python深度学习分类后的 混淆矩阵的意义

Question

有大佬可以解释混淆矩阵上面还有（200，2）是什么意思吗，我设置的目标值为1或者2，但不知道为什么200后面是2，这个2和设置的1，2有关系吗

import numpy as np
import urllib.request
import pandas as pd
from pandas import DataFrame
import numpy as np
import pandas as pd
import xlrd
from sklearn import preprocessing
def excel_to_matrix(path):
    table = xlrd.open_workbook(path).sheets()[0]  # 获取第一个sheet表
    row = table.nrows  # 行数
    col = table.ncols  # 列数
    datamatrix = np.zeros((row, col))
    for x in range(col):
        cols = np.matrix(table.col_values(x))

        datamatrix[:, x] = cols
    return datamatrix


datafile = u'C:\\Users\\asus\\PycharmProjects\\2\\venv\\Lib\\附件2：数据.xls'
datamatrix=excel_to_matrix(datafile)
data=pd.DataFrame(datamatrix)

y=data[10]
data=data.drop(10,1)
x=data


from sklearn import preprocessing
x_MinMax=preprocessing.MinMaxScaler()

y=np.array(y).reshape((len(y),1))

x=x_MinMax.fit_transform(x)
x.mean(axis=0)
import random
from sklearn.cross_validation import train_test_split
np.random.seed(2016)
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2)

from sknn.mlp import Classifier,Layer #预测模型

fit3=Classifier(layers=[Layer('Tanh',units=38),Layer('Tanh',units=45),
                       Layer('Tanh', units=28),
                       Layer('Softmax')],
               learning_rate=0.02,
               random_state=2016,
               n_iter=100,
               dropout_rate=0.05,
                batch_size=50,
                learning_rule=u'adadelta',
                learning_momentum=0.005


               )
fit3.fit(x_train,y_train)

from sklearn.metrics import confusion_matrix
predict3_train=fit3.predict(x_train)
print(predict3_train)

predict3_test=fit3.predict(x_test)
confu3_test=confusion_matrix(y_test,predict3_test)
print(confu3_test)

Answer 1

这是因为你在倒数第五行有一句‘print(predict3_train)’啊，所以输出了它的维度