我正在建立一个神经网络，从扑克游戏中对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？
错误提示：
ValueError：无法将字符串转换为float：“'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'
答案 0 :(得分：0)
$error = false;
if(empty($_POST['email_subject'])){
$error = true;
$msg = "<span class='text-danger'>Please select a subject for your email.
</span>";
}
if(empty($_POST['message'])){
$error = true;
$msg_2 = "<span class='text-danger'>Please enter your message.</span>";
}
if(empty($_POST['email_2'])){
$error = true;
$msg_4 = "<span class='text-danger'>Please enter a valid email address.</span>";
}
if($error===false) {
$mail->Send();
header('Location: thankYou_2.php');
}
，其字符串为dtype：
loadtxt 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')
，dtype为genfromtxt：
None这没有列；而是有命名字段。它是一个结构化数组。但是请注意，“ f5”列现在以浮点数加载，而其他列为字符串。
与熊猫
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])
请注意，这是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)
dtype，以适应字符串和浮点数的混合（加上object始终使用pandas而不是object字符串dtype。
或者更接近结构化数组：
numpy