BatchNorm with Fashion-MNIST

BatchNorm_FMINIST_2.py

import numpy as np
import matplotlib.pyplot as plt
import pickle
import time
from keras.datasets import fashion_mnist
from keras.models import Model
from keras.layers import Input, Activation, Conv2D, BatchNormalization, Flatten, Dense
from keras.optimizers import Adam
from keras.utils import to_categorical

(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()
# 標準化
X_train, X_test = X_train / 255.0, X_test / 255.0
# ランク4にする
X_train, X_test = X_train[:, :, :, np.newaxis], X_test[:, :, :, np.newaxis]
# One-hotベクトル化
y_train, y_test = to_categorical(y_train), to_categorical(y_test)

# モデルの作成
def create_model(nb_layers, use_bn, bn_freq=1):
    # 入力層
    input = Input(shape=(28, 28, 1))
    x = input
    # 隠れ層
    for i in range(nb_layers):
        x = Conv2D(filters=2*(i+1), kernel_size=(3, 3))(x)
        if use_bn and ((i+1)%bn_freq==0):
            x = BatchNormalization()(x)
        x = Activation("relu")(x)
    # FC
    x = Flatten()(x)
    # 出力層
    y = Dense(10, activation="softmax")(x)
    # モデル
    model = Model(inputs=input, outputs=y)
    return model


# 定数
nb_epoch = 20
# 結果の保存用
result = []
times = []
# レイヤー数6、BN有りの計7
for i in range(7):
    print(f"★★ i = {i}★★")
    # モデルの取得
    model = create_model(6, True, i+1)
    # コンパイル
    model.compile(optimizer=Adam(), loss="categorical_crossentropy", metrics=["accuracy"])
    # フィット
    start_time = time.time()
    history = model.fit(X_train, y_train, batch_size=64,
                        epochs=nb_epoch, validation_data=(X_test, y_test)).history
    elapsed = time.time() - start_time
    # 結果に追加
    result.append(history)
    times.append(elapsed)
# 保存
with open("result_fq.dat", "wb") as fp:
    pickle.dump(result, fp)
with open("times_fq.dat", "wb") as fp:
    pickle.dump(times, fp)