《Keras 3 ：AI 从头开始的时间序列分类》

作者： hfawaz
创建日期： 2020/07/21
最后修改时间： 2023/11/10
描述： 在 UCR/UEA 档案中的 FordA 数据集上从头开始训练时间序列分类器。

（i）此示例使用 Keras 3

介绍

此示例说明如何从原始开始从头开始进行时间序列分类 CSV 时间序列文件。我们演示了 UCR/UEA 档案中 FordA 数据集的工作流程。

设置

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

加载数据：FordA 数据集

数据集描述

我们在这里使用的数据集称为 FordA。数据来自 UCR 存档。该数据集包含 3601 个训练实例和另外 1320 个测试实例。每个时间序列对应于电机传感器捕获的发动机噪声测量值。对于此任务，目标是自动检测是否存在特定问题引擎。问题是平衡的二元分类任务。的完整描述此数据集可在此处找到。

读取 TSV 数据

我们将使用该文件进行训练，将该文件用于测试。此数据集的简单性使我们能够有效地演示如何使用 ConvNets 进行时间序列分类。在此文件中，第一列对应于标签。FordA_TRAINFordA_TEST

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")

可视化数据

在这里，我们可视化数据集中每个类的一个时间序列示例。

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()

PNG 格式

标准化数据

我们的时间序列已经是单个长度（500）。但是，它们的值是通常在不同的范围内。这对于神经网络来说并不理想; 通常，我们应该设法使 Importing 值标准化。对于此特定数据集，数据已 z 规范化：每个时间序列样本的平均值等于 0，标准差等于 1。这种类型的归一化对于时间序列分类问题来说非常常见，参见 Bagnall et al. （2016）。

请注意，这里使用的时间序列数据是单变量的，这意味着我们只有一个通道每个时间序列示例。因此，我们将时间序列转换为具有一个通道的多元时间序列通过 NumPy 使用简单的 reshaping 。这将使我们能够构建一个易于适用于多变量时间的模型系列。

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))

最后，为了使用，我们将不得不计数预先的类数。sparse_categorical_crossentropy

num_classes = len(np.unique(y_train))

现在我们随机排列训练集，因为我们将使用选项以后在训练时。validation_split

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

将标签标准化为正整数。然后，预期的标签将为 0 和 1。

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

构建模型

我们构建了一个最初在本文中提出的全卷积神经网络。该实现基于此处提供的 TF 2 版本。找到以下超参数（kernel_size、过滤器、BatchNorm 的用法）通过使用 KerasTuner 进行随机搜索。

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)

PNG 格式

训练模型

epochs = 500
batch_size = 32

callbacks = [
    keras.callbacks.ModelCheckpoint(
        "best_model.keras", 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 ━━━━━━━━━━━━━━━━━━━━ 5s 32ms/step - loss: 0.6056 - sparse_categorical_accuracy: 0.6818 - val_loss: 0.9692 - val_sparse_categorical_accuracy: 0.4591 - learning_rate: 0.0010 Epoch 2/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.4623 - sparse_categorical_accuracy: 0.7619 - val_loss: 0.9336 - val_sparse_categorical_accuracy: 0.4591 - learning_rate: 0.0010 Epoch 3/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.4383 - sparse_categorical_accuracy: 0.7888 - val_loss: 0.6842 - val_sparse_categorical_accuracy: 0.5409 - learning_rate: 0.0010 Epoch 4/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.4295 - sparse_categorical_accuracy: 0.7826 - val_loss: 0.6632 - val_sparse_categorical_accuracy: 0.5118 - learning_rate: 0.0010 Epoch 5/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.4311 - sparse_categorical_accuracy: 0.7831 - val_loss: 0.5693 - val_sparse_categorical_accuracy: 0.6935 - learning_rate: 0.0010 Epoch 6/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.4250 - sparse_categorical_accuracy: 0.7832 - val_loss: 0.5001 - val_sparse_categorical_accuracy: 0.7712 - learning_rate: 0.0010 Epoch 7/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.4179 - sparse_categorical_accuracy: 0.8079 - val_loss: 0.5151 - val_sparse_categorical_accuracy: 0.7379 - learning_rate: 0.0010 Epoch 8/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.3929 - sparse_categorical_accuracy: 0.8073 - val_loss: 0.3992 - val_sparse_categorical_accuracy: 0.8377 - learning_rate: 0.0010 Epoch 9/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.4074 - sparse_categorical_accuracy: 0.7947 - val_loss: 0.4053 - val_sparse_categorical_accuracy: 0.8225 - learning_rate: 0.0010 Epoch 10/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.4067 - sparse_categorical_accuracy: 0.7984 - val_loss: 0.3727 - val_sparse_categorical_accuracy: 0.8377 - learning_rate: 0.0010 Epoch 11/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.3910 - sparse_categorical_accuracy: 0.8083 - val_loss: 0.3687 - val_sparse_categorical_accuracy: 0.8363 - learning_rate: 0.0010 Epoch 12/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.3872 - sparse_categorical_accuracy: 0.8001 - val_loss: 0.3773 - val_sparse_categorical_accuracy: 0.8169 - learning_rate: 0.0010 Epoch 13/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.3684 - sparse_categorical_accuracy: 0.8138 - val_loss: 0.3566 - val_sparse_categorical_accuracy: 0.8474 - learning_rate: 0.0010 Epoch 14/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.3843 - sparse_categorical_accuracy: 0.8102 - val_loss: 0.3674 - val_sparse_categorical_accuracy: 0.8322 - learning_rate: 0.0010 Epoch 15/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.3774 - sparse_categorical_accuracy: 0.8260 - val_loss: 0.4040 - val_sparse_categorical_accuracy: 0.7614 - learning_rate: 0.0010 Epoch 16/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.3547 - sparse_categorical_accuracy: 0.8351 - val_loss: 0.6609 - val_sparse_categorical_accuracy: 0.6671 - learning_rate: 0.0010 Epoch 17/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.3797 - sparse_categorical_accuracy: 0.8194 - val_loss: 0.3379 - val_sparse_categorical_accuracy: 0.8599 - learning_rate: 0.0010 Epoch 18/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.3544 - sparse_categorical_accuracy: 0.8373 - val_loss: 0.3363 - val_sparse_categorical_accuracy: 0.8613 - learning_rate: 0.0010 Epoch 19/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.3372 - sparse_categorical_accuracy: 0.8477 - val_loss: 0.4554 - val_sparse_categorical_accuracy: 0.7545 - learning_rate: 0.0010 Epoch 20/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.3509 - sparse_categorical_accuracy: 0.8330 - val_loss: 0.4411 - val_sparse_categorical_accuracy: 0.7490 - learning_rate: 0.0010 Epoch 21/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.3771 - sparse_categorical_accuracy: 0.8195 - val_loss: 0.3526 - val_sparse_categorical_accuracy: 0.8225 - learning_rate: 0.0010 Epoch 22/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.3448 - sparse_categorical_accuracy: 0.8373 - val_loss: 0.3296 - val_sparse_categorical_accuracy: 0.8669 - learning_rate: 0.0010 Epoch 23/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.3400 - sparse_categorical_accuracy: 0.8455 - val_loss: 0.3938 - val_sparse_categorical_accuracy: 0.7656 - learning_rate: 0.0010 Epoch 24/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.3243 - sparse_categorical_accuracy: 0.8626 - val_loss: 0.8280 - val_sparse_categorical_accuracy: 0.5534 - learning_rate: 0.0010 Epoch 25/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.3263 - sparse_categorical_accuracy: 0.8518 - val_loss: 0.3881 - val_sparse_categorical_accuracy: 0.8031 - learning_rate: 0.0010 Epoch 26/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.3424 - sparse_categorical_accuracy: 0.8491 - val_loss: 0.3140 - val_sparse_categorical_accuracy: 0.8766 - learning_rate: 0.0010 Epoch 27/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.3236 - sparse_categorical_accuracy: 0.8551 - val_loss: 0.3138 - val_sparse_categorical_accuracy: 0.8502 - learning_rate: 0.0010 Epoch 28/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.3161 - sparse_categorical_accuracy: 0.8605 - val_loss: 0.3419 - val_sparse_categorical_accuracy: 0.8294 - learning_rate: 0.0010 Epoch 29/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.3077 - sparse_categorical_accuracy: 0.8660 - val_loss: 0.3326 - val_sparse_categorical_accuracy: 0.8460 - learning_rate: 0.0010 Epoch 30/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.3257 - sparse_categorical_accuracy: 0.8527 - val_loss: 0.2964 - val_sparse_categorical_accuracy: 0.8932 - learning_rate: 0.0010 Epoch 31/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2990 - sparse_categorical_accuracy: 0.8754 - val_loss: 0.3273 - val_sparse_categorical_accuracy: 0.8405 - learning_rate: 0.0010 Epoch 32/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.3046 - sparse_categorical_accuracy: 0.8618 - val_loss: 0.2882 - val_sparse_categorical_accuracy: 0.8641 - learning_rate: 0.0010 Epoch 33/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2998 - sparse_categorical_accuracy: 0.8759 - val_loss: 0.3532 - val_sparse_categorical_accuracy: 0.7989 - learning_rate: 0.0010 Epoch 34/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2750 - sparse_categorical_accuracy: 0.8753 - val_loss: 0.5120 - val_sparse_categorical_accuracy: 0.7365 - learning_rate: 0.0010 Epoch 35/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2784 - sparse_categorical_accuracy: 0.8862 - val_loss: 0.3159 - val_sparse_categorical_accuracy: 0.8752 - learning_rate: 0.0010 Epoch 36/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2661 - sparse_categorical_accuracy: 0.8982 - val_loss: 0.3643 - val_sparse_categorical_accuracy: 0.8433 - learning_rate: 0.0010 Epoch 37/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2769 - sparse_categorical_accuracy: 0.8814 - val_loss: 0.4004 - val_sparse_categorical_accuracy: 0.7947 - learning_rate: 0.0010 Epoch 38/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2963 - sparse_categorical_accuracy: 0.8679 - val_loss: 0.4778 - val_sparse_categorical_accuracy: 0.7323 - learning_rate: 0.0010 Epoch 39/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2688 - sparse_categorical_accuracy: 0.8851 - val_loss: 0.2490 - val_sparse_categorical_accuracy: 0.9043 - learning_rate: 0.0010 Epoch 40/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2696 - sparse_categorical_accuracy: 0.8872 - val_loss: 0.2792 - val_sparse_categorical_accuracy: 0.8821 - learning_rate: 0.0010 Epoch 41/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2880 - sparse_categorical_accuracy: 0.8868 - val_loss: 0.2775 - val_sparse_categorical_accuracy: 0.8752 - learning_rate: 0.0010 Epoch 42/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2884 - sparse_categorical_accuracy: 0.8774 - val_loss: 0.3545 - val_sparse_categorical_accuracy: 0.8128 - learning_rate: 0.0010 Epoch 43/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2840 - sparse_categorical_accuracy: 0.8709 - val_loss: 0.3292 - val_sparse_categorical_accuracy: 0.8350 - learning_rate: 0.0010 Epoch 44/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.3000 - sparse_categorical_accuracy: 0.8569 - val_loss: 1.5013 - val_sparse_categorical_accuracy: 0.5479 - learning_rate: 0.0010 Epoch 45/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2618 - sparse_categorical_accuracy: 0.8896 - val_loss: 0.2766 - val_sparse_categorical_accuracy: 0.8835 - learning_rate: 0.0010 Epoch 46/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2604 - sparse_categorical_accuracy: 0.8955 - val_loss: 0.2397 - val_sparse_categorical_accuracy: 0.9098 - learning_rate: 0.0010 Epoch 47/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2520 - sparse_categorical_accuracy: 0.8975 - val_loss: 0.3794 - val_sparse_categorical_accuracy: 0.7975 - learning_rate: 0.0010 Epoch 48/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2521 - sparse_categorical_accuracy: 0.9067 - val_loss: 0.2871 - val_sparse_categorical_accuracy: 0.8641 - learning_rate: 0.0010 Epoch 49/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2554 - sparse_categorical_accuracy: 0.8904 - val_loss: 0.8962 - val_sparse_categorical_accuracy: 0.7115 - learning_rate: 0.0010 Epoch 50/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2501 - sparse_categorical_accuracy: 0.8989 - val_loss: 0.4592 - val_sparse_categorical_accuracy: 0.7864 - learning_rate: 0.0010 Epoch 51/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2362 - sparse_categorical_accuracy: 0.8944 - val_loss: 0.4599 - val_sparse_categorical_accuracy: 0.7684 - learning_rate: 0.0010 Epoch 52/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2538 - sparse_categorical_accuracy: 0.8986 - val_loss: 0.2748 - val_sparse_categorical_accuracy: 0.8849 - learning_rate: 0.0010 Epoch 53/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2648 - sparse_categorical_accuracy: 0.8934 - val_loss: 0.2725 - val_sparse_categorical_accuracy: 0.9001 - learning_rate: 0.0010 Epoch 54/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2292 - sparse_categorical_accuracy: 0.9117 - val_loss: 0.2617 - val_sparse_categorical_accuracy: 0.8766 - learning_rate: 0.0010 Epoch 55/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2704 - sparse_categorical_accuracy: 0.8826 - val_loss: 0.2929 - val_sparse_categorical_accuracy: 0.8488 - learning_rate: 0.0010 Epoch 56/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2388 - sparse_categorical_accuracy: 0.9022 - val_loss: 0.2365 - val_sparse_categorical_accuracy: 0.9112 - learning_rate: 0.0010 Epoch 57/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2309 - sparse_categorical_accuracy: 0.9087 - val_loss: 1.1993 - val_sparse_categorical_accuracy: 0.5784 - learning_rate: 0.0010 Epoch 58/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2639 - sparse_categorical_accuracy: 0.8893 - val_loss: 0.2410 - val_sparse_categorical_accuracy: 0.9098 - learning_rate: 0.0010 Epoch 59/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2229 - sparse_categorical_accuracy: 0.9104 - val_loss: 0.6126 - val_sparse_categorical_accuracy: 0.7212 - learning_rate: 0.0010 Epoch 60/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2451 - sparse_categorical_accuracy: 0.9084 - val_loss: 0.3189 - val_sparse_categorical_accuracy: 0.8655 - learning_rate: 0.0010 Epoch 61/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2200 - sparse_categorical_accuracy: 0.9169 - val_loss: 0.7695 - val_sparse_categorical_accuracy: 0.7212 - learning_rate: 0.0010 Epoch 62/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2249 - sparse_categorical_accuracy: 0.9149 - val_loss: 0.2900 - val_sparse_categorical_accuracy: 0.8835 - learning_rate: 0.0010 Epoch 63/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2476 - sparse_categorical_accuracy: 0.8988 - val_loss: 0.2863 - val_sparse_categorical_accuracy: 0.8682 - learning_rate: 0.0010 Epoch 64/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2263 - sparse_categorical_accuracy: 0.9010 - val_loss: 0.4034 - val_sparse_categorical_accuracy: 0.7961 - learning_rate: 0.0010 Epoch 65/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2404 - sparse_categorical_accuracy: 0.9041 - val_loss: 0.2965 - val_sparse_categorical_accuracy: 0.8696 - learning_rate: 0.0010 Epoch 66/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2257 - sparse_categorical_accuracy: 0.9051 - val_loss: 0.2227 - val_sparse_categorical_accuracy: 0.9029 - learning_rate: 0.0010 Epoch 67/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2218 - sparse_categorical_accuracy: 0.9088 - val_loss: 0.2274 - val_sparse_categorical_accuracy: 0.9154 - learning_rate: 0.0010 Epoch 68/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2106 - sparse_categorical_accuracy: 0.9159 - val_loss: 0.2703 - val_sparse_categorical_accuracy: 0.8877 - learning_rate: 0.0010 Epoch 69/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1945 - sparse_categorical_accuracy: 0.9278 - val_loss: 0.2688 - val_sparse_categorical_accuracy: 0.8724 - learning_rate: 0.0010 Epoch 70/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2269 - sparse_categorical_accuracy: 0.9108 - val_loss: 0.2003 - val_sparse_categorical_accuracy: 0.9196 - learning_rate: 0.0010 Epoch 71/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2312 - sparse_categorical_accuracy: 0.9041 - val_loss: 0.3678 - val_sparse_categorical_accuracy: 0.8322 - learning_rate: 0.0010 Epoch 72/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1828 - sparse_categorical_accuracy: 0.9290 - val_loss: 0.2397 - val_sparse_categorical_accuracy: 0.9043 - learning_rate: 0.0010 Epoch 73/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1723 - sparse_categorical_accuracy: 0.9364 - val_loss: 0.2070 - val_sparse_categorical_accuracy: 0.9098 - learning_rate: 0.0010 Epoch 74/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1830 - sparse_categorical_accuracy: 0.9317 - val_loss: 0.3114 - val_sparse_categorical_accuracy: 0.8391 - learning_rate: 0.0010 Epoch 75/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1786 - sparse_categorical_accuracy: 0.9345 - val_loss: 0.7721 - val_sparse_categorical_accuracy: 0.6824 - learning_rate: 0.0010 Epoch 76/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1680 - sparse_categorical_accuracy: 0.9444 - val_loss: 0.1898 - val_sparse_categorical_accuracy: 0.9293 - learning_rate: 0.0010 Epoch 77/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1606 - sparse_categorical_accuracy: 0.9426 - val_loss: 0.1803 - val_sparse_categorical_accuracy: 0.9293 - learning_rate: 0.0010 Epoch 78/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1705 - sparse_categorical_accuracy: 0.9292 - val_loss: 0.6892 - val_sparse_categorical_accuracy: 0.7226 - learning_rate: 0.0010 Epoch 79/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1428 - sparse_categorical_accuracy: 0.9534 - val_loss: 0.2448 - val_sparse_categorical_accuracy: 0.8932 - learning_rate: 0.0010 Epoch 80/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1527 - sparse_categorical_accuracy: 0.9441 - val_loss: 0.3191 - val_sparse_categorical_accuracy: 0.8377 - learning_rate: 0.0010 Epoch 81/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1398 - sparse_categorical_accuracy: 0.9447 - val_loss: 0.9834 - val_sparse_categorical_accuracy: 0.6366 - learning_rate: 0.0010 Epoch 82/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1615 - sparse_categorical_accuracy: 0.9405 - val_loss: 0.3857 - val_sparse_categorical_accuracy: 0.8391 - learning_rate: 0.0010 Epoch 83/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1371 - sparse_categorical_accuracy: 0.9525 - val_loss: 0.1597 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 0.0010 Epoch 84/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1377 - sparse_categorical_accuracy: 0.9548 - val_loss: 0.4212 - val_sparse_categorical_accuracy: 0.8058 - learning_rate: 0.0010 Epoch 85/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1315 - sparse_categorical_accuracy: 0.9585 - val_loss: 0.3091 - val_sparse_categorical_accuracy: 0.8447 - learning_rate: 0.0010 Epoch 86/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1381 - sparse_categorical_accuracy: 0.9517 - val_loss: 0.1539 - val_sparse_categorical_accuracy: 0.9487 - learning_rate: 0.0010 Epoch 87/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1169 - sparse_categorical_accuracy: 0.9581 - val_loss: 0.1927 - val_sparse_categorical_accuracy: 0.9168 - learning_rate: 0.0010 Epoch 88/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1438 - sparse_categorical_accuracy: 0.9512 - val_loss: 0.1696 - val_sparse_categorical_accuracy: 0.9293 - learning_rate: 0.0010 Epoch 89/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1471 - sparse_categorical_accuracy: 0.9464 - val_loss: 0.2523 - val_sparse_categorical_accuracy: 0.8988 - learning_rate: 0.0010 Epoch 90/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1389 - sparse_categorical_accuracy: 0.9535 - val_loss: 0.2452 - val_sparse_categorical_accuracy: 0.8849 - learning_rate: 0.0010 Epoch 91/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1209 - sparse_categorical_accuracy: 0.9599 - val_loss: 0.3986 - val_sparse_categorical_accuracy: 0.8183 - learning_rate: 0.0010 Epoch 92/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1278 - sparse_categorical_accuracy: 0.9520 - val_loss: 0.2153 - val_sparse_categorical_accuracy: 0.9334 - learning_rate: 0.0010 Epoch 93/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1080 - sparse_categorical_accuracy: 0.9656 - val_loss: 0.1532 - val_sparse_categorical_accuracy: 0.9459 - learning_rate: 0.0010 Epoch 94/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1236 - sparse_categorical_accuracy: 0.9671 - val_loss: 0.1580 - val_sparse_categorical_accuracy: 0.9404 - learning_rate: 0.0010 Epoch 95/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0982 - sparse_categorical_accuracy: 0.9645 - val_loss: 0.1922 - val_sparse_categorical_accuracy: 0.9417 - learning_rate: 0.0010 Epoch 96/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1165 - sparse_categorical_accuracy: 0.9630 - val_loss: 0.3719 - val_sparse_categorical_accuracy: 0.8377 - learning_rate: 0.0010 Epoch 97/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1207 - sparse_categorical_accuracy: 0.9655 - val_loss: 0.2266 - val_sparse_categorical_accuracy: 0.8988 - learning_rate: 0.0010 Epoch 98/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1431 - sparse_categorical_accuracy: 0.9530 - val_loss: 0.1165 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 0.0010 Epoch 99/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1262 - sparse_categorical_accuracy: 0.9553 - val_loss: 0.1814 - val_sparse_categorical_accuracy: 0.9320 - learning_rate: 0.0010 Epoch 100/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0983 - sparse_categorical_accuracy: 0.9714 - val_loss: 0.1264 - val_sparse_categorical_accuracy: 0.9501 - learning_rate: 0.0010 Epoch 101/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1366 - sparse_categorical_accuracy: 0.9552 - val_loss: 0.1222 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 0.0010 Epoch 102/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1156 - sparse_categorical_accuracy: 0.9602 - val_loss: 0.3325 - val_sparse_categorical_accuracy: 0.8904 - learning_rate: 0.0010 Epoch 103/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1231 - sparse_categorical_accuracy: 0.9544 - val_loss: 0.7861 - val_sparse_categorical_accuracy: 0.7074 - learning_rate: 0.0010 Epoch 104/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1081 - sparse_categorical_accuracy: 0.9653 - val_loss: 0.1329 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 0.0010 Epoch 105/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1171 - sparse_categorical_accuracy: 0.9585 - val_loss: 0.1094 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 0.0010 Epoch 106/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1110 - sparse_categorical_accuracy: 0.9633 - val_loss: 0.1403 - val_sparse_categorical_accuracy: 0.9390 - learning_rate: 0.0010 Epoch 107/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1308 - sparse_categorical_accuracy: 0.9523 - val_loss: 0.2915 - val_sparse_categorical_accuracy: 0.8863 - learning_rate: 0.0010 Epoch 108/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1062 - sparse_categorical_accuracy: 0.9662 - val_loss: 0.1033 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 0.0010 Epoch 109/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1167 - sparse_categorical_accuracy: 0.9614 - val_loss: 0.1259 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 0.0010 Epoch 110/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1037 - sparse_categorical_accuracy: 0.9676 - val_loss: 0.1180 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 0.0010 Epoch 111/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1156 - sparse_categorical_accuracy: 0.9626 - val_loss: 0.1534 - val_sparse_categorical_accuracy: 0.9473 - learning_rate: 0.0010 Epoch 112/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1165 - sparse_categorical_accuracy: 0.9559 - val_loss: 0.2067 - val_sparse_categorical_accuracy: 0.9362 - learning_rate: 0.0010 Epoch 113/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1163 - sparse_categorical_accuracy: 0.9574 - val_loss: 0.4253 - val_sparse_categorical_accuracy: 0.8044 - learning_rate: 0.0010 Epoch 114/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1148 - sparse_categorical_accuracy: 0.9601 - val_loss: 0.1323 - val_sparse_categorical_accuracy: 0.9376 - learning_rate: 0.0010 Epoch 115/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1055 - sparse_categorical_accuracy: 0.9627 - val_loss: 0.1076 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 0.0010 Epoch 116/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0910 - sparse_categorical_accuracy: 0.9700 - val_loss: 0.7235 - val_sparse_categorical_accuracy: 0.6963 - learning_rate: 0.0010 Epoch 117/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1308 - sparse_categorical_accuracy: 0.9597 - val_loss: 0.1575 - val_sparse_categorical_accuracy: 0.9348 - learning_rate: 0.0010 Epoch 118/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1368 - sparse_categorical_accuracy: 0.9433 - val_loss: 0.1076 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 0.0010 Epoch 119/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0995 - sparse_categorical_accuracy: 0.9674 - val_loss: 0.1788 - val_sparse_categorical_accuracy: 0.9196 - learning_rate: 0.0010 Epoch 120/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1221 - sparse_categorical_accuracy: 0.9506 - val_loss: 0.1161 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 0.0010 Epoch 121/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0921 - sparse_categorical_accuracy: 0.9741 - val_loss: 0.1154 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 0.0010 Epoch 122/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1081 - sparse_categorical_accuracy: 0.9618 - val_loss: 0.1153 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 0.0010 Epoch 123/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0962 - sparse_categorical_accuracy: 0.9667 - val_loss: 0.1808 - val_sparse_categorical_accuracy: 0.9390 - learning_rate: 0.0010 Epoch 124/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1115 - sparse_categorical_accuracy: 0.9634 - val_loss: 0.1017 - val_sparse_categorical_accuracy: 0.9723 - learning_rate: 0.0010 Epoch 125/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1032 - sparse_categorical_accuracy: 0.9657 - val_loss: 0.1763 - val_sparse_categorical_accuracy: 0.9390 - learning_rate: 0.0010 Epoch 126/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1088 - sparse_categorical_accuracy: 0.9628 - val_loss: 0.1823 - val_sparse_categorical_accuracy: 0.9307 - learning_rate: 0.0010 Epoch 127/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1095 - sparse_categorical_accuracy: 0.9637 - val_loss: 0.1089 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 0.0010 Epoch 128/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1316 - sparse_categorical_accuracy: 0.9547 - val_loss: 0.1416 - val_sparse_categorical_accuracy: 0.9307 - learning_rate: 0.0010 Epoch 129/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1051 - sparse_categorical_accuracy: 0.9642 - val_loss: 0.2307 - val_sparse_categorical_accuracy: 0.8904 - learning_rate: 0.0010 Epoch 130/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1051 - sparse_categorical_accuracy: 0.9692 - val_loss: 1.0068 - val_sparse_categorical_accuracy: 0.6338 - learning_rate: 0.0010 Epoch 131/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1052 - sparse_categorical_accuracy: 0.9620 - val_loss: 0.2687 - val_sparse_categorical_accuracy: 0.9112 - learning_rate: 0.0010 Epoch 132/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1045 - sparse_categorical_accuracy: 0.9647 - val_loss: 0.0941 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 0.0010 Epoch 133/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0953 - sparse_categorical_accuracy: 0.9701 - val_loss: 0.1996 - val_sparse_categorical_accuracy: 0.9390 - learning_rate: 0.0010 Epoch 134/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1149 - sparse_categorical_accuracy: 0.9612 - val_loss: 0.4479 - val_sparse_categorical_accuracy: 0.8044 - learning_rate: 0.0010 Epoch 135/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0913 - sparse_categorical_accuracy: 0.9715 - val_loss: 0.0993 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 0.0010 Epoch 136/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1211 - sparse_categorical_accuracy: 0.9586 - val_loss: 0.1036 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 0.0010 Epoch 137/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0910 - sparse_categorical_accuracy: 0.9700 - val_loss: 0.1525 - val_sparse_categorical_accuracy: 0.9279 - learning_rate: 0.0010 Epoch 138/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0986 - sparse_categorical_accuracy: 0.9633 - val_loss: 0.1699 - val_sparse_categorical_accuracy: 0.9251 - learning_rate: 0.0010 Epoch 139/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0886 - sparse_categorical_accuracy: 0.9722 - val_loss: 0.0957 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 0.0010 Epoch 140/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1050 - sparse_categorical_accuracy: 0.9652 - val_loss: 1.6603 - val_sparse_categorical_accuracy: 0.6366 - learning_rate: 0.0010 Epoch 141/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0922 - sparse_categorical_accuracy: 0.9676 - val_loss: 0.1741 - val_sparse_categorical_accuracy: 0.9209 - learning_rate: 0.0010 Epoch 142/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1383 - sparse_categorical_accuracy: 0.9476 - val_loss: 0.2704 - val_sparse_categorical_accuracy: 0.8821 - learning_rate: 0.0010 Epoch 143/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1104 - sparse_categorical_accuracy: 0.9576 - val_loss: 0.3363 - val_sparse_categorical_accuracy: 0.8447 - learning_rate: 0.0010 Epoch 144/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1037 - sparse_categorical_accuracy: 0.9666 - val_loss: 0.4437 - val_sparse_categorical_accuracy: 0.8169 - learning_rate: 0.0010 Epoch 145/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0939 - sparse_categorical_accuracy: 0.9688 - val_loss: 0.2474 - val_sparse_categorical_accuracy: 0.9029 - learning_rate: 0.0010 Epoch 146/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1130 - sparse_categorical_accuracy: 0.9564 - val_loss: 0.1531 - val_sparse_categorical_accuracy: 0.9362 - learning_rate: 0.0010 Epoch 147/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1022 - sparse_categorical_accuracy: 0.9626 - val_loss: 0.1573 - val_sparse_categorical_accuracy: 0.9348 - learning_rate: 0.0010 Epoch 148/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0815 - sparse_categorical_accuracy: 0.9774 - val_loss: 0.1416 - val_sparse_categorical_accuracy: 0.9390 - learning_rate: 0.0010 Epoch 149/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0937 - sparse_categorical_accuracy: 0.9701 - val_loss: 0.2065 - val_sparse_categorical_accuracy: 0.9112 - learning_rate: 0.0010 Epoch 150/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0955 - sparse_categorical_accuracy: 0.9672 - val_loss: 0.1146 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 0.0010 Epoch 151/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1097 - sparse_categorical_accuracy: 0.9560 - val_loss: 0.3142 - val_sparse_categorical_accuracy: 0.8599 - learning_rate: 0.0010 Epoch 152/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1017 - sparse_categorical_accuracy: 0.9636 - val_loss: 0.3406 - val_sparse_categorical_accuracy: 0.8433 - learning_rate: 0.0010 Epoch 153/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0930 - sparse_categorical_accuracy: 0.9684 - val_loss: 0.0928 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 5.0000e-04 Epoch 154/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0969 - sparse_categorical_accuracy: 0.9685 - val_loss: 0.2657 - val_sparse_categorical_accuracy: 0.8904 - learning_rate: 5.0000e-04 Epoch 155/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1045 - sparse_categorical_accuracy: 0.9634 - val_loss: 0.1027 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 5.0000e-04 Epoch 156/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0915 - sparse_categorical_accuracy: 0.9699 - val_loss: 0.1175 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 5.0000e-04 Epoch 157/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0949 - sparse_categorical_accuracy: 0.9634 - val_loss: 0.1001 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 5.0000e-04 Epoch 158/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0830 - sparse_categorical_accuracy: 0.9733 - val_loss: 0.0899 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 5.0000e-04 Epoch 159/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0827 - sparse_categorical_accuracy: 0.9758 - val_loss: 0.1171 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 5.0000e-04 Epoch 160/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0903 - sparse_categorical_accuracy: 0.9686 - val_loss: 0.1056 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 5.0000e-04 Epoch 161/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0765 - sparse_categorical_accuracy: 0.9777 - val_loss: 0.1604 - val_sparse_categorical_accuracy: 0.9376 - learning_rate: 5.0000e-04 Epoch 162/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0848 - sparse_categorical_accuracy: 0.9707 - val_loss: 0.0911 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 5.0000e-04 Epoch 163/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0891 - sparse_categorical_accuracy: 0.9684 - val_loss: 0.0882 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 5.0000e-04 Epoch 164/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0796 - sparse_categorical_accuracy: 0.9721 - val_loss: 0.0989 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 5.0000e-04 Epoch 165/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0810 - sparse_categorical_accuracy: 0.9720 - val_loss: 0.2738 - val_sparse_categorical_accuracy: 0.8655 - learning_rate: 5.0000e-04 Epoch 166/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0903 - sparse_categorical_accuracy: 0.9712 - 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learning_rate: 5.0000e-04 Epoch 172/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0853 - sparse_categorical_accuracy: 0.9692 - val_loss: 0.0941 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 5.0000e-04 Epoch 173/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0922 - sparse_categorical_accuracy: 0.9679 - val_loss: 0.0924 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 5.0000e-04 Epoch 174/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0954 - sparse_categorical_accuracy: 0.9699 - val_loss: 0.0898 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 5.0000e-04 Epoch 175/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0823 - sparse_categorical_accuracy: 0.9701 - val_loss: 0.1449 - val_sparse_categorical_accuracy: 0.9431 - learning_rate: 5.0000e-04 Epoch 176/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0853 - sparse_categorical_accuracy: 0.9692 - val_loss: 0.0877 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 5.0000e-04 Epoch 177/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - 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learning_rate: 2.5000e-04 Epoch 236/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0661 - sparse_categorical_accuracy: 0.9750 - val_loss: 0.0843 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 2.5000e-04 Epoch 237/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0722 - sparse_categorical_accuracy: 0.9775 - val_loss: 0.1315 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 2.5000e-04 Epoch 238/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0802 - sparse_categorical_accuracy: 0.9744 - val_loss: 0.0969 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 2.5000e-04 Epoch 239/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0697 - sparse_categorical_accuracy: 0.9795 - val_loss: 0.0890 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 2.5000e-04 Epoch 240/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0640 - sparse_categorical_accuracy: 0.9811 - val_loss: 0.0812 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 2.5000e-04 Epoch 241/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0637 - sparse_categorical_accuracy: 0.9852 - val_loss: 0.0750 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 2.5000e-04 Epoch 242/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0645 - sparse_categorical_accuracy: 0.9772 - val_loss: 0.0864 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 2.5000e-04 Epoch 243/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0776 - sparse_categorical_accuracy: 0.9746 - val_loss: 0.0885 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 2.5000e-04 Epoch 244/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0635 - sparse_categorical_accuracy: 0.9835 - val_loss: 0.1270 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 2.5000e-04 Epoch 245/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0669 - sparse_categorical_accuracy: 0.9761 - val_loss: 0.0803 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 2.5000e-04 Epoch 246/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0635 - sparse_categorical_accuracy: 0.9796 - val_loss: 0.0791 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 2.5000e-04 Epoch 247/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0622 - sparse_categorical_accuracy: 0.9801 - val_loss: 0.0928 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 2.5000e-04 Epoch 248/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0715 - sparse_categorical_accuracy: 0.9756 - val_loss: 0.0817 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 2.5000e-04 Epoch 249/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0652 - sparse_categorical_accuracy: 0.9821 - val_loss: 0.0804 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 2.5000e-04 Epoch 250/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0689 - sparse_categorical_accuracy: 0.9788 - val_loss: 0.0765 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 2.5000e-04 Epoch 251/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0720 - sparse_categorical_accuracy: 0.9773 - val_loss: 0.1128 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 2.5000e-04 Epoch 252/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0670 - sparse_categorical_accuracy: 0.9762 - val_loss: 0.0896 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 2.5000e-04 Epoch 253/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0743 - sparse_categorical_accuracy: 0.9776 - val_loss: 0.1141 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 2.5000e-04 Epoch 254/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0648 - sparse_categorical_accuracy: 0.9783 - val_loss: 0.1578 - val_sparse_categorical_accuracy: 0.9362 - learning_rate: 2.5000e-04 Epoch 255/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0554 - sparse_categorical_accuracy: 0.9862 - val_loss: 0.0835 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 2.5000e-04 Epoch 256/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0645 - sparse_categorical_accuracy: 0.9796 - val_loss: 0.0930 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 2.5000e-04 Epoch 257/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0645 - sparse_categorical_accuracy: 0.9838 - val_loss: 0.0784 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 2.5000e-04 Epoch 258/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0733 - sparse_categorical_accuracy: 0.9757 - val_loss: 0.0867 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 2.5000e-04 Epoch 259/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0601 - sparse_categorical_accuracy: 0.9836 - val_loss: 0.1279 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 2.5000e-04 Epoch 260/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0795 - sparse_categorical_accuracy: 0.9742 - val_loss: 0.1646 - val_sparse_categorical_accuracy: 0.9445 - learning_rate: 2.5000e-04 Epoch 261/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0755 - sparse_categorical_accuracy: 0.9755 - val_loss: 0.0781 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 2.5000e-04 Epoch 262/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0620 - sparse_categorical_accuracy: 0.9798 - val_loss: 0.0775 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.2500e-04 Epoch 263/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0671 - sparse_categorical_accuracy: 0.9777 - val_loss: 0.1033 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 1.2500e-04 Epoch 264/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0580 - sparse_categorical_accuracy: 0.9831 - val_loss: 0.0797 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 1.2500e-04 Epoch 265/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0620 - sparse_categorical_accuracy: 0.9828 - val_loss: 0.0770 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.2500e-04 Epoch 266/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0653 - sparse_categorical_accuracy: 0.9795 - val_loss: 0.0834 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.2500e-04 Epoch 267/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0646 - sparse_categorical_accuracy: 0.9808 - val_loss: 0.0911 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.2500e-04 Epoch 268/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0690 - sparse_categorical_accuracy: 0.9796 - val_loss: 0.0795 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.2500e-04 Epoch 269/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0727 - sparse_categorical_accuracy: 0.9737 - val_loss: 0.0812 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.2500e-04 Epoch 270/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0613 - sparse_categorical_accuracy: 0.9843 - val_loss: 0.0905 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.2500e-04 Epoch 271/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0624 - sparse_categorical_accuracy: 0.9782 - val_loss: 0.1130 - val_sparse_categorical_accuracy: 0.9542 - learning_rate: 1.2500e-04 Epoch 272/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0654 - sparse_categorical_accuracy: 0.9794 - val_loss: 0.0784 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04 Epoch 273/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0693 - sparse_categorical_accuracy: 0.9804 - val_loss: 0.0980 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 1.2500e-04 Epoch 274/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0627 - sparse_categorical_accuracy: 0.9842 - val_loss: 0.0864 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 1.2500e-04 Epoch 275/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0713 - sparse_categorical_accuracy: 0.9778 - val_loss: 0.0956 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 1.2500e-04 Epoch 276/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0631 - sparse_categorical_accuracy: 0.9812 - val_loss: 0.0805 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04 Epoch 277/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0613 - sparse_categorical_accuracy: 0.9797 - val_loss: 0.0982 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 1.2500e-04 Epoch 278/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0649 - sparse_categorical_accuracy: 0.9818 - val_loss: 0.0857 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.2500e-04 Epoch 279/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0668 - sparse_categorical_accuracy: 0.9788 - val_loss: 0.0845 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.2500e-04 Epoch 280/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0679 - sparse_categorical_accuracy: 0.9762 - val_loss: 0.0835 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 1.2500e-04 Epoch 281/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0766 - sparse_categorical_accuracy: 0.9734 - val_loss: 0.0810 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 1.2500e-04 Epoch 282/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0589 - sparse_categorical_accuracy: 0.9815 - val_loss: 0.0829 - val_sparse_categorical_accuracy: 0.9626 - learning_rate: 1.0000e-04 Epoch 283/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0676 - sparse_categorical_accuracy: 0.9771 - val_loss: 0.0856 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 1.0000e-04 Epoch 284/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0607 - sparse_categorical_accuracy: 0.9832 - val_loss: 0.0850 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.0000e-04 Epoch 285/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0723 - sparse_categorical_accuracy: 0.9782 - val_loss: 0.0844 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.0000e-04 Epoch 286/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0620 - sparse_categorical_accuracy: 0.9789 - val_loss: 0.1347 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 1.0000e-04 Epoch 287/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0641 - sparse_categorical_accuracy: 0.9802 - val_loss: 0.0765 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.0000e-04 Epoch 288/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0654 - sparse_categorical_accuracy: 0.9797 - val_loss: 0.1081 - val_sparse_categorical_accuracy: 0.9528 - learning_rate: 1.0000e-04 Epoch 289/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0690 - sparse_categorical_accuracy: 0.9785 - val_loss: 0.1734 - val_sparse_categorical_accuracy: 0.9362 - learning_rate: 1.0000e-04 Epoch 290/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0771 - sparse_categorical_accuracy: 0.9750 - val_loss: 0.0821 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 1.0000e-04 Epoch 291/500 90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0605 - sparse_categorical_accuracy: 0.9839 - val_loss: 0.0770 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 1.0000e-04 Epoch 291: early stopping

根据测试数据评估模型

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

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

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

 42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - loss: 0.0997 - sparse_categorical_accuracy: 0.9687 Test accuracy 0.9696969985961914 Test loss 0.09916326403617859

绘制模型的训练和验证损失

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()

PNG 格式

我们可以看到，在 100 个 epoch 后，训练准确率如何达到近 0.95。但是，通过观察验证准确性，我们可以看到网络仍然需要训练，直到验证和训练准确率都达到几乎 0.97 在 200 个 epoch 之后。在第 200 个 epoch 之后，如果我们继续训练，验证准确率将开始降低，而训练准确率将继续提高：模型开始过拟合。