Training Loss Curve — Makie.jl

A line plot showing training and validation loss curves over epochs during neural network training. This visualization is essential for monitoring model training, detecting overfitting (when validation loss diverges from training loss), and determining optimal early stopping points. The dual-curve display reveals the gap between training and generalization performance.

Training Loss Curve rendered with Makie.jl

Renders

Julia source (Makie.jl)

# anyplot.ai
# line-loss-training: Training Loss Curve
# Library: makie 0.21.9 | Julia 1.11.9
# Quality: 92/100 | Created: 2026-09-05

using CairoMakie
using Colors
using Random

Random.seed!(42)

# --- Theme tokens -----------------------------------------------------------
const THEME    = get(ENV, "ANYPLOT_THEME", "light")
const PAGE_BG  = THEME == "light" ? colorant"#FAF8F1" : colorant"#1A1A17"
const INK      = THEME == "light" ? colorant"#1A1A17" : colorant"#F0EFE8"
const INK_SOFT = THEME == "light" ? colorant"#4A4A44" : colorant"#B8B7B0"
const IMPRINT_PALETTE = [
    colorant"#009E73", colorant"#C475FD", colorant"#4467A3", colorant"#BD8233",
    colorant"#AE3030", colorant"#2ABCCD", colorant"#954477", colorant"#99B314",
]
const ANYPLOT_AMBER = colorant"#DDCC77"  # warning / caution — early-stopping marker

# --- Data ---------------------------------------------------------------
# Synthetic training history: training loss decays smoothly, validation loss
# decays alongside it until the model starts overfitting past epoch 45.
epochs = collect(1:80)
n = length(epochs)

train_loss_base = 2.0 .* exp.(-0.065 .* epochs) .+ 0.03
train_loss = train_loss_base .* (1 .+ 0.04 .* randn(n))
train_loss = clamp.(train_loss, 0.02, Inf)

overfit_start = 45
val_loss = zeros(n)
for (i, e) in enumerate(epochs)
    base = 2.2 * exp(-0.058 * e) + 0.16
    if e > overfit_start
        base += 0.0035 * (e - overfit_start)^1.3
    end
    val_loss[i] = clamp(base + 0.035 * randn(), 0.05, Inf)
end

best_epoch = epochs[argmin(val_loss)]
best_val_loss = minimum(val_loss)

# --- Plot -----------------------------------------------------------------
fig = Figure(
    resolution      = (1600, 900),
    fontsize        = 14,
    backgroundcolor = PAGE_BG,
)

ax = Axis(
    fig[1, 1];
    title              = "line-loss-training · julia · makie · anyplot.ai",
    titlesize          = 20,
    titlecolor         = INK,
    xlabel             = "Epoch",
    ylabel             = "Cross-Entropy Loss (log scale)",
    xlabelsize         = 14,
    ylabelsize         = 14,
    xlabelcolor        = INK,
    ylabelcolor        = INK,
    xticklabelsize     = 12,
    yticklabelsize     = 12,
    xticklabelcolor    = INK_SOFT,
    yticklabelcolor    = INK_SOFT,
    xtickcolor         = INK_SOFT,
    ytickcolor         = INK_SOFT,
    backgroundcolor    = PAGE_BG,
    yscale             = log10,
    topspinevisible    = false,
    rightspinevisible  = false,
    leftspinecolor     = INK_SOFT,
    bottomspinecolor   = INK_SOFT,
    xgridvisible       = false,
    ygridcolor         = RGBAf(INK.r, INK.g, INK.b, 0.15),
    xminorgridvisible  = false,
    yminorgridvisible  = false,
)

vlines!(ax, [best_epoch]; color = ANYPLOT_AMBER, linewidth = 1.5, linestyle = :dash)

lines!(ax, epochs, train_loss; color = IMPRINT_PALETTE[1], linewidth = 3.0, label = "Training loss")
lines!(ax, epochs, val_loss; color = IMPRINT_PALETTE[2], linewidth = 3.0, label = "Validation loss")

scatter!(ax, [best_epoch], [best_val_loss];
    color = ANYPLOT_AMBER, markersize = 18, strokewidth = 1.5, strokecolor = PAGE_BG,
    label = "Early-stopping epoch")

text!(ax, best_epoch + 3, best_val_loss * 1.9;
    text     = "Best val loss @ epoch $(best_epoch)",
    color    = INK_SOFT,
    fontsize = 13,
    align    = (:left, :baseline),
)

axislegend(ax;
    position      = :rt,
    labelcolor    = INK_SOFT,
    framevisible  = false,
    backgroundcolor = PAGE_BG,
)

# --- Save -------------------------------------------------------------------
save("plot-$(THEME).png", fig; px_per_unit = 2)

Retrieve this implementation

Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/line-loss-training/makie/code. Any spec id and library id listed in llms-full.txt fit the same URL shape; every URL below is complete and callable.

{
  "spec_id": "line-loss-training",
  "language": "julia",
  "library": "makie",
  "page": "https://anyplot.ai/line-loss-training/julia/makie",
  "hub": "https://anyplot.ai/line-loss-training",
  "code_json": "https://api.anyplot.ai/specs/line-loss-training/makie/code",
  "spec_json": "https://api.anyplot.ai/specs/line-loss-training",
  "render_light_png": "https://storage.googleapis.com/anyplot-images/plots/line-loss-training/julia/makie/plot-light.png",
  "render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/line-loss-training/julia/makie/plot-dark.png",
  "quality_score": 92.0,
  "license": "MIT",
  "guide": "https://anyplot.ai/llms.txt"
}

Part of Training Loss Curve on anyplot.ai.

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