def read_attributes(path):
print("Reading execution attributes from ", path)
attr = ExecutionAttribute()
lines = [line.rstrip('\n') for line in open(path)]
attr.seq = int(lines[0].split("=", 1)[1])
attr.img_width = int(lines[1].split("=", 1)[1])
attr.img_height = int(lines[2].split("=", 1)[1])
attr.path = lines[3].split("=", 1)[1]
attr.summ_basename = lines[4].split("=", 1)[1]
attr.epochs = int(lines[5].split("=", 1)[1])
attr.batch_size = int(lines[6].split("=", 1)[1])
attr.train_data_dir = lines[7].split("=", 1)[1]
attr.validation_data_dir = lines[8].split("=", 1)[1]
attr.test_data_dir = lines[9].split("=", 1)[1]
attr.steps_train = int(lines[10].split("=", 1)[1])
attr.steps_valid = int(lines[11].split("=", 1)[1])
attr.steps_test = int(lines[12].split("=", 1)[1])
attr.architecture = lines[13].split("=", 1)[1]
attr.curr_basename = lines[14].split("=", 1)[1]
return attr
#tf.set_random_seed(seed=seed) # Summary Information SUMMARY_PATH = "/mnt/data/results" # SUMMARY_PATH="c:/temp/results" # SUMMARY_PATH="/tmp/results" NETWORK_FORMAT = "Unimodal" IMAGE_FORMAT = "2D" SUMMARY_BASEPATH = create_results_dir(SUMMARY_PATH, NETWORK_FORMAT, IMAGE_FORMAT) # how many times to execute the training/validation/test cycle CYCLES = 20 # # Execution Attributes attr = ExecutionAttribute() # 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.path = '/mnt/data/image/2d/com_pre_proc/' attr.summ_basename = get_base_name(SUMMARY_BASEPATH) attr.s3_path = NETWORK_FORMAT + '/' + IMAGE_FORMAT attr.epochs = 100 attr.batch_size = 128 attr.set_dir_names() if K.image_data_format() == 'channels_first':