Question

编辑（抱歉，确实应该发布更多详细信息）：

下面是整个代码示例。

from __future__ import absolute_import
from __future__ import print_function
import numpy as np
import random
from keras.datasets import mnist
from keras.models import Model
from keras.layers import Input, Flatten, Dense, Dropout, Lambda
from keras.optimizers import RMSprop
from keras import backend as K
from sklearn import metrics

num_classes = 10
epochs = 2


def euclidean_distance(vects):
    x, y = vects
    return K.sqrt(K.maximum(K.sum(K.square(x - y), axis=1, keepdims=True), K.epsilon()))


def eucl_dist_output_shape(shapes):
    shape1, shape2 = shapes
    return (shape1[0], 1)


def contrastive_loss(y_true, y_pred):
    '''Contrastive loss from Hadsell-et-al.'06
    http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
    '''
    margin = 1
    return K.mean(y_true * K.square(y_pred) +
                  (1 - y_true) * K.square(K.maximum(margin - y_pred, 0)))


def create_pairs(x, digit_indices):
    '''Positive and negative pair creation.
    Alternates between positive and negative pairs.
    '''
    pairs = []
    labels = []
    n = min([len(digit_indices[d]) for d in range(num_classes)]) - 1
    for d in range(num_classes):
        for i in range(n):
            z1, z2 = digit_indices[d][i], digit_indices[d][i + 1]
            pairs += [[x[z1], x[z2]]]
            inc = random.randrange(1, num_classes)
            dn = (d + inc) % num_classes
            z1, z2 = digit_indices[d][i], digit_indices[dn][i]
            pairs += [[x[z1], x[z2]]]
            labels += [1, 0]
    return np.array(pairs), np.array(labels)


def create_base_network(input_shape):
    '''Base network to be shared (eq. to feature extraction).
    '''
    input = Input(shape=input_shape)
    x = Flatten()(input)
    x = Dense(128, activation='relu')(x)
    x = Dropout(0.1)(x)
    x = Dense(128, activation='relu')(x)
    x = Dropout(0.1)(x)
    x = Dense(128, activation='relu')(x)
    return Model(input, x)


def compute_accuracy(y_true, y_pred):
    '''Compute classification accuracy with a fixed threshold on distances.
    '''
    pred = y_pred.ravel() < 0.5
    return np.mean(pred == y_true)


def accuracy(y_true, y_pred):
    '''Compute classification accuracy with a fixed threshold on distances.
    '''
    return K.mean(K.equal(y_true, K.cast(y_pred < 0.5, y_true.dtype)))


# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
input_shape = x_train.shape[1:]

# create training+test positive and negative pairs
digit_indices = [np.where(y_train == i)[0] for i in range(num_classes)]
tr_pairs, tr_y = create_pairs(x_train, digit_indices)

digit_indices = [np.where(y_test == i)[0] for i in range(num_classes)]
te_pairs, te_y = create_pairs(x_test, digit_indices)

# network definition
base_network = create_base_network(input_shape)

input_a = Input(shape=input_shape)
input_b = Input(shape=input_shape)

# because we re-use the same instance `base_network`,
# the weights of the network
# will be shared across the two branches
processed_a = base_network(input_a)
processed_b = base_network(input_b)

distance = Lambda(euclidean_distance,
                  output_shape=eucl_dist_output_shape)([processed_a, processed_b])

model = Model([input_a, input_b], distance)

# train
rms = RMSprop()
model.compile(loss=contrastive_loss, optimizer=rms, metrics=[accuracy])
model.fit([tr_pairs[:, 0], tr_pairs[:, 1]], tr_y,
          batch_size=128,
          epochs=epochs,
          validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y))

# compute final accuracy on training and test sets
    y_pred = model.predict([tr_pairs[:, 0], tr_pairs[:, 1]])
tr_acc = compute_accuracy(tr_y, y_pred)
y_pred = model.predict([te_pairs[:, 0], te_pairs[:, 1]])
te_acc = compute_accuracy(te_y, y_pred)

print('* Accuracy on training set: %0.2f%%' % (100 * tr_acc))
print('* Accuracy on test set: %0.2f%%' % (100 * te_acc))

roc_auc_score = metrics.roc_auc_score(te_y, 1-y_pred)
print("roc_auc_score:  %0.2f" % roc_auc_score)

我正在尝试通过暹罗网络来学习对比损失功能的用法。我从the Keras example here开始。我正在尝试绘制roc_auc_score from the scikit-learn，它给了我0.00

Train on 108400 samples, validate on 17820 samples
Epoch 1/2
108400/108400 [==============================] - 6s 52us/step - loss: 0.0930 - accuracy: 0.8910 - val_loss: 0.0420 - val_accuracy: 0.9582
Epoch 2/2
108400/108400 [==============================] - 5s 49us/step - loss: 0.0390 - accuracy: 0.9615 - val_loss: 0.0295 - val_accuracy: 0.9710
* Accuracy on training set: 97.80%
* Accuracy on test set: 96.82%
roc_auc_score:  0.01

我觉得这里肯定有问题。像肯定标签和否定标签可能不会以正确的方式传递给roc_auc_score。

有人知道为什么会发生这种情况，以及如何在不手动设置pos_label的情况下解决此问题。请告诉我。谢谢您的宝贵时间。

Answer 1

ROC曲线是通过对分数进行阈值获得的，通常使用大于运算符（>）来完成，但是模型产生的距离具有其他排序，其中接近零意味着两个样本相似，并且较大的距离表示不同的样本。这意味着必须使用<运算符将这些分数（您的距离）阈值化。

一个简单的解决方案是翻转模型的预测：

>>> metrics.roc_auc_score(tr_y, 1.0 - y_pred)
0.9954217433041488

从模型预测中减去一个就意味着可以使用>运算符对它们进行阈值设置，这使AUC变得有意义。

roc_auc_score即将到来0测试准确性97％可能吗？

1 个答案: