我正在测试下面的代码。
#%matplotlib inlineimport seaborn as snsimport pandas as pdimport numpy as npfrom sklearn.model_selection import cross_validatefrom sklearn.model_selection import train_test_splitfrom sklearn.linear_model import LogisticRegressionCViris = 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) = 0.93import kerasfrom keras.models import Sequentialfrom keras.layers.core import Dense, Activationfrom keras.utils import np_utilstrain_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，直到倒数第二行代码。
当我尝试运行这个时：
loss, accuracy = model.evaluate(test_X, test_y_ohe, show_accuracy=True, verbose=0)我得到了这个错误：
TypeError: evaluate() got an unexpected keyword argument 'show_accuracy'我做了一些研究，发现’show_accuracy=True’可能在不久前已被废弃。现在有其他方法可以做到这一点吗？我如何评估并打印模型的准确性？
我在这里找到了代码样本：
https://blog.fastforwardlabs.com/2016/02/24/hello-world-in-keras-or-scikit-learn-versus.html
回答：
show_accuracy参数在新版本的keras中已被废弃，从model.evaluate()中移除此参数，并在model.compile()中使用metrics=['accuracy']代替
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])# fit modeltrain_y_ohe = pd.get_dummies(train_y)model.fit(train_X, train_y_ohe,epochs=1000,batch_size=20)loss, accuracy = model.evaluate(test_X, test_y_ohe, verbose=0)print("Test fraction correct (Accuracy) = {:.2f}".format(accuracy))#Test fraction correct (Accuracy) = 0.97