# 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':
input_s = (3, attr.img_width, attr.img_height)
else:
input_s = (attr.img_width, attr.img_height, 3)
for i in range(0, CYCLES):
# define model
attr.model = Sequential()
attr.model.add(Conv2D(128, (3, 3), input_shape=input_s, kernel_initializer='he_normal', kernel_regularizer=regularizers.l2(0.0005)))
attr.model.add(BatchNormalization())
# tf.set_random_seed(seed=seed)
# Summary Information
SUMMARY_PATH = "/mnt/data/results"
NETWORK_FORMAT = "Unimodal"
IMAGE_FORMAT = "2D"
SUMMARY_BASEPATH = create_results_dir(SUMMARY_PATH, NETWORK_FORMAT,
IMAGE_FORMAT)
# Execution Attributes
attr = ExecutionAttribute()
attr.architecture = 'InceptionV3'
results_path = create_results_dir(SUMMARY_BASEPATH, 'fine-tuning',
attr.architecture)
attr.summ_basename = get_base_name(results_path)
attr.s3_path = NETWORK_FORMAT + '/' + IMAGE_FORMAT
attr.path = '/mnt/data/image/2d/sem_pre_proc'
attr.set_dir_names()
attr.batch_size = 128 # try 4, 8, 16, 32, 64, 128, 256 dependent on CPU/GPU memory capacity (powers of 2 values).
attr.epochs = 500
# how many times to execute the training/validation/test cycle
CYCLES = 1
for i in range(0, CYCLES):
# create the base pre-trained model
base_model = InceptionV3(weights='imagenet', include_top=False)
# dimensions of our images.