@@ -0,0 +1,1420 @@
+#!/usr/bin/env python%0A%0Aimport argparse%0A%0Aimport pandas as pd%0Aimport matplotlib.pyplot as plt%0A%0A%0Adef learning_curve(csv_file):%0A    df = pd.read_csv(csv_file)%0A    df_train  = df.query(%22type == 'train'%22)%0A    df_val = df.query(%22type == 'val'%22)%0A%0A    plt.figure()%0A%0A    # train loss%0A    plt.subplot(221)%0A    plt.semilogy(df_train.i_iter, df_train.loss, 'o', markersize=1, color='r',%0A                 alpha=.5, label='train loss')%0A    plt.title('train loss')%0A    plt.xlabel('iteration')%0A    plt.ylabel('loss')%0A%0A    # val loss%0A    plt.subplot(222)%0A    plt.semilogy(df_val.i_iter, df_val.loss, 'o-', color='r',%0A                 alpha=.5, label='val loss')%0A    plt.title('val loss')%0A    plt.xlabel('iteration')%0A    plt.ylabel('loss')%0A%0A    # train accuracy%0A    plt.subplot(223)%0A    plt.plot(df_train.i_iter, df_train.accuracy, 'o', markersize=1, color='g',%0A             alpha=.5, label='train accuracy')%0A    plt.title('train accuracy')%0A    plt.xlabel('iteration')%0A    plt.ylabel('accuracy')%0A%0A    # val accuracy%0A    plt.subplot(224)%0A    plt.plot(df_val.i_iter, df_val.accuracy, 'o-', color='g',%0A             alpha=.5, label='val accuracy')%0A    plt.title('val accuracy')%0A    plt.xlabel('iteration')%0A    plt.ylabel('accuracy')%0A%0A    plt.show()%0A%0A%0Adef main():%0A    parser = argparse.ArgumentParser()%0A    parser.add_argument('csv_file')%0A    args = parser.parse_args()%0A    learning_curve(args.csv_file)%0A%0A%0Aif __name__ == '__main__':%0A    main()%0A

Add script to draw learning curve