比较 MLPClassifier 的随机学习策略#
此示例可视化了一些不同随机学习策略的训练损失曲线，包括 SGD 和 Adam。由于时间限制，我们使用了一些小型数据集，对于这些数据集，L-BFGS 可能更适合。但是，在这些示例中显示的总体趋势似乎也适用于更大的数据集。
请注意，这些结果可能高度依赖于 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
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()
脚本的总运行时间：（0 分钟 3.176 秒）
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