本文整理汇总了Python中nolearn.lasagne.NeuralNet.update_learning_rate方法的典型用法代码示例。如果您正苦于以下问题：Python NeuralNet.update_learning_rate方法的具体用法？Python NeuralNet.update_learning_rate怎么用？Python NeuralNet.update_learning_rate使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类nolearn.lasagne.NeuralNet的用法示例。
在下文中一共展示了NeuralNet.update_learning_rate方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: CompileNetwork
# 需要导入模块: from nolearn.lasagne import NeuralNet [as 别名]
# 或者: from nolearn.lasagne.NeuralNet import update_learning_rate [as 别名]
def CompileNetwork(l_out, epochs, update, update_learning_rate, objective_l2,
earlystopping, patience, batch_size, verbose):
update_fn = getattr(updates, update)
earlystop = EarlyStopping(patience=patience, verbose=verbose)
net = NeuralNet(
l_out,
max_epochs=epochs,
update=update_fn,
objective_l2=objective_l2,
batch_iterator_train = BatchIterator(batch_size=batch_size),
batch_iterator_test = BatchIterator(batch_size=batch_size),
verbose=verbose,
on_training_finished = [earlystop.load_best_weights]
)
if earlystopping == True:
net.on_epoch_finished.append(earlystop)
if update_learning_rate is not None:
net.update_learning_rate=update_learning_rate
return net示例2: createNet
# 需要导入模块: from nolearn.lasagne import NeuralNet [as 别名]
# 或者: from nolearn.lasagne.NeuralNet import update_learning_rate [as 别名]
def createNet(X, Y, ln, loadFile = ""):
net1 = NeuralNet(
layers=[ # four layers: two hidden layers
('input', layers.InputLayer),
('hidden', layers.DenseLayer),
('hidden1', layers.DenseLayer),
('hidden2', layers.DenseLayer),
('hidden3', layers.DenseLayer),
('output', layers.DenseLayer),
],
# layer parameters: Best 400 400
input_shape=(None, numInputs), # 31 inputs
hidden_num_units=400, # number of units in hidden layer
hidden1_num_units=400,
hidden2_num_units=400,
hidden3_num_units=400,
output_nonlinearity=None, # output layer uses identity function
output_num_units=numOutputs, # 4 outputs
# optimization method:
update=nesterov_momentum,
update_learning_rate=ln,
update_momentum=0.9,
regression=True, # flag to indicate we're dealing with regression problem
max_epochs=1500, # we want to train this many epochs
verbose=1,
)
#if (loadFile != ""):
#net1.load_params_from(loadFile)
net1.max_epochs = 50
net1.update_learning_rate = ln;
return net1