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ImageClassifier

ImageClassifier

A model type for constructing a image classifier, based on MLJFlux.jl, and implementing the MLJ model interface.

From MLJ, the type can be imported using

ImageClassifier = @load ImageClassifier pkg=MLJFlux

Do model = ImageClassifier() to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in ImageClassifier(builder=...).

ImageClassifier classifies images using a neural network adapted to the type of images provided (color or gray scale). Predictions are probabilistic. Users provide a recipe for constructing the network, based on properties of the image encountered, by specifying an appropriate builder. See MLJFlux documentation for more on builders.

Training data

In MLJ or MLJBase, bind an instance model to data with

mach = machine(model, X, y)

Here:

  • X is any AbstractVector of images with ColorImage or GrayImage scitype; check the scitype with scitype(X) and refer to ScientificTypes.jl documentation on coercing typical image formats into an appropriate type.
  • y is the target, which can be any AbstractVector whose element scitype is Multiclass; check the scitype with scitype(y).

Train the machine with fit!(mach, rows=...).

Hyper-parameters

  • builder: An MLJFlux builder that constructs the neural network. The fallback builds a depth-16 VGG architecture adapted to the image size and number of target classes, with no batch normalization; see the Metalhead.jl documentation for details. See the example below for a user-specified builder. A convenience macro @builder is also available. See also finaliser below.

  • optimiser::Optimisers.Adam(): An Optimisers.jl optimiser. The optimiser performs the updating of the weights of the network. To choose a learning rate (the update rate of the optimizer), a good rule of thumb is to start out at 10e-3, and tune using powers of 10 between 1 and 1e-7.

  • loss=Flux.crossentropy: The loss function which the network will optimize. Should be a function which can be called in the form loss(yhat, y). Possible loss functions are listed in the Flux loss function documentation. For a classification task, the most natural loss functions are:

    • Flux.crossentropy: Standard multiclass classification loss, also known as the log loss.
    • Flux.logitcrossentopy: Mathematically equal to crossentropy, but numerically more stable than finalising the outputs with softmax and then calculating crossentropy. You will need to specify finaliser=identity to remove MLJFlux's default softmax finaliser, and understand that the output of predict is then unnormalized (no longer probabilistic).
    • Flux.tversky_loss: Used with imbalanced data to give more weight to false negatives.
    • Flux.focal_loss: Used with highly imbalanced data. Weights harder examples more than easier examples.

    Currently MLJ measures are not supported values of loss.

  • epochs::Int=10: The duration of training, in epochs. Typically, one epoch represents one pass through the complete the training dataset.

  • batch_size::int=1: the batch size to be used for training, representing the number of samples per update of the network weights. Typically, batch size is between 8 and

    1. Increassing batch size may accelerate training if acceleration=CUDALibs() and a

    GPU is available.

  • lambda::Float64=0: The strength of the weight regularization penalty. Can be any value in the range [0, ∞). Note the history reports unpenalized losses.

  • alpha::Float64=0: The L2/L1 mix of regularization, in the range [0, 1]. A value of 0 represents L2 regularization, and a value of 1 represents L1 regularization.

  • rng::Union{AbstractRNG, Int64}: The random number generator or seed used during training. The default is Random.default_rng().

  • optimizer_changes_trigger_retraining::Bool=false: Defines what happens when re-fitting a machine if the associated optimiser has changed. If true, the associated machine will retrain from scratch on fit! call, otherwise it will not.

  • acceleration::AbstractResource=CPU1(): Defines on what hardware training is done. For Training on GPU, use CUDALibs().

  • finaliser=Flux.softmax: The final activation function of the neural network (applied after the network defined by builder). Defaults to Flux.softmax.

Operations

  • predict(mach, Xnew): return predictions of the target given new features Xnew, which should have the same scitype as X above. Predictions are probabilistic but uncalibrated.
  • predict_mode(mach, Xnew): Return the modes of the probabilistic predictions returned above.

Fitted parameters

The fields of fitted_params(mach) are:

  • chain: The trained "chain" (Flux.jl model), namely the series of layers, functions, and activations which make up the neural network. This includes the final layer specified by finaliser (eg, softmax).

Report

The fields of report(mach) are:

  • training_losses: A vector of training losses (penalised if lambda != 0) in historical order, of length epochs + 1. The first element is the pre-training loss.

Examples

In this example we use MLJFlux and a custom builder to classify the MNIST image dataset.

using MLJ
using Flux
import MLJFlux
import Optimisers
import MLJIteration ## for `skip` control

First we want to download the MNIST dataset, and unpack into images and labels:

import MLDatasets: MNIST
data = MNIST(split=:train)
images, labels = data.features, data.targets

In MLJ, integers cannot be used for encoding categorical data, so we must coerce them into the Multiclass scitype:

labels = coerce(labels, Multiclass);

Above images is a single array but MLJFlux requires the images to be a vector of individual image arrays:

images = coerce(images, GrayImage);
images[1]

We start by defining a suitable builder object. This is a recipe for building the neural network. Our builder will work for images of any (constant) size, whether they be color or black and white (ie, single or multi-channel). The architecture always consists of six alternating convolution and max-pool layers, and a final dense layer; the filter size and the number of channels after each convolution layer is customizable.

import MLJFlux

struct MyConvBuilder
    filter_size::Int
    channels1::Int
    channels2::Int
    channels3::Int
end

make2d(x::AbstractArray) = reshape(x, :, size(x)[end])

function MLJFlux.build(b::MyConvBuilder, rng, n_in, n_out, n_channels)
    k, c1, c2, c3 = b.filter_size, b.channels1, b.channels2, b.channels3
    mod(k, 2) == 1 || error("`filter_size` must be odd. ")
    p = div(k - 1, 2) ## padding to preserve image size
    init = Flux.glorot_uniform(rng)
    front = Chain(
        Conv((k, k), n_channels => c1, pad=(p, p), relu, init=init),
        MaxPool((2, 2)),
        Conv((k, k), c1 => c2, pad=(p, p), relu, init=init),
        MaxPool((2, 2)),
        Conv((k, k), c2 => c3, pad=(p, p), relu, init=init),
        MaxPool((2 ,2)),
        make2d)
    d = Flux.outputsize(front, (n_in..., n_channels, 1)) |> first
    return Chain(front, Dense(d, n_out, init=init))
end

It is important to note that in our build function, there is no final softmax. This is applied by default in all MLJFlux classifiers (override this using the finaliser hyperparameter).

Now that our builder is defined, we can instantiate the actual MLJFlux model. If you have a GPU, you can substitute in acceleration=CUDALibs() below to speed up training.

ImageClassifier = @load ImageClassifier pkg=MLJFlux
clf = ImageClassifier(builder=MyConvBuilder(3, 16, 32, 32),
                      batch_size=50,
                      epochs=10,
                      rng=123)

You can add Flux options such as optimiser and loss in the snippet above. Currently, loss must be a flux-compatible loss, and not an MLJ measure.

Next, we can bind the model with the data in a machine, and train using the first 500 images:

mach = machine(clf, images, labels);
fit!(mach, rows=1:500, verbosity=2);
report(mach)
chain = fitted_params(mach)
Flux.params(chain)[2]

We can tack on 20 more epochs by modifying the epochs field, and iteratively fit some more:

clf.epochs = clf.epochs + 20
fit!(mach, rows=1:500, verbosity=2);

We can also make predictions and calculate an out-of-sample loss estimate, using any MLJ measure (loss/score):

predicted_labels = predict(mach, rows=501:1000);
cross_entropy(predicted_labels, labels[501:1000])

The preceding fit!/predict/evaluate workflow can be alternatively executed as follows:

evaluate!(mach,
          resampling=Holdout(fraction_train=0.5),
          measure=cross_entropy,
          rows=1:1000,
          verbosity=0)

See also NeuralNetworkClassifier.