Coursera Machine LearningをPythonで実装 - [Week4]ニューラルネットワーク(1) [3]ニューラルネットワーク、自分で実装

ex3_nn.py

import numpy as np
import matplotlib.pyplot as plt

from scipy.io import loadmat

# データの読み込み
def load_data1():
    data = loadmat("ex3data1")
    # yが元データだと5000x1の行列なので、ベクトルに変換する
    return np.array(data['X']), np.ravel(np.array(data['y']))

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

# 計算済みの係数を読み込み
def load_weights():
    data = loadmat("ex3weights")
    return np.array(data['Theta1']), np.array(data['Theta2'])

Theta1, Theta2 = load_weights()

# Forward propagation
def predict(Theta1, Theta2, X):
    XX = np.c_[np.ones(X.shape[0]), X]
    Z2 = 1 / (1 + np.exp(-np.dot(XX, Theta1.T)))
    XX = np.c_[np.ones(Z2.shape[0]), Z2]
    Z3 = 1 / (1 + np.exp(-np.dot(XX, Theta2.T)))
    return np.argmax(Z3, axis=1)+1

# 精度
pred = predict(Theta1, Theta2, X_data)
print("Training Set Accuracy: ", np.mean(pred == y) * 100)