比较MLPClassifier的随机学习策略¶
此示例显示了针对不同随机学习策略（包括SGD和Adam）的一些训练损失曲线。 由于时间限制，我们使用了几个较小的数据集，对于这些数据集，L-BFGS可能更适合。 这些示例中显示的总体趋势似乎可以延续到更大的数据集。
请注意，这些结果可能高度依赖于learning_rate_init的值。
print(__doc__)
import warnings
import matplotlib.pyplot as plt
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import MinMaxScaler
from sklearn import datasets
from sklearn.exceptions import ConvergenceWarning
# 不同的学习率时间表和动量参数
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 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):
# 对于每个数据集，对每种学习策略进行学习
print("\nlearning on dataset %s" % name)
ax.set_title(name)
X = MinMaxScaler().fit_transform(X)
mlps = []
if name == "digits":
# 数字较大，但收敛很快
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)
# 一些参数组合将不会收敛，如在图上看到的那样，因此此处将其忽略
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))
# 加载/生成一些玩具数据集
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()
输出：
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: 0.503452
training: inv-scaling with Nesterov's momentum
Training set score: 0.860000
Training set loss: 0.504185
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: 0.912632
Training set loss: 0.290584
training: inv-scaling with Nesterov's momentum
Training set score: 0.909293
Training set loss: 0.318387
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: 0.689143
training: inv-scaling with Nesterov's momentum
Training set score: 0.500000
Training set loss: 0.689751
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: 0.512595
training: inv-scaling with Nesterov's momentum
Training set score: 0.830000
Training set loss: 0.513034
training: adam
Training set score: 0.930000
Training set loss: 0.170087
脚本的总运行时间：（0分钟6.181秒）
