Python sklearn.neural_network() Examples

The following are 4 code examples of sklearn.neural_network(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module sklearn , or try the search function .
Example #1
Source File: field_based_ml_field_detection.py    From lexpredict-contraxsuite with GNU Affero General Public License v3.0 5 votes vote down vote up 
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)
Example #2
Source File: transfer.py    From rmnist with MIT License 5 votes vote down vote up 
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))
Example #3
Source File: baselines.py    From rmnist with MIT License 5 votes vote down vote up 
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))
Example #4
Source File: clf_helpers.py    From ibeis with Apache License 2.0 4 votes vote down vote up 
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