测试时Tensorflow batch_norm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())

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

和inception_V3 在这里定义.我的模型训练很好,损失从60到小于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')

它给了我无意义的结果,或者更确切地说,损失是1e-8针对所有列车和测试样本的.当我改变is_training=True它时会给出更多的逻辑结果,但是损失仍然大于训练阶段(即使我正在测试训练数据)我对VGG16也有同样的问题.当我使用不带batch_norm的VGG和使用batch_norm时为0%时,我的测试精度为100%.

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。当你使用时slim.batch_norm，一定要使用slim.learning.create_train_opinstead oftf.train.GradientDecentOptimizer(lr).minimize(loss)或其他优化器。尝试一下看看是否有效！