Question

在使用fit transform方法编译我的代码时，它显示有关数组形状的错误 ” ValueError：检查输入时出错：预期density_1_input的形状为（6，），但数组的形状为（11，）“

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



dataset = pd.read_csv('Churn_Modelling.csv')
x = dataset.iloc[:, 3:13].values
y = dataset.iloc[:, 13].values


from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_x_1 = LabelEncoder()
x[:, 1] = labelencoder_x_1.fit_transform(x[:, 1])
labelencoder_x_2 = LabelEncoder()
x[:, 2] = labelencoder_x_2.fit_transform(x[:, 2])
onehotencoder = OneHotEncoder(categorical_features =[1])
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 =0.2, 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 =6, init = 'uniform', activation= 'relu', input_dim= 6))

classifier.add(Dense(output_dim =6, 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)

y_pred = classifier.predict(x_test)
y_pred = (y_pred > 0.5)


from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)

Answer 1

差异在x_train中，可能在x_test中。如果您查看print(x_train.shape)，可能会得到类似(N, 11)的信息，其中N是每个样本包含11个特征的数量。但是，等等，您的模型已定义为具有6个input_dim功能。因此您可以：

在第一层更改input_dim=11
或查看预处理以确保您获得6个功能。

keras中的拟合方法（数组的形状）

1 个答案: