testing - Tensorflow batch_norm 在测试时无法正常工作(is_training=False)

Question

我正在训练以下模型:

with slim.arg_scope(inception_arg_scope(is_training=True)):
    logits_v, endpoints_v = inception_v3(all_v, num_classes=25, is_training=True, dropout_keep_prob=0.8,
                     spatial_squeeze=True, reuse=reuse_variables, scope='vis')
    logits_p, endpoints_p = inception_v3(all_p, num_classes=25, is_training=True, dropout_keep_prob=0.8,
                     spatial_squeeze=True, reuse=reuse_variables, scope='pol')
    pol_features = endpoints_p['pol/features']
    vis_features = endpoints_v['vis/features']

eps = 1e-08
loss = tf.sqrt(tf.maximum(tf.reduce_sum(tf.square(pol_features - vis_features), axis=1, keep_dims=True), eps))

# rest of code
saver = tf.train.Saver(tf.global_variables())

在哪里

def inception_arg_scope(weight_decay=0.00004,
                    batch_norm_decay=0.9997,
                    batch_norm_epsilon=0.001, is_training=True):
normalizer_params = {
    'decay': batch_norm_decay,
    'epsilon': batch_norm_epsilon,
    'is_training': is_training
}
normalizer_fn = tf.contrib.layers.batch_norm

# Set weight_decay for weights in Conv and FC layers.
with slim.arg_scope([slim.conv2d, slim.fully_connected],
                    weights_regularizer=slim.l2_regularizer(weight_decay)):
    with slim.arg_scope([slim.batch_norm, slim.dropout], is_training=is_training):
        with slim.arg_scope(
                [slim.conv2d],
                weights_initializer=slim.variance_scaling_initializer(),
                activation_fn=tf.nn.relu,
                normalizer_fn=normalizer_fn,
                normalizer_params=normalizer_params) as sc:
            return sc

并且 inception_V3 被定义为 here .我的模型训练得很好，损失从 60 减少到不到 1。但是当我想在另一个文件中测试模型时:

with slim.arg_scope(inception_arg_scope(is_training=False)):
    logits_v, endpoints_v = inception_v3(all_v, num_classes=25, is_training=False, dropout_keep_prob=0.8,
                     spatial_squeeze=True, reuse=reuse_variables, scope='vis')
    logits_p, endpoints_p = inception_v3(all_p, num_classes=25, is_training=False, dropout_keep_prob=0.8,
                     spatial_squeeze=True, reuse=reuse_variables, scope='pol')

它给了我毫无意义的结果，或者更准确地说，所有训练和测试样本的损失是 1e-8。当我更改 is_training=True 时，它会给出更合乎逻辑的结果，但损失仍然大于训练阶段(即使我正在测试训练数据)我对 VGG16 有同样的问题。当我使用不带 batch_norm 的 VGG 时，我的测试准确率为 %100，而当我使用 batch_norm 时，准确率为 0%。

我在这里错过了什么？谢谢，

Answer 1

我遇到了同样的问题并解决了。当你使用slim.batch_norm时，一定要使用slim.learning.create_train_op而不是tf.train.GradientDecentOptimizer(lr).minimize(loss) 或其他优化器。试试吧，看看它是否有效!