# NNC Common Neural Network Primitives¶

Computation graph is a powerful abstraction for scheduling and managing computations. Often times though, this can feel too *raw*. Conceptually, in a computation graph, all tensors are equal. But for neural networks, parameters (weights) and activations are different. Parameters are the configurations, while activations are the temporary states given the input.

Both weights and activations in computation graph are represented as ordinary tensors.

## Model¶

**Model** is the core abstraction in **common neural network primitives (CNNP)** interface. It can represent both a layer or a group of layers. An ordinary neural network layer contains parameters, and applies the parameters to input neurons to generate activations in output neurons. The **model** abstraction goes beyond one input and one output. It can take multiple sets of input neurons, and generate activations on multiple sets of output neurons. The main difference between a **model** and a **command** in a concrete graph is that the **model** contains states (parameters).

The **model** itself is incredibly flexible. You don’t need to know any shape of the inputs or outputs to compose a model. **Models** are composable. The most simple way to compose a new model from a list of models is to use `ccv_cnnp_sequential_new`

. This function takes a list of models, and runs activations through them sequentially (the input of current model is the output of the model prior). Alternatively, `ccv_cnnp_model_new`

can compose a new model out of a set of model inputs and outputs. More on this in Model IO.

The **model** concept is meta in a sense that a **model** is not materialized until you call `ccv_cnnp_model_compile`

with tensor inputs / outputs parameters. Internally, this method will materialize **model** into a symbolic graph that has proper shape. After a **model** compiled, either evaluate the **model** against inputs or train the **model** is possible.

## Model IO¶

Composing a **model** with `ccv_cnnp_model_new`

requires model inputs and model outputs. The concept of model inputs / outputs is remarkably similar to tensor symbols. In this case, it is broader. Ordinarily, `ccv_cnnp_input`

gives a `ccv_cnnp_model_io_t`

that represents a tensor as input. When `ccv_cnnp_model_apply`

called with a **model** and set of inputs, its `ccv_cnnp_model_io_t`

output represents a set of tensors generated by applying inputs against the said **model**. Thus, `ccv_cnnp_model_io_t`

can conceptually both be a single tensor and a set of tensors. For the given model inputs and outputs, a set of **models** that are used to generate the outputs from the inputs can be traced to compose a new **model**. This also means a composed **model** can be used to compose a more complex **model**. In this way, the model IO abstraction is very natural to compose ever complex **models**.

## Fit, Evaluate, Backward, Apply Gradients¶

CNNP provides two sets of APIs such that you can control different aspects of the training process yourselves. The first one is rather straightforward, `ccv_cnnp_model_fit`

is the one-stop API that handles both compute losses and gradients’ update. If you want to have finer control over what losses propagated back, or accumulating gradients from multiple batches, you can use `ccv_cnnp_model_evaluate`

, `ccv_cnnp_model_backward`

and `ccv_cnnp_model_apply_gradients`

.

By separating `ccv_cnnp_model_fit`

API into 3 pieces, we can integrate CNNP models with **dynamic graph** easily. The `ccv_dynamic_graph_evaluate`

under the hood calls into `ccv_cnnp_model_evaluate`

, so does `ccv_dynamic_graph_backward`

and `ccv_dynamic_graph_apply_gradients`

. With this design, CNNP models with **dynamic graph** are now the recommended way to build models.