Question

对不起，如果查询是原始查询。我有一些代码试图对整数进行分类，如果它们不是质数。我已经使用Keras训练了模型。我正在尝试使用以下方法进行预测：

predict( x, batch_size=None, verbose=0, steps=None)

我不断收到以下错误消息：

---->预测（x = 5000003，batch_size = None，verbose = 0，steps = None）

NameError：名称'predict'未定义

当我使用以下命令时：“ model.predict（x = 5000003，batch_size = None，verbose = 0，steps = None）”，我收到此错误消息“ AttributeError：'KerasClassifier'对象没有属性'model '“

代码：

import numpy
from numpy import array
import pandas
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import GridSearchCV




seed = 7
numpy.random.seed(seed)



def isPrime(number):
    if number == 1:
        return 0
    elif number == 2:
        return 1
    elif number % 2 == 0:
        return 0
    for d in range(3, int(number**(0.5)+1), 2):
        if number % d == 0:
            return 0
    else:
        return 1


p=[]
N=[]
for i in range (1,10000):
    p=[i,isPrime(i)]
    N=N+[p]

a=array (N)

X=a[:10000,0]
Y=a[:10000,1]



def create_model(optimizer='rmsprop', init='glorot_uniform'):
    # create model
    model = Sequential()
    model.add(Dense(2, input_dim=1, kernel_initializer=init, activation='selu'))
    model.add(Dense(1, kernel_initializer=init, activation='sigmoid'))
    # Compile model
    model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])
    return model



# create model

        model = KerasClassifier(build_fn=create_model, epochs=1000, batch_size=100, init='glorot_uniform', verbose=0)
        kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=seed)
        results = cross_val_score(model, X, Y, cv=kfold)
        print(results.mean())
        predict(x=5000003, batch_size=None, verbose=0, steps=None)

Answer 1

predict是model对象的功能，因此您可以将其用作：

model = KerasClassifier(build_fn=create_model, epochs=1000, batch_size=100, init='glorot_uniform', verbose=0)
kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=seed)
results = cross_val_score(model, X, Y, cv=kfold)
print(results.mean())
# Call on model
model.predict(x=5000003, batch_size=None, verbose=0, steps=None)

这里是source code，以调查其幕后行为。

使用Keras进行预测。我不断收到错误消息

1 个答案: