
from sklearn.neural_network import MLPClassifier as MLPC
X,y = [[0., 0.], [1., 1.]], [0, 1]

clf = MLPC(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1)
clf.fit(X, y)
print(clf.predict([[2., 2.], [-1., -2.]]))

[1 0]


[coef.shape for coef in clf.coefs_]

[(2, 5), (5, 2), (2, 1)]


clf.predict_proba([[2., 2.], [1., 2.]])

array([[1.96718015e-04, 9.99803282e-01],
       [1.96718015e-04, 9.99803282e-01]])


X,y = [[0., 0.], [1., 1.]], [[0, 1], [1, 1]]

clf = MLPC(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(15,), random_state=1)
clf.fit(X, y)

print(clf.predict([[1., 2.]]))
print(clf.predict([[0., 0.]]))

[[1 1]]
[[0 1]]


import matplotlib.pyplot as plt

from sklearn.neural_network import MLPClassifier as MLPC
from sklearn.preprocessing import MinMaxScaler as MMS
from sklearn import datasets
from sklearn.exceptions import ConvergenceWarning

# different learning rate schedules and momentum parameters
params = [{'solver': 'sgd', 'learning_rate': 'constant',   'momentum': 0, 
           'learning_rate_init': 0.2},
          {'solver': 'sgd', 'learning_rate': 'constant',   'momentum': .9,
           'nesterovs_momentum': False, 'learning_rate_init': 0.2},
          {'solver': 'sgd', 'learning_rate': 'constant',   'momentum': .9,
           'nesterovs_momentum': True, 'learning_rate_init': 0.2},
          {'solver': 'sgd', 'learning_rate': 'invscaling', 'momentum': 0,
           'learning_rate_init': 0.2},
          {'solver': 'sgd', 'learning_rate': 'invscaling', 'momentum': .9,
           'nesterovs_momentum': True, 'learning_rate_init': 0.2},
          {'solver': 'sgd', 'learning_rate': 'invscaling', 'momentum': .9,
           'nesterovs_momentum': False, 'learning_rate_init': 0.2},
          {'solver': 'adam', 'learning_rate_init': 0.01}]

labels = ["constant", 
          "constant/momentum",
          "constant/momentum/nesterov",
          "inv-scaling", 
          "inv-scaling/momentum",
          "inv-scaling/momentum/nesterov", 
          "adam"]

plot_args = [{'c': 'red', 'linestyle': '-'},
             {'c': 'green', 'linestyle': '-'},
             {'c': 'blue', 'linestyle': '-'},
             {'c': 'red', 'linestyle': '--'},
             {'c': 'green', 'linestyle': '--'},
             {'c': 'blue', 'linestyle': '--'},
             {'c': 'black', 'linestyle': '-'}]


def plot_on_dataset(X, y, ax, name):
    ax.set_title(name)

    X = MMS().fit_transform(X)
    mlps = []
    if name == "digits": # digits is larger but converges fairly quickly
        max_iter = 15
    else:
        max_iter = 400

    for label, param in zip(labels, params):
        mlp = MLPC(random_state=0, max_iter=max_iter, **param)

        # some combinations will not converge as can be seen on the
        # plots so they are ignored here
        '''
        with warnings.catch_warnings():
            warnings.filterwarnings("ignore", category=ConvergenceWarning, module="sklearn")
        '''
        mlp.fit(X, y)

        mlps.append(mlp)
        print("Training score: %f" % mlp.score(X, y))
        print("Training loss: %f" % mlp.loss_)
    for mlp, label, args in zip(mlps, labels, plot_args):
        ax.plot(mlp.loss_curve_, label=label, **args)

fig, axes = plt.subplots(2, 2, figsize=(15, 10))
iris = datasets.load_iris()
X_digits, y_digits = datasets.load_digits(return_X_y=True)

data_sets = [(iris.data, iris.target),
             (X_digits, y_digits),
             datasets.make_circles(noise=0.2, factor=0.5, random_state=1),
             datasets.make_moons(noise=0.3, random_state=0)]

for ax, data, name in zip(axes.ravel(), data_sets, ['iris', 'digits',
                                                    'circles', 'moons']):
    plot_on_dataset(*data, ax=ax, name=name)

fig.legend(ax.get_lines(), labels, ncol=3, loc="upper center")
plt.show()

/home/bjpcjp/.local/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.
  warnings.warn(

Training score: 0.980000
Training loss: 0.096950
Training score: 0.980000
Training loss: 0.049530
Training score: 0.980000
Training loss: 0.049540
Training score: 0.360000
Training loss: 0.978444
Training score: 0.860000
Training loss: 0.503452

/home/bjpcjp/.local/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.
  warnings.warn(
/home/bjpcjp/.local/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.
  warnings.warn(

Training score: 0.860000
Training loss: 0.504185
Training score: 0.980000
Training loss: 0.045311

/home/bjpcjp/.local/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (15) reached and the optimization hasn't converged yet.
  warnings.warn(

Training score: 0.956038
Training loss: 0.243802

/home/bjpcjp/.local/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (15) reached and the optimization hasn't converged yet.
  warnings.warn(

Training score: 0.992766
Training loss: 0.041297

/home/bjpcjp/.local/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (15) reached and the optimization hasn't converged yet.
  warnings.warn(

Training score: 0.993879
Training loss: 0.042898


from sklearn.neural_network import MLPRegressor as MLPR
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split as TTS

X, y                             = make_regression(n_samples=200, random_state=1)
X_train, X_test, y_train, y_test = TTS(X, y, random_state=1)
regr                             = MLPR(random_state=1, max_iter=5000).fit(X_train, y_train)

print(regr.predict(X_test[:2]))
print(regr.score(X_test, y_test))

[8.69448846 6.5006531 ]
0.5209157440819883


import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap as LCM
from sklearn.model_selection import train_test_split as TTS
from sklearn.preprocessing import StandardScaler as SS
from sklearn.datasets import make_moons, make_circles, make_classification
from sklearn.neural_network import MLPClassifier as MLPC
from sklearn.pipeline import make_pipeline


h           = .02  # step size in the mesh
alphas      = np.logspace(-1, 1, 5)
classifiers = []
names       = []

for alpha in alphas:
    classifiers.append(make_pipeline(
        SS(),
        MLPC(
            solver='lbfgs', alpha=alpha, random_state=1, max_iter=2000,
            early_stopping=True, hidden_layer_sizes=[100, 100],
        )
    ))
    names.append(f"alpha {alpha:.2f}")

X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,
                           random_state=0, n_clusters_per_class=1)
rng = np.random.RandomState(2)
X  += 2 * rng.uniform(size=X.shape)
linearly_separable = (X, y)

datasets = [make_moons(noise=0.3, random_state=0),
            make_circles(noise=0.2, factor=0.5, random_state=1),
            linearly_separable]


figure = plt.figure(figsize=(17, 9))
i = 1
for X, y in datasets:
    X_train, X_test, y_train, y_test = TTS(X, y, test_size=.4)

    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
    
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                         np.arange(y_min, y_max, h))

    # just plot the dataset first
    cm = plt.cm.RdBu
    cm_bright = LCM(['#FF0000', '#0000FF'])
    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
    # training & testing points
    ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
    ax.scatter(X_test[:, 0],  X_test[:, 1],  c=y_test,  cmap=cm_bright, alpha=0.6)
    ax.set_xlim(xx.min(), xx.max())
    ax.set_ylim(yy.min(), yy.max())
    ax.set_xticks(())
    ax.set_yticks(())
    i += 1

    # iterate over classifiers
    for name, clf in zip(names, classifiers):
        ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
        clf.fit(X_train, y_train)
        score = clf.score(X_test, y_test)

        # Plot the decision boundary. 
        if hasattr(clf, "decision_function"):
            Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
        else:
            Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]

        # Put the result into a color plot
        Z = Z.reshape(xx.shape)
        ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)

        # training & testing points
        ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors='black', s=25)
        ax.scatter(X_test[:, 0],  X_test[:, 1],  c=y_test,  cmap=cm_bright, alpha=0.6, edgecolors='black', s=25)

        ax.set_xlim(xx.min(), xx.max())
        ax.set_ylim(yy.min(), yy.max())
        ax.set_xticks(())
        ax.set_yticks(())
        ax.set_title(name)
        ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'),
                size=15, horizontalalignment='right')
        i += 1

figure.subplots_adjust(left=.02, right=.98)
plt.show()

/home/bjpcjp/.local/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:500: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
  self.n_iter_ = _check_optimize_result("lbfgs", opt_res, self.max_iter)

Perceptrons ¶

Classification ¶

Multiclass & Multilabel Classification¶

Example: MLP Classifier learning strategies, compared ¶

Regression ¶

Regularization¶

Perceptrons¶

Classification¶

Multiclass & Multilabel Classification¶

Example: MLP Classifier learning strategies, compared¶

Regression¶

Regularization¶

Perceptrons ¶

Classification ¶

Example: MLP Classifier learning strategies, compared ¶

Regression ¶