我正在使用Keras建模神经网络，并尝试使用val_acc和print(history.keys())的图形对其进行评估。我在以下代码行中有3个错误：
function' object has not attribute 'keys'中，错误为y_pred = classifier.predict(X_test) name 'classifier' is not defined中，错误为plt.plot(history.history['acc']) 'History' object is not subscriptable中，错误为import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import keras
from keras.models import Sequential
from keras.layers import Dense
from sklearn import cross_validation
from matplotlib import pyplot
from keras.utils import plot_model
dataset = pd.read_csv('Data_BP.csv')
X = dataset.iloc[:, 0:11].values
y = dataset.iloc[:, -1].values
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size = 0.2, random_state = 0)
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
def Model():
classifier = Sequential()
classifier.add(Dense(units = 12, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
classifier.add(Dense(units = 8, kernel_initializer = 'uniform', activation = 'relu'))
classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
classifier.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics = ['mse', 'acc'])
return classifier
classifier = Model()
history = classifier.fit(X_train, y_train, validation_split=0.25, batch_size = 10, epochs = 5)
print('\n', history.history.keys())
y_pred = classifier.predict(X_test)
y_pred = (y_pred > 0.5)
from sklearn.metrics import recall_score, classification_report, auc, roc_curve
cm = confusion_matrix(y_test, y_pred)
print(cm)
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
我也在尝试绘制ROC曲线，该怎么做？
<p>应该添加什么功能？
答案 0 :(得分：0)
在以下几行中将history更改为classifier（实际上History是在fit对象上调用的Model方法的返回值），如下所示：
classifier = Model()
history = classifier.fit(...)
不要将fit方法的返回值与模型混淆。顾名思义，History对象仅包含训练的历史记录。但是，您的模型是classifier和it is the one that has methods，例如fit()，predict()，evaluate()，compile()等。
此外，History对象具有一个名为history的属性，该属性是一个字典，其中包含训练期间的损失值和指标。因此，您需要改用print(history.history.keys())。
现在，如果您想例如在训练过程中绘制损失曲线（即每个时期结束时的损失），则可以这样做：
loss_values = history.history['loss']
epochs = range(1, len(loss_values)+1)
plt.plot(epochs, loss_values, label='Training Loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()