Примечание
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Сравнение стохастических стратегий обучения для MLPClassifier
В этом примере визуализированы кривые потерь обучения для различных стохастических стратегий обучения, включая SGD и Adam. Из-за ограничений по времени мы используем несколько небольших наборов данных, для которых L-BFGS может быть более подходящим. Однако общая тенденция, показанная в этих примерах, кажется, сохраняется и для больших наборов данных.
Обратите внимание, что эти результаты могут сильно зависеть от значения learning_rate_init.
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()
Общее время выполнения сценария: (0 минут 3.131 секунды)
Связанные примеры
© 2007–2025 The scikit-learn developers
Licensed under the 3-clause BSD License.
https://scikit-learn.org/1.6/auto_examples/neural_networks/plot_mlp_training_curves.html
