Sequential Models in Keras
A Functional API model needs a defined route from data entering the model to data leaving it.
The Route View
The Keras Functional API describes a model as a connected route of tensor transformations. The central question is not simply which layers appear in a file. Instead, ask which tensor begins the route, which layers receive that tensor or a later tensor, and which tensor ends the route.
A Functional API model needs a defined route from data entering the model to data leaving it.
This route-based view gives you a reliable way to read and check model construction. Create or identify an input tensor, pass it through connected layers, retain the resulting tensors, and use the related starting and ending tensors to form the model.
Following Tensor Transformations
In this route, the input tensor is handed to the first layer. That layer returns another tensor. The returned tensor becomes the input to the next layer, which returns the output tensor. The important relationship is the handoff from one tensor to the next layer, not merely the fact that the layers are written near one another.
Tracing a Two-Layer Route
Suppose input_tensor is passed to layer_a, the returned tensor is saved as tensor_a, and tensor_a is passed to layer_b. The tensor returned by layer_b is saved as output_tensor. Identify the route and the model boundaries.
- 1Start
The route begins at
input_tensor. - 2First handoff
layer_a receives
input_tensorand returns tensor_a. - 3Second handoff
layer_b receives tensor_a and returns
output_tensor. - 4Choose endpoints
The related input and output tensors are
input_tensorandoutput_tensor.
The route is input_tensor to layer_a to tensor_a to layer_b to output_tensor. The model boundaries are the related starting tensor input_tensor and ending tensor output_tensor.
Layers as Tensor Functions
In the Functional API, a layer is used as a function: it accepts tensors and returns tensors.
For route tracing, record both sides of every layer use. First identify the tensor entering the layer. Then identify the tensor returned by that layer. The returned tensor is the next object that can continue the connected route or become the selected output endpoint.
A layer call creates a tensor handoff: one tensor enters, and a returned tensor continues the route.
Defining Model Boundaries
The Model class uses an input tensor and a related output tensor to assemble the model. Keras uses the connected path between those endpoints as the model route. The endpoint perspective is therefore useful when reading Functional API construction: locate the tensor at the beginning, locate the tensor at the end, and verify that the two are connected through the intended transformations.
Checking the Layer Path
- 1Identify the tensor at the beginning of the intended route.
- 2For each layer, identify which tensor enters it.
- 3Identify and save the tensor returned by that layer.
- 4Continue tracing the returned tensor into the next layer.
- 5Confirm that the selected output tensor is reached through the connected route.
- 6Check that the input and output tensors supplied to the Model class are related.
This procedure is especially useful when the assembled model does not match the intended design. Inspect the tensor handoff one step at a time. A line appearing earlier or later in a file does not by itself establish the route; the connected relationships between tensors do.
Assembly Mistakes
Common mistake Choosing an output tensor that is not connected to the selected input tensor.
What it looks likeThe proposed model boundaries come from separate routes rather than from one connected sequence of transformations.
Why it failsThe Model class assembles the route between related input and output tensors. A disconnected pair causes a
RuntimeError.Do this insteadTrace backward from the proposed output and verify each tensor handoff until the route reaches the intended input tensor.
Common mistake Checking only the visual order of layer statements.
What it looks likeA layer appears in the expected place in the file, but the tensor passed into it is not the tensor returned by the preceding intended layer.
Why it failsThe Functional API is defined by connected tensor transformations, not by visual placement alone.
Do this insteadFor every layer, name the incoming tensor and the returned tensor.
Common mistake Losing track of the tensor returned by a layer.
What it looks likeA layer receives one tensor, but the next step is described without identifying the tensor produced by that layer.
Why it failsThe returned tensor is the object that continues the transformation route.
Do this insteadSave or identify the returned tensor before tracing the next layer.
Common mistake Treating the Model boundary as a list of layers rather than as related tensors.
What it looks likeThe explanation names the layers but does not identify the starting and ending tensors.
Why it failsThe Model class uses an input tensor and a related output tensor to assemble the model.
Do this insteadState the input endpoint and output endpoint explicitly, then verify the connected path between them.
Route-Checking Practice
Imagine a route with input_tensor, layer_a, tensor_a, layer_b, and output_tensor. Explain which tensor enters each layer, which tensor each layer returns, which two tensors should define the Model boundaries, and what you would inspect first if the selected output were disconnected from input_tensor.
Hints
- Start with the tensor at the beginning of the route.
- For each layer, name its incoming tensor and its returned tensor.
- The model boundaries are the related starting and ending tensors.
- For a disconnected endpoint, inspect the tensor handoff and the selected endpoints.
What do you think happens?
If a layer receives tensor_a and returns output_tensor, which tensor should be checked as the next point in the route?
Reveal answer
The tensor returned by the layer
A layer accepts a tensor and returns a tensor. The returned tensor continues the connected transformation route and may become the selected output endpoint.
Essential Takeaways
- 1The Keras Functional API describes a model as a connected route of tensor transformations.
- 2A layer can be used as a function: it accepts tensors and returns tensors.
- 3The Model class uses related input and output tensors to assemble the route between them.
- 4Tracing each tensor handoff is the practical way to verify model assembly.
- 5A disconnected input and output pair causes a
RuntimeError.
At a glance
- A Functional API model is a connected route from an input tensor to an output tensor.
- Each layer receives a tensor and produces a returned tensor that can continue the route.
- The Model class uses related endpoint tensors to assemble the model.
- To check assembly, trace the tensor entering and leaving every layer.
- Disconnected endpoints cause a
RuntimeError.