比較 MLPClassifier 的隨機學習策略#

此範例視覺化了一些不同隨機學習策略的訓練損失曲線，包括 SGD 和 Adam。由於時間限制，我們使用了一些小型資料集，對於這些資料集，L-BFGS 可能更適合。但是，這些範例中顯示的一般趨勢似乎可以應用於較大的資料集。

請注意，這些結果可能高度依賴於 learning_rate_init 的值。

learning on dataset iris
training: constant learning-rate
Training set score: 0.980000
Training set loss: 0.096950
training: constant with momentum
Training set score: 0.980000
Training set loss: 0.049530
training: constant with Nesterov's momentum
Training set score: 0.980000
Training set loss: 0.049540
training: inv-scaling learning-rate
Training set score: 0.360000
Training set loss: 0.978444
training: inv-scaling with momentum
Training set score: 0.860000
Training set loss: 0.504185
training: inv-scaling with Nesterov's momentum
Training set score: 0.860000
Training set loss: 0.503452
training: adam
Training set score: 0.980000
Training set loss: 0.045311

learning on dataset digits
training: constant learning-rate
Training set score: 0.956038
Training set loss: 0.243802
training: constant with momentum
Training set score: 0.992766
Training set loss: 0.041297
training: constant with Nesterov's momentum
Training set score: 0.993879
Training set loss: 0.042898
training: inv-scaling learning-rate
Training set score: 0.638843
Training set loss: 1.855465
training: inv-scaling with momentum
Training set score: 0.909293
Training set loss: 0.318387
training: inv-scaling with Nesterov's momentum
Training set score: 0.912632
Training set loss: 0.290584
training: adam
Training set score: 0.991653
Training set loss: 0.045934

learning on dataset circles
training: constant learning-rate
Training set score: 0.840000
Training set loss: 0.601052
training: constant with momentum
Training set score: 0.940000
Training set loss: 0.157334
training: constant with Nesterov's momentum
Training set score: 0.940000
Training set loss: 0.154453
training: inv-scaling learning-rate
Training set score: 0.500000
Training set loss: 0.692470
training: inv-scaling with momentum
Training set score: 0.500000
Training set loss: 0.689751
training: inv-scaling with Nesterov's momentum
Training set score: 0.500000
Training set loss: 0.689143
training: adam
Training set score: 0.940000
Training set loss: 0.150527

learning on dataset moons
training: constant learning-rate
Training set score: 0.850000
Training set loss: 0.341523
training: constant with momentum
Training set score: 0.850000
Training set loss: 0.336188
training: constant with Nesterov's momentum
Training set score: 0.850000
Training set loss: 0.335919
training: inv-scaling learning-rate
Training set score: 0.500000
Training set loss: 0.689015
training: inv-scaling with momentum
Training set score: 0.830000
Training set loss: 0.513034
training: inv-scaling with Nesterov's momentum
Training set score: 0.830000
Training set loss: 0.512595
training: adam
Training set score: 0.930000
Training set loss: 0.170087

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

import warnings

import matplotlib.pyplot as plt

from sklearn import datasets
from sklearn.exceptions import ConvergenceWarning
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import MinMaxScaler

# 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": 0.9,
        "nesterovs_momentum": False,
        "learning_rate_init": 0.2,
    },
    {
        "solver": "sgd",
        "learning_rate": "constant",
        "momentum": 0.9,
        "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": 0.9,
        "nesterovs_momentum": False,
        "learning_rate_init": 0.2,
    },
    {
        "solver": "sgd",
        "learning_rate": "invscaling",
        "momentum": 0.9,
        "nesterovs_momentum": True,
        "learning_rate_init": 0.2,
    },
    {"solver": "adam", "learning_rate_init": 0.01},
]

labels = [
    "constant learning-rate",
    "constant with momentum",
    "constant with Nesterov's momentum",
    "inv-scaling learning-rate",
    "inv-scaling with momentum",
    "inv-scaling with Nesterov's momentum",
    "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):
    # for each dataset, plot learning for each learning strategy
    print("\nlearning on dataset %s" % name)
    ax.set_title(name)

    X = MinMaxScaler().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):
        print("training: %s" % label)
        mlp = MLPClassifier(random_state=0, max_iter=max_iter, **param)

        # some parameter 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 set score: %f" % mlp.score(X, y))
        print("Training set 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))
# load / generate some toy datasets
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()