Question

我想建立一个可以做到的端到端可训练模型：

使用CNN从面部图像中提取特征
对先前提取的特征应用KNN算法

我使用python3.5构建我的cnn网络

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=4)

X_train = X_train.astype('float32')
X_test = X_test.astype('float32')

X_train /= 255
X_test /= 255

Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)    
model = Sequential()
    X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)
    X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)

    model.add(Convolution2D(nb_filters, (nb_conv, nb_conv),
                            padding='valid',
                            input_shape=(1, img_cols, img_rows),
                            data_format='channels_first'))
    model.add(Activation('relu'))
    model.add(Convolution2D(nb_filters, (nb_conv, nb_conv)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
    model.add(Dropout(0.5))
    model.add(Flatten())
    model.add(Dense(128))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))
    model.add(Dense(nb_classes))
    model.add(Activation('softmax'))

    model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy'])


    hist = model.fit(X_train, Y_train, batch_size=batch_size, epochs=nb_epoch,
                     verbose=1, validation_data=(X_test, Y_test))

    hist = model.fit(X_train, Y_train, batch_size=batch_size, epochs=nb_epoch,
                     verbose=1, validation_split=0.2)

我也有https://machinelearningmastery.com/tutorial-to-implement-k-nearest-neighbors-in-python-from-scratch/

的knn算法

def loadDataset(filename, split, trainingSet=[], testSet=[]):
    with open(filename, 'r') as csvfile:
        lines = csv.reader(csvfile)
        dataset = list(lines)
        for x in range(len(dataset) - 1):
            for y in range(4):
                dataset[x][y] = float(dataset[x][y])
            if random.random() < split:
                trainingSet.append(dataset[x])
            else:
                testSet.append(dataset[x])

def euclideanDistance(instance1, instance2, length):
    distance = 0
    for x in range(length):
        distance += pow((instance1[x] - instance2[x]), 2)
    return math.sqrt(distance)

def getNeighbors(trainingSet, testInstance, k):
    distances = []
    length = len(testInstance) - 1
    for x in range(len(trainingSet)):
        dist = euclideanDistance(testInstance, trainingSet[x], length)
        distances.append((trainingSet[x], dist))
    distances.sort(key=operator.itemgetter(1))
    neighbors = []
    for x in range(k):
        neighbors.append(distances[x][0])
    return neighbors

def getResponse(neighbors):
    classVotes = {}
    for x in range(len(neighbors)):
        response = neighbors[x][-1]
        if response in classVotes:
            classVotes[response] += 1
        else:
            classVotes[response] = 1
    sortedVotes = sorted(classVotes.items(), key=operator.itemgetter(1), reverse=True)
    return sortedVotes[0][0]

我想使用从cnn提取的特征并将其提供给knn算法。我如何获取，可视化提取的特征并通过获取每个k个最近的特征并将其与knn中的特定组进行分类进行交互？我无法链接两个代码并无法获得所需的结果。

在python中将CNN功能馈送到KNN算法

0 个答案: