本文整理汇总了Python中nolearn.dbn.DBN.chunked_decision_function方法的典型用法代码示例。如果您正苦于以下问题：Python DBN.chunked_decision_function方法的具体用法？Python DBN.chunked_decision_function怎么用？Python DBN.chunked_decision_function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.dbn.DBN的用法示例。
在下文中一共展示了DBN.chunked_decision_function方法的1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: DBNRegressor
# 需要导入模块: from nolearn.dbn import DBN [as 别名]
# 或者: from nolearn.dbn.DBN import chunked_decision_function [as 别名]
class DBNRegressor(BaseEstimator, RegressorMixin):
def __init__(self, n_hidden_layers=2, n_units=1000, epochs=100,
epochs_pretrain=0, scales=0.05,
real_valued_vis=True,
use_re_lu=False,
uniforms=False,
learn_rates_pretrain=0.1,
learn_rates=0.1,
learn_rate_decays=1.0,
learn_rate_minimums=0.0,
momentum=0.9,
momentum_pretrain=0.9,
l2_costs=0.0001,
l2_costs_pretrain=0.0001,
dropouts=None,
minibatch_size=64,
verbose=2,
fine_tune_callback=None,
nest_compare=True,
nest_compare_pretrain=None,
fan_outs=None,
nesterov=False,
):
self.n_hidden_layers = n_hidden_layers
self.n_units = n_units
self.epochs = epochs
self.epochs_pretrain = epochs_pretrain
self.learn_rates_pretrain = learn_rates_pretrain
self.learn_rates = learn_rates
self.learn_rate_decays = learn_rate_decays
self.learn_rate_minimums = learn_rate_minimums
self.l2_costs_pretrain = l2_costs_pretrain
self.l2_costs = l2_costs
self.momentum = momentum
self.momentum_pretrain = momentum_pretrain
self.verbose = verbose
self.real_valued_vis = real_valued_vis
self.use_re_lu = use_re_lu
self.scales = scales
self.minibatch_size = minibatch_size
if dropouts is None:
dropouts = [0.2] + [0.5] * n_hidden_layers
self.dropouts = dropouts
self.fine_tune_callback = fine_tune_callback
self.nest_compare = nest_compare
self.nest_compare_pretrain = nest_compare_pretrain
self.fan_outs = fan_outs
self.nesterov = nesterov
def fit(self, X, y, X_pretrain=None):
from nolearn.dbn import DBN
if y.ndim == 2:
n_outputs = y.shape[1]
else:
y = y[:, np.newaxis]
n_outputs = 1
params = dict(self.__dict__)
from gdbn.activationFunctions import Linear
params['output_act_funct'] = Linear()
n_units = params.pop('n_units')
n_hidden_layers = params.pop('n_hidden_layers')
if isinstance(n_units, int):
units = [n_units] * n_hidden_layers
else:
units = n_units
units = [X.shape[1]] + units + [n_outputs]
self.dbn = DBN(units, **params)
print X.shape
self.dbn.fit(X, y, X_pretrain=X_pretrain)
def predict(self, X):
return self.dbn.chunked_decision_function(X)