Question

好吧，我是4年级学生，最近我需要教自己如何为机器学习编写代码。我目前正在为一个项目使用葡萄酒数据集，并且需要使用神经网络基于11种输入来预测葡萄酒的质量。我需要将此作为回归示例。一切正常，直到我适合模型为止。我的准确性没有增加，并且保持不变。我对此很陌生，并且确定这个问题并没有那么难解决，我只是菜鸟。

到目前为止，我已经尝试研究过拟合和欠拟合，但是我认为在此处发布会很快


import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

df = pd.read_csv('wineQualityReds.csv', usecols=lambda x: 'Unnamed' not in x,)  #there is a column i needed to remove here

df.head(3)
df.describe(include = 'all')

sns.pairplot(df)

X = df.iloc[:,0:11]
Y = df.iloc[:,11]

from sklearn.preprocessing import MinMaxScaler
sc = MinMaxScaler()
X = sc.fit_transform(X)
Y = Y.values.reshape(-1,1)  
Y = sc.fit_transform(Y)

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)

from keras import Sequential
from keras.layers import Dense

def build_regressor():
    regressor = Sequential()
    regressor.add(Dense(units = 11, input_dim = 11))
    regressor.add(Dense(units = 11))
    regressor.add(Dense(units = 1))
    regressor.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics = ['mae', 'accuracy'])
    return regressor


from keras.wrappers.scikit_learn import KerasRegressor
regressor = KerasRegressor(build_fn = build_regressor, batch_size = 32, 
epochs = 100)

results = regressor.fit(X_train,y_train)

我的神经网络无法正常工作

0 个答案: