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@koshian2
Created May 29, 2018 21:18
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CIFAR-10
import numpy as np
import matplotlib.pyplot as plt
from keras.datasets import cifar10
from sklearn.linear_model import LogisticRegression
import time
start_time = time.time()
# データの読み込み
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
# 小数化
x_train = x_train / 255
x_test = x_test / 255
# データ数
m_train, m_test = x_train.shape[0], x_test.shape[0]
# 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)
# ロジスティック回帰
logr = LogisticRegression()
logr.fit(x_train, y_train)
print("Elapsed[s] : ", time.time() - start_time)
print("Train :", logr.score(x_train, y_train))
print("Test :", logr.score(x_test, y_test))
#Elapsed[s] : 329.31931829452515
#Train : 0.41996
#Test : 0.4072
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