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
attr.architecture = 'vgg19'
attr.csv_path = 'csv/clinical_data.csv'
attr.s3_path = NETWORK_FORMAT + '/' + IMAGE_FORMAT
attr.numpy_path = '/mnt/data/image/2d/numpy/' + IMG_TYPE
# attr.numpy_path = '/home/amenegotto/dataset/2d/numpy/' + IMG_TYPE
attr.path = '/mnt/data/image/2d/' + IMG_TYPE
results_path = create_results_dir(SUMMARY_BASEPATH, 'fine-tuning',
attr.architecture)
attr.summ_basename = get_base_name(results_path)
attr.set_dir_names()
attr.batch_size = 128
attr.epochs = 500
attr.img_width = 224
attr.img_height = 224
input_attributes_s = (20, )
# how many times to execute the training/validation/test cycle
CYCLES = 1
for i in range(0, CYCLES):
#Load the VGG model
vgg_conv = VGG19(weights='imagenet',
include_top=False,
input_shape=(attr.img_width, attr.img_height, 3))
# Freeze the layers except the last 4 layers
for layer in vgg_conv.layers[:-4]: