Coursera Machine LearningをPythonで実装 - [Week5]ニューラルネットワーク(2) [2]組み込み

ex4_builtin.py

import numpy as np
import matplotlib.pyplot as plt

from scipy.io import loadmat
from sklearn.neural_network import MLPClassifier

# データの読み込み
def load_data1():
    data = loadmat("ex4data1")
    return np.array(data['X']), np.ravel(np.array(data['y']))

X_data, y = load_data1()
m = len(X_data[:, 1])

## ネットワークの可視化
def display_data(X, alpha, accuracy):
    fig = plt.figure(figsize = (5, 5))
    fig.subplots_adjust(hspace=0.05, wspace=0.05)
    for i in range(X.shape[0]):
        ax = fig.add_subplot(5, 5, i+1, xticks=[], yticks=[])
        ax.imshow(X[i, :].reshape(20, 20, order="F"), cmap="gray")
    plt.suptitle(f"Alpha = {alpha}, Accuracy = {accuracy}")
    plt.show()

# 正則化の値を変えてみる
alphas = [0.0001, 1, 50, 1e10]
for a in alphas:
    print("Alpha =", a)
    clf = MLPClassifier(hidden_layer_sizes=(26, ), solver="adam", random_state=114514, max_iter=500, alpha=a)
    clf.fit(X_data, y)
    accuracy = clf.score(X_data, y)*100
    print("Training Set Accuracy:" , accuracy)
    print()
    display_data(clf.coefs_[0][:, 1:].T, a, np.round(accuracy, 3))