本文整理匯總了Python中Engine.Engine.epoch_model_filename方法的典型用法代碼示例。如果您正苦於以下問題：Python Engine.epoch_model_filename方法的具體用法？Python Engine.epoch_model_filename怎麽用？Python Engine.epoch_model_filename使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類Engine.Engine的用法示例。
在下文中一共展示了Engine.epoch_model_filename方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: saveCrnnNetwork
# 需要導入模塊: from Engine import Engine [as 別名]
# 或者: from Engine.Engine import epoch_model_filename [as 別名]
def saveCrnnNetwork(epoch, layers):
"""
:type epoch: int
:type layers: list[(numpy.ndarray, numpy.ndarray)]
"""
print("Loading Crnn")
from Network import LayerNetwork
from NetworkHiddenLayer import ForwardLayer
from NetworkOutputLayer import OutputLayer
from Pretrain import pretrainFromConfig
from Engine import Engine
pretrain = pretrainFromConfig(config)
is_pretrain_epoch = pretrain and epoch <= pretrain.get_train_num_epochs()
modelFilename = config.value("model", None)
assert modelFilename, "need 'model' in config"
filename = Engine.epoch_model_filename(modelFilename, epoch, is_pretrain_epoch)
assert not os.path.exists(filename), "already exists"
if is_pretrain_epoch:
network = pretrain.get_network_for_epoch(epoch)
else:
network = LayerNetwork.from_config_topology(config)
nHiddenLayers = len(network.hidden)
# print network topology
print "Crnn Network layer topology:"
print "input dim:", network.n_in
print "hidden layer count:", nHiddenLayers
print "output dim:", network.n_out["classes"]
print "net weights #:", network.num_params()
print "net params:", network.train_params_vars
print "net output:", network.output["output"]
assert network.n_in == inputDim
#assert network.n_out == outputDim
assert nHiddenLayers + 1 == layerCount # hidden + output layer
assert len(layers) == layerCount
for i, (layerName, hidden) in enumerate(sorted(network.hidden.items())):
# Some checks whether this is a forward-layer.
assert isinstance(hidden, ForwardLayer)
saveCrnnLayer(hidden, *layers[i])
assert isinstance(network.output["output"], OutputLayer)
saveCrnnLayer(network.output["output"], *layers[len(layers) - 1])
import h5py
print("Save Crnn model under %s" % filename)
model = h5py.File(filename, "w")
network.save_hdf(model, epoch)
model.close()示例2: initBase
# 需要導入模塊: from Engine import Engine [as 別名]
# 或者: from Engine.Engine import epoch_model_filename [as 別名]
def initBase(configfile=None, targetMode=None, epoch=None):
"""
:type configfile: str | None
"""
global isInitialized
isInitialized = True
# Run through in any case. Maybe just to set targetMode.
global config
if not config:
if configfile is None:
configfile = DefaultSprintCrnnConfig
assert os.path.exists(configfile)
rnn.initThreadJoinHack()
rnn.initConfig(configfile, [])
config = rnn.config
rnn.initLog()
rnn.initConfigJsonNetwork()
if targetMode:
setTargetMode(targetMode)
initDataset()
if targetMode and targetMode == "forward" and epoch:
model_filename = config.value('model', '')
fns = [Engine.epoch_model_filename(model_filename, epoch, is_pretrain) for is_pretrain in [False, True]]
fns_existing = [fn for fn in fns if os.path.exists(fn)]
assert len(fns_existing) == 1, "%s not found" % fns
model_epoch_filename = fns_existing[0]
config.set('load', model_epoch_filename)
assert Engine.get_epoch_model(config)[1] == model_epoch_filename
global engine
if not engine:
devices = rnn.initDevices()
rnn.printTaskProperties(devices)
rnn.initEngine(devices)
engine = rnn.engine
assert isinstance(engine, Engine)