from __future__ import division
import lasagne
import nolearn.lasagne
import pecdeeplearn as pdl
import data_path
import time
import numpy as np
# Create an experiment object to keep track of parameters and facilitate data
# loading and saving.
exp = pdl.utils.Experiment(data_path.get())
exp.create_experiment('single_cube_dense_three_landmark_targeted_more')
exp.add_param('num_training_volumes', 45)
exp.add_param('max_points_per_volume', 25000)
exp.add_param('margins', (12, 12, 12))
exp.add_param('local_patch_shape', [25, 25, 25])
exp.add_param('local_patch_input_shape', [25 * 25 * 25])
exp.add_param('landmark_1', 'Sternal angle')
exp.add_param('landmark_2', 'Left nipple')
exp.add_param('landmark_3', 'Right nipple')
exp.add_param('join_dense1_num_units', 25000)
exp.add_param('batch_size', 5000)
exp.add_param('update_learning_rate', 0.001)
exp.add_param('update_momentum', 0.9)
exp.add_param('max_epochs', 100)
exp.add_param('validation_prop', 0.2)
exp.add_param('prediction_margins', (40, 40, 40))
# List and load all volumes.
vol_list = exp.list_volumes()
from __future__ import division
import lasagne
import nolearn.lasagne
import pecdeeplearn as pdl
import data_path
import time
import numpy as np
# Create an experiment object to keep track of parameters and facilitate data
# loading and saving.
exp = pdl.utils.Experiment(data_path.get())
exp.create_experiment('single_s_dense_six_landmark')
exp.add_param('num_training_volumes', 45)
exp.add_param('max_points_per_volume', 25000)
exp.add_param('margins', (12, 12, 0))
exp.add_param('local_patch_shape', [1, 25, 25])
exp.add_param('local_patch_input_shape', [25 * 25])
exp.add_param('landmark_1', 'Sternal angle')
exp.add_param('landmark_2', 'Left nipple')
exp.add_param('landmark_3', 'Right nipple')
exp.add_param('landmark_4', 'Left humerus ball')
exp.add_param('landmark_5', 'Right humerus ball')
exp.add_param('landmark_6', 'Spinal cord')
exp.add_param('join_dense1_num_units', 972)
exp.add_param('batch_size', 5000)
exp.add_param('update_learning_rate', 0.001)
exp.add_param('update_momentum', 0.9)
exp.add_param('max_epochs', 100)
exp.add_param('validation_prop', 0.2)
