bayesiancnn-data-parsing/cifar_100_epoch.jl

include("aux_func.jl")
using Statistics
using PlotlyJS
using PlotlyJS: savefig

folder = "CIFAR_100_epoch/"

bayes_exp_1 = load_pickle("$(folder)bayes_exp_data_1.pkl")
bayes_exp_2 = load_pickle("$(folder)bayes_exp_data_2.pkl")
bayes_exp_3 = load_pickle("$(folder)bayes_exp_data_3.pkl")
bayes_exp_4 = load_pickle("$(folder)bayes_exp_data_4.pkl")
bayes_exp_5 = load_pickle("$(folder)bayes_exp_data_5.pkl")

bayes_all_mean_tloss =
    (1 / 5) * (
        bayes_exp_1[:, 2] +
        bayes_exp_2[:, 2] +
        bayes_exp_3[:, 2] +
        bayes_exp_4[:, 2] +
        bayes_exp_5[:, 2]
    )

bayes_all_mean_iloss =
    (1 / 5) * (
        bayes_exp_1[:, 4] +
        bayes_exp_2[:, 4] +
        bayes_exp_3[:, 4] +
        bayes_exp_4[:, 4] +
        bayes_exp_5[:, 4]
    )

bayes_all_mean_acc =
    (1 / 5) * (
        bayes_exp_1[:, 3] +
        bayes_exp_2[:, 3] +
        bayes_exp_3[:, 3] +
        bayes_exp_4[:, 3] +
        bayes_exp_5[:, 3]
    )

bayes_all_mean_pre =
    (1 / 5) * (
        bayes_exp_1[:, 5] +
        bayes_exp_2[:, 5] +
        bayes_exp_3[:, 5] +
        bayes_exp_4[:, 5] +
        bayes_exp_5[:, 5]
    )

b_exp_1_tls = bayes_exp_1[:, 2]
b_exp_1_acc = bayes_exp_1[:, 3]
b_exp_1_vls = bayes_exp_1[:, 4]
b_exp_1_pre = bayes_exp_1[:, 5]
println("Training accuracy bayes 1 $(mean(b_exp_1_acc))")
println("Testing accuracy bayes 1 $(mean(b_exp_1_pre))")

b_exp_2_tls = bayes_exp_2[:, 2]
b_exp_2_acc = bayes_exp_2[:, 3]
b_exp_2_vls = bayes_exp_2[:, 4]
b_exp_2_pre = bayes_exp_2[:, 5]
println("Training accuracy bayes 2 $(mean(b_exp_2_acc))")
println("Testing accuracy bayes 2 $(mean(b_exp_2_pre))")

b_exp_3_tls = bayes_exp_3[:, 2]
b_exp_3_acc = bayes_exp_3[:, 3]
b_exp_3_vls = bayes_exp_3[:, 4]
b_exp_3_pre = bayes_exp_3[:, 5]
println("Training accuracy bayes 3 $(mean(b_exp_3_acc))")
println("Testing accuracy bayes 3 $(mean(b_exp_3_pre))")

b_exp_4_tls = bayes_exp_4[:, 2]
b_exp_4_acc = bayes_exp_4[:, 3]
b_exp_4_vls = bayes_exp_4[:, 4]
b_exp_4_pre = bayes_exp_4[:, 5]
println("Training accuracy bayes 4 $(mean(b_exp_4_acc))")
println("Testing accuracy bayes 4 $(mean(b_exp_4_pre))")

b_exp_5_tls = bayes_exp_5[:, 2]
b_exp_5_acc = bayes_exp_5[:, 3]
b_exp_5_vls = bayes_exp_5[:, 4]
b_exp_5_pre = bayes_exp_5[:, 5]
println("Training accuracy bayes 5 $(mean(b_exp_5_acc))")
println("Testing accuracy bayes 5 $(mean(b_exp_5_pre))")



cnn_exp_1 = load_pickle("$(folder)freq_exp_data_1.pkl")
cnn_exp_2 = load_pickle("$(folder)freq_exp_data_2.pkl")
cnn_exp_3 = load_pickle("$(folder)freq_exp_data_3.pkl")
cnn_exp_4 = load_pickle("$(folder)freq_exp_data_4.pkl")
cnn_exp_5 = load_pickle("$(folder)freq_exp_data_5.pkl")

cnn_all_mean_tloss =
    (1 / 5) * (
        cnn_exp_1[:, 2] +
        cnn_exp_2[:, 2] +
        cnn_exp_3[:, 2] +
        cnn_exp_4[:, 2] +
        cnn_exp_5[:, 2]
    )

cnn_all_mean_iloss =
    (1 / 5) * (
        cnn_exp_1[:, 4] +
        cnn_exp_2[:, 4] +
        cnn_exp_3[:, 4] +
        cnn_exp_4[:, 4] +
        cnn_exp_5[:, 4]
    )

cnn_all_mean_acc =
    (1 / 5) * (
        cnn_exp_1[:, 3] +
        cnn_exp_2[:, 3] +
        cnn_exp_3[:, 3] +
        cnn_exp_4[:, 3] +
        cnn_exp_5[:, 3]
    )

cnn_all_mean_pre =
    (1 / 5) * (
        cnn_exp_1[:, 5] +
        cnn_exp_2[:, 5] +
        cnn_exp_3[:, 5] +
        cnn_exp_4[:, 5] +
        cnn_exp_5[:, 5]
    )

f_exp_1_tls = cnn_exp_1[:, 2]
f_exp_1_acc = cnn_exp_1[:, 3]
f_exp_1_vls = cnn_exp_1[:, 4]
f_exp_1_pre = cnn_exp_1[:, 5]
println("Training accuracy freq 1 $(mean(f_exp_1_acc))")
println("Testing accuracy freq 1 $(mean(f_exp_1_pre))")

f_exp_2_tls = cnn_exp_2[:, 2]
f_exp_2_acc = cnn_exp_2[:, 3]
f_exp_2_vls = cnn_exp_2[:, 4]
f_exp_2_pre = cnn_exp_2[:, 5]
println("Training accuracy freq 2 $(mean(f_exp_2_acc))")
println("Testing accuracy freq 2 $(mean(f_exp_2_pre))")

f_exp_3_tls = cnn_exp_3[:, 2]
f_exp_3_acc = cnn_exp_3[:, 3]
f_exp_3_vls = cnn_exp_3[:, 4]
f_exp_3_pre = cnn_exp_3[:, 5]
println("Training accuracy freq 3 $(mean(f_exp_3_acc))")
println("Testing accuracy freq 3 $(mean(f_exp_3_pre))")

f_exp_4_tls = cnn_exp_4[:, 2]
f_exp_4_acc = cnn_exp_4[:, 3]
f_exp_4_vls = cnn_exp_4[:, 4]
f_exp_4_pre = cnn_exp_4[:, 5]
println("Training accuracy freq 4 $(mean(f_exp_4_acc))")
println("Testing accuracy freq 4 $(mean(f_exp_4_pre))")

f_exp_5_tls = cnn_exp_5[:, 2]
f_exp_5_acc = cnn_exp_5[:, 3]
f_exp_5_vls = cnn_exp_5[:, 4]
f_exp_5_pre = cnn_exp_5[:, 5]
println("Training accuracy freq 5 $(mean(f_exp_5_acc))")
println("Testing accuracy freq 5 $(mean(f_exp_5_pre))")



en_plot = plot(
    [
        scatter(
            y = f_exp_1_acc,
            name = "LeNet 1",
            marker = attr(color = "rgb(211,120,000)"),
        ),
        scatter(
            y = f_exp_2_acc,
            name = "LeNet 2",
            marker = attr(color = "rgb(255,170,017)"),
        ),
        scatter(
            y = f_exp_3_acc,
            name = "LeNet 3",
            marker = attr(color = "rgb(255,187,034)"),
        ),
        scatter(
            y = f_exp_4_acc,
            name = "LeNet 4",
            marker = attr(color = "rgb(255,204,051)"),
        ),
        scatter(
            y = f_exp_5_acc,
            name = "LeNet 5",
            marker = attr(color = "rgb(255,221,068)"),
        ),
        scatter(
            y = b_exp_1_acc,
            name = "BCNN 1",
            marker = attr(color = "rgb(055,033,240)"),
        ),
        scatter(
            y = b_exp_2_acc,
            name = "BCNN 2",
            marker = attr(color = "rgb(033,081,240)"),
        ),
        scatter(
            y = b_exp_3_acc,
            name = "BCNN 3",
            marker = attr(color = "rgb(033,115,240)"),
        ),
        scatter(
            y = b_exp_4_acc,
            name = "BCNN 4",
            marker = attr(color = "rgb(151,177,255)"),
        ),
        scatter(
            y = b_exp_5_acc,
            name = "BCNN 5",
            marker = attr(color = "rgb(051,215,255)"),
        ),
    ],
    Layout(
        mode = "lines",
        opacity = 0.4,
        xaxis_tickangle = -45,
        yaxis_title_text = "Accuracy",
        xaxis_title_text = "Epoch";
        yaxis_range = [0, 1],
    ),
)
savefig(en_plot, "cifar_100_tacc.png")


en_plot = plot(
    [
        scatter(
            y = f_exp_1_pre,
            name = "LeNet 1",
            marker = attr(color = "rgb(211,120,000)"),
        ),
        scatter(
            y = f_exp_2_pre,
            name = "LeNet 2",
            marker = attr(color = "rgb(255,170,017)"),
        ),
        scatter(
            y = f_exp_3_pre,
            name = "LeNet 3",
            marker = attr(color = "rgb(255,187,034)"),
        ),
        scatter(
            y = f_exp_4_pre,
            name = "LeNet 4",
            marker = attr(color = "rgb(255,204,051)"),
        ),
        scatter(
            y = f_exp_5_pre,
            name = "LeNet 5",
            marker = attr(color = "rgb(255,221,068)"),
        ),
        scatter(
            y = b_exp_1_pre,
            name = "BCNN 1",
            marker = attr(color = "rgb(055,033,240)"),
        ),
        scatter(
            y = b_exp_2_pre,
            name = "BCNN 2",
            marker = attr(color = "rgb(033,081,240)"),
        ),
        scatter(
            y = b_exp_3_pre,
            name = "BCNN 3",
            marker = attr(color = "rgb(033,115,240)"),
        ),
        scatter(
            y = b_exp_4_pre,
            name = "BCNN 4",
            marker = attr(color = "rgb(151,177,255)"),
        ),
        scatter(
            y = b_exp_5_pre,
            name = "BCNN 5",
            marker = attr(color = "rgb(051,215,255)"),
        ),
    ],
    Layout(
        mode = "lines",
        opacity = 0.4,
        xaxis_tickangle = -45,
        yaxis_title_text = "Accuracy",
        xaxis_title_text = "Epoch";
        yaxis_range = [0, 1],
    ),
)
savefig(en_plot, "cifar_100_tpre.png")