

Quick Start: SimpleCNN

Quick Start: Simple CNN

Low-level Graph API

In Serrano, a Graph represent your computation model. It allows you to construct arbitrary graphs of Operators. Operator in a graph, can be viewed as layer in other frameworks, is the basic calculation unit which takes into some tensors and output some tensors. The Tensor object is a N-D array implemented in Serrano, like array in NumPy.

First, let's construct a empty graph:

import Serrano
let g = ComputationGraph()
// In Serrano, class ComputationGraph conforms to protocol Graph and you should use ComputationGraph to initialize a graph model.

Then, we can add an input tensor for our constructed graph

let shape = TensorShape(dataType: .float, shape: [244, 244, 3]) // shape of the tensor
let input = g.tensor(shape: shape) // add an input tensor

Next, let's add a convolutional layer, max pooling layer and a fully connected layer into this graph.

// CNN
let convOp  = ConvOperator2D(numFilters: 96,
         kernelSize: [11,11],
         stride: [4, 4],
         padMode: PaddingMode.Valid,
         channelPosition: TensorChannelOrder.Last,
         inputShape: input.shape)
let (convOut, _, _) = g.operation(inputs: [input], op: convOp)

// MaxPooling
let maxPool = MaxPool2DOperator(kernelSize: [2, 2], stride: [2, 2],
        channelPosition: TensorChannelOrder.Last,
        paddingMode: PaddingMode.Valid)
let (poolOut, _, _) = g.operation(inputs: [convOut], op: maxPool)

// Fully connected
let fc = FullyconnectedOperator(inputDim: poolOut.first!.shape.count,
        numUnits: 200)
let _ = g.operation(inputs: [poolOut], op: fc)

Right now we have a computation graph. Next you can bind the weight of layers from your trained models. Here we can quickly initialize weights and do forward computation.

g.allocateAllTensors()
g.forwardPrepare()

let _ = SerranoEngine.configuredEngine.configureEngine(computationMode: .GPU) // prepare GPU device
let results = g.forward(mode: .GPU) // or g.forward(mode: .CPU)

The complete and executable code are in Serrano/Examples/Graph/SimpleCNN.swift

Also, there's an VGG16 example listed in Serrano/Examples/Graph/VGG16.swift.

High-level Model API

• Note: Model is high-level API which allow user to construct graph more quickly and easily. Currently, it is under developing.

---

Imperative Computation with Serrano

All codes talked about before is focusing on constructing a static computation graph and then feed the data and get result. During this process, Serrano will try best to reuse memory and do multiprocessing. However, in case if you need imperative programming, i.e. get result immediately after each operation, Serrano is coming with this capability.

The way is simple, you just do not construct a Graph! Directly play with operators and tensors. Below is a code fragment using convolutional operator to do calcualtion:

let inputTensor = randomTensor(fromShape: TensorShape(dataType: .float, shape: [244, 244, 3]))

// conv
let convOp = ConvOperator2D(numFilters: 96,
       kernelSize: [11,11],
       stride: [4, 4],
       padMode: PaddingMode.Valid,
       channelPosition: TensorChannelOrder.Last,
       weight: randomTensor(fromShape: TensorShape(dataType: .float, shape: [96, 3, 11, 11])))

// Initialize a tensor object to store convOp's result.
// In serrano, operator cannot allocate memeory for output tensors so that it can control memory allcoation precisely.
let convOutputs = SerranoResourceManager.globalManager.allocateUnamangedTensors(convOp.outputShape(shapeArray: [inputTensor.shape])!)
convOp.inputTensors = [inputTensor]
convOp.outputTensors = convOutputs
convOp.compute(.GPU)

Complete code can be found Serrano/Examples/Imperative/SimpleCNN_imperative.swift

---