本文整理匯總了Python中sklearn.neural_network方法的典型用法代碼示例。如果您正苦於以下問題：Python sklearn.neural_network方法的具體用法？Python sklearn.neural_network怎麽用？Python sklearn.neural_network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sklearn的用法示例。
在下文中一共展示了sklearn.neural_network方法的4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: init_classifier_impl
# 需要導入模塊: import sklearn [as 別名]
# 或者: from sklearn import neural_network [as 別名]
def init_classifier_impl(field_code: str, init_script: str):
if init_script is not None:
init_script = init_script.strip()
if not init_script:
from sklearn import tree as sklearn_tree
return sklearn_tree.DecisionTreeClassifier()
from sklearn import tree as sklearn_tree
from sklearn import neural_network as sklearn_neural_network
from sklearn import neighbors as sklearn_neighbors
from sklearn import svm as sklearn_svm
from sklearn import gaussian_process as sklearn_gaussian_process
from sklearn.gaussian_process import kernels as sklearn_gaussian_process_kernels
from sklearn import ensemble as sklearn_ensemble
from sklearn import naive_bayes as sklearn_naive_bayes
from sklearn import discriminant_analysis as sklearn_discriminant_analysis
from sklearn import linear_model as sklearn_linear_model
eval_locals = {
'sklearn_linear_model': sklearn_linear_model,
'sklearn_tree': sklearn_tree,
'sklearn_neural_network': sklearn_neural_network,
'sklearn_neighbors': sklearn_neighbors,
'sklearn_svm': sklearn_svm,
'sklearn_gaussian_process': sklearn_gaussian_process,
'sklearn_gaussian_process_kernels': sklearn_gaussian_process_kernels,
'sklearn_ensemble': sklearn_ensemble,
'sklearn_naive_bayes': sklearn_naive_bayes,
'sklearn_discriminant_analysis': sklearn_discriminant_analysis
}
return eval_script('classifier init script of field {0}'.format(field_code), init_script, eval_locals) 示例2: transfer
# 需要導入模塊: import sklearn [as 別名]
# 或者: from sklearn import neural_network [as 別名]
def transfer(n):
td, vd, ts = data_loader.load_data(n, abstract=True, expanded=expanded)
classifiers = [
#sklearn.svm.SVC(),
#sklearn.svm.SVC(kernel="linear", C=0.1),
#sklearn.neighbors.KNeighborsClassifier(1),
#sklearn.tree.DecisionTreeClassifier(),
#sklearn.ensemble.RandomForestClassifier(max_depth=10, n_estimators=500, max_features=1),
sklearn.neural_network.MLPClassifier(alpha=1.0, hidden_layer_sizes=(300,), max_iter=500)
]
for clf in classifiers:
clf.fit(td[0], td[1])
print "\n{}: {}".format(type(clf).__name__, round(clf.score(vd[0], vd[1])*100, 2)) 示例3: baselines
# 需要導入模塊: import sklearn [as 別名]
# 或者: from sklearn import neural_network [as 別名]
def baselines(n):
td, vd, ts = data_loader.load_data(n)
classifiers = [
sklearn.svm.SVC(C=1000),
sklearn.svm.SVC(kernel="linear", C=0.1),
sklearn.neighbors.KNeighborsClassifier(1),
sklearn.tree.DecisionTreeClassifier(),
sklearn.ensemble.RandomForestClassifier(max_depth=10, n_estimators=500, max_features=1),
sklearn.neural_network.MLPClassifier(alpha=1, hidden_layer_sizes=(500, 100))
]
for clf in classifiers:
clf.fit(td[0], td[1])
print "\n{}: {}".format(type(clf).__name__, round(clf.score(vd[0], vd[1])*100, 2)) 示例4: _get_estimator
# 需要導入模塊: import sklearn [as 別名]
# 或者: from sklearn import neural_network [as 別名]
def _get_estimator(pblm, clf_key):
"""
Returns sklearn classifier
"""
tup = clf_key.split('-')
wrap_type = None if len(tup) == 1 else tup[1]
est_type = tup[0]
multiclass_wrapper = {
None: ut.identity,
'OVR': sklearn.multiclass.OneVsRestClassifier,
'OVO': sklearn.multiclass.OneVsOneClassifier,
}[wrap_type]
est_class = {
'RF': sklearn.ensemble.RandomForestClassifier,
'SVC': sklearn.svm.SVC,
'Logit': sklearn.linear_model.LogisticRegression,
'MLP': sklearn.neural_network.MLPClassifier,
}[est_type]
est_kw1, est_kw2 = pblm._estimator_params(est_type)
est_params = ut.merge_dicts(est_kw1, est_kw2)
# steps = []
# steps.append((est_type, est_class(**est_params)))
# if wrap_type is not None:
# steps.append((wrap_type, multiclass_wrapper))
if est_type == 'MLP':
def clf_partial():
pipe = sklearn.pipeline.Pipeline([
('inputer', sklearn.preprocessing.Imputer(
missing_values='NaN', strategy='mean', axis=0)),
# ('scale', sklearn.preprocessing.StandardScaler),
('est', est_class(**est_params)),
])
return multiclass_wrapper(pipe)
elif est_type == 'Logit':
def clf_partial():
pipe = sklearn.pipeline.Pipeline([
('inputer', sklearn.preprocessing.Imputer(
missing_values='NaN', strategy='mean', axis=0)),
('est', est_class(**est_params)),
])
return multiclass_wrapper(pipe)
else:
def clf_partial():
return multiclass_wrapper(est_class(**est_params))
return clf_partial