Sklearn与Keras的比较— Keras带来的小错误

我正在测试下面的代码。

#%matplotlib inline
import seaborn as sns
import pandas as pd
import numpy as np
from sklearn.model_selection import cross_validate
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegressionCV


iris = sns.load_dataset("iris")
iris.head()

sns.pairplot(iris, hue='species')

X = iris.values[:, 0:4]
y = iris.values[:, 4]

train_X, test_X, train_y, test_y = train_test_split(X, y, train_size=0.5, random_state=0)

lr = LogisticRegressionCV()
lr.fit(train_X, train_y)

pred_y = lr.predict(test_X)
print("Test fraction correct (Accuracy) = {:.2f}".format(lr.score(test_X, test_y)))
# Test fraction correct (Accuracy) = 0.93


import keras
from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.utils import np_utils


train_y_ohe = pd.get_dummies(train_y)
test_y_ohe = pd.get_dummies(test_y)


model = Sequential()
model.add(Dense(16, input_shape=(4,)))
model.add(Activation('sigmoid'))
model.add(Dense(3))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam')


loss, accuracy = model.evaluate(test_X, test_y_ohe, show_accuracy=True, verbose=0)
print("Test fraction correct (Accuracy) = {:.2f}".format(accuracy))

一切正常，直到代码的倒数第二行。

当我尝试运行此命令时：

loss, accuracy = model.evaluate(test_X, test_y_ohe, show_accuracy=True, verbose=0)

我收到此错误：

TypeError: evaluate() got an unexpected keyword argument 'show_accuracy'

我做了一些研究，发现'show_accuracy = True'可能在不久前已贬值。现在还有其他方法吗？我如何评估和打印模型的准确性？

我在这里找到了代码示例：

https://blog.fastforwardlabs.com/2016/02/24/hello-world-in-keras-or-scikit- 学习，versus.html

问题来源：stackoverflow