DL中的ValueError

Question

我得到了这个错误，post选择时期: ValueError:输入数组应该有相同数量的样本作为目标数组。找到5,516个输入样本和12870个目标样本。欢迎提出任何建议。提前感谢

我正在使用一个有很多分类变量的数据集，它们在创建虚拟变量后加起来是95，代码直到我选择了纪元的数量才能完美地运行，然后我得到了这个错误，这个错误的原因是什么，它很重要，1，我可以在将来使用它，2，我无法继续:)

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

dataset = pd.read_csv('zrpl_data.csv')
X = dataset.iloc[:, 0:6].values
y = dataset.iloc[:, 6].values

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X_1 = LabelEncoder()
X[:, 0] = labelencoder_X_1.fit_transform(X[:, 0])

labelencoder_X_2 = LabelEncoder()
X[:, 1] = labelencoder_X_2.fit_transform(X[:, 1])
labelencoder_X_3 = LabelEncoder()
X[:, 2] = labelencoder_X_1.fit_transform(X[:, 2])

labelencoder_X_4 = LabelEncoder()
X[:, 3] = labelencoder_X_1.fit_transform(X[:, 3])

labelencoder_X_5 = LabelEncoder()
X[:, 4] = labelencoder_X_1.fit_transform(X[:, 4])

onehotencoder = OneHotEncoder(categorical_features = [0,1,2,3,4])
X = onehotencoder.fit_transform(X).toarray()
X = X[:, 1:]

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3, 
random_state = 0)

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

import keras
from keras.models import Sequential
from keras.layers import Dense

classifier = Sequential()

classifier.add(Dense(output_dim=47, 
init='uniform',activation='relu',input_dim=95)) 

classifier.add(Dense(output_dim=47, init='uniform',activation='relu'))
classifier.add(Dense(output_dim=1, 
init='uniform',activation='sigmoid'))

classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

classifier.fit (X_train, y_train, batch_size=10,nb_epoch=100)

Answer 1

你有5,516个特征样本和12870个目标样本(你应该有相等的)，在训练模型之前仔细检查它们的尺寸。