If I just use a single layer like this:
layer = tf.layers.dense(tf_x, 1, tf.nn.relu)
Is this just a single layer with a single node?
Or
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)
My network seemed to work properly with just 1 layer, so I was curious about the setup.
That is possible, for some tasks you will get decent results without hidden layers.