Study how to implement a CNN using Golang

Golang implements CNN

Deep learning plays a vital role in the field of computer science. In the field of computer vision, convolutional neural network (CNN) is a very popular technology. In this article, we will study how to implement a CNN using Golang.

In order to understand CNN, we need to first understand the convolution operation. The convolution operation is the core operation of CNN. The input data can be multiplied by the kernel by sliding the kernel to generate the output feature map. In Golang, we can use GoCV to process images. GoCV is a Golang library written by the OpenCV C library, specialized for computer vision and image processing.

In GoCV, we can use the Mat type to represent images and feature maps. The Mat type is a multidimensional matrix that can store the values ​​of one or more channels. In CNN, three layers of Mat are usually used: input Mat, convolution kernel Mat and output Mat. We can implement the convolution operation by multiplying the input Mat and the convolution kernel Mat, and then accumulating the result into the output Mat.

The following is a simple convolution function implemented using Golang:

func convolve(input, kernel *gocv.Mat, stride int) *gocv.Mat {
    out := gocv.NewMatWithSize((input.Rows()-kernel.Rows())/stride+1, (input.Cols()-kernel.Cols())/stride+1, gocv.MatTypeCV32F)
    for row := 0; row < out.Rows(); row++ {
        for col := 0; col < out.Cols(); col++ {
            sum := float32(0)
            for i := 0; i < kernel.Rows(); i++ {
                for j := 0; j < kernel.Cols(); j++ {
                    inputRow := row*stride + i
                    inputCol := col*stride + j
                    value := input.GetFloatAt(inputRow, inputCol, 0)
                    kernelValue := kernel.GetFloatAt(i, j, 0)
                    sum += value * kernelValue
                }
            }
            out.SetFloatAt(row, col, 0, sum)
        }
    }
    return out
}

In this simple convolution function, we will input Mat and convolution kernel Mat as input parameters, and specify Movement step size. We iterate through each element of the output Mat, multiply the input Mat and the convolution kernel Mat and accumulate them into the output Mat. Finally, we will output Mat as the return value of the function.

Now let's take a look at how to use the convolution function to implement a CNN. We will use Golang to implement a simple two-layer CNN for classifying handwritten digits.

Our network will consist of two convolutional layers and two fully connected layers. After the first convolutional layer, we will apply a max pooling layer to reduce the size of the data. After the second convolutional layer, we perform average pooling on the data to further reduce the size of the data. Finally, we will use two fully connected layers to classify the feature data.

The following is the code of a simple CNN implemented using Golang:

func main() {
    inputSize := image.Point{28, 28}
    batchSize := 32
    trainData, trainLabels, testData, testLabels := loadData()

    batchCount := len(trainData) / batchSize

    conv1 := newConvLayer(inputSize, 5, 20, 1)
    pool1 := newMaxPoolLayer(conv1.outSize, 2)
    conv2 := newConvLayer(pool1.outSize, 5, 50, 1)
    pool2 := newAvgPoolLayer(conv2.outSize, 2)
    fc1 := newFcLayer(pool2.totalSize(), 500)
    fc2 := newFcLayer(500, 10)

    for i := 0; i < 10; i++ {
        for j := 0; j < batchCount; j++ {
            start := j * batchSize
            end := start + batchSize

            inputs := make([]*gocv.Mat, batchSize)
            for k := start; k < end; k++ {
                inputs[k-start] = preprocess(trainData[k])
            }
            labels := trainLabels[start:end]

            conv1Out := convolveBatch(inputs, conv1)
            relu(conv1Out)
            pool1Out := maxPool(conv1Out, pool1)

            conv2Out := convolveBatch(pool1Out, conv2)
            relu(conv2Out)
            pool2Out := avgPool(conv2Out, pool2)

            fc1Out := fc(pool2Out, fc1)
            relu(fc1Out)
            fc2Out := fc(fc1Out, fc2)

            softmax(fc2Out)
            costGradient := costDerivative(fc2Out, labels)
            fcBackward(fc1, costGradient, fc2Out)
            fcBackward(pool2, fc1.gradient, fc1.out)
            reluBackward(conv2.gradient, pool2.gradient, conv2.out)
            convBackward(pool1, conv2.gradient, conv2.kernels, conv2.out, pool1.out)
            maxPoolBackward(conv1.gradient, pool1.gradient, conv1.out)
            convBackward(inputs, conv1.gradient, conv1.kernels, nil, conv1.out)

            updateParameters([]*layer{conv1, conv2, fc1, fc2})
        }

        accuracy := evaluate(testData, testLabels, conv1, pool1, conv2, pool2, fc1, fc2)
        fmt.Printf("Epoch %d, Accuracy: %f\n", i+1, accuracy)
    }
}

In this simple CNN implementation, we use the underlying Mat operation to implement it. We first call the loadData function to load training and test data. Then we define the structure of the convolutional layer, pooling layer and fully connected layer. We loop through all batches of data and feed them into the network using a new preprocessing function. Finally, we use the backpropagation algorithm to calculate the gradients and update the weights and biases.

Summary:

In this article, we learned about the basic principles of convolution operations and CNN, and implemented a simple CNN using Golang. We use the underlying Mat operation to calculate the convolution and pooling operations, and use the backpropagation algorithm to update the weights and biases. By implementing this simple CNN, we can better understand neural networks and start exploring more advanced CNNs.

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