 b/config.py
+"""
+The required configurations for training phase ('prepare_Data.py', 'train.py').
+"""
+cfg = dict()
+"""
+The coordinates to crop brain volumes. For example, a brain volume with the
+One can set the x0,x1,... by calculating none zero pixels through dataset.
+Note that the final three shapes must be divisible by the network downscale rate.
+"""
+cfg['crop_coord']            =  {'x0':42, 'x1':194,
+                                 'y0':29, 'y1':221,
+                                 'z0':2,  'z1':146}
+"""
+The path to all brain volumes (ex: suppose we have a folder 'MICCAI_BraTS_2019_Data_Training'
+that contains two HGG and LGG folders so:
+data_dir='./MICCAI_BraTS_2019_Data_Training/*/*')
+"""
+cfg['data_dir']              = '/path/to/data/'
+"""
+The final data shapes of saved table file.
+"""
+cfg['table_data_shape']      =  (cfg["crop_coord"]['z1']-cfg["crop_coord"]['z0'],
+                                 cfg["crop_coord"]['y1']-cfg["crop_coord"]['y0'],
+                                 cfg["crop_coord"]['x1']-cfg["crop_coord"]['x0'])
+"""
+BraTS datasets contain 4 channels: (FLAIR, T1, T1ce, T2)
+"""
+cfg['data_channels']         = 4
+"""
+The path to save table file + k-fold files
+"""
+cfg['save_data_dir']         = './data/'
+"""
+The path to save models + log files + tensorboards
+"""
+cfg['save_dir']        = './save/'
+"""
+k-fold cross-validation
+"""
+cfg['k_fold']                = 5
+"""
+The defualt path of saved table.
+"""
+cfg['hdf5_dir']              = './data/data.hdf5'
+"""
+The path to brain indexes of specific fold (a numpy file that was saved in ./data/ by default)
+"""
+cfg['brains_idx_dir']        = './data/fold0_idx.npy'
+"""
+'axial', 'sagittal' or 'coronal'. The 'view' has no effect in "prepare_data.py".
+All 2D slices and the model will be prepared  with respect to 'view'.
+"""
+cfg['view']                  = 'axial'
+"""
+The batch size for training and validating the model
+"""
+cfg['batch_size']            = 16
+cfg['val_batch_size']        = 32
+"""
+The augmentation parameters.
+"""
+cfg['hor_flip']              = True
+cfg['ver_flip']              = True
+cfg['rotation_range']        = 0
+cfg['zoom_range']            = 0.
+"""
+The leraning rate and the number of epochs for training the model
+"""
+cfg['epochs']                = 100
+cfg['lr']                    = 0.008
+"""
+If True, use process-based threading. "https://keras.io/models/model/"
+"""
+cfg['multiprocessing']       = False
+"""
+Maximum number of processes to spin up when using process-based threading.
+If unspecified, workers will default to 1. If 0, will execute the generator
+on the main thread. "https://keras.io/models/model/"
+"""
+cfg['workers']               = 1
+"""
+Whether to use the proposed modifid UNet or the original UNet
+"""
+cfg['modified_unet']         = True
+"""
+The depth of the U-structure
+"""
+cfg['levels']                = 3
+"""
+The number of channels of the first conv
+"""
+cfg['start_chs']             = 64
+"""
+If specified, before training, the model weights will be loaded from this path otherwise
+the model will be trained from scratch.
+"""
+cfg['load_model_dir']        = None