from keras.utils import plot_model from Datasets import create_image_generator import multiprocessing # fix seed for reproducible results (only works on CPU, not GPU) #seed = 9 #np.random.seed(seed=seed) #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/'
from TrainingResume import save_execution_attributes
from keras.utils import plot_model
from Datasets import create_image_generator
import multiprocessing
from keras import backend as K
# fix seed for reproducible results (only works on CPU, not GPU)
# seed = 9
# np.random.seed(seed=seed)
# 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