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首页 > 解决方案 > 获取ValueError：输入字符串和数字数据集时无法将字符串转换为浮点数：“'H4'”

python - 获取ValueError：输入字符串和数字数据集时无法将字符串转换为浮点数：“'H4'”

问题描述

我正在制作一个神经网络来从我的扑克游戏中对我的扑克机器人的行为进行分类。我正在使用一个简单的神经网络代码来执行我的任务。但是当我将自己的数据集放入代码中时，会出现错误。神经网络是否接受像我这样的字符串和数字数据集？

错误说：

ValueError：无法将字符串转换为浮点数：“'H4'”

这是我的代码：

from keras.models import Sequential
from keras.layers import Dense, Dropout
from sklearn.model_selection import train_test_split
import numpy
import matplotlib.pyplot as plt
numpy.random.seed(2)

# e load ang dataset
dataset = numpy.loadtxt("monteCarlo.csv", delimiter=",")

# split input (X) and output (Y) variables, splitting csv data
X = dataset[:,0:8]
Y = dataset[:,8]

#split x,y train,test

x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2,     random_state=42)

# create model, add dense layers one by one specifying activation function sigmoid

model = Sequential()
model.add(Dense(15, input_dim=8, activation='relu')) # input layer requires     input_dim param
model.add(Dense(10, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dropout(.2))
model.add(Dense(1, activation='sigmoid'))

# compile the model, adam gradient descent (optimized)
# adam or adamax


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

# call the function to fit to the data (training the network)
history = model.fit_(x_train, y_train, epochs = 1000, batch_size=20,     validation_data=(x_test, y_test))

# save the model
model.save('pokerClassifier.h5')

#evaluate model
scores = model.evaluate(X, Y, verbose=1)
print('Test loss: ',scores[0])
print('accuracy: ',scores[1]*100 ,'%')


#plot accuracy

plt.figure(1)

plt.plot(history.history['acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epochs')
plt.legend(['test','train'], loc='upper left')
plt.show()

plt.figure(2)
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epochs')
plt.legend(['train','test'], loc='upper left')

plt.show()

这是我的 CSV 或数据集：

'H4','D7','D3','C5','C6',0.82,'C'

'H4','D1','D3','C2','C6',0.22,'F'

'H4','D7','D9','C9','C9',0.55,'C'

'H4','D7','D3','C5','C6',0.82,'C'

'H4','D1','D3','C2','C6',0.22,'F'

'H4','D7','D9','C9','C9',0.55,'C'

'H4','D7','D3','C5','C6',0.82,'C'

'H4','D1','D3','C2','C6',0.22,'F'

'H4','D7','D9','C9','C9',0.55,'C'

'H4','D7','A3','C5','C6',0.84,'C'

'H4','D1','D3','C9','C6',0.44,'F'

标签： pythonnumpyneural-networkclassification

解决方案

loadtxt使用字符串 dtype：

In [4]: data = np.loadtxt('h4.csv', delimiter=',', dtype='U4')                  
In [5]: data                                                                    
Out[5]: 
array([["'H4'", "'D7'", "'D3'", "'C5'", "'C6'", '0.82', "'C'"],
       ["'H4'", "'D1'", "'D3'", "'C2'", "'C6'", '0.22', "'F'"],
       ["'H4'", "'D7'", "'D9'", "'C9'", "'C9'", '0.55', "'C'"],
       ["'H4'", "'D7'", "'D3'", "'C5'", "'C6'", '0.82', "'C'"],
       ["'H4'", "'D1'", "'D3'", "'C2'", "'C6'", '0.22', "'F'"],
       ["'H4'", "'D7'", "'D9'", "'C9'", "'C9'", '0.55', "'C'"],
       ["'H4'", "'D7'", "'D3'", "'C5'", "'C6'", '0.82', "'C'"],
       ["'H4'", "'D1'", "'D3'", "'C2'", "'C6'", '0.22', "'F'"],
       ["'H4'", "'D7'", "'D9'", "'C9'", "'C9'", '0.55', "'C'"],
       ["'H4'", "'D7'", "'A3'", "'C5'", "'C6'", '0.84', "'C'"],
       ["'H4'", "'D1'", "'D3'", "'C9'", "'C6'", '0.44', "'F'"]],
      dtype='<U4')

genfromtxt使用None数据类型：

In [7]: data = np.genfromtxt('h4.csv', delimiter=',', dtype=None, encoding=None)
   ...:                                                                         
In [8]: data                                                                    
Out[8]: 
array([("'H4'", "'D7'", "'D3'", "'C5'", "'C6'", 0.82, "'C'"),
       ("'H4'", "'D1'", "'D3'", "'C2'", "'C6'", 0.22, "'F'"),
       ("'H4'", "'D7'", "'D9'", "'C9'", "'C9'", 0.55, "'C'"),
       ("'H4'", "'D7'", "'D3'", "'C5'", "'C6'", 0.82, "'C'"),
       ("'H4'", "'D1'", "'D3'", "'C2'", "'C6'", 0.22, "'F'"),
       ("'H4'", "'D7'", "'D9'", "'C9'", "'C9'", 0.55, "'C'"),
       ("'H4'", "'D7'", "'D3'", "'C5'", "'C6'", 0.82, "'C'"),
       ("'H4'", "'D1'", "'D3'", "'C2'", "'C6'", 0.22, "'F'"),
       ("'H4'", "'D7'", "'D9'", "'C9'", "'C9'", 0.55, "'C'"),
       ("'H4'", "'D7'", "'A3'", "'C5'", "'C6'", 0.84, "'C'"),
       ("'H4'", "'D1'", "'D3'", "'C9'", "'C6'", 0.44, "'F'")],
      dtype=[('f0', '<U4'), ('f1', '<U4'), ('f2', '<U4'), ('f3', '<U4'), ('f4', '<U4'), ('f5', '<f8'), ('f6', '<U3')])
In [9]: data['f0']                                                              
Out[9]: 
array(["'H4'", "'H4'", "'H4'", "'H4'", "'H4'", "'H4'", "'H4'", "'H4'",
       "'H4'", "'H4'", "'H4'"], dtype='<U4')
In [11]: data['f5']                                                             
Out[11]: array([0.82, 0.22, 0.55, 0.82, 0.22, 0.55, 0.82, 0.22, 0.55, 0.84, 0.44])

这没有列；取而代之的是命名字段。它是一个结构化数组。但请注意，“f5”列现在加载为浮点数，而其他列是字符串。

与熊猫

In [15]: df = pd.read_csv('h4.csv')                                             
In [16]: df                                                                     
Out[16]: 
   'H4'  'D7'  'D3'  'C5'  'C6'  0.82  'C'
0  'H4'  'D1'  'D3'  'C2'  'C6'  0.22  'F'
1  'H4'  'D7'  'D9'  'C9'  'C9'  0.55  'C'
2  'H4'  'D7'  'D3'  'C5'  'C6'  0.82  'C'
3  'H4'  'D1'  'D3'  'C2'  'C6'  0.22  'F'
4  'H4'  'D7'  'D9'  'C9'  'C9'  0.55  'C'
5  'H4'  'D7'  'D3'  'C5'  'C6'  0.82  'C'
6  'H4'  'D1'  'D3'  'C2'  'C6'  0.22  'F'
7  'H4'  'D7'  'D9'  'C9'  'C9'  0.55  'C'
8  'H4'  'D7'  'A3'  'C5'  'C6'  0.84  'C'
9  'H4'  'D1'  'D3'  'C9'  'C6'  0.44  'F'
In [17]: df.dtypes                                                              
Out[17]: 
'H4'     object
'D7'     object
'D3'     object
'C5'     object
'C6'     object
0.82    float64
'C'      object
dtype: object
In [18]: df.values                                                              
Out[18]: 
array([["'H4'", "'D1'", "'D3'", "'C2'", "'C6'", 0.22, "'F'"],
       ["'H4'", "'D7'", "'D9'", "'C9'", "'C9'", 0.55, "'C'"],
       ["'H4'", "'D7'", "'D3'", "'C5'", "'C6'", 0.82, "'C'"],
       ["'H4'", "'D1'", "'D3'", "'C2'", "'C6'", 0.22, "'F'"],
       ["'H4'", "'D7'", "'D9'", "'C9'", "'C9'", 0.55, "'C'"],
       ["'H4'", "'D7'", "'D3'", "'C5'", "'C6'", 0.82, "'C'"],
       ["'H4'", "'D1'", "'D3'", "'C2'", "'C6'", 0.22, "'F'"],
       ["'H4'", "'D7'", "'D9'", "'C9'", "'C9'", 0.55, "'C'"],
       ["'H4'", "'D7'", "'A3'", "'C5'", "'C6'", 0.84, "'C'"],
       ["'H4'", "'D1'", "'D3'", "'C9'", "'C6'", 0.44, "'F'"]],
      dtype=object)

请注意，这是objectdtype，以适应字符串和浮点数的混合（加上pandas始终使用object而不是numpy字符串 dtypes。

或者作为更接近结构化数组的东西：

In [19]: df.to_records()                                                        
Out[19]: 
rec.array([(0, "'H4'", "'D1'", "'D3'", "'C2'", "'C6'", 0.22, "'F'"),
           (1, "'H4'", "'D7'", "'D9'", "'C9'", "'C9'", 0.55, "'C'"),
           (2, "'H4'", "'D7'", "'D3'", "'C5'", "'C6'", 0.82, "'C'"),
           (3, "'H4'", "'D1'", "'D3'", "'C2'", "'C6'", 0.22, "'F'"),
           (4, "'H4'", "'D7'", "'D9'", "'C9'", "'C9'", 0.55, "'C'"),
           (5, "'H4'", "'D7'", "'D3'", "'C5'", "'C6'", 0.82, "'C'"),
           (6, "'H4'", "'D1'", "'D3'", "'C2'", "'C6'", 0.22, "'F'"),
           (7, "'H4'", "'D7'", "'D9'", "'C9'", "'C9'", 0.55, "'C'"),
           (8, "'H4'", "'D7'", "'A3'", "'C5'", "'C6'", 0.84, "'C'"),
           (9, "'H4'", "'D1'", "'D3'", "'C9'", "'C6'", 0.44, "'F'")],
          dtype=[('index', '<i8'), ("'H4'", 'O'), ("'D7'", 'O'), ("'D3'", 'O'), ("'C5'", 'O'), ("'C6'", 'O'), ('0.82', '<f8'), ("'C'", 'O')])

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