从头开始进行时间序列分类
作者： hfawaz
创建日期： 2020/07/21
最后修改： 2023/11/10
描述： 在来自UCR/UEA存档的FordA数据集上从头开始训练时间序列分类器。
介绍
本示例展示了如何从头开始进行时间序列分类，从硬盘上的原始CSV时间序列文件开始。我们在来自UCR/UEA存档的FordA数据集上演示整个工作流程。
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1320个测试实例。每个时间序列对应于由电机传感器捕获的发动机噪声测量。该任务的目标是自动检测发动机出现特定问题的情况。这个问题是一个平衡的二分类任务。对此数据集的完整描述可以在这里找到。
读取TSV数据
我们将使用FordA_TRAIN文件进行训练，使用FordA_TEST文件进行测试。这个数据集的简单性使我们能够有效地演示如何使用卷积网络进行时间序列分类。在这个文件中，第一列对应于标签。
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，我们为数据集中每个类可视化一个时间序列示例。
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()
标准化数据
我们的时间序列已经都是相同的长度（500）。但是，它们的值通常在不同的范围内。这对于神经网络来说并不理想；一般来说，我们应该寻求使输入值归一化。对于这个特定数据集，数据已经是z标准化的：每个时间序列样本的均值等于零，标准差等于一。这种类型的标准化对于时间序列分类问题是非常常见的，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，这意味着我们每个时间序列示例只有一个通道。因此，我们将通过numpy的简单重塑将时间序列转换为具有一个通道的多变量形式。这将使我们能够构建一个可以轻松应用于多变量时间序列的模型。
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sparse_categorical_crossentropy，我们必须提前计算类别的数量。
num_classes = len(np.unique(y_train))
现在我们打乱训练集，因为稍后在训练时我们将使用validation_split选项。
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。期望的标签将是0和1。
y_train[y_train == -1] = 0
y_test[y_test == -1] = 0
构建模型
我们构建一个完整的卷积神经网络，最初是在this paper中提出的。该实现基于在这里提供的TF 2版本。以下超参数（kernel_size、filters、使用BatchNorm）是通过使用KerasTuner的随机搜索找到的。
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)
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 - val_loss: 0.0985 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 5.0000e-04
Epoch 167/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0835 - sparse_categorical_accuracy: 0.9712 - val_loss: 0.1081 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 5.0000e-04
Epoch 168/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1182 - sparse_categorical_accuracy: 0.9519 - val_loss: 0.1212 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 5.0000e-04
Epoch 169/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0909 - sparse_categorical_accuracy: 0.9666 - val_loss: 0.0909 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 5.0000e-04
Epoch 170/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0882 - sparse_categorical_accuracy: 0.9708 - val_loss: 0.0912 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 5.0000e-04
Epoch 171/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0863 - sparse_categorical_accuracy: 0.9735 - val_loss: 0.1391 - val_sparse_categorical_accuracy: 0.9487 - 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 - loss: 0.0834 - sparse_categorical_accuracy: 0.9692 - val_loss: 0.2338 - val_sparse_categorical_accuracy: 0.8974 - learning_rate: 5.0000e-04
Epoch 178/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0940 - sparse_categorical_accuracy: 0.9639 - val_loss: 0.1609 - val_sparse_categorical_accuracy: 0.9431 - learning_rate: 5.0000e-04
Epoch 179/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0965 - sparse_categorical_accuracy: 0.9628 - val_loss: 0.5213 - val_sparse_categorical_accuracy: 0.7947 - learning_rate: 5.0000e-04
Epoch 180/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0926 - sparse_categorical_accuracy: 0.9720 - val_loss: 0.0898 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 5.0000e-04
Epoch 181/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0854 - sparse_categorical_accuracy: 0.9732 - val_loss: 0.0949 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 5.0000e-04
Epoch 182/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0691 - sparse_categorical_accuracy: 0.9764 - val_loss: 0.0841 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 5.0000e-04
Epoch 183/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0768 - sparse_categorical_accuracy: 0.9766 - val_loss: 0.1021 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 5.0000e-04
Epoch 184/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0842 - sparse_categorical_accuracy: 0.9692 - val_loss: 0.1105 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 5.0000e-04
Epoch 185/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0731 - sparse_categorical_accuracy: 0.9760 - val_loss: 0.1487 - val_sparse_categorical_accuracy: 0.9404 - learning_rate: 5.0000e-04
Epoch 186/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0762 - sparse_categorical_accuracy: 0.9724 - val_loss: 0.1126 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 5.0000e-04
Epoch 187/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0783 - sparse_categorical_accuracy: 0.9723 - val_loss: 0.0954 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 5.0000e-04
Epoch 188/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0960 - sparse_categorical_accuracy: 0.9671 - val_loss: 0.1957 - val_sparse_categorical_accuracy: 0.9085 - learning_rate: 5.0000e-04
Epoch 189/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0831 - sparse_categorical_accuracy: 0.9695 - val_loss: 0.1711 - val_sparse_categorical_accuracy: 0.9431 - learning_rate: 5.0000e-04
Epoch 190/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0881 - sparse_categorical_accuracy: 0.9693 - val_loss: 0.0861 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 5.0000e-04
Epoch 191/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0735 - sparse_categorical_accuracy: 0.9769 - val_loss: 0.1154 - val_sparse_categorical_accuracy: 0.9584 - learning_rate: 5.0000e-04
Epoch 192/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0877 - sparse_categorical_accuracy: 0.9708 - val_loss: 0.0845 - val_sparse_categorical_accuracy: 0.9736 - learning_rate: 5.0000e-04
Epoch 193/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0899 - sparse_categorical_accuracy: 0.9709 - val_loss: 0.0977 - val_sparse_categorical_accuracy: 0.9709 - learning_rate: 5.0000e-04
Epoch 194/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0843 - sparse_categorical_accuracy: 0.9739 - val_loss: 0.0969 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 5.0000e-04
Epoch 195/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0755 - sparse_categorical_accuracy: 0.9765 - val_loss: 0.1345 - val_sparse_categorical_accuracy: 0.9473 - learning_rate: 5.0000e-04
Epoch 196/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0768 - sparse_categorical_accuracy: 0.9733 - val_loss: 0.0844 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 5.0000e-04
Epoch 197/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0751 - sparse_categorical_accuracy: 0.9801 - val_loss: 0.2736 - val_sparse_categorical_accuracy: 0.8793 - learning_rate: 5.0000e-04
Epoch 198/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0860 - sparse_categorical_accuracy: 0.9719 - val_loss: 0.0843 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 5.0000e-04
Epoch 199/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0835 - sparse_categorical_accuracy: 0.9712 - val_loss: 0.1799 - val_sparse_categorical_accuracy: 0.9209 - learning_rate: 5.0000e-04
Epoch 200/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0760 - sparse_categorical_accuracy: 0.9745 - val_loss: 0.1790 - val_sparse_categorical_accuracy: 0.9112 - learning_rate: 5.0000e-04
Epoch 201/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0714 - sparse_categorical_accuracy: 0.9742 - val_loss: 0.0918 - val_sparse_categorical_accuracy: 0.9667 - learning_rate: 5.0000e-04
Epoch 202/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0734 - sparse_categorical_accuracy: 0.9748 - val_loss: 0.1168 - val_sparse_categorical_accuracy: 0.9515 - learning_rate: 5.0000e-04
Epoch 203/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0654 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.0825 - val_sparse_categorical_accuracy: 0.9723 - learning_rate: 2.5000e-04
Epoch 204/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0717 - sparse_categorical_accuracy: 0.9796 - val_loss: 0.1186 - val_sparse_categorical_accuracy: 0.9556 - learning_rate: 2.5000e-04
Epoch 205/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0935 - sparse_categorical_accuracy: 0.9679 - val_loss: 0.0847 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 2.5000e-04
Epoch 206/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0897 - sparse_categorical_accuracy: 0.9687 - val_loss: 0.0820 - val_sparse_categorical_accuracy: 0.9723 - learning_rate: 2.5000e-04
Epoch 207/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0661 - sparse_categorical_accuracy: 0.9763 - val_loss: 0.0790 - val_sparse_categorical_accuracy: 0.9723 - learning_rate: 2.5000e-04
Epoch 208/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0683 - sparse_categorical_accuracy: 0.9739 - val_loss: 0.0991 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 2.5000e-04
Epoch 209/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0744 - sparse_categorical_accuracy: 0.9792 - val_loss: 0.1057 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 2.5000e-04
Epoch 210/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0715 - sparse_categorical_accuracy: 0.9747 - val_loss: 0.0858 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 2.5000e-04
Epoch 211/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0715 - sparse_categorical_accuracy: 0.9764 - val_loss: 0.0856 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 2.5000e-04
Epoch 212/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0783 - sparse_categorical_accuracy: 0.9708 - val_loss: 0.0835 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 2.5000e-04
Epoch 213/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0680 - sparse_categorical_accuracy: 0.9761 - val_loss: 0.0894 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 2.5000e-04
Epoch 214/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0661 - sparse_categorical_accuracy: 0.9800 - val_loss: 0.0788 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 2.5000e-04
Epoch 215/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0736 - sparse_categorical_accuracy: 0.9744 - val_loss: 0.1047 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 2.5000e-04
Epoch 216/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0655 - sparse_categorical_accuracy: 0.9819 - val_loss: 0.1158 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 2.5000e-04
Epoch 217/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0722 - sparse_categorical_accuracy: 0.9777 - val_loss: 0.0940 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 2.5000e-04
Epoch 218/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0750 - sparse_categorical_accuracy: 0.9761 - val_loss: 0.0966 - val_sparse_categorical_accuracy: 0.9653 - learning_rate: 2.5000e-04
Epoch 219/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0695 - sparse_categorical_accuracy: 0.9753 - val_loss: 0.1727 - val_sparse_categorical_accuracy: 0.9293 - learning_rate: 2.5000e-04
Epoch 220/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0748 - sparse_categorical_accuracy: 0.9760 - val_loss: 0.1067 - val_sparse_categorical_accuracy: 0.9570 - learning_rate: 2.5000e-04
Epoch 221/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0848 - sparse_categorical_accuracy: 0.9740 - val_loss: 0.0818 - val_sparse_categorical_accuracy: 0.9681 - learning_rate: 2.5000e-04
Epoch 222/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0675 - sparse_categorical_accuracy: 0.9808 - val_loss: 0.0931 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 2.5000e-04
Epoch 223/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0695 - sparse_categorical_accuracy: 0.9760 - val_loss: 0.0785 - val_sparse_categorical_accuracy: 0.9723 - learning_rate: 2.5000e-04
Epoch 224/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0680 - sparse_categorical_accuracy: 0.9822 - val_loss: 0.0820 - val_sparse_categorical_accuracy: 0.9695 - learning_rate: 2.5000e-04
Epoch 225/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0637 - sparse_categorical_accuracy: 0.9772 - val_loss: 0.1084 - val_sparse_categorical_accuracy: 0.9612 - learning_rate: 2.5000e-04
Epoch 226/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0703 - sparse_categorical_accuracy: 0.9797 - val_loss: 0.1029 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 2.5000e-04
Epoch 227/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0821 - sparse_categorical_accuracy: 0.9704 - val_loss: 0.1545 - val_sparse_categorical_accuracy: 0.9431 - learning_rate: 2.5000e-04
Epoch 228/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0826 - sparse_categorical_accuracy: 0.9714 - val_loss: 0.0819 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 2.5000e-04
Epoch 229/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0755 - sparse_categorical_accuracy: 0.9750 - val_loss: 0.0788 - val_sparse_categorical_accuracy: 0.9723 - learning_rate: 2.5000e-04
Epoch 230/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0702 - sparse_categorical_accuracy: 0.9776 - val_loss: 0.1514 - val_sparse_categorical_accuracy: 0.9445 - learning_rate: 2.5000e-04
Epoch 231/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0749 - sparse_categorical_accuracy: 0.9775 - val_loss: 0.1150 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 2.5000e-04
Epoch 232/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0732 - sparse_categorical_accuracy: 0.9794 - val_loss: 0.1110 - val_sparse_categorical_accuracy: 0.9598 - learning_rate: 2.5000e-04
Epoch 233/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0667 - sparse_categorical_accuracy: 0.9781 - val_loss: 0.1451 - val_sparse_categorical_accuracy: 0.9445 - learning_rate: 2.5000e-04
Epoch 234/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0812 - sparse_categorical_accuracy: 0.9793 - val_loss: 0.0954 - val_sparse_categorical_accuracy: 0.9639 - learning_rate: 2.5000e-04
Epoch 235/500
90/90 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0629 - sparse_categorical_accuracy: 0.9844 - val_loss: 0.0982 - val_sparse_categorical_accuracy: 0.9639 - 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
在测试数据上评估模型
model = keras.models.load_model("best_model.keras")
test_loss, test_acc = model.evaluate(x_test, y_test)
print("测试准确率", test_acc)
print("测试损失", test_loss)
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - loss: 0.0997 - sparse_categorical_accuracy: 0.9687
测试准确率 0.9696969985961914
测试损失 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("模型 " + metric)
plt.ylabel(metric, fontsize="large")
plt.xlabel("轮次", fontsize="large")
plt.legend(["训练", "验证"], loc="best")
plt.show()
plt.close()
我们可以看到训练准确率在100个轮次后几乎达到了0.95。 然而，通过观察验证准确率，我们可以看到网络仍然需要 训练，直到在200个轮次后验证和训练准确率都几乎达到0.97。超过第200个轮次后，若继续训练，验证准确率将开始下降，而训练准确率将继续上升： 模型开始过拟合。