def __init__(self, control_port, max_mb, patience, validFreq): """ Initialize the LSTMController Parameters ---------- max_mb : int Max number of minibatches to train on. patience: : int Training stops when this many minibatches have been trained on without any reported improvement. validFreq : int Number of minibatches to train on between every monitoring step. """ Controller.__init__(self, control_port) self.patience = patience self.max_mb = int(max_mb) self.validFreq = validFreq self.uidx = 0 self.eidx = 0 self.history_errs = [] self.bad_counter = 0 self.valid = False self.start_time = None
def __init__(self, control_port, max_mb, valid_freq): """ Initialize the LSTMController Parameters ---------- max_mb : int Max number of minibatches to train on. valid_freq : int Number of minibatches to train on between every monitoring step. """ Controller.__init__(self, control_port) self.max_mb = max_mb self.valid_freq = valid_freq self.nb_mb = 0 self.valid = False self.start_time = None config = get_config() self.experiment_dir = "{}exp_{}".format(config['plot_output_directory'], time.strftime("%Y-%m-%d_%H-%M-%S")) os.mkdir(self.experiment_dir)
def __init__(self, control_port, batch_port, dataset, batch_size): Controller.__init__(self, control_port, None) # The data socket should be initialized in the process that will handle the batch. # That is why it's not initialized in the parent constructor. Second param = None self._batch_port = batch_port self._start_time = None self._should_stop = False self._batch_size = batch_size self._dataset = dataset self._nb_batch_processed = 0 self._nb_batch_to_process = (dataset.shape[0] // batch_size)
def __init__(self, control_port, batch_port, dataset, batch_size): Controller.__init__(self, control_port, None) # The data socket should be initialized in the process that will handle # the batch. # That is why it's not initialized in the parent constructor. Second # param = None self._batch_port = batch_port self._start_time = None self._should_stop = False self._batch_size = batch_size self._dataset = dataset self._nb_batch_processed = 0 self._nb_batch_to_process = (dataset.shape[0] // batch_size)
def __init__( self, port=CONTROLLER_PORT, easgd_alpha=0.5, # Following arguments can be received from workers start_halving_at=6, end_at=10, step_len=10, valid_freq=1500, learning_rate=0.1, log_path=None): """ Initialize the controller. Args: port (int): batches in one training step easgd_alpha (float) """ Controller.__init__(self, port) self.epoch_start_halving = start_halving_at self.end_at = end_at self.step_len = step_len self.start_time = None self.rand = np.random.RandomState(3) self.epoch = 0 self._current_iter = 0 self._iters_from_last_valid = 0 self._evaluating = False self._valid_freq = valid_freq self._done = False self._lr = learning_rate self._easgd_alpha = easgd_alpha self._training_names = [] self._evaluation_names = [] self._best_valid_cost = sys.float_info.max self._lock = Lock() self.num_train_batches = 0 self.batch_pool = [] self._train_costs = [] self._epoch_start_time = None self.prepared_worker_pool = set() self.log_file = open(log_path, "w") if log_path else None if log_path: logging.info("write logs into {}".format(log_path)) logging.info("multi-gpu server is listening port {}".format(port))
def __init__(self, control_port, max_mb, valid_freq): """ Initialize the LSTMController Parameters ---------- max_mb : int Max number of minibatches to train on. valid_freq : int Number of minibatches to train on between every monitoring step. """ Controller.__init__(self, control_port) self.max_mb = max_mb self.valid_freq = valid_freq self.nb_mb = 0 self.valid = False self.start_time = None config = get_config()
def __init__(self, port=CONTROLLER_PORT, easgd_alpha=0.5, # Following arguments can be received from workers start_halving_at=6, end_at=10, step_len=10, valid_freq=1500, learning_rate=0.1, log_path=None): """ Initialize the controller. Args: port (int): batches in one training step easgd_alpha (float) """ Controller.__init__(self, port) self.epoch_start_halving = start_halving_at self.end_at = end_at self.step_len = step_len self.start_time = None self.rand = np.random.RandomState(3) self.epoch = 0 self._current_iter = 0 self._iters_from_last_valid = 0 self._evaluating = False self._valid_freq = valid_freq self._done = False self._lr = learning_rate self._easgd_alpha = easgd_alpha self._training_names = [] self._evaluation_names = [] self._best_valid_cost = sys.float_info.max self._lock = Lock() self.num_train_batches = 0 self.batch_pool = [] self._train_costs = [] self._epoch_start_time = None self.prepared_worker_pool = set() self.log_file = open(log_path, "w") if log_path else None if log_path: logging.info("write logs into {}".format(log_path)) logging.info("multi-gpu server is listening port {}".format(port))
def __init__(self, control_port,max_mb,validFreq): """ Initialize the ConvNetController Parameters ---------- max_mb : int Max number of minibatches to train on. validFreq : int Number of minibatches to train on between every monitoring step. """ Controller.__init__(self,control_port) self.validFreq = validFreq self.max_mb = int(max_mb) self.uidx = {} self.valid = {} self.start_time = None self.uepoch = 0 self.last_uepoch = 0 self.epoch_time=[] self.last = None self.last_uidx = 0
def __init__(self, control_port): Controller.__init__(self, control_port) self.wps = {}