CSV 数据集缩放为输入

如何解决CSV 数据集缩放为输入

我想制作一个使用深度学习对 csv 文件进行分类的项目，但我在输入缩放部分遇到了问题。现在我将数据集分为两类，即X_data 和Y_data。请关注下面的代码，谁知道请帮助我，在导入的包下面，将张量流导入为 tf

import keras.backend as K
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping
from keras.utils import to_categorical
import keras

import numpy as np

from keras.layers import Batchnormalization
from keras.layers import Dropout
from keras import regularizers

import pandas as pd

import sklearn
from sklearn import preprocessing
from sklearn.model_selection import train_test_split

import matplotlib
from matplotlib import pyplot as plt
%matplotlib inline
%config InlineBackend.figure_format='retina'

导入输入 (x) 和输出 (y) 数据，并将它们分配给 df1 和 df2

df1 = pd.read_csv('X_data.csv')
df2 = pd.read_csv('Y_data.csv')

缩放输入数据

df1 = preprocessing.scale(df1)   //I have faced error here

下面的错误是，

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-3-aec70d746687> in <module>
      1 # Scale input data
      2 
----> 3 df1 = preprocessing.scale(df1)

~/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py in inner_f(*args,**kwargs)
     71                           FutureWarning)
     72         kwargs.update({k: arg for k,arg in zip(sig.parameters,args)})
---> 73         return f(**kwargs)
     74     return inner_f
     75 

~/anaconda3/lib/python3.8/site-packages/sklearn/preprocessing/_data.py in scale(X,axis,with_mean,with_std,copy)
    139 
    140     """  # noqa
--> 141     X = check_array(X,accept_sparse='csc',copy=copy,ensure_2d=False,142                     estimator='the scale function',dtype=FLOAT_DTYPES,143                     force_all_finite='allow-nan')

~/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py in inner_f(*args,args)})
---> 73         return f(**kwargs)
     74     return inner_f
     75 

~/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py in check_array(array,accept_sparse,accept_large_sparse,dtype,order,copy,force_all_finite,ensure_2d,allow_nd,ensure_min_samples,ensure_min_features,estimator)
    597                     array = array.astype(dtype,casting="unsafe",copy=False)
    598                 else:
--> 599                     array = np.asarray(array,order=order,dtype=dtype)
    600             except ComplexWarning:
    601                 raise ValueError("Complex data not supported\n"

~/anaconda3/lib/python3.8/site-packages/numpy/core/_asarray.py in asarray(a,order)
     83 
     84     """
---> 85     return array(a,copy=False,order=order)
     86 
     87 

ValueError: Could not convert string to float: 'discrete'

# Split the data into input (x) training and testing data,and ouput (y)