LATE_FUSION = False
# how many times to execute the training/validation/test cycle
CYCLES = 1
# Execution Attributes
attr = ExecutionAttribute()
# dimensions of our images.
attr.img_width, attr.img_height = 96, 96
# network parameters
attr.csv_path = 'csv/clinical_data.csv'
attr.path = '/mnt/data/image/2d/' + IMG_TYPE
# attr.path = '/home/amenegotto/dataset/2d/' + IMG_TYPE
attr.numpy_path = '/mnt/data/image/2d/numpy/' + IMG_TYPE
# attr.numpy_path = '/home/amenegotto/dataset/2d/numpy/' + IMG_TYPE
attr.summ_basename = get_base_name(SUMMARY_BASEPATH)
attr.epochs = 2
attr.batch_size = 32
attr.set_dir_names()
if K.image_data_format() == 'channels_first':
input_image_s = (3, attr.img_width, attr.img_height)
else:
input_image_s = (attr.img_width, attr.img_height, 3)
input_attributes_s = (20,)
for i in range(0, CYCLES):
# SUMMARY_PATH="/tmp/results"
NETWORK_FORMAT = "Multimodal"
IMAGE_FORMAT = "2D"
SUMMARY_BASEPATH = create_results_dir(SUMMARY_PATH, NETWORK_FORMAT, IMAGE_FORMAT)
INTERMEDIATE_FUSION = False
LATE_FUSION = True
# how many times to execute the training/validation/test cycle
CYCLES = 1
#
# Execution Attributes
attr = ExecutionAttribute()
# numpy_path = '/home/amenegotto/dataset/2d/numpy/sem_pre_proc_mini/'
attr.numpy_path = '/mnt/data/image/2d/numpy/sem_pre_proc/'
# dimensions of our images.
attr.img_width, attr.img_height = 96, 96
# network parameters
# attr.path='C:/Users/hp/Downloads/cars_train'
# attr.path='/home/amenegotto/dataset/2d/sem_pre_proc_mini/
attr.csv_path = 'csv/clinical_data.csv'
# attr.path = '/mnt/data/image/2d/com_pre_proc/'
attr.summ_basename = get_base_name(SUMMARY_BASEPATH)
attr.epochs = 5
attr.batch_size = 32
attr.set_dir_names()
if K.image_data_format() == 'channels_first':
input_image_s = (3, attr.img_width, attr.img_height)