_systole.load_weights('weights_systole_best.hdf5')
print('Loading training data...')
X, y = load_train_data()
print('Pre-processing images...') # denoising filter
X = preprocess(X)
# split to training and test
X_train, y_train, X_test, y_test = split_data(X, y, split_ratio=0.2)
# save test subset
with open('y_test.txt', mode='w+') as f:
f.write(str(y_test))
f.write('\n')
nb_iter = 20
epochs_per_iter = 1
batch_size = 32 # if too small, will converge to unreliable models
# if too big, it wont fit into memory
calc = 4 # Every n-th iteration (0 if not needed)
# apply function to rotate the images
print('Augmenting images - rotations')
X_train_aug = rotation_augmentation(X_train, 15)
# apply function to shift the images
print('Augmenting images - shifts')
X_train_aug = shift_augmentation(X_train_aug, 0.1, 0.1)
# remember min val. losses (best iterations)
min_val_loss_systole = sys.float_info.max
print('-'*50)
print('Training...')
print('-'*50)
for i in range(nb_iter):
print('-'*50)
print('Iteration {0}/{1}'.format(i + 1, nb_iter))
print('-'*50)
print('Fitting diastole/systole model...')
hist_systole = model_systole.fit(X_train_aug, y_train[:, :], shuffle=True, nb_epoch=epochs_per_iter,
batch_size=batch_size, validation_data=(X_test, y_test[:, :]))
# loss function values (RMSE)
loss_last = hist_systole.history['loss'][-1] # one number for the iter
val_loss_last = hist_systole.history['val_loss'][-1]
# since hist_systole.history['loss'] returns an array
# pick the last value with [-1]
loss = hist_systole.history['loss'][:] # all iter
val_loss = hist_systole.history['val_loss'][:]
with open('loss_last.txt', mode='a') as f:
f.write(str(loss_last))
f.write('\n')
with open('val_loss_last.txt', mode='a') as f:
f.write(str(val_loss_last))
f.write('\n')
with open('loss.txt', mode='a') as f:
f.write(str(loss))
f.write('\n')
with open('val_loss.txt', mode='a') as f:
f.write(str(val_loss))
f.write('\n')
# usually accuracy = correct predictions / total predictions
# using RMSE as a loss function, means if value of loss function is 20
# - that is an indicator the model usually misses the true value by ~20ml
if calc > 0 and i % calc == 0:
print('Getting predictions...')
pred_systole = model_systole.predict(X_train, batch_size=batch_size, verbose=1)
# npy 1283 x 2
val_pred_systole = model_systole.predict(X_test, batch_size=batch_size, verbose=1)
# npy 320 x 2
# save predictions
with open('pred_systole.txt', mode='a') as f:
f.write(str(pred_systole))
f.write('\n')
with open('val_pred_systole.txt', mode='a') as f:
f.write(str(val_pred_systole))
f.write('\n')
# save weights so they can be loaded later
print('Saving weights...')
model_systole.save_weights('weights_systole.hdf5', overwrite=True)
# for best (lowest) val losses, save weights
if val_loss_last < min_val_loss_systole:
min_val_loss_systole = val_loss_last
model_systole.save_weights('weights_systole_best.hdf5', overwrite=True)
# save best (lowest) val losses in file (to be later used for submission)
with open('min_val_loss.txt', mode='w+') as f:
f.write(str(min_val_loss_systole))
f.write('\n')
def train():
"""
Training systole and diastole models.
"""
print('Loading and compiling models...')
model_systole = get_model(img_size)
model_diastole = get_model(img_size)
print('Loading training data...')
X, y = load_train_data()
print('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=0.2)
# define image generator for random rotations
datagen = ImageDataGenerator(featurewise_center=False,
featurewise_std_normalization=False,
rotation_range=15)
nb_iter = 300
epochs_per_iter = 1
batch_size = 64
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
if not os.path.exists(STATS):
os.makedirs(STATS)
with open(STATS + 'RMSE_CRPS.txt', 'w') as f:
names = ['train_RMSE_d', 'train_RMSE_s', 'test_RMSE_d', 'test_RMSE_s', 'train_crps', 'test_crps']
f.write('\t'.join([str(name) for name in names]) + '\n')
print('-'*50)
print('Training...')
print('-'*50)
for i in range(nb_iter):
print('-'*50)
print('Iteration {0}/{1}'.format(i + 1, nb_iter))
print('-'*50)
print('Augmenting images - rotations')
X_train_aug = rotation_augmentation(X_train, 15)
print('Augmenting images - shifts')
X_train_aug = shift_augmentation(X_train_aug, 0.1, 0.1)
print('Fitting systole model...')
hist_systole = model_systole.fit(X_train_aug, y_train[:, 0], shuffle=True, nb_epoch=epochs_per_iter, batch_size=batch_size, validation_data=(X_test, y_test[:, 0]))
print('Fitting diastole model...')
hist_diastole = model_diastole.fit(X_train_aug, y_train[:, 1], shuffle=True, nb_epoch=epochs_per_iter, batch_size=batch_size, validation_data=(X_test, 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:
print('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 = real_to_cdf(pred_systole, loss_systole)
cdf_pred_diastole = real_to_cdf(pred_diastole, loss_diastole)
cdf_val_pred_systole = real_to_cdf(val_pred_systole, val_loss_systole)
cdf_val_pred_diastole = real_to_cdf(val_pred_diastole, val_loss_diastole)
# evaluate CRPS on training data
crps_train = crps(cdf_train, np.concatenate((cdf_pred_systole, cdf_pred_diastole)))
print('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)))
print('CRPS(test) = {0}'.format(crps_test))
print('Saving weights...')
# save weights so they can be loaded later
model_systole.save_weights(MODELS + 'weights_systole.hdf5', overwrite=True)
model_diastole.save_weights(MODELS + '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_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_diastole_best.hdf5', overwrite=True)
# save best (lowest) val losses in file (to be later used for generating submission)
with open(MODELS + 'val_loss.txt', mode='w+') as f:
f.write(str(min_val_loss_systole))
f.write('\n')
f.write(str(min_val_loss_diastole))
with open(STATS + 'RMSE_CRPS.txt', 'a') as f:
# train_RMSE_d train_RMSE_s test_RMSE_d test_RMSE_s train_crps test_crps
rmse_values = [loss_diastole, loss_systole, val_loss_diastole, val_loss_systole]
crps_values = [crps_train, crps_test]
f.write('\t'.join([str(val) for val in rmse_values + crps_values]) + '\n')
print('Saving stats images...')
write_images(STATS)
if (i != 0) & ((i + 1) % 100 == 0):
print('Submitting learned model....')
SUBMISSION_FOLDER = SUBMISSION + preproc_type + "/" + model_name + "/" + get_name() + "_ITERS" + str(i + 1) + "/"
if not os.path.exists(SUBMISSION_FOLDER):
os.makedirs(SUBMISSION_FOLDER)
copyfile(MODELS + 'weights_systole_best.hdf5', SUBMISSION_FOLDER + 'weights_systole_best.hdf5')
copyfile(MODELS + 'weights_diastole_best.hdf5', SUBMISSION_FOLDER + 'weights_diastole_best.hdf5')
copyfile(MODELS + 'val_loss.txt', SUBMISSION_FOLDER + 'val_loss.txt')
os.system('python submission.py %s %s %s' % (preproc_type, model_name, SUBMISSION_FOLDER))
def train():
"""
Training systole and diastole models.
"""
print('Loading and compiling models...')
model_systole = get_model()
model_diastole = get_model()
print('Loading training data...')
X, y = load_train_data()
print('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=0.2)
#Iteraties was 200
nb_iter = 250
epochs_per_iter = 1
## Batch-size was 32, ivm processing op laptop heb ik hiervan 12 gemaakt voor test #3
batch_size = 12
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
print('-' * 50)
print('Training...')
start = datetime.now()
print('-' * 50)
for i in range(nb_iter):
print('-' * 50)
print('Iteration {0}/{1}'.format(i + 1, nb_iter))
print('-' * 50)
print('Augmenting images - rotations')
X_train_aug = rotation_augmentation(X_train, 15)
print('Augmenting images - shifts')
X_train_aug = shift_augmentation(X_train_aug, 0.1, 0.1)
csv_logger_diastole = CSVLogger('training_diastole.log',
append=True,
separator=';')
csv_logger_systole = CSVLogger('training_systole.log',
append=True,
separator=';')
print('Fitting systole model...')
hist_systole = model_systole.fit(X_train_aug,
y_train[:, 0],
shuffle=True,
nb_epoch=epochs_per_iter,
batch_size=batch_size,
validation_data=(X_test, y_test[:,
0]),
callbacks=[csv_logger_systole])
print('Fitting diastole model...')
hist_diastole = model_diastole.fit(X_train_aug,
y_train[:, 1],
shuffle=True,
nb_epoch=epochs_per_iter,
batch_size=batch_size,
validation_data=(X_test, y_test[:,
1]),
callbacks=[csv_logger_diastole])
dialoss_history = hist_diastole.history["loss"]
sysloss_history = hist_systole.history["loss"]
import numpy
numpy_dialoss_history = numpy.array(dialoss_history)
numpy_sysloss_history = numpy.array(sysloss_history)
numpy.savetxt("dialoss_history.txt",
numpy_dialoss_history,
delimiter=",")
numpy.savetxt("sysloss_history.txt",
numpy_sysloss_history,
delimiter=",")
# 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:
print('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)
## DEZE BOVENSTAANDE VALUES ZIJN DE RESULTATEN ##
## try
## accuracy_systole = pred_systole - val_pred_systole
print("Pred_diastole:")
print(pred_diastole)
print("Pred_systole:")
print(pred_systole)
print("Val_pred_sysyole:")
print(val_pred_systole)
print("Val_pred_diastole:")
print(val_pred_diastole)
# 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 = real_to_cdf(pred_systole, loss_systole)
cdf_pred_diastole = real_to_cdf(pred_diastole, loss_diastole)
cdf_val_pred_systole = real_to_cdf(val_pred_systole,
val_loss_systole)
cdf_val_pred_diastole = real_to_cdf(val_pred_diastole,
val_loss_diastole)
# evaluate CRPS on training data
crps_train = crps(
cdf_train, np.concatenate(
(cdf_pred_systole, cdf_pred_diastole)))
print('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)))
print('CRPS(test) = {0}'.format(crps_test))
""" BEGIN PLOTTING RESULTS """
import matplotlib.pyplot as plt
import numpy
# score = model_systole.evaluate(X_test, Y_test, verbose=0)
# print("Score systole")
# print(score)
""" EIND PLOTTING RESULT """
print('Saving weights...')
# save weights so they can be loaded later
model_systole.save_weights('weights_systole.hdf5', overwrite=True)
model_diastole.save_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('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('weights_diastole_best.hdf5',
overwrite=True)
##Start accuracy plot for systole and diastole
## pyplot.plot(history.history['acc'])
##pyplot.show()
# save best (lowest) val losses in file (to be later used for generating submission)
with open('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 model.
"""
# Compile training and testing functions
[model, train_fn, val_fn, predict_fn] = get_model()
# Load training data
print('Loading training data...')
X, y = load_train_data()
#print('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=0.2)
nb_epoch = 200
batch_size = 32
calc_crps = 0 # calculate CRPS every n-th iteration (set to 0 if CRPS estimation is not needed) NOT IMPLEMENTED YET
print('-' * 50)
print('Training...')
print('-' * 50)
min_val_err = sys.float_info.max
patience = 0
for i in range(nb_epoch):
print('-' * 50)
print('Iteration {0}/{1}'.format(i + 1, nb_epoch))
print('-' * 50)
print('Augmenting images - rotations')
X_train_aug = rotation_augmentation(X_train, 15)
print('Augmenting images - shifts')
X_train_aug = shift_augmentation(X_train_aug, 0.1, 0.1)
# In each epoch, we do a full pass over the training data:
print('Fitting model...')
train_err = 0
train_batches = 0
for batch in iterate_minibatches(X_train_aug,
y_train,
batch_size,
shuffle=True):
inputs, targets = batch
train_err += train_fn(inputs, targets)
train_batches += 1
# And a full pass over the validation data:
val_err = 0
val_batches = 0
for batch in iterate_minibatches(X_test,
y_test,
batch_size,
shuffle=False):
inputs, targets = batch
val_err += val_fn(inputs, targets)
val_batches += 1
print('Saving weights...')
# save weights so they can be loaded later
# np.savez('weights.npz', *get_all_param_values(model))
# for best (lowest) val losses, save weights
if val_err < min_val_err:
patience = 0
min_val_err = val_err
np.savez('weights_best.npz', *get_all_param_values(model))
else:
patience += 1
print('error on validation set: ' + str(val_err))
print('patience variable is: ' + str(patience))
print('\n')
# save best (lowest) val losses in file (to be later used for generating submission)
with open('val_loss.txt', mode='a') as f:
f.write(str(val_err))
f.write('\n')
if (patience == 8):
break