python – Tensorboard权重直方图只有最后一层可见的变化

我在网络中添加了TensorBoard可视化,并注意到只有外层变化很多.为什么网络的权重不会发生很大变化？这在叠加直方图中尤为明显.

直方图

相同但覆盖视图

我的模特

def neural_network_model(inputdata):
    """The blueprint of the network and the tensorboard information
        :param inputdata: the placeholder for the inputdata
        :returns: the output of the network?
            """
    W1 = tf.get_variable("W1",shape=[set.input,nodes_h1],initializer=tf.contrib.layers.xavier_initializer())
    B1 = tf.get_variable("B1",shape=[nodes_h1],initializer=tf.random_normal_initializer())
    layer1 = tf.matmul(inputdata,W1)
    layer1_bias = tf.add(layer1,B1)
    layer1_act = tf.nn.relu(layer1)

    W2 = tf.get_variable("W2",shape=[nodes_h1,nodes_h2],initializer=tf.contrib.layers.xavier_initializer())
    B2 = tf.get_variable("B2",shape=[nodes_h2],initializer=tf.random_normal_initializer())
    layer2 = tf.matmul(layer1_act,W2)
    layer2_bias = tf.add(layer2,B2)
    layer2_act = tf.nn.relu(layer2)

    W3 = tf.get_variable("W3",shape=[nodes_h2,nodes_h3],initializer=tf.contrib.layers.xavier_initializer())
    B3 = tf.get_variable("B3",shape=[nodes_h3],initializer=tf.random_normal_initializer())

    layer3 = tf.matmul(layer2_act,W3)
    layer3_bias = tf.add(layer3,B3)
    layer3_act = tf.nn.relu(layer3)
    WO = tf.get_variable("WO",shape=[nodes_h3,set.output],initializer=tf.contrib.layers.xavier_initializer())
    layerO = tf.matmul(layer3_act,WO)

    with tf.name_scope('Layer1'):
        tf.summary.histogram("weights",W1)
        tf.summary.histogram("layer",layer1)
        tf.summary.histogram("bias",layer1_bias)
        tf.summary.histogram("activations",layer1_act)
    with tf.name_scope('Layer2'):
        tf.summary.histogram("weights",W2)
        tf.summary.histogram("layer",layer2)
        tf.summary.histogram("bias",layer2_bias)
        tf.summary.histogram("activations",layer2_act)
    with tf.name_scope('Layer3'):
        tf.summary.histogram("weights",W3)
        tf.summary.histogram("layer",layer3)
        tf.summary.histogram("bias",layer3_bias)
        tf.summary.histogram("activations",layer3_act)
    with tf.name_scope('Output'):
        tf.summary.histogram("weights",WO)
        tf.summary.histogram("layer",layerO)
    return layerO

我对训练过程的理解是,应该调整重量,这在图像中几乎不会发生.然而,损失已经完成.我已经训练了10000个时代的网络,所以我期望整体上有一点变化.特别是重量不足我不明白.有人可以详细说明吗？

解决方法

我的神经网络中的重量直方图遇到了类似的问题.尽管Relu确实处理了隐藏层的消失梯度问题,但您应该检查您的学习速率并确保每个变量的更新不会太小.这可能导致接近零的更新,导致随时间的变化无关紧要.您只需使用以下代码段检查每个图层的渐变：

def replace_none_with_zero(tensor):
   return[0 if i==None else i for i in tensor]

with tf.name_scope('Gradients'):
   gradient_for_variable_of_interest=replace_none_with_zero(
                              tf.gradients(loss,[variable_of_interest]))

然后通过调用渐变上的tf.summary.histogram来检查tensorboard中的渐变.