如何获得keras模型的准确性，F1，准确性和召回率？

我想为我的二进制KerasClassifier模型计算精度，召回率和F1分数，但找不到任何解决方案。

这是我的实际代码：

# Split dataset in train and test data 
X_train, X_test, Y_train, Y_test = train_test_split(normalized_X, Y, test_size=0.3, random_state=seed)

# Build the model
model = Sequential()
model.add(Dense(23, input_dim=45, kernel_initializer='normal', activation='relu'))
model.add(Dense(1, kernel_initializer='normal', activation='sigmoid'))

# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])


tensorboard = TensorBoard(log_dir="logs/{}".format(time.time()))

time_callback = TimeHistory()

# Fit the model
history = model.fit(X_train, Y_train, validation_split=0.3, epochs=200, batch_size=5, verbose=1, callbacks=[tensorboard, time_callback]) 

然后，我根据新的测试数据进行预测，并得到如下的混淆矩阵：

y_pred = model.predict(X_test)
y_pred =(y_pred>0.5)
list(y_pred)

cm = confusion_matrix(Y_test, y_pred)
print(cm)

但是，是否有解决方案来获得准确性分数，F1分数，准确性和召回率？（如果不复杂，也可以使用交叉验证分数，但此答案不是必需的）

感谢您的任何帮助！

指标已从Keras核心中删除。您需要手动计算它们。他们在2.0版本上删除了它们。这些指标都是全局指标，但是Keras批量工作。结果，这可能会产生误导，而不是有所帮助。

但是，如果您确实需要它们，可以这样做

from keras import backend as K

def recall_m(y_true, y_pred):
    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
    recall = true_positives / (possible_positives + K.epsilon())
    return recall

def precision_m(y_true, y_pred):
    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
    precision = true_positives / (predicted_positives + K.epsilon())
    return precision

def f1_m(y_true, y_pred):
    precision = precision_m(y_true, y_pred)
    recall = recall_m(y_true, y_pred)
    return 2*((precision*recall)/(precision+recall+K.epsilon()))

# compile the model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc',f1_m,precision_m, recall_m])

# fit the model
history = model.fit(Xtrain, ytrain, validation_split=0.3, epochs=10, verbose=0)

# evaluate the model
loss, accuracy, f1_score, precision, recall = model.evaluate(Xtest, ytest, verbose=0)

您可以使用scikit-learn分类报告。要将标签转换为数字或二进制格式，请查看scikit-learn标签编码器。

from sklearn.metrics import classification_report

y_pred = model.predict(x_test, batch_size=64, verbose=1)
y_pred_bool = np.argmax(y_pred, axis=1)

print(classification_report(y_test, y_pred_bool))

这会给您（从scikit-learn示例复制的输出）：

             precision  recall   f1-score    support

 class 0       0.50      1.00      0.67         1
 class 1       0.00      0.00      0.00         1
 class 2       1.00      0.67      0.80         3

您也可以尝试以下所述。

from sklearn.metrics import f1_score, precision_score, recall_score, confusion_matrix
y_pred1 = model.predict(X_test)
y_pred = np.argmax(y_pred1, axis=1)

# Print f1, precision, and recall scores
print(precision_score(y_test, y_pred , average="macro"))
print(recall_score(y_test, y_pred , average="macro"))
print(f1_score(y_test, y_pred , average="macro"))

试试这个：https : //scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_fscore_support.html，以Y_test和y_pred为参数。