clear all

clc

format long

model1     = readmatrix("Model_1/run-.-tag-loss.csv");

model2     = readmatrix("Model_2/run-.-tag-loss.csv");

model3     = readmatrix("Model_3/run-.-tag-loss.csv");

model4     = readmatrix("Model_4/run-.-tag-loss.csv");

model5     = readmatrix("Model_5/run-.-tag-loss.csv");

model6     = readmatrix("Model_6/run-.-tag-loss.csv");

model7     = readmatrix("Model_7/run-.-tag-loss.csv");

model8     = readmatrix("Model_8/run-.-tag-loss.csv");

model9     = readmatrix("Model_9/run-.-tag-loss.csv");

model10    = readmatrix("Model_10/run-.-tag-loss.csv");

model11    = readmatrix("Model_11/run-.-tag-loss.csv");

model12    = readmatrix("Model_12/run-.-tag-loss.csv");

model1_x_axis  = model1(:, 2);

model1_y_axis  = model1(:, 3);

model2_x_axis  = model2(:, 2);

model2_y_axis  = model2(:, 3);

model3_x_axis  = model3(:, 2);

model3_y_axis  = model3(:, 3);

model4_x_axis  = model4(:, 2);

model4_y_axis  = model4(:, 3);

model5_x_axis  = model5(:, 2);

model5_y_axis  = model5(:, 3);

model6_x_axis  = model6(:, 2);

model6_y_axis  = model6(:, 3);

model7_x_axis  = model7(:, 2);

model7_y_axis  = model7(:, 3);

model8_x_axis  = model8(:, 2);

model8_y_axis  = model8(:, 3);

model9_x_axis  = model9(:, 2);

model9_y_axis  = model9(:, 3);

model10_x_axis = model10(:, 2);

model10_y_axis = model10(:, 3);

model11_x_axis = model11(:, 2);

model11_y_axis = model11(:, 3);

model12_x_axis = model12(:, 2);

model12_y_axis = model12(:, 3);

color=[1 0 0; 0 1 0; 0 0 1; 0.5 1 1; 

       1 1 0.5; 1 0.5 1; 0 0 0.5; 0.5 0 0;

       0 0.5 0; 1 0.5 0.5; 0.5 1 0.5; 0.5 0.5 1;

       1 1 0;0 1 1;1 0 1];

% Draw the Images

figure(1)

plot(model1_x_axis,  model1_y_axis,  'linewidth', 1.2, 'color', color(8, :));

hold on

plot(model2_x_axis,  model2_y_axis,  'linewidth', 1.2, 'color', color(2, :));

hold on

plot(model3_x_axis,  model3_y_axis,  'linewidth', 1.2, 'color', color(3, :));

hold on

plot(model4_x_axis,  model4_y_axis,  'linewidth', 1.2, 'color', color(4, :));

hold on 

plot(model5_x_axis,  model5_y_axis,  'linewidth', 1.2, 'color', color(5, :));

hold on

plot(model6_x_axis,  model6_y_axis,  'linewidth', 1.2, 'color', color(6, :));

hold on

plot(model7_x_axis,  model7_y_axis,  'linewidth', 1.2, 'color', color(7, :));

hold on

plot(model8_x_axis,  model8_y_axis,  'linewidth', 1.2, 'color', color(1, :));

hold on

plot(model9_x_axis,  model9_y_axis,  'linewidth', 1.2, 'color', color(9, :));

hold on

plot(model10_x_axis, model10_y_axis, 'linewidth', 1.2, 'color', color(10, :));

hold on

plot(model11_x_axis, model11_y_axis, 'linewidth', 1.2, 'color', 'k');

hold on

plot(model12_x_axis, model12_y_axis, 'linewidth', 1.2, 'color', color(12, :));

hold on

grid on

xlim([0, 310])

title({'Loss w.r.t. RNN-based Models'}, 'FontName', 'Times New Roman', 'FontSize', 16, 'FontWeight', 'bold')

xlabel('Iterations')

ylabel('Loss Value')

set(gca, 'FontName', 'Times New Roman', 'FontSize', 16, 'FontWeight', 'bold');

legend('RNN', 'BiRNN', 'RNN with Attention', 'BiRNN with Attention', ...

       'LSTM', 'BiLSTM', 'LSTM with Attention', 'BiLSTM with Attention', ...

       'GRU', 'BiGRU', 'GRU with Attention', 'BiGRU with Attention', ...

       'location', 'EastOutside', 'FontName', 'Times New Roman', 'FontSize', 16)

legend('boxoff')

print('Loss_RNN_basedModels', '-dpng',  '-r600')