config.py

import tensorflow as tf
import os

flags = tf.flags

# to keep compatibility with CLI arg
flags.DEFINE_string('task', 'jps', 'what the hell')

# hamming set
flags.DEFINE_integer('hammingSetSize', 40, 'Hamming set size')
flags.DEFINE_string('selectionMethod', 'max', 'max or mean')
flags.DEFINE_string('hammingFileName', 'max_hamming_set_', 'Name of the file to be saved')

# jigsaw
flags.DEFINE_integer('numCrops', 9, 'The number of jigsaw-puzzle crops')
flags.DEFINE_integer('cellSize', 75, 'The dimensions of the jigsaw input')
flags.DEFINE_integer('tileSize', 64, 'The dimensions of the jigsaw input')
flags.DEFINE_integer('colorJitter', 2, 'Number of pixels for color jittering')
flags.DEFINE_integer('cropSize', 225, 'Size of the crop extracted from each input image')
flags.DEFINE_integer('original_dim', 256, 'Size of the input image')

# Training logs
flags.DEFINE_integer('max_epoch', 69, 'maximum number of training epochs')
# to reach in order to solve 69 puzzles per image

# Hyper-parameters
flags.DEFINE_integer('batchSize', 100, 'training batch size')
flags.DEFINE_integer('val_batch_size', 100, 'validation batch size')
flags.DEFINE_float('init_lr', 4e-5, 'Initial learning dropout_rate')
flags.DEFINE_float('decay_rate', 0.99, 'Decay rate for learning rate')
flags.DEFINE_string('optimizer', 'SGD', 'select optimizer for the model. Choose between "adam" and "SGD"')
flags.DEFINE_float('sgd_momentum', 0.93, 'Momentum of the SGD optimizer')


# data
flags.DEFINE_string('data_path', 'C:\\Users\\giova\\Downloads\\Dataset_NoLabels\\ILSVRC_500000', 'Data path')
flags.DEFINE_integer('height', 64, 'Input height size')
flags.DEFINE_integer('width', 64, 'Input width size')
flags.DEFINE_integer('numChannels', 3, 'Input channel size')

# Directories and settings
flags.DEFINE_string('resources', os.path.join('resources', 'hamming_sets'), 'Path to h5 files folder')
flags.DEFINE_string('run_name', '10_da_continuare', 'Run name')
flags.DEFINE_string('trial_dir', os.path.join('ResultsJPS'), 'Results saving directory')
flags.DEFINE_string('model_name', 'model_on_input', 'Model file name')
flags.DEFINE_integer('reload_step', 10, 'Reload step to continue training')
flags.DEFINE_string('eval_weight', 'weights.10-3.05.hdf5', 'Select the weight with which evaluate the model_on_input')

conf = tf.flags.FLAGS