plot_results.py

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

if __name__ == "__main__":
    # plot EEG
    EEGNet_dict = {
        'elu': 'elu_5e-3_amsgrad',
        'relu': 'relu_1e-3',
        'leaky_relu': 'leaky_relu_1e-2_init_amsgrad'
    }

    plt.figure()
    plt.rcParams["font.family"] = "serif"
    X = np.linspace(1, 500, 500, endpoint=True)

    for act in EEGNet_dict:
        name = EEGNet_dict[act]
        filename = "./results/EEGNet/EEGNet_" + name + '-train_acc.csv'
        data = pd.read_csv(filename)
        acc = data['Value']
        acc *= 100
        plt.plot(X, acc, label=act + '_train')

        filename = "./results/EEGNet/EEGNet_" + name + '-eval_acc.csv'
        data = pd.read_csv(filename)
        acc = data['Value']
        acc *= 100
        plt.plot(X, acc, label=act + '_test')

    plt.xlabel("Epoch")
    plt.ylabel("Accuracy(%)")
    plt.legend()
    plt.title("Activation function comparison(EEGNet)")
    plt.savefig("./results/EEGNet/EEGNet_acc.jpg")

    # plot DeepConvNet
    DeepConvNet_dict = {
        'elu': 'elu_1e-3_amsgrad',
        'relu': 'relu_1e-2',
        'leaky_relu': 'leaky_relu_1e-2_init_amsgrad'
    }

    plt.figure()
    plt.rcParams["font.family"] = "serif"
    X = np.linspace(1, 500, 500, endpoint=True)

    for act in DeepConvNet_dict:
        name = DeepConvNet_dict[act]
        filename = "./results/DeepConvNet/DeepConvNet_" + name + '-train_acc.csv'
        data = pd.read_csv(filename)
        acc = data['Value']
        acc *= 100
        plt.plot(X, acc, label=act + '_train')

        filename = "./results/DeepConvNet/DeepConvNet_" + name + '-eval_acc.csv'
        data = pd.read_csv(filename)
        acc = data['Value']
        acc *= 100
        plt.plot(X, acc, label=act + '_test')

    plt.xlabel("Epoch")
    plt.ylabel("Accuracy(%)")
    plt.legend()
    plt.title("Activation function comparison(DeepConvNet)")
    plt.savefig("./results/DeepConvNet/DeepConvNet_acc.jpg")