Machine Learning for Industrial Applications

15Advanced Deep Learning Using Julia Programming

15.1 Convolutional Neural Network

using Flux

using Flux: @epochs, onehotbatch, onecold, throttle

using Statistics

using Base.Iterators: repeated

# Define your CNN model

function build_model()

  return Chain(

    Conv((3, 3), 1=>16, relu),

    MaxPool((2,2)),

    Conv((3, 3), 16=>32, relu),

    MaxPool((2,2)),

    Conv((3, 3), 32=>64, relu),

    MaxPool((2,2)),

    flatten,

    Dense(64*4*4, 128, relu),

    Dense(128, 10),

    softmax

) end

# Load your dataset (for example, MNIST)

using Flux.Data.MNIST

imgs = MNIST.images()

labels = MNIST.labels()

# Preprocess your data

X = hcat(float.(reshape.(imgs, :))…) |> gpu

Y = onehotbatch(labels, 0:9) |> gpu

# Split data into training and testing sets

train_indices = 1:50000

test_indices = 50001:60000

train_data = (X[:, train_indices], Y[:, train_indices])

test_data = (X[:, test_indices], Y[:, test_indices])

# Build the model

model = build_model() |> gpu

# Define loss function and optimizer

loss(x, y) = Flux.crossentropy(model(x), y)

optimizer = Flux.ADAM(params(model))

# Train the model

epochs = 10

@epochs epochs Flux.train!(loss, train_data, optimizer, cb = throttle(() ->

println(“Loss: “, loss(X, Y)), 10))

# Test the model

accuracy(x, y) = mean(onecold(model(x)) .== onecold(y))

acc = accuracy(test_data…)

println(“Test accuracy: $acc”)

15.2 Deep Neural Network Implementation

using Flux

using Flux: @epochs, onecold, onehotbatch

using Statistics

# Define your DNN model

function build_model() ...