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 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.
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 is my input features tensor.