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, warm_st...
# _random_state就是第五点中根据seed实例出的对象 # uniform代表在随机生成,参数分别为下限,上限,size # Generate weights and bias coef_init = self._random_state.uniform( -init_bound, init_bound, (fan_in, fan_out) ) intercept_init = self._random_state.uniform(-init_bound, init_bound, fan...
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_state=1) clf.fit(X, y) clf.predict([[2., 2.], [-1., -2.]])...
from sklearn.neural_network import MLPClassifier 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(solver='sgd',activation = 'identity',max_iter = 10,alpha = 1e-5,hidden_layer_sizes = (100,50),random_state = 1,verbose = True) clf.fit(train_data[0][:10000],train_data[1][:10000]) print clf.predict(test_data[0][:10]) ...
clf = MLPClassifier(solver='sgd',activation = 'identity',max_iter = 10,alpha = 1e-5,hidden_layer_sizes = (100,50),random_state = 1,verbose = True) clf.fit(train_data[0][:10000],train_data[1][:10000]) print clf.predict(test_data[0][:10]) ...
random_state:随机数种子, default=None max_iterint:default=1000,要运行的最大迭代次数。 decision_function_shape:{‘ovo’, ‘ovr’}, default=’ovr’。 Decision tree-based models tree.DecisionTreeClassifier函数参数: criterion:{“gini”, “entropy”, “log_loss”}, default=”gini”,衡量分割质量的...
from sklearn.neural_network import MLPClassifier # 现在导入sklearn中的用于评测预测结果指标的库,如混淆矩阵和分类报告 from sklearn.metrics import confusion_matrix,classification_report 1. 2. 3. 4. 5. 6. 7. 8. 数据读取和处理部分 #以txt文本数据为例,我们用numpy读取文本数据到整个程序中,以逗号分...
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...
# MLP神经网络算法 def mx_MLP(train_x, train_y): #mx = MLPClassifier(solver='lbfgs', alpha=1e-5,hidden_layer_sizes=(5, 2), random_state=1) mx = MLPClassifier() mx.fit(train_x, train_y) return mx #结果验证函数 def ai_acc_xed(df9,ky0=5,fgDebug=True): ...