CIFAR-10

CIFAR10_SVM_conv1.py

import numpy as np
import matplotlib.pyplot as plt
from keras.datasets import cifar10
from sklearn.svm import LinearSVC
import time

def convolution_filter(img):
    # 縦方向のフィルター
    prewitt_v = np.array([[-1,0,1], [-2,0,2], [-1,0,1]])
    # 横方向のフィルター
    prewitt_h = np.array([[-1,-2,-1], [0,0,0], [1,2,1]])
    # カーネルサイズ
    kernel_size = 3
    # 出力画像
    out_img = np.zeros((img.shape[0]-kernel_size+1, img.shape[1]-kernel_size+1, 3))

    for i in range(kernel_size-1, img.shape[0]-kernel_size+1):
        for j in range(kernel_size-1, img.shape[1]-kernel_size+1):
            # スライス
            img_slice = img[(i-2):(i+1), (j-2):(j+1), 0:3]
            # 畳み込み
            conv_v = np.sum(img_slice * prewitt_v, axis=(1,2))
            conv_h = np.sum(img_slice * prewitt_h, axis=(1,2))
            # 代入
            out_img[i, j, :] = np.sqrt(conv_v**2 + conv_h**2)
    return out_img

start_time = time.time()
# データの読み込み
(x_train_origin, y_train), (x_test_origin, y_test) = cifar10.load_data()
# Infを出さないように255で割る
x_train_origin = x_train_origin / 255
x_test_origin = x_test_origin / 255
# データ数
m_train, m_test = x_train_origin.shape[0], x_test_origin.shape[0]
# 畳み込み変換用
x_train = np.zeros((m_train, x_train_origin.shape[1]-2, x_train_origin.shape[2]-2, 3))
x_test = np.zeros((m_test, x_test_origin.shape[1]-2, x_test_origin.shape[2]-2, 3))
# 畳み込み
for i in range(m_train):
    x_train[i, :, :, :] = convolution_filter(x_train_origin[i, :, :, :])
    x_train[i, :, :, :] = x_train[i, :, :, :] / np.max(x_train[i, :, :, :], axis=(0,1))
for i in range(m_test):
    x_test[i, :, :, :] = convolution_filter(x_test_origin[i, :, :, :])
    x_test[i, :, :, :] = x_test[i, :, :, :] / np.max(x_test[i, :, :, :], axis=(0,1))
# ベクトル化
x_train, x_test = x_train.reshape(m_train, -1), x_test.reshape(m_test, -1)
# ノルムで標準化
x_train = x_train / np.linalg.norm(x_train, ord=2, axis=1, keepdims=True)
x_test = x_test / np.linalg.norm(x_test, ord=2, axis=1, keepdims=True)

# サポートベクトルマシン
svc = LinearSVC()
svc.fit(x_train, y_train)
print("Elapsed[s] : ", time.time() - start_time)
print("Train :", svc.score(x_train, y_train))
print("Test :", svc.score(x_test, y_test))

# デフォルト
#Elapsed[s] :  1282.1308102607727
#Train : 0.26964
#Test : 0.2153