用法: classsklearn.neural_network.MLPClassifier(hidden_layer_sizes=(100,), activation='relu', *, solver='adam', alpha=0.0001, batch_size='auto', learning_rate='constant', learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, ...
predict_proba(X):概率估计。 score(X, y):返回给定测试数据和标签的平均准确度。 set_params():设置参数。 from sklearn.neural_network import MLPClassifier X =[[0., 0.], [1., 1.]]y = [0,1] clf = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(5,2), random_stat...
random_state int,RandomState实例或None,可选,默认无随机数生成器的状态或种子。如果是int,则random_state是随机数生成器使用的种子;如果是RandomState实例,则random_state是随机数生成器;如果为None,则随机数生成器是np.random使用的RandomState实例。 tol float,optional,默认1e-4 优化的容忍度,容差优化。当n_iter...
sklearn.neural_network.MLPClassifier(hidden_layer_sizes=(100, ), activation=’relu’, solver=’adam’, alpha=0.0001, batch_size=’auto’, learning_rate=’constant’, learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_star...
iloc[:,4] X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.3,random_state=0) # 标准化数据 sc=StandardScaler() standard_train=sc.fit_transform(X_train) standard_test=sc.transform(X_test) # 训练并预测结果 mlp=MLPClassifier(solver='lbfgs',alpha=1e-5,hidden...
-`random_state`:默认为None,表示随机数生成器的种子。 参数调整实例 在实际使用中,根据数据集的特点和分类任务的需求,我们可能需要进行参数调整以提高模型的性能。下面给出了一些常见的参数调整实例: 调整隐藏层的数量和神经元数量 默认情况下,MLPClassifier模型只有一个隐藏层,且每个隐藏层只有100个神经元。如果数据...
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 se...
net = MLPClassifier(hidden_layer_sizes=(100), activation='relu', solver='adam', alpha=0.0001, batch_size='auto', learning_rate='constant', learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, ...
clf = MLPClassifier(algorithm='l-bfgs', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1, warm_start=True) TypeError: MLPClassifier() got an unexpected keyword argument 'algorithm' Run Code Online (Sandbox Code Playgroud)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) 创建MLPClassifier模型并进行训练: 代码语言:txt 复制 model = MLPClassifier(hidden_layer_sizes=(100, 100), max_iter=1000, random_state=42) model.fit(X_train, y_...