基本上，我想计算精度的最高k @ n并回忆keras神经网络中每个时期的@n。经过长时间的搜索，我找到了自定义功能......
def precision（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召回（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
//这是用于模型编译
model.compile（optimizer = RMSprop（lr = learning_rate），loss =＆＃39; binary_crossentropy＆＃39;，metrics = [precision，recall]）
for xoch（epochs）中的纪元： t1 =时间（） user_input，item_input，labels = get_train_instances（train，num_negatives）
hist = model.fit([np.array(user_input), np.array(item_input)], np.array(labels),
batch_size=batch_size, nb_epoch=1, verbose=0, shuffle=True)
loss,precision,recall = hist.history['loss'][0],hist.history["precision"][0] , hist.history["recall"][0]
print('Iteration %d [%.1f s]: loss = %.4f , precision= %.4f , recall=%.4f [%.1f s]' % (epoch, t2-t1, loss,precision,recall, time()-t2))
//这是我的输出，似乎错了...... enter image description here
我希望你明白......
答案 0 :(得分：1)
最好的办法是使用classification report capability in SciKitLearn。它是一般的分类评估功能，但我经常与Keras一起使用。
from sklearn.preprocessing import LabelBinarizer
from sklearn.metrics import classification_report
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
from keras.datasets import cifar10
# Load CIFAR-10 dataset
(trainX, trainY), (testX, testY) = cifar10.load_data()
trainX = trainX / 255.0
testX = testX / 255.0
# Transform labels from int to one-hot vectors
lb = LabelBinarizer()
trainY = lb.fit_transform(trainY)
testY = lb.fit_transform(testY)
# CNN architecture with Keras
model = Sequential()
model.add(Conv2D(input_shape=trainX[0,:,:,:].shape, filters=32,
use_bias=True, kernel_size=(3,3)))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Conv2D(filters=96, use_bias=False, kernel_size=(5,5), strides=2))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Conv2D(filters=96, use_bias=False, kernel_size=(5,5), strides=2))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(BatchNormalization())
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dropout(0.4))
model.add(Dense(n_classes, activation="softmax"))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# Train the model
n_epochs = 25
batch_size = 256
callbacks_list = None
H = model.fit(trainX, trainY, validation_data=(testX, testY),
epochs=n_epochs, batch_size=batch_size, callbacks=callbacks_list)
print('Done!!!')
# Evaluate TEST model class prediction accuracy
print("[INFO] Evaluating network...")
predictions = model.predict(testX, batch_size=batch_size)
target_names = [str(x) for x in lb.classes_]
print(classification_report(testY.argmax(axis=1),
predictions.argmax(axis=1),
target_names=target_names))
# Evaluate TRAIN model class prediction accuracy
print("[INFO] Evaluating network...")
trainPreds = model.predict(trainX, batch_size=batch_size)
target_names = [str(x) for x in lb.classes_]
print(classification_report(trainY.argmax(axis=1),
trainPreds.argmax(axis=1),
target_names=target_names))
对分类报告的调用将为您提供每个类的精确度和召回统计数据，如下所示：
[INFO]评估网络...
精确召回f1-得分支持0 0.78 0.83 0.80 1000 1 0.85 0.92 0.88 1000 2 0.77 0.58 0.66 1000 3 0.66 0.54 0.59 1000 4 0.72 0.72 0.72 1000 5 0.72 0.63 0.67 1000 6 0.69 0.91 0.78 1000 7 0.81 0.83 0.82 1000 8 0.85 0.87 0.86 1000 9 0.82 0.86 0.84 1000 avg 0.77 0.77 0.76 10000