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当没有任何帮助时,如何克服卷积神经网络中的过度拟合?

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我在训练和验证准确性方面面临巨大差异,从字面上的前三或五个时期开始 . 当训练精度达到95%时,我的验证准确度大约为65% . 它在70%左右波动,但从未达到这个数字 . these are training and validation accuracy plotted on one chart

所以为了避免这种情况,我在过度拟合时尝试了一系列标准技术,但在将它们列在这里之前,我应该说它们都没有真正改变画面 . 训练和验证准确性之间的差距保持不变 . 所以我用过:

  • L1正则化,lambda从0.0001变化到10000.0

  • L2正则化,lambda从0.0001变化到10000.0

  • 辍学率从0.2到0.8

  • 数据增强技术(旋转,移位,缩放)

  • 删除除最后一层以外的完全连接的图层 .

这些都没有真正的帮助,所以我感谢你们的任何建议 . 以及有关网络本身的一些信息 . 我正在使用tensorflow . 这就是模型本身的样子:

net = tf.layers.conv2d(
    inputs,
    kernel_size=(7, 7),
    filters=15,
    strides=1,
    activation=tf.nn.relu,
    kernel_initializer=w_init,
    kernel_regularizer=reg)
# 15 x 58 x 58
net = tf.layers.max_pooling2d(net, pool_size=(2, 2), strides=2)
# 15 x 29 x 29
net = tf.layers.conv2d(
    net,
    kernel_size=(6, 6),
    filters=45,
    strides=1,
    activation=tf.nn.relu,
    kernel_initializer=w_init,
    kernel_regularizer=reg)
# 45 x 24 x 24
net = tf.layers.max_pooling2d(net, pool_size=(4, 4), strides=4)
# 45 x 6 x 6
net = tf.layers.conv2d(
    net,
    kernel_size=(6, 6),
    filters=256,
    strides=1,
    activation=tf.nn.relu,
    kernel_initializer=w_init,
    kernel_regularizer=reg)
# 256 x 1 x 1
net = tf.reshape(net, [-1, 256])
net = tf.layers.dense(net, units=512, activation=tf.nn.relu, kernel_regularizer=reg, kernel_initializer=w_init)
net = tf.layers.dropout(net, rate=0.2)
# net = tf.layers.dense(net, units=256, activation=tf.nn.relu, kernel_regularizer=reg, kernel_initializer=w_init)
# net = tf.layers.dropout(net, rate=0.75)
return tf.layers.dense(net, units=embedding_size, activation=tf.nn.relu, kernel_initializer=w_init)

这是损失函数的实现方式:

def contrastive_loss(out1, out2, labels, margin):
distance = compute_euclidian_distance_square(out1, out2)
positive_part = labels * distance
negative_part = (1 - labels) * tf.maximum(tf.square(margin) - distance, 0.0)
return tf.reduce_mean(positive_part + negative_part) / 2

这是我获取和扩充数据的方式(我正在使用LFW数据集):

ROTATIONS_RANGE = range(1, 25)
SHIFTS_RANGE = range(1, 18)
ZOOM_RANGE = (1.05, 1.075, 1.1, 1.125, 1.15, 1.175, 1.2, 1.225, 1.25, 1.275, 1.3, 1.325, 1.35, 1.375, 1.4)
IMG_SLICE = (slice(0, 64), slice(0, 64))


def pad_img(img):
    return np.pad(img, ((0, 2), (0, 17)), mode='constant')


def get_data(rotation=False, shifting=False, zooming=False):
    train_data = fetch_lfw_pairs(subset='train')
    test_data = fetch_lfw_pairs(subset='test')

    x1s_trn, x2s_trn, ys_trn, x1s_vld, x2s_vld = [], [], [], [], []

    for (pair, y) in zip(train_data.pairs, train_data.target):
        img1, img2 = pad_img(pair[0]), pad_img(pair[1])
        x1s_trn.append(img1)
        x2s_trn.append(img2)
        ys_trn.append(y)

        if rotation:
            for angle in ROTATIONS_RANGE:
                x1s_trn.append(np.asarray(rotate(img1, angle))[IMG_SLICE])
                x2s_trn.append(np.asarray(rotate(img2, angle))[IMG_SLICE])
                ys_trn.append(y)
                x1s_trn.append(np.asarray(rotate(img1, -angle))[IMG_SLICE])
                x2s_trn.append(np.asarray(rotate(img2, -angle))[IMG_SLICE])
                ys_trn.append(y)

        if shifting:
            for pixels_to_shift in SHIFTS_RANGE:
                x1s_trn.append(shift(img1, pixels_to_shift))
                x2s_trn.append(shift(img2, pixels_to_shift))
                ys_trn.append(y)
                x1s_trn.append(shift(img1, -pixels_to_shift))
                x2s_trn.append(shift(img2, -pixels_to_shift))
                ys_trn.append(y)

        if zooming:
            for zm in ZOOM_RANGE:
                x1s_trn.append(np.asarray(zoom(img1, zm))[IMG_SLICE])
                x2s_trn.append(np.asarray(zoom(img2, zm))[IMG_SLICE])
                ys_trn.append(y)

    for (img1, img2) in test_data.pairs:
        x1s_vld.append(pad_img(img1))
        x2s_vld.append(pad_img(img2))

    return (
        np.array(x1s_trn),
        np.array(x2s_trn),
        np.array(ys_trn),
        np.array(x1s_vld),
        np.array(x2s_vld),
        np.array(test_data.target)
    )

谢谢大家!

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