def __init__(self, **kwargs):
self.macro_batched = False
self.__dict__.update(kwargs)
opt_param(self, ['backend_type'], 'np.float32')
self.backend_type = ensure_dtype(self.backend_type) # string to dtype
logger.info("Setting dtype to" + str(self.backend_type))
def __init__(self, **kwargs):
self.initialized = False
self.__dict__.update(kwargs)
opt_param(self, ['name'], 'layer')
opt_param(self, [
'pre_act_dtype', 'output_dtype', 'deltas_dtype', 'weight_dtype',
'updates_dtype'
], np.float32)
opt_param(self, ['prev_layer'])
opt_param(self, ['activation'], Linear())
opt_param(self, ['is_local', 'is_data', 'is_cost'], False)
opt_param(self, ['is_random'], False)
opt_param(self, ['skip_act', 'has_params'], False)
opt_param(self, ['prev_names'], [])
opt_param(self, ['backend_type'], 'np.float32')
self.backend_type = ensure_dtype(self.backend_type) # string to dtype
logger.info("Setting layer dtype to" + str(self.backend_type))
for some_type in [
'pre_act_dtype', 'output_dtype', 'deltas_dtype',
'weight_dtype', 'updates_dtype'
]:
setattr(self, some_type, self.backend_type)
def __init__(self, **kwargs):
self.macro_batched = False
self.__dict__.update(kwargs)
opt_param(self, ['backend_type'], 'np.float32')
self.backend_type = ensure_dtype(self.backend_type) # string to dtype
logger.info("Setting dtype to" + str(self.backend_type))
def __init__(self, **kwargs):
self.__dict__.update(kwargs)
opt_param(self, ['outputbuf', 'temp'], None)
opt_param(self, ['scale'], 1.0)
opt_param(self, ['backend_type'], np.float32)
self.temp_dtype = ensure_dtype(self.backend_type) # string to dtype
logger.info("Setting dtype to" + str(self.backend_type))
def __init__(self, **kwargs):
self.__dict__.update(kwargs)
opt_param(self, ['outputbuf', 'temp'], None)
opt_param(self, ['scale'], 1.0)
opt_param(self, ['backend_type'], np.float32)
self.temp_dtype = ensure_dtype(self.backend_type) # string to dtype
logger.info("Setting dtype to" + str(self.backend_type))
def fit(self, dataset):
error = self.backend.empty((1, 1))
self.print_layers()
self.data_layer.init_dataset(dataset)
self.data_layer.use_set('train')
if (self.num_grad_params is not None) \
and (str(ensure_dtype(self.backend_type)) ==
"<type 'numpy.float64'>"):
self.grad_checker(numgrad=self.num_grad_params)
if self.make_plots is True:
viz = VisualizeRNN()
errorlist = []
suberrorlist = []
logger.info('commencing model fitting')
suberror = self.backend.zeros((1, 1))
while self.epochs_complete < self.num_epochs:
self.backend.begin(Block.epoch, self.epochs_complete)
error.fill(0.0)
mb_id = 1
self.data_layer.reset_counter()
dlnb = self.data_layer.num_batches
while self.data_layer.has_more_data():
self.backend.begin(Block.minibatch, mb_id)
self.reset(mb_id)
self.backend.begin(Block.fprop, mb_id)
self.fprop(debug=(True if (mb_id is -1) else False))
self.backend.end(Block.fprop, mb_id)
self.backend.begin(Block.bprop, mb_id)
self.bprop(debug=(True if (mb_id is -1) else False))
self.backend.end(Block.bprop, mb_id)
self.backend.begin(Block.update, mb_id)
self.update(self.epochs_complete)
self.backend.end(Block.update, mb_id)
self.cost_layer.cost.set_outputbuf(
self.class_layer.output_list[-1])
suberror = self.cost_layer.get_cost()
if self.make_plots is True:
suberrorlist.append(float(suberror.asnumpyarray()))
self.backend.add(error, suberror, error)
if self.step_print > 0 and mb_id % self.step_print == 0:
logger.info('%d:%d logloss=%0.5f', self.epochs_complete,
mb_id / self.step_print,
float(error.asnumpyarray()) / dlnb)
self.backend.end(Block.minibatch, mb_id)
mb_id += 1
self.epochs_complete += 1
if self.make_plots is True:
errorlist.append(float(error.asnumpyarray()) / dlnb)
logger.info('epoch: %d, total training error: %0.5f',
self.epochs_complete,
float(error.asnumpyarray()) / dlnb)
self.backend.end(Block.epoch, self.epochs_complete - 1)
self.save_snapshot()
if self.make_plots is True:
self.plot_layers(viz, suberrorlist, errorlist)
self.data_layer.cleanup()
def fit(self, dataset):
error = self.backend.empty((1, 1))
self.print_layers()
self.data_layer.init_dataset(dataset)
self.data_layer.use_set("train")
if (self.num_grad_params is not None) and (str(ensure_dtype(self.backend_type)) == "<type 'numpy.float64'>"):
self.grad_checker(numgrad=self.num_grad_params)
if self.make_plots is True:
viz = VisualizeRNN()
errorlist = []
suberrorlist = []
logger.info("commencing model fitting")
suberror = self.backend.zeros((1, 1))
while self.epochs_complete < self.num_epochs:
self.backend.begin(Block.epoch, self.epochs_complete)
error.fill(0.0)
mb_id = 1
self.data_layer.reset_counter()
dlnb = self.data_layer.num_batches
while self.data_layer.has_more_data():
self.backend.begin(Block.minibatch, mb_id)
self.reset(mb_id)
self.backend.begin(Block.fprop, mb_id)
self.fprop(debug=(True if (mb_id is -1) else False))
self.backend.end(Block.fprop, mb_id)
self.backend.begin(Block.bprop, mb_id)
self.bprop(debug=(True if (mb_id is -1) else False))
self.backend.end(Block.bprop, mb_id)
self.backend.begin(Block.update, mb_id)
self.update(self.epochs_complete)
self.backend.end(Block.update, mb_id)
self.cost_layer.cost.set_outputbuf(self.class_layer.output_list[-1])
suberror = self.cost_layer.get_cost()
if self.make_plots is True:
suberrorlist.append(float(suberror.asnumpyarray()))
self.backend.add(error, suberror, error)
if self.step_print > 0 and mb_id % self.step_print == 0:
logger.info(
"%d:%d logloss=%0.5f",
self.epochs_complete,
mb_id / self.step_print,
float(error.asnumpyarray()) / dlnb,
)
self.backend.end(Block.minibatch, mb_id)
mb_id += 1
self.epochs_complete += 1
if self.make_plots is True:
errorlist.append(float(error.asnumpyarray()) / dlnb)
logger.info(
"epoch: %d, total training error: %0.5f", self.epochs_complete, float(error.asnumpyarray()) / dlnb
)
self.backend.end(Block.epoch, self.epochs_complete - 1)
self.save_snapshot()
if self.make_plots is True:
self.plot_layers(viz, suberrorlist, errorlist)
self.data_layer.cleanup()
def __init__(self, **kwargs):
self.initialized = False
self.__dict__.update(kwargs)
opt_param(self, ['name'], 'layer')
opt_param(self, ['pre_act_dtype', 'output_dtype', 'deltas_dtype',
'weight_dtype', 'updates_dtype'], np.float32)
opt_param(self, ['prev_layer'])
opt_param(self, ['activation'], Linear())
opt_param(self, ['is_local', 'is_data', 'is_cost'], False)
opt_param(self, ['is_random'], False)
opt_param(self, ['skip_act', 'has_params'], False)
opt_param(self, ['prev_names'], [])
opt_param(self, ['backend_type'], 'np.float32')
self.backend_type = ensure_dtype(self.backend_type) # string to dtype
logger.info("Setting layer dtype to" + str(self.backend_type))
for some_type in ['pre_act_dtype', 'output_dtype', 'deltas_dtype',
'weight_dtype', 'updates_dtype']:
setattr(self, some_type, self.backend_type)