Keras 2 : examples : 時系列 – ゼロからの時系列分類 (翻訳/解説)

翻訳 : (株)クラスキャット セールスインフォメーション
作成日時 : 06/18/2022 (keras 2.9.0)

* 本ページは、Keras の以下のドキュメントを翻訳した上で適宜、補足説明したものです：

• Code examples : Timeseries : Timeseries classification from scratch (Author: hfawaz)

* サンプルコードの動作確認はしておりますが、必要な場合には適宜、追加改変しています。
* ご自由にリンクを張って頂いてかまいませんが、sales-info@classcat.com までご一報いただけると嬉しいです。

 

 

 

Keras 2 : examples : 時系列 – ゼロからの時系列分類

Description : UCR/UEA アーカイブからの FordA データセットで時系列分類器をゼロから訓練します。

 

イントロダクション

このサンプルはディスクの raw CSV 時系列ファイルからはじめて、時系列分類をゼロから行う方法を示します。UCR/UEA アーカイブからの FordA データセットでワークフローを実演します。

 

セットアップ

from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt

 

データのロード : FordA データセット

データセットの説明

ここで使用しているデータセットは FordA と呼ばれるものです。データは UCR アーカイブに由来します。データセットは 3601 訓練インスタンスと別の 1320 テストインスタンスを含みます。各時系列はモーターセンサーにより捕捉されたエンジンのノイズの測定に対応しています。このタスクについて、目標はエンジンの特定の問題の存在を自動的に検出することです。この問題はバランスの取れた二値分類タスクです。このデータセットの完全な記述は ここ で見つかります。

 

TSV データを読む

訓練には FordA_TRAIN ファイルをそしてテストには FordA_TEST ファイルを使用します。このデータセットの単純さは時系列分類のために ConvNet を使用する方法を効果的に実演することを可能にします。このファイルでは、最初のカラムがラベルに対応しています。

def readucr(filename):
    data = np.loadtxt(filename, delimiter="\t")
    y = data[:, 0]
    x = data[:, 1:]
    return x, y.astype(int)


root_url = "https://raw.githubusercontent.com/hfawaz/cd-diagram/master/FordA/"

x_train, y_train = readucr(root_url + "FordA_TRAIN.tsv")
x_test, y_test = readucr(root_url + "FordA_TEST.tsv")

 

データの可視化

ここではデータセットの各々のクラスに対する一つの時系列サンプルを可視化します。

classes = np.unique(np.concatenate((y_train, y_test), axis=0))

plt.figure()
for c in classes:
    c_x_train = x_train[y_train == c]
    plt.plot(c_x_train[0], label="class " + str(c))
plt.legend(loc="best")
plt.show()
plt.close()

 

データの標準化

私達の時系列は既に単一の長さ (500) にあります。けれども、それらの値は通常は様々な範囲にあります。これはニューラルネットワークに対しては理想的ではありません ; 一般には入力値を正規化するように努めるべきです。この特定のデータセットについては、データは既に z-正規化されています : 各時系列サンプルはゼロに等しい平均と 1 に等しい標準偏差を持ちます。このタイプの正規化は時系列分類問題に対して非常に一般的です、Bagnall et al. (2016) を見てください。

ここで使用される時系列は単変量であることに注意してください、つまり時系列サンプル毎に 1 つのチャネルだけを持ちます。そのため numpy による単純な reshape で、時系列を 1 つのチャネルの多変量のものに変換します。これは多変量時系列に容易に適用可能なモデルを構築することを可能にします。

x_train = x_train.reshape((x_train.shape[0], x_train.shape[1], 1))
x_test = x_test.reshape((x_test.shape[0], x_test.shape[1], 1))

最後に、sparse_categorical_crossentropy を使用するために、事前にクラス数を数えておかなければなりません。

num_classes = len(np.unique(y_train))

そして訓練セットをシャッフルします、後で訓練のときに validation_split オプションを使用していくからです。

idx = np.random.permutation(len(x_train))
x_train = x_train[idx]
y_train = y_train[idx]

ラベルを正の整数に標準化します。すると想定されるラベルは 0 か 1 になります。

y_train[y_train == -1] = 0
y_test[y_test == -1] = 0

 

モデルの構築

元々は この論文 で提案された Fully 畳み込みニューラルネットワークを構築します。実装は ここ で提供されている TF 2 バージョンに基づいています。以下のハイパーパラメータ (kernel_size, filters, BatchNorm の使用) は KerasTuner を使用した random search により探索されました。

def make_model(input_shape):
    input_layer = keras.layers.Input(input_shape)

    conv1 = keras.layers.Conv1D(filters=64, kernel_size=3, padding="same")(input_layer)
    conv1 = keras.layers.BatchNormalization()(conv1)
    conv1 = keras.layers.ReLU()(conv1)

    conv2 = keras.layers.Conv1D(filters=64, kernel_size=3, padding="same")(conv1)
    conv2 = keras.layers.BatchNormalization()(conv2)
    conv2 = keras.layers.ReLU()(conv2)

    conv3 = keras.layers.Conv1D(filters=64, kernel_size=3, padding="same")(conv2)
    conv3 = keras.layers.BatchNormalization()(conv3)
    conv3 = keras.layers.ReLU()(conv3)

    gap = keras.layers.GlobalAveragePooling1D()(conv3)

    output_layer = keras.layers.Dense(num_classes, activation="softmax")(gap)

    return keras.models.Model(inputs=input_layer, outputs=output_layer)


model = make_model(input_shape=x_train.shape[1:])
keras.utils.plot_model(model, show_shapes=True)

 

モデルの訓練

epochs = 500
batch_size = 32

callbacks = [
    keras.callbacks.ModelCheckpoint(
        "best_model.h5", save_best_only=True, monitor="val_loss"
    ),
    keras.callbacks.ReduceLROnPlateau(
        monitor="val_loss", factor=0.5, patience=20, min_lr=0.0001
    ),
    keras.callbacks.EarlyStopping(monitor="val_loss", patience=50, verbose=1),
]
model.compile(
    optimizer="adam",
    loss="sparse_categorical_crossentropy",
    metrics=["sparse_categorical_accuracy"],
)
history = model.fit(
    x_train,
    y_train,
    batch_size=batch_size,
    epochs=epochs,
    callbacks=callbacks,
    validation_split=0.2,
    verbose=1,
)

Epoch 1/500
90/90 [==============================] - 1s 8ms/step - loss: 0.5531 - sparse_categorical_accuracy: 0.7017 - val_loss: 0.7335 - val_sparse_categorical_accuracy: 0.4882
Epoch 2/500
90/90 [==============================] - 1s 6ms/step - loss: 0.4520 - sparse_categorical_accuracy: 0.7729 - val_loss: 0.7446 - val_sparse_categorical_accuracy: 0.4882
Epoch 3/500
90/90 [==============================] - 1s 6ms/step - loss: 0.4404 - sparse_categorical_accuracy: 0.7733 - val_loss: 0.7706 - val_sparse_categorical_accuracy: 0.4882
Epoch 4/500
90/90 [==============================] - 1s 6ms/step - loss: 0.4234 - sparse_categorical_accuracy: 0.7899 - val_loss: 0.9741 - val_sparse_categorical_accuracy: 0.4882
Epoch 5/500
90/90 [==============================] - 1s 6ms/step - loss: 0.4180 - sparse_categorical_accuracy: 0.7972 - val_loss: 0.6679 - val_sparse_categorical_accuracy: 0.5936
Epoch 6/500
90/90 [==============================] - 1s 6ms/step - loss: 0.3988 - sparse_categorical_accuracy: 0.8066 - val_loss: 0.5399 - val_sparse_categorical_accuracy: 0.6990
Epoch 7/500
90/90 [==============================] - 1s 6ms/step - loss: 0.4012 - sparse_categorical_accuracy: 0.8024 - val_loss: 0.4051 - val_sparse_categorical_accuracy: 0.8225
Epoch 8/500
90/90 [==============================] - 1s 6ms/step - loss: 0.3903 - sparse_categorical_accuracy: 0.8080 - val_loss: 0.9671 - val_sparse_categorical_accuracy: 0.5340
Epoch 9/500
90/90 [==============================] - 1s 6ms/step - loss: 0.3948 - sparse_categorical_accuracy: 0.7986 - val_loss: 0.5778 - val_sparse_categorical_accuracy: 0.6436
Epoch 10/500
90/90 [==============================] - 1s 6ms/step - loss: 0.3731 - sparse_categorical_accuracy: 0.8260 - val_loss: 0.4307 - val_sparse_categorical_accuracy: 0.7698
Epoch 11/500
90/90 [==============================] - 1s 6ms/step - loss: 0.3645 - sparse_categorical_accuracy: 0.8260 - val_loss: 0.4010 - val_sparse_categorical_accuracy: 0.7698
Epoch 12/500
90/90 [==============================] - 1s 6ms/step - loss: 0.3666 - sparse_categorical_accuracy: 0.8247 - val_loss: 0.3574 - val_sparse_categorical_accuracy: 0.8350
Epoch 13/500
90/90 [==============================] - 1s 6ms/step - loss: 0.3618 - sparse_categorical_accuracy: 0.8271 - val_loss: 0.3942 - val_sparse_categorical_accuracy: 0.8044
Epoch 14/500
90/90 [==============================] - 1s 6ms/step - loss: 0.3619 - sparse_categorical_accuracy: 0.8257 - val_loss: 0.4104 - val_sparse_categorical_accuracy: 0.7906
Epoch 15/500
90/90 [==============================] - 1s 6ms/step - loss: 0.3353 - sparse_categorical_accuracy: 0.8521 - val_loss: 0.3819 - val_sparse_categorical_accuracy: 0.7684
Epoch 16/500
90/90 [==============================] - 1s 6ms/step - loss: 0.3287 - sparse_categorical_accuracy: 0.8514 - val_loss: 0.3776 - val_sparse_categorical_accuracy: 0.8252
Epoch 17/500
90/90 [==============================] - 1s 6ms/step - loss: 0.3299 - sparse_categorical_accuracy: 0.8545 - val_loss: 0.3555 - val_sparse_categorical_accuracy: 0.8350
Epoch 18/500
90/90 [==============================] - 1s 6ms/step - loss: 0.3206 - sparse_categorical_accuracy: 0.8601 - val_loss: 0.4051 - val_sparse_categorical_accuracy: 0.7906
Epoch 19/500
90/90 [==============================] - 1s 6ms/step - loss: 0.3125 - sparse_categorical_accuracy: 0.8608 - val_loss: 0.3792 - val_sparse_categorical_accuracy: 0.8114
Epoch 20/500
90/90 [==============================] - 1s 6ms/step - loss: 0.3052 - sparse_categorical_accuracy: 0.8750 - val_loss: 0.3448 - val_sparse_categorical_accuracy: 0.8377
Epoch 21/500
90/90 [==============================] - 1s 6ms/step - loss: 0.3023 - sparse_categorical_accuracy: 0.8736 - val_loss: 0.3325 - val_sparse_categorical_accuracy: 0.8363
Epoch 22/500
90/90 [==============================] - 1s 6ms/step - loss: 0.2955 - sparse_categorical_accuracy: 0.8736 - val_loss: 0.3447 - val_sparse_categorical_accuracy: 0.8225
Epoch 23/500
90/90 [==============================] - 1s 6ms/step - loss: 0.2934 - sparse_categorical_accuracy: 0.8788 - val_loss: 0.2943 - val_sparse_categorical_accuracy: 0.8779
Epoch 24/500
90/90 [==============================] - 1s 6ms/step - loss: 0.2972 - sparse_categorical_accuracy: 0.8715 - val_loss: 0.4946 - val_sparse_categorical_accuracy: 0.7462
Epoch 25/500
90/90 [==============================] - 1s 6ms/step - loss: 0.2800 - sparse_categorical_accuracy: 0.8865 - val_loss: 0.2860 - val_sparse_categorical_accuracy: 0.8821
Epoch 26/500
90/90 [==============================] - 1s 6ms/step - loss: 0.2752 - sparse_categorical_accuracy: 0.8847 - val_loss: 0.2924 - val_sparse_categorical_accuracy: 0.8655
Epoch 27/500
90/90 [==============================] - 1s 6ms/step - loss: 0.2769 - sparse_categorical_accuracy: 0.8847 - val_loss: 0.6254 - val_sparse_categorical_accuracy: 0.6879
Epoch 28/500
90/90 [==============================] - 1s 6ms/step - loss: 0.2821 - sparse_categorical_accuracy: 0.8799 - val_loss: 0.2764 - val_sparse_categorical_accuracy: 0.8821
Epoch 29/500
90/90 [==============================] - 1s 6ms/step - loss: 0.2713 - sparse_categorical_accuracy: 0.8892 - val_loss: 0.7015 - val_sparse_categorical_accuracy: 0.6422
Epoch 30/500
90/90 [==============================] - 1s 6ms/step - loss: 0.2633 - sparse_categorical_accuracy: 0.8885 - val_loss: 0.8508 - val_sparse_categorical_accuracy: 0.7254
Epoch 31/500
90/90 [==============================] - 1s 6ms/step - loss: 0.2673 - sparse_categorical_accuracy: 0.8896 - val_loss: 0.4354 - val_sparse_categorical_accuracy: 0.7725
Epoch 32/500
90/90 [==============================] - 1s 6ms/step - loss: 0.2518 - sparse_categorical_accuracy: 0.8997 - val_loss: 0.9172 - val_sparse_categorical_accuracy: 0.6394
Epoch 33/500
90/90 [==============================] - 1s 6ms/step - loss: 0.2484 - sparse_categorical_accuracy: 0.9024 - val_loss: 0.5055 - val_sparse_categorical_accuracy: 0.7531
Epoch 34/500
90/90 [==============================] - 1s 6ms/step - loss: 0.2352 - sparse_categorical_accuracy: 0.9059 - val_loss: 0.6289 - val_sparse_categorical_accuracy: 0.7115
Epoch 35/500
90/90 [==============================] - 1s 6ms/step - loss: 0.2389 - sparse_categorical_accuracy: 0.9104 - val_loss: 0.2776 - val_sparse_categorical_accuracy: 0.8946
Epoch 36/500
90/90 [==============================] - 1s 6ms/step - loss: 0.2218 - sparse_categorical_accuracy: 0.9122 - val_loss: 1.3105 - val_sparse_categorical_accuracy: 0.6408
Epoch 37/500
90/90 [==============================] - 1s 6ms/step - loss: 0.2237 - sparse_categorical_accuracy: 0.9125 - val_loss: 0.4860 - val_sparse_categorical_accuracy: 0.7628
Epoch 38/500
90/90 [==============================] - 1s 6ms/step - loss: 0.2008 - sparse_categorical_accuracy: 0.9281 - val_loss: 0.5553 - val_sparse_categorical_accuracy: 0.7226
Epoch 39/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1999 - sparse_categorical_accuracy: 0.9233 - val_loss: 0.4511 - val_sparse_categorical_accuracy: 0.8058
Epoch 40/500
90/90 [==============================] - 0s 6ms/step - loss: 0.1857 - sparse_categorical_accuracy: 0.9330 - val_loss: 0.2912 - val_sparse_categorical_accuracy: 0.8516
Epoch 41/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1736 - sparse_categorical_accuracy: 0.9399 - val_loss: 0.9930 - val_sparse_categorical_accuracy: 0.5506
Epoch 42/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1649 - sparse_categorical_accuracy: 0.9396 - val_loss: 0.5852 - val_sparse_categorical_accuracy: 0.7198
Epoch 43/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1501 - sparse_categorical_accuracy: 0.9538 - val_loss: 0.1911 - val_sparse_categorical_accuracy: 0.9168
Epoch 44/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1512 - sparse_categorical_accuracy: 0.9455 - val_loss: 0.8169 - val_sparse_categorical_accuracy: 0.6130
Epoch 45/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1358 - sparse_categorical_accuracy: 0.9552 - val_loss: 0.4748 - val_sparse_categorical_accuracy: 0.7795
Epoch 46/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1401 - sparse_categorical_accuracy: 0.9535 - val_loss: 1.7678 - val_sparse_categorical_accuracy: 0.5881
Epoch 47/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1444 - sparse_categorical_accuracy: 0.9545 - val_loss: 1.7005 - val_sparse_categorical_accuracy: 0.5950
Epoch 48/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1320 - sparse_categorical_accuracy: 0.9542 - val_loss: 0.1550 - val_sparse_categorical_accuracy: 0.9431
Epoch 49/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1333 - sparse_categorical_accuracy: 0.9576 - val_loss: 0.1665 - val_sparse_categorical_accuracy: 0.9362
Epoch 50/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1367 - sparse_categorical_accuracy: 0.9549 - val_loss: 0.4227 - val_sparse_categorical_accuracy: 0.8308
Epoch 51/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1391 - sparse_categorical_accuracy: 0.9503 - val_loss: 0.1729 - val_sparse_categorical_accuracy: 0.9390
Epoch 52/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1237 - sparse_categorical_accuracy: 0.9573 - val_loss: 0.1338 - val_sparse_categorical_accuracy: 0.9487
Epoch 53/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1397 - sparse_categorical_accuracy: 0.9531 - val_loss: 0.1667 - val_sparse_categorical_accuracy: 0.9487
Epoch 54/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1205 - sparse_categorical_accuracy: 0.9601 - val_loss: 0.2904 - val_sparse_categorical_accuracy: 0.8821
Epoch 55/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1302 - sparse_categorical_accuracy: 0.9538 - val_loss: 0.9437 - val_sparse_categorical_accuracy: 0.7060
Epoch 56/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1241 - sparse_categorical_accuracy: 0.9580 - val_loss: 0.1346 - val_sparse_categorical_accuracy: 0.9501
Epoch 57/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1158 - sparse_categorical_accuracy: 0.9646 - val_loss: 0.9489 - val_sparse_categorical_accuracy: 0.6907
Epoch 58/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1175 - sparse_categorical_accuracy: 0.9573 - val_loss: 0.6089 - val_sparse_categorical_accuracy: 0.7212
Epoch 59/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1160 - sparse_categorical_accuracy: 0.9611 - val_loss: 0.1294 - val_sparse_categorical_accuracy: 0.9487
Epoch 60/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1096 - sparse_categorical_accuracy: 0.9642 - val_loss: 0.1527 - val_sparse_categorical_accuracy: 0.9417
Epoch 61/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1163 - sparse_categorical_accuracy: 0.9611 - val_loss: 0.5554 - val_sparse_categorical_accuracy: 0.7684
Epoch 62/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1090 - sparse_categorical_accuracy: 0.9656 - val_loss: 0.2433 - val_sparse_categorical_accuracy: 0.8904
Epoch 63/500
90/90 [==============================] - 0s 6ms/step - loss: 0.1105 - sparse_categorical_accuracy: 0.9656 - val_loss: 0.3426 - val_sparse_categorical_accuracy: 0.8571
Epoch 64/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1058 - sparse_categorical_accuracy: 0.9667 - val_loss: 2.1389 - val_sparse_categorical_accuracy: 0.5520
Epoch 65/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1037 - sparse_categorical_accuracy: 0.9674 - val_loss: 0.3875 - val_sparse_categorical_accuracy: 0.8738
Epoch 66/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1135 - sparse_categorical_accuracy: 0.9622 - val_loss: 0.1783 - val_sparse_categorical_accuracy: 0.9459
Epoch 67/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1006 - sparse_categorical_accuracy: 0.9681 - val_loss: 0.1462 - val_sparse_categorical_accuracy: 0.9515
Epoch 68/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0994 - sparse_categorical_accuracy: 0.9684 - val_loss: 0.1140 - val_sparse_categorical_accuracy: 0.9584
Epoch 69/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1095 - sparse_categorical_accuracy: 0.9635 - val_loss: 1.6500 - val_sparse_categorical_accuracy: 0.5589
Epoch 70/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1118 - sparse_categorical_accuracy: 0.9628 - val_loss: 1.3355 - val_sparse_categorical_accuracy: 0.6768
Epoch 71/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1155 - sparse_categorical_accuracy: 0.9608 - val_loss: 0.3167 - val_sparse_categorical_accuracy: 0.8793
Epoch 72/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1041 - sparse_categorical_accuracy: 0.9677 - val_loss: 0.1329 - val_sparse_categorical_accuracy: 0.9417
Epoch 73/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1001 - sparse_categorical_accuracy: 0.9677 - val_loss: 0.1385 - val_sparse_categorical_accuracy: 0.9417
Epoch 74/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0997 - sparse_categorical_accuracy: 0.9642 - val_loss: 0.1369 - val_sparse_categorical_accuracy: 0.9473
Epoch 75/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1051 - sparse_categorical_accuracy: 0.9667 - val_loss: 0.5135 - val_sparse_categorical_accuracy: 0.7781
Epoch 76/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0945 - sparse_categorical_accuracy: 0.9688 - val_loss: 0.1440 - val_sparse_categorical_accuracy: 0.9556
Epoch 77/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1081 - sparse_categorical_accuracy: 0.9618 - val_loss: 0.2210 - val_sparse_categorical_accuracy: 0.9196
Epoch 78/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1109 - sparse_categorical_accuracy: 0.9618 - val_loss: 0.2181 - val_sparse_categorical_accuracy: 0.9196
Epoch 79/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1047 - sparse_categorical_accuracy: 0.9608 - val_loss: 0.2074 - val_sparse_categorical_accuracy: 0.9237
Epoch 80/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1035 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.3792 - val_sparse_categorical_accuracy: 0.8571
Epoch 81/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1040 - sparse_categorical_accuracy: 0.9674 - val_loss: 0.7353 - val_sparse_categorical_accuracy: 0.7420
Epoch 82/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1106 - sparse_categorical_accuracy: 0.9649 - val_loss: 0.2948 - val_sparse_categorical_accuracy: 0.9140
Epoch 83/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1066 - sparse_categorical_accuracy: 0.9656 - val_loss: 0.1338 - val_sparse_categorical_accuracy: 0.9570
Epoch 84/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0988 - sparse_categorical_accuracy: 0.9691 - val_loss: 0.1095 - val_sparse_categorical_accuracy: 0.9570
Epoch 85/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1065 - sparse_categorical_accuracy: 0.9622 - val_loss: 0.1717 - val_sparse_categorical_accuracy: 0.9417
Epoch 86/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1087 - sparse_categorical_accuracy: 0.9660 - val_loss: 0.1206 - val_sparse_categorical_accuracy: 0.9570
Epoch 87/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0991 - sparse_categorical_accuracy: 0.9656 - val_loss: 0.4285 - val_sparse_categorical_accuracy: 0.8474
Epoch 88/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0984 - sparse_categorical_accuracy: 0.9667 - val_loss: 0.1589 - val_sparse_categorical_accuracy: 0.9334
Epoch 89/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1023 - sparse_categorical_accuracy: 0.9701 - val_loss: 1.5442 - val_sparse_categorical_accuracy: 0.6782
Epoch 90/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0995 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.1211 - val_sparse_categorical_accuracy: 0.9528
Epoch 91/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0908 - sparse_categorical_accuracy: 0.9705 - val_loss: 0.0987 - val_sparse_categorical_accuracy: 0.9556
Epoch 92/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0919 - sparse_categorical_accuracy: 0.9677 - val_loss: 0.2109 - val_sparse_categorical_accuracy: 0.9140
Epoch 93/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0890 - sparse_categorical_accuracy: 0.9715 - val_loss: 0.1509 - val_sparse_categorical_accuracy: 0.9431
Epoch 94/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0958 - sparse_categorical_accuracy: 0.9694 - val_loss: 0.1761 - val_sparse_categorical_accuracy: 0.9417
Epoch 95/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1000 - sparse_categorical_accuracy: 0.9663 - val_loss: 0.1466 - val_sparse_categorical_accuracy: 0.9293
Epoch 96/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0913 - sparse_categorical_accuracy: 0.9698 - val_loss: 0.6963 - val_sparse_categorical_accuracy: 0.7725
Epoch 97/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0954 - sparse_categorical_accuracy: 0.9667 - val_loss: 0.3042 - val_sparse_categorical_accuracy: 0.8738
Epoch 98/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0866 - sparse_categorical_accuracy: 0.9722 - val_loss: 0.1115 - val_sparse_categorical_accuracy: 0.9584
Epoch 99/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1017 - sparse_categorical_accuracy: 0.9615 - val_loss: 0.1195 - val_sparse_categorical_accuracy: 0.9584
Epoch 100/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1012 - sparse_categorical_accuracy: 0.9677 - val_loss: 0.1975 - val_sparse_categorical_accuracy: 0.9196
Epoch 101/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1058 - sparse_categorical_accuracy: 0.9622 - val_loss: 0.1960 - val_sparse_categorical_accuracy: 0.9487
Epoch 102/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0914 - sparse_categorical_accuracy: 0.9705 - val_loss: 0.1086 - val_sparse_categorical_accuracy: 0.9598
Epoch 103/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0907 - sparse_categorical_accuracy: 0.9701 - val_loss: 0.1117 - val_sparse_categorical_accuracy: 0.9584
Epoch 104/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0959 - sparse_categorical_accuracy: 0.9674 - val_loss: 3.9192 - val_sparse_categorical_accuracy: 0.4993
Epoch 105/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0991 - sparse_categorical_accuracy: 0.9632 - val_loss: 0.1232 - val_sparse_categorical_accuracy: 0.9473
Epoch 106/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0953 - sparse_categorical_accuracy: 0.9653 - val_loss: 0.1328 - val_sparse_categorical_accuracy: 0.9584
Epoch 107/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0835 - sparse_categorical_accuracy: 0.9750 - val_loss: 0.1480 - val_sparse_categorical_accuracy: 0.9542
Epoch 108/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0865 - sparse_categorical_accuracy: 0.9701 - val_loss: 0.1095 - val_sparse_categorical_accuracy: 0.9598
Epoch 109/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0940 - sparse_categorical_accuracy: 0.9681 - val_loss: 3.4316 - val_sparse_categorical_accuracy: 0.6422
Epoch 110/500
90/90 [==============================] - 1s 6ms/step - loss: 0.1015 - sparse_categorical_accuracy: 0.9632 - val_loss: 4.1126 - val_sparse_categorical_accuracy: 0.4965
Epoch 111/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0882 - sparse_categorical_accuracy: 0.9698 - val_loss: 0.1968 - val_sparse_categorical_accuracy: 0.9390
Epoch 112/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0778 - sparse_categorical_accuracy: 0.9764 - val_loss: 0.1051 - val_sparse_categorical_accuracy: 0.9584
Epoch 113/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0784 - sparse_categorical_accuracy: 0.9743 - val_loss: 0.1120 - val_sparse_categorical_accuracy: 0.9612
Epoch 114/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0765 - sparse_categorical_accuracy: 0.9792 - val_loss: 0.1347 - val_sparse_categorical_accuracy: 0.9556
Epoch 115/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0771 - sparse_categorical_accuracy: 0.9736 - val_loss: 0.1268 - val_sparse_categorical_accuracy: 0.9556
Epoch 116/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0787 - sparse_categorical_accuracy: 0.9743 - val_loss: 0.1014 - val_sparse_categorical_accuracy: 0.9626
Epoch 117/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0802 - sparse_categorical_accuracy: 0.9726 - val_loss: 0.0995 - val_sparse_categorical_accuracy: 0.9695
Epoch 118/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0770 - sparse_categorical_accuracy: 0.9774 - val_loss: 0.1022 - val_sparse_categorical_accuracy: 0.9598
Epoch 119/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0758 - sparse_categorical_accuracy: 0.9764 - val_loss: 0.2318 - val_sparse_categorical_accuracy: 0.9098
Epoch 120/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0751 - sparse_categorical_accuracy: 0.9750 - val_loss: 0.3361 - val_sparse_categorical_accuracy: 0.8793
Epoch 121/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0708 - sparse_categorical_accuracy: 0.9792 - val_loss: 0.1739 - val_sparse_categorical_accuracy: 0.9362
Epoch 122/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0764 - sparse_categorical_accuracy: 0.9753 - val_loss: 0.1351 - val_sparse_categorical_accuracy: 0.9556
Epoch 123/500
90/90 [==============================] - 0s 6ms/step - loss: 0.0724 - sparse_categorical_accuracy: 0.9750 - val_loss: 0.1064 - val_sparse_categorical_accuracy: 0.9556
Epoch 124/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0788 - sparse_categorical_accuracy: 0.9736 - val_loss: 0.1159 - val_sparse_categorical_accuracy: 0.9598
Epoch 125/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0806 - sparse_categorical_accuracy: 0.9719 - val_loss: 0.1268 - val_sparse_categorical_accuracy: 0.9612
Epoch 126/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0755 - sparse_categorical_accuracy: 0.9753 - val_loss: 0.1175 - val_sparse_categorical_accuracy: 0.9528
Epoch 127/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0741 - sparse_categorical_accuracy: 0.9757 - val_loss: 0.1049 - val_sparse_categorical_accuracy: 0.9612
Epoch 128/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0720 - sparse_categorical_accuracy: 0.9767 - val_loss: 0.1756 - val_sparse_categorical_accuracy: 0.9376
Epoch 129/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0734 - sparse_categorical_accuracy: 0.9757 - val_loss: 0.1165 - val_sparse_categorical_accuracy: 0.9639
Epoch 130/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0743 - sparse_categorical_accuracy: 0.9778 - val_loss: 0.1398 - val_sparse_categorical_accuracy: 0.9417
Epoch 131/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0764 - sparse_categorical_accuracy: 0.9726 - val_loss: 0.1193 - val_sparse_categorical_accuracy: 0.9459
Epoch 132/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0741 - sparse_categorical_accuracy: 0.9747 - val_loss: 0.1661 - val_sparse_categorical_accuracy: 0.9473
Epoch 133/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0677 - sparse_categorical_accuracy: 0.9792 - val_loss: 0.1016 - val_sparse_categorical_accuracy: 0.9612
Epoch 134/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0673 - sparse_categorical_accuracy: 0.9778 - val_loss: 0.1049 - val_sparse_categorical_accuracy: 0.9584
Epoch 135/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0681 - sparse_categorical_accuracy: 0.9802 - val_loss: 0.1109 - val_sparse_categorical_accuracy: 0.9515
Epoch 136/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0673 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1198 - val_sparse_categorical_accuracy: 0.9542
Epoch 137/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0679 - sparse_categorical_accuracy: 0.9767 - val_loss: 0.1130 - val_sparse_categorical_accuracy: 0.9528
Epoch 138/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0717 - sparse_categorical_accuracy: 0.9774 - val_loss: 0.1009 - val_sparse_categorical_accuracy: 0.9612
Epoch 139/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0657 - sparse_categorical_accuracy: 0.9771 - val_loss: 0.1046 - val_sparse_categorical_accuracy: 0.9528
Epoch 140/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0711 - sparse_categorical_accuracy: 0.9767 - val_loss: 0.0977 - val_sparse_categorical_accuracy: 0.9639
Epoch 141/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0719 - sparse_categorical_accuracy: 0.9774 - val_loss: 0.1071 - val_sparse_categorical_accuracy: 0.9612
Epoch 142/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0663 - sparse_categorical_accuracy: 0.9826 - val_loss: 0.1027 - val_sparse_categorical_accuracy: 0.9612
Epoch 143/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0699 - sparse_categorical_accuracy: 0.9781 - val_loss: 0.1131 - val_sparse_categorical_accuracy: 0.9626
Epoch 144/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0670 - sparse_categorical_accuracy: 0.9771 - val_loss: 0.1025 - val_sparse_categorical_accuracy: 0.9626
Epoch 145/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0653 - sparse_categorical_accuracy: 0.9785 - val_loss: 0.0935 - val_sparse_categorical_accuracy: 0.9653
Epoch 146/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0616 - sparse_categorical_accuracy: 0.9812 - val_loss: 0.1075 - val_sparse_categorical_accuracy: 0.9556
Epoch 147/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0643 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.0960 - val_sparse_categorical_accuracy: 0.9584
Epoch 148/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0681 - sparse_categorical_accuracy: 0.9792 - val_loss: 0.0944 - val_sparse_categorical_accuracy: 0.9639
Epoch 149/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0661 - sparse_categorical_accuracy: 0.9792 - val_loss: 0.1311 - val_sparse_categorical_accuracy: 0.9501
Epoch 150/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0693 - sparse_categorical_accuracy: 0.9781 - val_loss: 0.1715 - val_sparse_categorical_accuracy: 0.9390
Epoch 151/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0658 - sparse_categorical_accuracy: 0.9802 - val_loss: 0.1010 - val_sparse_categorical_accuracy: 0.9612
Epoch 152/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0652 - sparse_categorical_accuracy: 0.9778 - val_loss: 0.0949 - val_sparse_categorical_accuracy: 0.9639
Epoch 153/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0640 - sparse_categorical_accuracy: 0.9812 - val_loss: 0.0996 - val_sparse_categorical_accuracy: 0.9598
Epoch 154/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0659 - sparse_categorical_accuracy: 0.9785 - val_loss: 0.0980 - val_sparse_categorical_accuracy: 0.9612
Epoch 155/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0666 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1490 - val_sparse_categorical_accuracy: 0.9501
Epoch 156/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0659 - sparse_categorical_accuracy: 0.9774 - val_loss: 0.1010 - val_sparse_categorical_accuracy: 0.9570
Epoch 157/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0650 - sparse_categorical_accuracy: 0.9792 - val_loss: 0.1040 - val_sparse_categorical_accuracy: 0.9570
Epoch 158/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0626 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.0965 - val_sparse_categorical_accuracy: 0.9612
Epoch 159/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0645 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1010 - val_sparse_categorical_accuracy: 0.9570
Epoch 160/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0691 - sparse_categorical_accuracy: 0.9774 - val_loss: 0.0987 - val_sparse_categorical_accuracy: 0.9626
Epoch 161/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0615 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.0936 - val_sparse_categorical_accuracy: 0.9612
Epoch 162/500
90/90 [==============================] - 0s 6ms/step - loss: 0.0625 - sparse_categorical_accuracy: 0.9792 - val_loss: 0.1129 - val_sparse_categorical_accuracy: 0.9626
Epoch 163/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0601 - sparse_categorical_accuracy: 0.9802 - val_loss: 0.0989 - val_sparse_categorical_accuracy: 0.9584
Epoch 164/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0624 - sparse_categorical_accuracy: 0.9812 - val_loss: 0.1512 - val_sparse_categorical_accuracy: 0.9515
Epoch 165/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0641 - sparse_categorical_accuracy: 0.9778 - val_loss: 0.0986 - val_sparse_categorical_accuracy: 0.9584
Epoch 166/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0558 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.0979 - val_sparse_categorical_accuracy: 0.9598
Epoch 167/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0607 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.1085 - val_sparse_categorical_accuracy: 0.9626
Epoch 168/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0585 - sparse_categorical_accuracy: 0.9812 - val_loss: 0.0976 - val_sparse_categorical_accuracy: 0.9639
Epoch 169/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0599 - sparse_categorical_accuracy: 0.9826 - val_loss: 0.1078 - val_sparse_categorical_accuracy: 0.9626
Epoch 170/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0608 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.0951 - val_sparse_categorical_accuracy: 0.9626
Epoch 171/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0612 - sparse_categorical_accuracy: 0.9812 - val_loss: 0.1004 - val_sparse_categorical_accuracy: 0.9612
Epoch 172/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0622 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.0949 - val_sparse_categorical_accuracy: 0.9653
Epoch 173/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0622 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.0923 - val_sparse_categorical_accuracy: 0.9639
Epoch 174/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0600 - sparse_categorical_accuracy: 0.9802 - val_loss: 0.1019 - val_sparse_categorical_accuracy: 0.9639
Epoch 175/500
90/90 [==============================] - 0s 6ms/step - loss: 0.0591 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1238 - val_sparse_categorical_accuracy: 0.9626
Epoch 176/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0588 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.0917 - val_sparse_categorical_accuracy: 0.9639
Epoch 177/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0598 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.1138 - val_sparse_categorical_accuracy: 0.9626
Epoch 178/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0566 - sparse_categorical_accuracy: 0.9826 - val_loss: 0.0938 - val_sparse_categorical_accuracy: 0.9639
Epoch 179/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0634 - sparse_categorical_accuracy: 0.9809 - val_loss: 0.0966 - val_sparse_categorical_accuracy: 0.9639
Epoch 180/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0579 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.1033 - val_sparse_categorical_accuracy: 0.9653
Epoch 181/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0601 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.0937 - val_sparse_categorical_accuracy: 0.9626
Epoch 182/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0545 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.0979 - val_sparse_categorical_accuracy: 0.9626
Epoch 183/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0569 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.0987 - val_sparse_categorical_accuracy: 0.9626
Epoch 184/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0569 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.0907 - val_sparse_categorical_accuracy: 0.9626
Epoch 185/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0579 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.0918 - val_sparse_categorical_accuracy: 0.9626
Epoch 186/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0571 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.0933 - val_sparse_categorical_accuracy: 0.9626
Epoch 187/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0577 - sparse_categorical_accuracy: 0.9826 - val_loss: 0.0933 - val_sparse_categorical_accuracy: 0.9626
Epoch 188/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0634 - sparse_categorical_accuracy: 0.9809 - val_loss: 0.1014 - val_sparse_categorical_accuracy: 0.9667
Epoch 189/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0582 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.0906 - val_sparse_categorical_accuracy: 0.9639
Epoch 190/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0571 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.0931 - val_sparse_categorical_accuracy: 0.9626
Epoch 191/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0602 - sparse_categorical_accuracy: 0.9812 - val_loss: 0.0903 - val_sparse_categorical_accuracy: 0.9626
Epoch 192/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0581 - sparse_categorical_accuracy: 0.9809 - val_loss: 0.0915 - val_sparse_categorical_accuracy: 0.9639
Epoch 193/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0574 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.0914 - val_sparse_categorical_accuracy: 0.9639
Epoch 194/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0530 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.0941 - val_sparse_categorical_accuracy: 0.9626
Epoch 195/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0557 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.0925 - val_sparse_categorical_accuracy: 0.9653
Epoch 196/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0576 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.1018 - val_sparse_categorical_accuracy: 0.9639
Epoch 197/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0562 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1003 - val_sparse_categorical_accuracy: 0.9626
Epoch 198/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0582 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.0917 - val_sparse_categorical_accuracy: 0.9612
Epoch 199/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0602 - sparse_categorical_accuracy: 0.9809 - val_loss: 0.1001 - val_sparse_categorical_accuracy: 0.9667
Epoch 200/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0580 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.0927 - val_sparse_categorical_accuracy: 0.9584
Epoch 201/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0573 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.1226 - val_sparse_categorical_accuracy: 0.9612
Epoch 202/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0581 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.0941 - val_sparse_categorical_accuracy: 0.9612
Epoch 203/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0602 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.0933 - val_sparse_categorical_accuracy: 0.9639
Epoch 204/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0539 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.0956 - val_sparse_categorical_accuracy: 0.9626
Epoch 205/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0561 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.0947 - val_sparse_categorical_accuracy: 0.9639
Epoch 206/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0604 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.1132 - val_sparse_categorical_accuracy: 0.9639
Epoch 207/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0564 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0930 - val_sparse_categorical_accuracy: 0.9653
Epoch 208/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0615 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.0941 - val_sparse_categorical_accuracy: 0.9626
Epoch 209/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0555 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.0900 - val_sparse_categorical_accuracy: 0.9626
Epoch 210/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0589 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0936 - val_sparse_categorical_accuracy: 0.9612
Epoch 211/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0615 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.0947 - val_sparse_categorical_accuracy: 0.9626
Epoch 212/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0599 - sparse_categorical_accuracy: 0.9799 - val_loss: 0.0943 - val_sparse_categorical_accuracy: 0.9612
Epoch 213/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0599 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.0908 - val_sparse_categorical_accuracy: 0.9653
Epoch 214/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0548 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.1143 - val_sparse_categorical_accuracy: 0.9639
Epoch 215/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0526 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.0965 - val_sparse_categorical_accuracy: 0.9626
Epoch 216/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0588 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.0958 - val_sparse_categorical_accuracy: 0.9639
Epoch 217/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0549 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.0942 - val_sparse_categorical_accuracy: 0.9612
Epoch 218/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0513 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.1027 - val_sparse_categorical_accuracy: 0.9612
Epoch 219/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0555 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1217 - val_sparse_categorical_accuracy: 0.9598
Epoch 220/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0572 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.0933 - val_sparse_categorical_accuracy: 0.9653
Epoch 221/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0545 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.0959 - val_sparse_categorical_accuracy: 0.9653
Epoch 222/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0545 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.1163 - val_sparse_categorical_accuracy: 0.9639
Epoch 223/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0556 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.0955 - val_sparse_categorical_accuracy: 0.9626
Epoch 224/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0566 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.0931 - val_sparse_categorical_accuracy: 0.9598
Epoch 225/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0543 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0915 - val_sparse_categorical_accuracy: 0.9667
Epoch 226/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0566 - sparse_categorical_accuracy: 0.9826 - val_loss: 0.0931 - val_sparse_categorical_accuracy: 0.9626
Epoch 227/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0528 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.0984 - val_sparse_categorical_accuracy: 0.9639
Epoch 228/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0576 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.1019 - val_sparse_categorical_accuracy: 0.9639
Epoch 229/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0572 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.0908 - val_sparse_categorical_accuracy: 0.9639
Epoch 230/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0543 - sparse_categorical_accuracy: 0.9826 - val_loss: 0.0923 - val_sparse_categorical_accuracy: 0.9639
Epoch 231/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0566 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.0960 - val_sparse_categorical_accuracy: 0.9626
Epoch 232/500
90/90 [==============================] - 0s 6ms/step - loss: 0.0539 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.0954 - val_sparse_categorical_accuracy: 0.9653
Epoch 233/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0536 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.0965 - val_sparse_categorical_accuracy: 0.9626
Epoch 234/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0512 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0945 - val_sparse_categorical_accuracy: 0.9639
Epoch 235/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0528 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0925 - val_sparse_categorical_accuracy: 0.9639
Epoch 236/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0497 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.0974 - val_sparse_categorical_accuracy: 0.9626
Epoch 237/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0529 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0957 - val_sparse_categorical_accuracy: 0.9612
Epoch 238/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0552 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.0961 - val_sparse_categorical_accuracy: 0.9626
Epoch 239/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0573 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.0943 - val_sparse_categorical_accuracy: 0.9598
Epoch 240/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0558 - sparse_categorical_accuracy: 0.9812 - val_loss: 0.0935 - val_sparse_categorical_accuracy: 0.9639
Epoch 241/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0526 - sparse_categorical_accuracy: 0.9826 - val_loss: 0.0958 - val_sparse_categorical_accuracy: 0.9626
Epoch 242/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0488 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.0976 - val_sparse_categorical_accuracy: 0.9626
Epoch 243/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0499 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0935 - val_sparse_categorical_accuracy: 0.9626
Epoch 244/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0505 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.0945 - val_sparse_categorical_accuracy: 0.9639
Epoch 245/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0483 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.0952 - val_sparse_categorical_accuracy: 0.9584
Epoch 246/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0524 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.0958 - val_sparse_categorical_accuracy: 0.9653
Epoch 247/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0507 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0934 - val_sparse_categorical_accuracy: 0.9653
Epoch 248/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0553 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.0946 - val_sparse_categorical_accuracy: 0.9598
Epoch 249/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0577 - sparse_categorical_accuracy: 0.9809 - val_loss: 0.0979 - val_sparse_categorical_accuracy: 0.9612
Epoch 250/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0535 - sparse_categorical_accuracy: 0.9826 - val_loss: 0.0979 - val_sparse_categorical_accuracy: 0.9626
Epoch 251/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0509 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.0937 - val_sparse_categorical_accuracy: 0.9626
Epoch 252/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0571 - sparse_categorical_accuracy: 0.9826 - val_loss: 0.0937 - val_sparse_categorical_accuracy: 0.9612
Epoch 253/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0525 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.1017 - val_sparse_categorical_accuracy: 0.9639
Epoch 254/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0551 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0930 - val_sparse_categorical_accuracy: 0.9639
Epoch 255/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0557 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.0896 - val_sparse_categorical_accuracy: 0.9653
Epoch 256/500
90/90 [==============================] - 0s 6ms/step - loss: 0.0494 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0908 - val_sparse_categorical_accuracy: 0.9612
Epoch 257/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0492 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.0953 - val_sparse_categorical_accuracy: 0.9612
Epoch 258/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0525 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0923 - val_sparse_categorical_accuracy: 0.9626
Epoch 259/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0514 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.0937 - val_sparse_categorical_accuracy: 0.9612
Epoch 260/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0511 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0934 - val_sparse_categorical_accuracy: 0.9612
Epoch 261/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0510 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.0914 - val_sparse_categorical_accuracy: 0.9639
Epoch 262/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0498 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0957 - val_sparse_categorical_accuracy: 0.9653
Epoch 263/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0543 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.0956 - val_sparse_categorical_accuracy: 0.9653
Epoch 264/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0564 - sparse_categorical_accuracy: 0.9812 - val_loss: 0.0917 - val_sparse_categorical_accuracy: 0.9598
Epoch 265/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0529 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.0928 - val_sparse_categorical_accuracy: 0.9626
Epoch 266/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0564 - sparse_categorical_accuracy: 0.9816 - val_loss: 0.0978 - val_sparse_categorical_accuracy: 0.9639
Epoch 267/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0497 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.0917 - val_sparse_categorical_accuracy: 0.9639
Epoch 268/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0538 - sparse_categorical_accuracy: 0.9795 - val_loss: 0.0913 - val_sparse_categorical_accuracy: 0.9626
Epoch 269/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0497 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.0928 - val_sparse_categorical_accuracy: 0.9598
Epoch 270/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0553 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0950 - val_sparse_categorical_accuracy: 0.9626
Epoch 271/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0501 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.0923 - val_sparse_categorical_accuracy: 0.9639
Epoch 272/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0575 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.0903 - val_sparse_categorical_accuracy: 0.9639
Epoch 273/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0490 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.1155 - val_sparse_categorical_accuracy: 0.9626
Epoch 274/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0553 - sparse_categorical_accuracy: 0.9809 - val_loss: 0.0923 - val_sparse_categorical_accuracy: 0.9653
Epoch 275/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0513 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.0915 - val_sparse_categorical_accuracy: 0.9598
Epoch 276/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0494 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.0918 - val_sparse_categorical_accuracy: 0.9639
Epoch 277/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0606 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.1049 - val_sparse_categorical_accuracy: 0.9639
Epoch 278/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0488 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0936 - val_sparse_categorical_accuracy: 0.9598
Epoch 279/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0535 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.0934 - val_sparse_categorical_accuracy: 0.9639
Epoch 280/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0493 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0997 - val_sparse_categorical_accuracy: 0.9626
Epoch 281/500
90/90 [==============================] - 0s 5ms/step - loss: 0.0485 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0943 - val_sparse_categorical_accuracy: 0.9626
Epoch 282/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0493 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0906 - val_sparse_categorical_accuracy: 0.9626
Epoch 283/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0491 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.0919 - val_sparse_categorical_accuracy: 0.9653
Epoch 284/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0482 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0895 - val_sparse_categorical_accuracy: 0.9639
Epoch 285/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0505 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0926 - val_sparse_categorical_accuracy: 0.9612
Epoch 286/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0466 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0950 - val_sparse_categorical_accuracy: 0.9639
Epoch 287/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0576 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.0935 - val_sparse_categorical_accuracy: 0.9639
Epoch 288/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0527 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.0943 - val_sparse_categorical_accuracy: 0.9639
Epoch 289/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0492 - sparse_categorical_accuracy: 0.9878 - val_loss: 0.0961 - val_sparse_categorical_accuracy: 0.9667
Epoch 290/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0466 - sparse_categorical_accuracy: 0.9882 - val_loss: 0.0947 - val_sparse_categorical_accuracy: 0.9612
Epoch 291/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0498 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0936 - val_sparse_categorical_accuracy: 0.9653
Epoch 292/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0489 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0922 - val_sparse_categorical_accuracy: 0.9653
Epoch 293/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0499 - sparse_categorical_accuracy: 0.9878 - val_loss: 0.0907 - val_sparse_categorical_accuracy: 0.9612
Epoch 294/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0511 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.0892 - val_sparse_categorical_accuracy: 0.9639
Epoch 295/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0502 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.0946 - val_sparse_categorical_accuracy: 0.9639
Epoch 296/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0504 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0902 - val_sparse_categorical_accuracy: 0.9639
Epoch 297/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0532 - sparse_categorical_accuracy: 0.9826 - val_loss: 0.0908 - val_sparse_categorical_accuracy: 0.9639
Epoch 298/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0526 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.0950 - val_sparse_categorical_accuracy: 0.9584
Epoch 299/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0478 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1001 - val_sparse_categorical_accuracy: 0.9612
Epoch 300/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0543 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.0929 - val_sparse_categorical_accuracy: 0.9639
Epoch 301/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0507 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.0935 - val_sparse_categorical_accuracy: 0.9653
Epoch 302/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0512 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.0897 - val_sparse_categorical_accuracy: 0.9612
Epoch 303/500
90/90 [==============================] - 0s 5ms/step - loss: 0.0480 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.1003 - val_sparse_categorical_accuracy: 0.9612
Epoch 304/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0538 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0997 - val_sparse_categorical_accuracy: 0.9612
Epoch 305/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0528 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.1028 - val_sparse_categorical_accuracy: 0.9626
Epoch 306/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0507 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0949 - val_sparse_categorical_accuracy: 0.9612
Epoch 307/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0534 - sparse_categorical_accuracy: 0.9812 - val_loss: 0.0902 - val_sparse_categorical_accuracy: 0.9639
Epoch 308/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0497 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0929 - val_sparse_categorical_accuracy: 0.9681
Epoch 309/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0510 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0904 - val_sparse_categorical_accuracy: 0.9626
Epoch 310/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0518 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0967 - val_sparse_categorical_accuracy: 0.9598
Epoch 311/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0521 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.0945 - val_sparse_categorical_accuracy: 0.9626
Epoch 312/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0586 - sparse_categorical_accuracy: 0.9806 - val_loss: 0.0957 - val_sparse_categorical_accuracy: 0.9626
Epoch 313/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0470 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0984 - val_sparse_categorical_accuracy: 0.9598
Epoch 314/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0533 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.0908 - val_sparse_categorical_accuracy: 0.9598
Epoch 315/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0502 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0908 - val_sparse_categorical_accuracy: 0.9639
Epoch 316/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0463 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0912 - val_sparse_categorical_accuracy: 0.9639
Epoch 317/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0515 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.1047 - val_sparse_categorical_accuracy: 0.9626
Epoch 318/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0522 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.0916 - val_sparse_categorical_accuracy: 0.9639
Epoch 319/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0494 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0919 - val_sparse_categorical_accuracy: 0.9639
Epoch 320/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0446 - sparse_categorical_accuracy: 0.9906 - val_loss: 0.0901 - val_sparse_categorical_accuracy: 0.9626
Epoch 321/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0527 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.0910 - val_sparse_categorical_accuracy: 0.9598
Epoch 322/500
90/90 [==============================] - 0s 6ms/step - loss: 0.0476 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.1029 - val_sparse_categorical_accuracy: 0.9598
Epoch 323/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0505 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0939 - val_sparse_categorical_accuracy: 0.9626
Epoch 324/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0505 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.0900 - val_sparse_categorical_accuracy: 0.9612
Epoch 325/500
90/90 [==============================] - 0s 6ms/step - loss: 0.0516 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1024 - val_sparse_categorical_accuracy: 0.9626
Epoch 326/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0512 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0946 - val_sparse_categorical_accuracy: 0.9598
Epoch 327/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0509 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.0988 - val_sparse_categorical_accuracy: 0.9626
Epoch 328/500
90/90 [==============================] - 0s 5ms/step - loss: 0.0427 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.0913 - val_sparse_categorical_accuracy: 0.9639
Epoch 329/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0515 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.0962 - val_sparse_categorical_accuracy: 0.9612
Epoch 330/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0477 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0917 - val_sparse_categorical_accuracy: 0.9598
Epoch 331/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0485 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0911 - val_sparse_categorical_accuracy: 0.9626
Epoch 332/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0479 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0999 - val_sparse_categorical_accuracy: 0.9612
Epoch 333/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0465 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.0877 - val_sparse_categorical_accuracy: 0.9639
Epoch 334/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0500 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.1073 - val_sparse_categorical_accuracy: 0.9626
Epoch 335/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0506 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0913 - val_sparse_categorical_accuracy: 0.9612
Epoch 336/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0473 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.1075 - val_sparse_categorical_accuracy: 0.9639
Epoch 337/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0494 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.0953 - val_sparse_categorical_accuracy: 0.9626
Epoch 338/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0510 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0904 - val_sparse_categorical_accuracy: 0.9639
Epoch 339/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0521 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.0913 - val_sparse_categorical_accuracy: 0.9584
Epoch 340/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0512 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.0908 - val_sparse_categorical_accuracy: 0.9626
Epoch 341/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0468 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.0990 - val_sparse_categorical_accuracy: 0.9626
Epoch 342/500
90/90 [==============================] - 0s 5ms/step - loss: 0.0494 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.0950 - val_sparse_categorical_accuracy: 0.9653
Epoch 343/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0518 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0937 - val_sparse_categorical_accuracy: 0.9598
Epoch 344/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0488 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0958 - val_sparse_categorical_accuracy: 0.9639
Epoch 345/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0523 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.1467 - val_sparse_categorical_accuracy: 0.9515
Epoch 346/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0482 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0917 - val_sparse_categorical_accuracy: 0.9667
Epoch 347/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0492 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.1134 - val_sparse_categorical_accuracy: 0.9626
Epoch 348/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0455 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.0976 - val_sparse_categorical_accuracy: 0.9612
Epoch 349/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0462 - sparse_categorical_accuracy: 0.9896 - val_loss: 0.0898 - val_sparse_categorical_accuracy: 0.9667
Epoch 350/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0497 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.0912 - val_sparse_categorical_accuracy: 0.9639
Epoch 351/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0462 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.0932 - val_sparse_categorical_accuracy: 0.9626
Epoch 352/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0515 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.0913 - val_sparse_categorical_accuracy: 0.9653
Epoch 353/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0455 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.0945 - val_sparse_categorical_accuracy: 0.9612
Epoch 354/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0452 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.0921 - val_sparse_categorical_accuracy: 0.9598
Epoch 355/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0430 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.0903 - val_sparse_categorical_accuracy: 0.9626
Epoch 356/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0471 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.1045 - val_sparse_categorical_accuracy: 0.9626
Epoch 357/500
90/90 [==============================] - 0s 5ms/step - loss: 0.0508 - sparse_categorical_accuracy: 0.9847 - val_loss: 0.0949 - val_sparse_categorical_accuracy: 0.9653
Epoch 358/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0468 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.0931 - val_sparse_categorical_accuracy: 0.9639
Epoch 359/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0466 - sparse_categorical_accuracy: 0.9851 - val_loss: 0.0913 - val_sparse_categorical_accuracy: 0.9612
Epoch 360/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0440 - sparse_categorical_accuracy: 0.9899 - val_loss: 0.0988 - val_sparse_categorical_accuracy: 0.9626
Epoch 361/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0448 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.0975 - val_sparse_categorical_accuracy: 0.9667
Epoch 362/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0477 - sparse_categorical_accuracy: 0.9875 - val_loss: 0.0914 - val_sparse_categorical_accuracy: 0.9639
Epoch 363/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0493 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.0906 - val_sparse_categorical_accuracy: 0.9626
Epoch 364/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0488 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0931 - val_sparse_categorical_accuracy: 0.9626
Epoch 365/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0491 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.0960 - val_sparse_categorical_accuracy: 0.9626
Epoch 366/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0477 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0891 - val_sparse_categorical_accuracy: 0.9612
Epoch 367/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0470 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.1026 - val_sparse_categorical_accuracy: 0.9626
Epoch 368/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0463 - sparse_categorical_accuracy: 0.9885 - val_loss: 0.0909 - val_sparse_categorical_accuracy: 0.9626
Epoch 369/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0459 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.0909 - val_sparse_categorical_accuracy: 0.9639
Epoch 370/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0511 - sparse_categorical_accuracy: 0.9868 - val_loss: 0.1036 - val_sparse_categorical_accuracy: 0.9626
Epoch 371/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0479 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.0922 - val_sparse_categorical_accuracy: 0.9626
Epoch 372/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0516 - sparse_categorical_accuracy: 0.9840 - val_loss: 0.0932 - val_sparse_categorical_accuracy: 0.9653
Epoch 373/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0451 - sparse_categorical_accuracy: 0.9858 - val_loss: 0.0928 - val_sparse_categorical_accuracy: 0.9639
Epoch 374/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0461 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.0911 - val_sparse_categorical_accuracy: 0.9612
Epoch 375/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0494 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.0895 - val_sparse_categorical_accuracy: 0.9639
Epoch 376/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0466 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.0902 - val_sparse_categorical_accuracy: 0.9639
Epoch 377/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0465 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0908 - val_sparse_categorical_accuracy: 0.9681
Epoch 378/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0430 - sparse_categorical_accuracy: 0.9882 - val_loss: 0.0906 - val_sparse_categorical_accuracy: 0.9626
Epoch 379/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0524 - sparse_categorical_accuracy: 0.9837 - val_loss: 0.0910 - val_sparse_categorical_accuracy: 0.9598
Epoch 380/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0467 - sparse_categorical_accuracy: 0.9872 - val_loss: 0.0947 - val_sparse_categorical_accuracy: 0.9639
Epoch 381/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0464 - sparse_categorical_accuracy: 0.9885 - val_loss: 0.0922 - val_sparse_categorical_accuracy: 0.9653
Epoch 382/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0449 - sparse_categorical_accuracy: 0.9885 - val_loss: 0.0918 - val_sparse_categorical_accuracy: 0.9639
Epoch 383/500
90/90 [==============================] - 1s 6ms/step - loss: 0.0438 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.0905 - val_sparse_categorical_accuracy: 0.9612
Epoch 00383: early stopping

 

テストデータでモデルを評価する

model = keras.models.load_model("best_model.h5")

test_loss, test_acc = model.evaluate(x_test, y_test)

print("Test accuracy", test_acc)
print("Test loss", test_loss)

42/42 [==============================] - 0s 2ms/step - loss: 0.0936 - sparse_categorical_accuracy: 0.9682
Test accuracy 0.9681817889213562
Test loss 0.0935916006565094

 

モデルの訓練と検証損失をプロットする

metric = "sparse_categorical_accuracy"
plt.figure()
plt.plot(history.history[metric])
plt.plot(history.history["val_" + metric])
plt.title("model " + metric)
plt.ylabel(metric, fontsize="large")
plt.xlabel("epoch", fontsize="large")
plt.legend(["train", "val"], loc="best")
plt.show()
plt.close()

100 エポック後に訓練精度がどのようにおよそ 0.95 に到達するかを見ることができます。しかし、検証精度を観察することにより、ネットワークが検証と訓練精度の両者に対してそれが 200 エポック後に殆ど 0.97 に達するまで訓練が依然として必要であることがわかります。200th エポックを超えると、訓練を続けた場合、訓練精度は上がり続ける一方で検証精度は下がり始めます : モデルは過剰適合し始めます。

以上