tf.layers.dense是单层吗?

问题描述

如果我只使用这样的单层:

If I just use a single layer like this:

layer = tf.layers.dense(tf_x, 1, tf.nn.relu)

这仅仅是一个具有单个节点的单层吗?

Is this just a single layer with a single node?

或者它实际上是一组具有1个节点的图层(输入，隐藏，输出)吗?我的网络似乎只能在1层上正常工作，所以我对设置感到好奇.

Or is it actually a set of layers (input, hidden, output) with 1 node? My network seemed to work properly with just 1 layer, so I was curious about the setup.

因此，下面的此设置是否具有2个隐藏层(layer1和layer2均为隐藏层)?还是实际上只有1个(仅layer 1)?

Consequently, does this setup below have 2 hidden layers (are layer1 and layer2 here both hidden layers)? Or actually just 1 (just layer 1)?

layer1 = tf.layers.dense(tf_x, 10, tf.nn.relu)
layer2 = tf.layers.dense(layer1, 1, tf.nn.relu)

tf_x是我的输入特征张量.

tf_x is my input features tensor.

推荐答案

tf.layers.dense将单个图层添加到您的网络.第二个参数是该层的神经元/节点数.例如:

tf.layers.dense adds a single layer to your network. The second argument is the number of neurons/nodes of the layer. For example:

# no hidden layers, dimension output layer = 1
output = tf.layers.dense(tf_x, 1, tf.nn.relu)

# one hidden layer, dimension hidden layer = 10,  dimension output layer = 1
hidden = tf.layers.dense(tf_x, 10, tf.nn.relu)
output = tf.layers.dense(hidden, 1, tf.nn.relu)

这是可能的，对于某些任务，您将获得体面的结果而没有隐藏的图层.

That is possible, for some tasks you will get decent results without hidden layers.

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