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4. The Functional class


There is another way of using the Model class besides subclassing it, which is the functional API. It connects the layers to each other to form a directed acyclic graph (DAG), where the nodes are layer call events, and the edges are KerasTensors. Please refer to this tutorial for more details of how to use it. Following is a code example of using the functional API.

inputs = tf.keras.Input(shape=(3,))
x = tf.keras.layers.Dense(4, activation=tf.nn.relu)(inputs)
outputs = tf.keras.layers.Dense(5, activation=tf.nn.softmax)(x)
model = tf.keras.Model(inputs=inputs, outputs=outputs)

Although it looks like it is still using the Model class, it is using the Functional class, which is an internal class not exposed to the public API. In Model.__new__(), it creates a Functional instance if using the functional API. The source code looks like this.


class Model(Layer):
  def __new__(cls, *args, **kwargs):
    if is_functional_model_init_params(args, kwargs) and cls == Model:
      return functional.Functional(skip_init=True, *args, **kwargs)

Now, let's see how Functional is tracking these layers and intermediate outputs in the computational graph.

The KerasTensor class

keras.Input(), which looks like a class, but it is a function, which returns a KerasTensor object.

print(type(keras.Input(shape=(28, 28, 1))))


<class 'keras.engine.keras_tensor.KerasTensor'>

KerasTensor is a class just to represent the intermediate output tensors of the layers in a Keras model, which has some useful properties like shape and dtype.


class KerasTensor(object):
  def shape(self):
  def dtype(self):

It is passed to each of the layers by calling them as shown in the functional API example. The purpose is for the layers to create the weights using the shape and type information of the input tensor. That is also why we have a special judge to see if the input tensor is a KerasTensor in Layer.__call__() as we introduced before.

class Layer(module.Module, ...):
  def __call__(self, inputs, **kwargs):
    if isinstance(inputs, keras_tensor.KerasTensor):
      inputs = convert_to_tf_tensor(inputs)
      outputs =
      return convert_to_keras_tensor(outputs)

From the source code above, we can see if we call a layer with a KerasTensor, the return value is also a KerasTensor, which will be used to call the next layer.

Connecting the layers

The question we try to answer here is: How did the computational graph being recorded and fetched only given the inputs and outputs? This functionality is implemented in Functional._init_graph_network().

The graph is being fetched starting from the outputs, which is a list of KerasTensors. Each KerasTensor records the Layer instance that produces it during the call of the Layer. The algorithm is like this. First, from the outputs, we got the Layer producing these outputs. Second, use the Layer to get the input KerasTensors. Third, use these KerasTensors to get the previous layers. Keep doing this until the inputs to the model are reached.

Here are another two questions to answer:

  1. How does a KerasTensor get the Layer producing it?

  2. How does the Layer get the input KerasTensors?

To answer these two questions, there are two important classes or concepts to make clear first: Node and KerasHistory.

A Node is created at each call of a layer, to represent the connectivity between the two Layers. In other words, a Node corresponds to a call of a Layer. Each Layer has an attribute of _inbound_nodes to track the input Nodes. The reason why there can be multiple inbound Nodes is that a Layer may be used in multiple places in a model for weight sharing.

In the following example, layer_a and layer_b are all called multiple times. Therefore, node1 and node4 are in layer_a._inbound_nodes. node2 and node5 are in layer_b._inbound_nodes.

node1 -> layer_a -> node2 -> layer_b -> node3

node4 -> layer_a -> node5 -> layer_b -> node6

When building a functional model, we can call a Layer with multiple KerasTensors. For example, the Add layer add multiple tensors together. Therefore, a call of a layer corresponds to multiple KerasTensors. A Node also corresponds to a call of a Layer. Therefore, a Node may correspond to multiple KerasTensors. A Node record these KerasTensors in Node._keras_inputs.

KerasHistory is for the KerasTensor to find the input Layer and the corresponding Node. It is stored in the attribute of KerasTensor._keras_history. KerasHistory.layer records the Layer producing it. KerasHistory.node_index records the index of the corresponding Node in the KerasHistory.layer._inbound_nodes.

For example, if node2 has a corresponding KerasTensor, named keras_tensor_2, keras_tensor_2._keras_history.node_index records the index of node1 in layer_a._inbound_nodes.

With all these recording mechanisms, we can have the following pseudo-code.


class Layer(tf.Module):
  def __call__(inputs):
    outputs =
    node = Node(self, inputs, outputs)


class Node:
  def __init__(self, layer, inputs, outputs):
    self._keras_inputs = inputs
    node_index = len(self.layer._inbound_nodes) - 1
    for keras_tensor in outputs:
        keras_tensor._keras_history = KerasHistory(layer, node_index)

Now, we have the answers to the two questions above. A KerasTensor find the layer producing it with KerasTensor._keras_history.layer. A Layer find the input KerasTensor with Layer._inbound_nodes[0]._keras_inputs.

Finally, we can fetch the entire computational graph with the algorithm described above. The pseudo-code is shown as follows.


def fetch_nodes(output_keras_tensor):
  if output_keras_tensor in model.inputs:
    return []
  layer = output_keras_tensor._keras_history.layer
  node_index = output_keras_tensor._keras_history.node_index
  node = layer._inbound_nodes[node_index]
  node_list = [node]
  for input_keras_tensor in node._keras_inputs:
    node_list += fetch_nodes(input_keras_tensor)
  return node_list

The actual code would not only fetch the nodes, but also the layers, and sort them in topological order. In, it calls the layer in topological order to produce the outputs.