def __init__(self):
PrepareData.__init__(self)
self.adadelta_rho = 0.95
self.opt_epsilon = 1.0
self.adagrad_initial_accumulator_value = 0.1
self.adam_beta1 = 0.9
self.adam_beta2 = 0.999
self.ftrl_learning_rate_power = -0.5
self.ftrl_initial_accumulator_value = 0.1
self.ftrl_l1 = 0.0
self.ftrl_l2 = 0.0
self.momentum = 0.9
self.rmsprop_decay = 0.9
self.rmsprop_momentum = 0.9
self.label_smoothing = 0
self.num_epochs_per_decay = 2.0
self.end_learning_rate = 0.0001
self.save_interval_secs = 60 * 60 #one hour
self.save_summaries_secs = 60
self.log_every_n_steps = 100
self.train_dir = './logs'
self.batch_size = 32
#optimiser
self.optimizer = 'rmsprop'
self.learning_rate = 0.01
self.learning_rate_decay_type = 'fixed'
self.max_number_of_steps = None
self.checkpoint_path = None
self.checkpoint_exclude_scopes = None
self.ignore_missing_vars = False
self.config_training()
return
def __init__(self):
PrepareData.__init__(self)
self.batch_size = 8
self.labels_offset = 0
self.eval_image_size = None
self.preprocessing_name = None
self.model_name = 'vgg-ssd'
self.num_preprocessing_threads = 4
self.checkpoint_path = None
self.eval_dir = None
return
def __init__(self):
PrepareData.__init__(self)
self.num_epochs_per_decay = 8.0
self.learning_rate_decay_type = 'exponential'
self.end_learning_rate = 0.0001
self.learning_rate = 0.1
#optimiser
self.optimizer = 'rmsprop'
self.adadelta_rho = 0.95
self.opt_epsilon = 1.0
self.adagrad_initial_accumulator_value = 0.1
self.adam_beta1 = 0.9
self.adam_beta2 = 0.999
self.ftrl_learning_rate_power = -0.5
self.ftrl_initial_accumulator_value = 0.1
self.ftrl_l1 = 0.0
self.ftrl_l2 = 0.0
self.momentum = 0.9
self.rmsprop_decay = 0.9
self.rmsprop_momentum = 0.9
self.train_dir = '/tmp/tfmodel/'
self.max_number_of_steps = None
self.checkpoint_path = None
self.checkpoint_exclude_scopes = None
self.ignore_missing_vars = False
self.batch_size = 1
self.save_interval_secs = 60 * 60 * 1 #one hour
self.save_summaries_secs = 30
self.learning_rate_decay_factor = 0.5
self.label_smoothing = 0
return
def __init__(self):
PrepareData.__init__(self)
return
def __init__(self):
PrepareData.__init__(self)
self.batch_size = 32
return