__author__ = 'adeb'
from spynet.utils.utilities import load_config
from data_brain_parcellation import generate_and_save
if __name__ == '__main__':
"""
Script to create the testing/training datasets given configuration files
"""
### Load the config file
data_cf = load_config("cfg_testing_data_creation.py")
### Generate and write on file the dataset
generate_and_save(data_cf)
### Load the config file
data_cf = load_config("cfg_training_data_creation.py")
### Generate and write on file the dataset
generate_and_save(data_cf)
def run(self):
data_path = "./datasets/final_exp_n_layers_2000/"
range_n_layers = np.arange(0, 6, 1)
error_rates = np.zeros(range_n_layers.shape)
dice_coeffs = np.zeros(range_n_layers.shape)
for idx, n_layers in enumerate(range_n_layers):
print "patch width {}".format(n_layers)
### Load the config file
data_cf_train = load_config("cfg_training_data_creation.py")
data_cf_test = load_config("cfg_testing_data_creation.py")
# Create the folder if it does not exist
if not os.path.exists(data_path):
os.makedirs(data_path)
data_cf_train.data_path = data_path
data_cf_test.data_path = data_path
data_cf_train.general["file_path"] = data_path + "train.h5"
data_cf_test.general["file_path"] = data_path + "test.h5"
### Generate and write on file the dataset
generate_and_save(data_cf_train)
generate_and_save(data_cf_test)
###### Create the datasets
training_data_path = data_path + "train.h5"
testing_data_path = data_path + "test.h5"
ds_training = DatasetBrainParcellation()
ds_training.read(training_data_path)
# image = PIL.Image.fromarray(tile_raster_images(X=ds_training.inputs[0:100],
# img_shape=(29, 29), tile_shape=(10, 10),
# tile_spacing=(1, 1)))
# image.save(self.path + "filters_corruption_30.png")
ds_validation = DatasetBrainParcellation()
ds_validation.read(testing_data_path)
# Few stats about the targets
analyse_classes(np.argmax(ds_training.outputs, axis=1))
# Scale some part of the data
# s = Scaler([slice(-134, None, None)])
# s.compute_parameters(ds_training.inputs)
# s.scale(ds_training.inputs)
# s.scale(ds_validation.inputs)
# s.scale(ds_testing.inputs)
# pickle.dump(s, open(self.path + "s.scaler", "wb"))
###### Create the network
net = MLP()
net.init([29**2] + [2000]*n_layers +[135])
print net
# image = PIL.Image.fromarray(tile_raster_images(X=net.get_layer(0).get_layer_block(0).w.get_value().reshape((10,-1)),
# img_shape=(5, 5), tile_shape=(3, 3),
# tile_spacing=(1, 1)))
# image.save(self.path + "filters_corruption_30.png")
###### Configure the trainer
# Cost function
cost_function = CostNegLL()
# Learning update
learning_rate = 0.05
momentum = 0.5
lr_update = LearningUpdateGDMomentum(learning_rate, momentum)
# Create monitors and add them to the trainer
err_validation = MonitorErrorRate(1, "Validation", ds_validation)
dice_validation = MonitorDiceCoefficient(1, "Validation", ds_validation, 135)
# Create stopping criteria and add them to the trainer
max_epoch = MaxEpoch(300)
early_stopping = EarlyStopping(err_validation, 5, 0.99, 5)
# Create the network selector
params_selector = ParamSelectorBestMonitoredValue(err_validation)
# Create the trainer object
batch_size = 200
t = Trainer(net, cost_function, params_selector, [max_epoch, early_stopping],
lr_update, ds_training, batch_size,
[err_validation, dice_validation])
###### Train the network
t.train()
###### Plot the records
error_rates[idx] = err_validation.get_minimum()
dice_coeffs[idx] = dice_validation.get_maximum()
print error_rates
print dice_coeffs
plt.figure()
plt.plot(range_n_layers, error_rates, label="Validation error rates")
plt.plot(range_n_layers, dice_coeffs, label="Validation dice coefficient")
plt.xlabel('Number of layers')
plt.savefig(self.path + "res.png")
tikz_save(self.path + "res.tikz", figureheight = '\\figureheighttik', figurewidth = '\\figurewidthtik')
__author__ = 'adeb'
from spynet.utils.utilities import load_config
from data_brain_parcellation import generate_and_save
if __name__ == '__main__':
"""
Script to create the testing/training datasets given configuration files
"""
### Load the config file
data_cf = load_config("cfg_testing_data_creation_very_small.py")
### Generate and write on file the dataset
generate_and_save(data_cf)
### Load the config file
data_cf = load_config("cfg_training_data_creation_very_small.py")
### Generate and write on file the dataset
generate_and_save(data_cf)
def run(self):
data_path = "./datasets/final_exp_size_patch/"
range_patch_size = np.arange(3, 37, 2)
error_rates = np.zeros(range_patch_size.shape)
dice_coeffs = np.zeros(range_patch_size.shape)
for idx, patch_size in enumerate(range_patch_size):
print "patch width {}".format(patch_size)
### Load the config file
data_cf_train = load_config("cfg_training_data_creation.py")
data_cf_test = load_config("cfg_testing_data_creation.py")
data_cf_train.data_path = data_path
data_cf_test.data_path = data_path
data_cf_train.pick_features[0]["patch_width"] = patch_size
data_cf_test.pick_features[0]["patch_width"] = patch_size
### Generate and write on file the dataset
generate_and_save(data_cf_train)
generate_and_save(data_cf_test)
###### Create the datasets
training_data_path = data_path + "train.h5"
testing_data_path = data_path + "test.h5"
ds_training = DatasetBrainParcellation()
ds_training.read(training_data_path)
# image = PIL.Image.fromarray(tile_raster_images(X=ds_training.inputs[0:100],
# img_shape=(29, 29), tile_shape=(10, 10),
# tile_spacing=(1, 1)))
# image.save(self.path + "filters_corruption_30.png")
ds_validation = DatasetBrainParcellation()
ds_validation.read(testing_data_path)
# Few stats about the targets
analyse_classes(np.argmax(ds_training.outputs, axis=1))
# Scale some part of the data
# s = Scaler([slice(-134, None, None)])
# s.compute_parameters(ds_training.inputs)
# s.scale(ds_training.inputs)
# s.scale(ds_validation.inputs)
# s.scale(ds_testing.inputs)
# pickle.dump(s, open(self.path + "s.scaler", "wb"))
###### Create the network
net = MLP()
net.init([
patch_size**2, patch_size**2, patch_size**2, patch_size**2, 135
])
print net
# image = PIL.Image.fromarray(tile_raster_images(X=net.get_layer(0).get_layer_block(0).w.get_value().reshape((10,-1)),
# img_shape=(5, 5), tile_shape=(3, 3),
# tile_spacing=(1, 1)))
# image.save(self.path + "filters_corruption_30.png")
###### Configure the trainer
# Cost function
cost_function = CostNegLL()
# Learning update
learning_rate = 0.05
momentum = 0.5
lr_update = LearningUpdateGDMomentum(learning_rate, momentum)
# Create monitors and add them to the trainer
err_validation = MonitorErrorRate(1, "Validation", ds_validation)
dice_validation = MonitorDiceCoefficient(1, "Validation",
ds_validation, 135)
# Create stopping criteria and add them to the trainer
max_epoch = MaxEpoch(300)
early_stopping = EarlyStopping(err_validation, 5, 0.99, 5)
# Create the network selector
params_selector = ParamSelectorBestMonitoredValue(err_validation)
# Create the trainer object
batch_size = 200
t = Trainer(net, cost_function, params_selector,
[max_epoch, early_stopping], lr_update, ds_training,
batch_size, [err_validation, dice_validation])
###### Train the network
t.train()
###### Plot the records
error_rates[idx] = err_validation.get_minimum()
dice_coeffs[idx] = dice_validation.get_maximum()
print error_rates
print dice_coeffs
plt.figure()
plt.plot(range_patch_size, error_rates, label="Validation error rates")
plt.plot(range_patch_size,
dice_coeffs,
label="Validation dice coefficient")
plt.xlabel('Patch size')
plt.savefig(self.path + "res.png")
tikz_save(self.path + "res.tikz",
figureheight='\\figureheighttik',
figurewidth='\\figurewidthtik')
