234 lines
7.1 KiB
Julia
234 lines
7.1 KiB
Julia
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include("aux_func.jl")
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using Statistics
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using PlotlyJS
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using PlotlyJS: savefig
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folder = "early_stop_res/"
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bayes_exp_1 = load_pickle("$(folder)bayes_exp_data_1.pkl")
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bayes_exp_2 = load_pickle("$(folder)bayes_exp_data_2.pkl")
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bayes_exp_3 = load_pickle("$(folder)bayes_exp_data_3.pkl")
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bayes_exp_4 = load_pickle("$(folder)bayes_exp_data_4.pkl")
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bayes_exp_5 = load_pickle("$(folder)bayes_exp_data_5.pkl")
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b_exp_1_tls = bayes_exp_1[:, 2]
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b_exp_1_acc = bayes_exp_1[:, 3]
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b_exp_1_vls = bayes_exp_1[:, 4]
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b_exp_1_pre = bayes_exp_1[:, 5]
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println("Training accuracy bayes 1 $(mean(b_exp_1_acc))")
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println("Testing accuracy bayes 1 $(mean(b_exp_1_pre))")
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b_exp_2_tls = bayes_exp_2[:, 2]
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b_exp_2_acc = bayes_exp_2[:, 3]
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b_exp_2_vls = bayes_exp_2[:, 4]
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b_exp_2_pre = bayes_exp_2[:, 5]
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println("Training accuracy bayes 2 $(mean(b_exp_2_acc))")
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println("Testing accuracy bayes 2 $(mean(b_exp_2_pre))")
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b_exp_3_tls = bayes_exp_3[:, 2]
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b_exp_3_acc = bayes_exp_3[:, 3]
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b_exp_3_vls = bayes_exp_3[:, 4]
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b_exp_3_pre = bayes_exp_3[:, 5]
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println("Training accuracy bayes 3 $(mean(b_exp_3_acc))")
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println("Testing accuracy bayes 3 $(mean(b_exp_3_pre))")
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b_exp_4_tls = bayes_exp_4[:, 2]
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b_exp_4_acc = bayes_exp_4[:, 3]
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b_exp_4_vls = bayes_exp_4[:, 4]
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b_exp_4_pre = bayes_exp_4[:, 5]
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println("Training accuracy bayes 4 $(mean(b_exp_4_acc))")
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println("Testing accuracy bayes 4 $(mean(b_exp_4_pre))")
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b_exp_5_tls = bayes_exp_5[:, 2]
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b_exp_5_acc = bayes_exp_5[:, 3]
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b_exp_5_vls = bayes_exp_5[:, 4]
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b_exp_5_pre = bayes_exp_5[:, 5]
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println("Training accuracy bayes 5 $(mean(b_exp_5_acc))")
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println("Testing accuracy bayes 5 $(mean(b_exp_5_pre))")
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println("Amount of epochs to reach bound Bayesian 1x: $(size(bayes_exp_1)[1])")
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println("Amount of epochs to reach bound Bayesian 2x: $(size(bayes_exp_2)[1])")
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println("Amount of epochs to reach bound Bayesian 3x: $(size(bayes_exp_3)[1])")
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println("Amount of epochs to reach bound Bayesian 4x: $(size(bayes_exp_4)[1])")
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println("Amount of epochs to reach bound Bayesian 5x: $(size(bayes_exp_5)[1])")
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cnn_exp_1 = load_pickle("$(folder)freq_exp_data_1.pkl")
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cnn_exp_2 = load_pickle("$(folder)freq_exp_data_2.pkl")
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cnn_exp_3 = load_pickle("$(folder)freq_exp_data_3.pkl")
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cnn_exp_4 = load_pickle("$(folder)freq_exp_data_4.pkl")
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cnn_exp_5 = load_pickle("$(folder)freq_exp_data_5.pkl")
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f_exp_1_tls = cnn_exp_1[:, 2]
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f_exp_1_acc = cnn_exp_1[:, 3]
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f_exp_1_vls = cnn_exp_1[:, 4]
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f_exp_1_pre = cnn_exp_1[:, 5]
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println("Training accuracy freq 1 $(mean(f_exp_1_acc))")
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println("Testing accuracy freq 1 $(mean(f_exp_1_pre))")
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f_exp_2_tls = cnn_exp_2[:, 2]
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f_exp_2_acc = cnn_exp_2[:, 3]
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f_exp_2_vls = cnn_exp_2[:, 4]
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f_exp_2_pre = cnn_exp_2[:, 5]
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println("Training accuracy freq 2 $(mean(f_exp_2_acc))")
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println("Testing accuracy freq 2 $(mean(f_exp_2_pre))")
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f_exp_3_tls = cnn_exp_3[:, 2]
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f_exp_3_acc = cnn_exp_3[:, 3]
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f_exp_3_vls = cnn_exp_3[:, 4]
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f_exp_3_pre = cnn_exp_3[:, 5]
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println("Training accuracy freq 3 $(mean(f_exp_3_acc))")
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println("Testing accuracy freq 3 $(mean(f_exp_3_pre))")
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f_exp_4_tls = cnn_exp_4[:, 2]
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f_exp_4_acc = cnn_exp_4[:, 3]
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f_exp_4_vls = cnn_exp_4[:, 4]
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f_exp_4_pre = cnn_exp_4[:, 5]
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println("Training accuracy freq 4 $(mean(f_exp_4_acc))")
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println("Testing accuracy freq 4 $(mean(f_exp_4_pre))")
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f_exp_5_tls = cnn_exp_5[:, 2]
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f_exp_5_acc = cnn_exp_5[:, 3]
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f_exp_5_vls = cnn_exp_5[:, 4]
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f_exp_5_pre = cnn_exp_5[:, 5]
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println("Training accuracy freq 5 $(mean(f_exp_5_acc))")
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println("Testing accuracy freq 5 $(mean(f_exp_5_pre))")
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println("Amount of epochs to reach bound LeNet 1x: $(size(cnn_exp_1)[1])")
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println("Amount of epochs to reach bound LeNet 2x: $(size(cnn_exp_2)[1])")
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println("Amount of epochs to reach bound LeNet 3x: $(size(cnn_exp_3)[1])")
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println("Amount of epochs to reach bound LeNet 4x: $(size(cnn_exp_4)[1])")
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println("Amount of epochs to reach bound LeNet 5x: $(size(cnn_exp_5)[1])")
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en_plot = plot(
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[
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scatter(
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y = f_exp_1_acc,
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name = "LeNet 1",
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marker = attr(color = "rgb(211,120,000)"),
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),
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scatter(
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y = f_exp_2_acc,
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name = "LeNet 2",
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marker = attr(color = "rgb(255,170,017)"),
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),
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scatter(
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y = f_exp_3_acc,
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name = "LeNet 3",
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marker = attr(color = "rgb(255,187,034)"),
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),
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scatter(
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y = f_exp_4_acc,
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name = "LeNet 4",
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marker = attr(color = "rgb(255,204,051)"),
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),
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scatter(
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y = f_exp_5_acc,
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name = "LeNet 5",
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marker = attr(color = "rgb(255,221,068)"),
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),
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scatter(
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y = b_exp_1_acc,
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name = "BCNN 1",
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marker = attr(color = "rgb(055,033,240)"),
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),
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scatter(
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y = b_exp_2_acc,
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name = "BCNN 2",
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marker = attr(color = "rgb(033,081,240)"),
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),
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scatter(
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y = b_exp_3_acc,
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name = "BCNN 3",
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marker = attr(color = "rgb(033,115,240)"),
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),
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scatter(
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y = b_exp_4_acc,
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name = "BCNN 4",
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marker = attr(color = "rgb(151,177,255)"),
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),
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scatter(
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y = b_exp_5_acc,
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name = "BCNN 5",
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marker = attr(color = "rgb(051,215,255)"),
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),
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],
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Layout(
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mode = "lines",
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opacity = 0.4,
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xaxis_tickangle = -45,
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yaxis_title_text = "Accuracy",
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xaxis_title_text = "Epoch";
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yaxis_range = [0, 1],
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),
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)
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savefig(en_plot, "mnist_es_tacc.png")
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en_plot = plot(
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[
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scatter(
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y = f_exp_1_pre,
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name = "LeNet 1",
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marker = attr(color = "rgb(211,120,000)"),
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),
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scatter(
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y = f_exp_2_pre,
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name = "LeNet 2",
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marker = attr(color = "rgb(255,170,017)"),
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),
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scatter(
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y = f_exp_3_pre,
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name = "LeNet 3",
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marker = attr(color = "rgb(255,187,034)"),
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),
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scatter(
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y = f_exp_4_pre,
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name = "LeNet 4",
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marker = attr(color = "rgb(255,204,051)"),
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),
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scatter(
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y = f_exp_5_pre,
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name = "LeNet 5",
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marker = attr(color = "rgb(255,221,068)"),
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),
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scatter(
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y = b_exp_1_pre,
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name = "BCNN 1",
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marker = attr(color = "rgb(055,033,240)"),
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),
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scatter(
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y = b_exp_2_pre,
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name = "BCNN 2",
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marker = attr(color = "rgb(033,081,240)"),
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),
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scatter(
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y = b_exp_3_pre,
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name = "BCNN 3",
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marker = attr(color = "rgb(033,115,240)"),
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),
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scatter(
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y = b_exp_4_pre,
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name = "BCNN 4",
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marker = attr(color = "rgb(151,177,255)"),
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),
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scatter(
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y = b_exp_5_pre,
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name = "BCNN 5",
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marker = attr(color = "rgb(051,215,255)"),
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),
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],
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Layout(
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mode = "lines",
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opacity = 0.4,
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xaxis_tickangle = -45,
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yaxis_title_text = "Accuracy",
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xaxis_title_text = "Epoch";
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yaxis_range = [0, 1],
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),
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)
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savefig(en_plot, "mnist_es_tpre.png")
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