► 代码示例 / 结构化数据 / 使用 Wide、Deep 和 Cross 网络进行结构化数据学习

使用 Wide、Deep 和 Cross 网络进行结构化数据学习

作者： Khalid Salama
创建日期 2020/12/31
最后修改日期 2025/01/03
描述： 使用 Wide & Deep 和 Deep & Cross 网络进行结构化数据分类。

ⓘ 本示例使用 Keras 3

在 Colab 中查看 • GitHub 源代码

简介

本示例演示了如何使用这两种建模技术进行结构化数据分类。

Wide & Deep 模型
Deep & Cross 模型

请注意，此示例应与 TensorFlow 2.5 或更高版本一起运行。

数据集

本示例使用了 UCI 机器学习存储库中的 Covertype 数据集。任务是从地图测绘变量预测森林覆盖类型。该数据集包含 506,011 个实例，具有 12 个输入特征：10 个数值特征和 2 个分类特征。每个实例被归入 7 个类别中的 1 个。

设置

import os

# Only the TensorFlow backend supports string inputs.
os.environ["KERAS_BACKEND"] = "tensorflow"

import math
import numpy as np
import pandas as pd
from tensorflow import data as tf_data
import keras
from keras import layers

准备数据

首先，我们将数据集从 UCI 机器学习存储库加载到 Pandas DataFrame 中

data_url = (
    "https://archive.ics.uci.edu/ml/machine-learning-databases/covtype/covtype.data.gz"
)
raw_data = pd.read_csv(data_url, header=None)
print(f"Dataset shape: {raw_data.shape}")
raw_data.head()

Dataset shape: (581012, 55)

	0	1	2	3	4	5	6	7	8	9	...	54
0	2596	51	3	258	0	510	221	232	148	6279	...	5
1	2590	56	2	212	-6	390	220	235	151	6225	...	5
2	2804	139	9	268	65	3180	234	238	135	6121	...	2
3	2785	155	18	242	118	3090	238	238	122	6211	...	2
4	2595	45	2	153	-1	391	220	234	150	6172	...	5

5 行 × 55 列

数据集中有两个分类特征是二元编码的。我们将把这种数据集表示转换为典型的表示，其中每个分类特征表示为一个单一的整数值。

soil_type_values = [f"soil_type_{idx+1}" for idx in range(40)]
wilderness_area_values = [f"area_type_{idx+1}" for idx in range(4)]

soil_type = raw_data.loc[:, 14:53].apply(
    lambda x: soil_type_values[0::1][x.to_numpy().nonzero()[0][0]], axis=1
)
wilderness_area = raw_data.loc[:, 10:13].apply(
    lambda x: wilderness_area_values[0::1][x.to_numpy().nonzero()[0][0]], axis=1
)

CSV_HEADER = [
    "Elevation",
    "Aspect",
    "Slope",
    "Horizontal_Distance_To_Hydrology",
    "Vertical_Distance_To_Hydrology",
    "Horizontal_Distance_To_Roadways",
    "Hillshade_9am",
    "Hillshade_Noon",
    "Hillshade_3pm",
    "Horizontal_Distance_To_Fire_Points",
    "Wilderness_Area",
    "Soil_Type",
    "Cover_Type",
]

data = pd.concat(
    [raw_data.loc[:, 0:9], wilderness_area, soil_type, raw_data.loc[:, 54]],
    axis=1,
    ignore_index=True,
)
data.columns = CSV_HEADER

# Convert the target label indices into a range from 0 to 6 (there are 7 labels in total).
data["Cover_Type"] = data["Cover_Type"] - 1

print(f"Dataset shape: {data.shape}")
data.head().T

Dataset shape: (581012, 13)

	0	1	2	3	4
海拔	2596	2590	2804	2785	2595
方位角	51	56	139	155	45
Slope	3	2	9	18	2
水平距离到水文	258	212	268	242	153
垂直距离到水文	0	-6	65	118	-1
水平距离到道路	510	390	3180	3090	391
9 AM 阴影	221	220	234	238	220
中午阴影	232	235	238	238	234
3 PM 阴影	148	151	135	122	150
水平距离到火点	6279	6225	6121	6211	6172
荒野区域	区域类型 1	区域类型 1	区域类型 1	区域类型 1	区域类型 1
土壤类型	土壤类型 29	土壤类型 29	土壤类型 12	土壤类型 30	土壤类型 29
覆盖类型	4	4	1	1	4

DataFrame 的形状显示每个样本有 13 列（12 列用于特征，1 列用于目标标签）。

让我们将数据分割为训练集（85%）和测试集（15%）。

train_splits = []
test_splits = []

for _, group_data in data.groupby("Cover_Type"):
    random_selection = np.random.rand(len(group_data.index)) <= 0.85
    train_splits.append(group_data[random_selection])
    test_splits.append(group_data[~random_selection])

train_data = pd.concat(train_splits).sample(frac=1).reset_index(drop=True)
test_data = pd.concat(test_splits).sample(frac=1).reset_index(drop=True)

print(f"Train split size: {len(train_data.index)}")
print(f"Test split size: {len(test_data.index)}")

Train split size: 494149
Test split size: 86863

接下来，将训练和测试数据存储在单独的 CSV 文件中。

train_data_file = "train_data.csv"
test_data_file = "test_data.csv"

train_data.to_csv(train_data_file, index=False)
test_data.to_csv(test_data_file, index=False)

定义数据集元数据

在这里，我们定义数据集的元数据，这将有助于读取和解析数据为输入特征，并根据其类型对输入特征进行编码。

TARGET_FEATURE_NAME = "Cover_Type"

TARGET_FEATURE_LABELS = ["0", "1", "2", "3", "4", "5", "6"]

NUMERIC_FEATURE_NAMES = [
    "Aspect",
    "Elevation",
    "Hillshade_3pm",
    "Hillshade_9am",
    "Hillshade_Noon",
    "Horizontal_Distance_To_Fire_Points",
    "Horizontal_Distance_To_Hydrology",
    "Horizontal_Distance_To_Roadways",
    "Slope",
    "Vertical_Distance_To_Hydrology",
]

CATEGORICAL_FEATURES_WITH_VOCABULARY = {
    "Soil_Type": list(data["Soil_Type"].unique()),
    "Wilderness_Area": list(data["Wilderness_Area"].unique()),
}

CATEGORICAL_FEATURE_NAMES = list(CATEGORICAL_FEATURES_WITH_VOCABULARY.keys())

FEATURE_NAMES = NUMERIC_FEATURE_NAMES + CATEGORICAL_FEATURE_NAMES

COLUMN_DEFAULTS = [
    [0] if feature_name in NUMERIC_FEATURE_NAMES + [TARGET_FEATURE_NAME] else ["NA"]
    for feature_name in CSV_HEADER
]

NUM_CLASSES = len(TARGET_FEATURE_LABELS)

实验设置

接下来，我们定义一个输入函数，该函数读取并解析文件，然后将特征和标签转换为 tf.data.Dataset 以用于训练或评估。

# To convert the datasets elements to from OrderedDict to Dictionary
def process(features, target):
    return dict(features), target


def get_dataset_from_csv(csv_file_path, batch_size, shuffle=False):
    dataset = tf_data.experimental.make_csv_dataset(
        csv_file_path,
        batch_size=batch_size,
        column_names=CSV_HEADER,
        column_defaults=COLUMN_DEFAULTS,
        label_name=TARGET_FEATURE_NAME,
        num_epochs=1,
        header=True,
        shuffle=shuffle,
    ).map(process)
    return dataset.cache()

在这里，我们配置参数并实现一个程序，该程序给定一个模型来运行训练和评估实验。

learning_rate = 0.001
dropout_rate = 0.1
batch_size = 265
num_epochs = 1

hidden_units = [32, 32]


def run_experiment(model):
    model.compile(
        optimizer=keras.optimizers.Adam(learning_rate=learning_rate),
        loss=keras.losses.SparseCategoricalCrossentropy(),
        metrics=[keras.metrics.SparseCategoricalAccuracy()],
    )

    train_dataset = get_dataset_from_csv(train_data_file, batch_size, shuffle=True)

    test_dataset = get_dataset_from_csv(test_data_file, batch_size)

    print("Start training the model...")
    history = model.fit(train_dataset, epochs=num_epochs)
    print("Model training finished")

    _, accuracy = model.evaluate(test_dataset, verbose=0)

    print(f"Test accuracy: {round(accuracy * 100, 2)}%")

创建模型输入

现在，将模型的输入定义为一个字典，其中键是特征名称，值是具有相应特征形状和数据类型的 keras.layers.Input 张量。

def create_model_inputs():
    inputs = {}
    for feature_name in FEATURE_NAMES:
        if feature_name in NUMERIC_FEATURE_NAMES:
            inputs[feature_name] = layers.Input(
                name=feature_name, shape=(), dtype="float32"
            )
        else:
            inputs[feature_name] = layers.Input(
                name=feature_name, shape=(), dtype="string"
            )
    return inputs

编码特征

我们创建了两种表示形式的输入特征：稀疏和密集：1. 在 **稀疏** 表示中，使用 CategoryEncoding 层通过独热编码对分类特征进行编码。这种表示形式可以帮助模型“记住”特定的特征值以做出某些预测。2. 在 **密集** 表示中，使用 Embedding 层将分类特征编码为低维嵌入。这种表示形式有助于模型很好地泛化到未见过的特征组合。

def encode_inputs(inputs, use_embedding=False):
    encoded_features = []
    for feature_name in inputs:
        if feature_name in CATEGORICAL_FEATURE_NAMES:
            vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[feature_name]
            # Create a lookup to convert string values to an integer indices.
            # Since we are not using a mask token nor expecting any out of vocabulary
            # (oov) token, we set mask_token to None and  num_oov_indices to 0.
            lookup = layers.StringLookup(
                vocabulary=vocabulary,
                mask_token=None,
                num_oov_indices=0,
                output_mode="int" if use_embedding else "binary",
            )
            if use_embedding:
                # Convert the string input values into integer indices.
                encoded_feature = lookup(inputs[feature_name])
                embedding_dims = int(math.sqrt(len(vocabulary)))
                # Create an embedding layer with the specified dimensions.
                embedding = layers.Embedding(
                    input_dim=len(vocabulary), output_dim=embedding_dims
                )
                # Convert the index values to embedding representations.
                encoded_feature = embedding(encoded_feature)
            else:
                # Convert the string input values into a one hot encoding.
                encoded_feature = lookup(
                    keras.ops.expand_dims(inputs[feature_name], -1)
                )
        else:
            # Use the numerical features as-is.
            encoded_feature = keras.ops.expand_dims(inputs[feature_name], -1)

        encoded_features.append(encoded_feature)

    all_features = layers.concatenate(encoded_features)
    return all_features

实验 1：基线模型

在第一个实验中，让我们创建一个多层前馈网络，其中分类特征被独热编码。

def create_baseline_model():
    inputs = create_model_inputs()
    features = encode_inputs(inputs)

    for units in hidden_units:
        features = layers.Dense(units)(features)
        features = layers.BatchNormalization()(features)
        features = layers.ReLU()(features)
        features = layers.Dropout(dropout_rate)(features)

    outputs = layers.Dense(units=NUM_CLASSES, activation="softmax")(features)
    model = keras.Model(inputs=inputs, outputs=outputs)
    return model


baseline_model = create_baseline_model()
keras.utils.plot_model(baseline_model, show_shapes=True, rankdir="LR")

png

让我们开始运行

run_experiment(baseline_model)

Start training the model...

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858/Unknown  228s 260ms/step - loss: 1.0959 - sparse_categorical_accuracy: 0.5790


859/Unknown  229s 260ms/step - loss: 1.0956 - sparse_categorical_accuracy: 0.5791


860/Unknown  229s 260ms/step - loss: 1.0954 - sparse_categorical_accuracy: 0.5792


861/Unknown  229s 260ms/step - loss: 1.0951 - sparse_categorical_accuracy: 0.5793


862/Unknown  229s 260ms/step - loss: 1.0948 - sparse_categorical_accuracy: 0.5794


863/Unknown  230s 260ms/step - loss: 1.0945 - sparse_categorical_accuracy: 0.5795


864/Unknown  230s 260ms/step - loss: 1.0943 - sparse_categorical_accuracy: 0.5796


865/Unknown  230s 260ms/step - loss: 1.0940 - sparse_categorical_accuracy: 0.5796


866/Unknown  231s 260ms/step - loss: 1.0937 - sparse_categorical_accuracy: 0.5797


867/Unknown  231s 260ms/step - loss: 1.0935 - sparse_categorical_accuracy: 0.5798


868/Unknown  231s 260ms/step - loss: 1.0932 - sparse_categorical_accuracy: 0.5799


869/Unknown  231s 260ms/step - loss: 1.0929 - sparse_categorical_accuracy: 0.5800


870/Unknown  232s 260ms/step - loss: 1.0927 - sparse_categorical_accuracy: 0.5801


871/Unknown  232s 260ms/step - loss: 1.0924 - sparse_categorical_accuracy: 0.5802


872/Unknown  232s 260ms/step - loss: 1.0921 - sparse_categorical_accuracy: 0.5802


873/Unknown  232s 260ms/step - loss: 1.0919 - sparse_categorical_accuracy: 0.5803


874/Unknown  233s 260ms/step - loss: 1.0916 - sparse_categorical_accuracy: 0.5804


875/Unknown  233s 260ms/step - loss: 1.0913 - sparse_categorical_accuracy: 0.5805


876/Unknown  233s 260ms/step - loss: 1.0911 - sparse_categorical_accuracy: 0.5806


877/Unknown  233s 260ms/step - loss: 1.0908 - sparse_categorical_accuracy: 0.5807


878/Unknown  234s 260ms/step - loss: 1.0906 - sparse_categorical_accuracy: 0.5808


879/Unknown  234s 260ms/step - loss: 1.0903 - sparse_categorical_accuracy: 0.5808


880/Unknown  234s 260ms/step - loss: 1.0900 - sparse_categorical_accuracy: 0.5809


881/Unknown  234s 260ms/step - loss: 1.0898 - sparse_categorical_accuracy: 0.5810


882/Unknown  235s 260ms/step - loss: 1.0895 - sparse_categorical_accuracy: 0.5811


883/Unknown  235s 260ms/step - loss: 1.0893 - sparse_categorical_accuracy: 0.5812


884/Unknown  235s 260ms/step - loss: 1.0890 - sparse_categorical_accuracy: 0.5813


885/Unknown  235s 260ms/step - loss: 1.0887 - sparse_categorical_accuracy: 0.5813


886/Unknown  236s 260ms/step - loss: 1.0885 - sparse_categorical_accuracy: 0.5814


887/Unknown  236s 260ms/step - loss: 1.0882 - sparse_categorical_accuracy: 0.5815


888/Unknown  237s 260ms/step - loss: 1.0880 - sparse_categorical_accuracy: 0.5816


889/Unknown  237s 260ms/step - loss: 1.0877 - sparse_categorical_accuracy: 0.5817


890/Unknown  237s 260ms/step - loss: 1.0874 - sparse_categorical_accuracy: 0.5818


891/Unknown  238s 261ms/step - loss: 1.0872 - sparse_categorical_accuracy: 0.5818


892/Unknown  238s 261ms/step - loss: 1.0869 - sparse_categorical_accuracy: 0.5819


893/Unknown  238s 261ms/step - loss: 1.0867 - sparse_categorical_accuracy: 0.5820


894/Unknown  239s 261ms/step - loss: 1.0864 - sparse_categorical_accuracy: 0.5821


895/Unknown  239s 261ms/step - loss: 1.0862 - sparse_categorical_accuracy: 0.5822


896/Unknown  239s 261ms/step - loss: 1.0859 - sparse_categorical_accuracy: 0.5823


897/Unknown  239s 261ms/step - loss: 1.0856 - sparse_categorical_accuracy: 0.5823


898/Unknown  240s 261ms/step - loss: 1.0854 - sparse_categorical_accuracy: 0.5824


899/Unknown  240s 261ms/step - loss: 1.0851 - sparse_categorical_accuracy: 0.5825


900/Unknown  240s 261ms/step - loss: 1.0849 - sparse_categorical_accuracy: 0.5826


901/Unknown  240s 261ms/step - loss: 1.0846 - sparse_categorical_accuracy: 0.5827


902/Unknown  241s 261ms/step - loss: 1.0844 - sparse_categorical_accuracy: 0.5827


903/Unknown  241s 261ms/step - loss: 1.0841 - sparse_categorical_accuracy: 0.5828


904/Unknown  241s 261ms/step - loss: 1.0839 - sparse_categorical_accuracy: 0.5829


905/Unknown  241s 261ms/step - loss: 1.0836 - sparse_categorical_accuracy: 0.5830


906/Unknown  242s 261ms/step - loss: 1.0834 - sparse_categorical_accuracy: 0.5831


907/Unknown  242s 261ms/step - loss: 1.0831 - sparse_categorical_accuracy: 0.5832


908/Unknown  242s 261ms/step - loss: 1.0829 - sparse_categorical_accuracy: 0.5832


909/Unknown  243s 261ms/step - loss: 1.0826 - sparse_categorical_accuracy: 0.5833


910/Unknown  243s 261ms/step - loss: 1.0824 - sparse_categorical_accuracy: 0.5834


911/Unknown  243s 261ms/step - loss: 1.0821 - sparse_categorical_accuracy: 0.5835


912/Unknown  243s 261ms/step - loss: 1.0819 - sparse_categorical_accuracy: 0.5836


913/Unknown  244s 261ms/step - loss: 1.0816 - sparse_categorical_accuracy: 0.5836


914/Unknown  244s 261ms/step - loss: 1.0814 - sparse_categorical_accuracy: 0.5837


915/Unknown  244s 261ms/step - loss: 1.0811 - sparse_categorical_accuracy: 0.5838


916/Unknown  244s 261ms/step - loss: 1.0809 - sparse_categorical_accuracy: 0.5839


917/Unknown  245s 261ms/step - loss: 1.0806 - sparse_categorical_accuracy: 0.5839


918/Unknown  245s 261ms/step - loss: 1.0804 - sparse_categorical_accuracy: 0.5840


919/Unknown  245s 261ms/step - loss: 1.0801 - sparse_categorical_accuracy: 0.5841


920/Unknown  246s 261ms/step - loss: 1.0799 - sparse_categorical_accuracy: 0.5842


921/Unknown  246s 261ms/step - loss: 1.0797 - sparse_categorical_accuracy: 0.5843


922/Unknown  246s 261ms/step - loss: 1.0794 - sparse_categorical_accuracy: 0.5843


923/Unknown  247s 261ms/step - loss: 1.0792 - sparse_categorical_accuracy: 0.5844


924/Unknown  247s 261ms/step - loss: 1.0789 - sparse_categorical_accuracy: 0.5845


925/Unknown  247s 261ms/step - loss: 1.0787 - sparse_categorical_accuracy: 0.5846


926/Unknown  248s 261ms/step - loss: 1.0784 - sparse_categorical_accuracy: 0.5847


927/Unknown  248s 261ms/step - loss: 1.0782 - sparse_categorical_accuracy: 0.5847


928/Unknown  248s 261ms/step - loss: 1.0780 - sparse_categorical_accuracy: 0.5848


929/Unknown  248s 261ms/step - loss: 1.0777 - sparse_categorical_accuracy: 0.5849


930/Unknown  249s 261ms/step - loss: 1.0775 - sparse_categorical_accuracy: 0.5850


931/Unknown  249s 261ms/step - loss: 1.0772 - sparse_categorical_accuracy: 0.5850


932/Unknown  249s 261ms/step - loss: 1.0770 - sparse_categorical_accuracy: 0.5851


933/Unknown  250s 262ms/step - loss: 1.0767 - sparse_categorical_accuracy: 0.5852


934/Unknown  250s 262ms/step - loss: 1.0765 - sparse_categorical_accuracy: 0.5853


935/Unknown  250s 262ms/step - loss: 1.0763 - sparse_categorical_accuracy: 0.5854


936/Unknown  250s 262ms/step - loss: 1.0760 - sparse_categorical_accuracy: 0.5854


937/Unknown  251s 262ms/step - loss: 1.0758 - sparse_categorical_accuracy: 0.5855


938/Unknown  251s 262ms/step - loss: 1.0755 - sparse_categorical_accuracy: 0.5856


939/Unknown  251s 262ms/step - loss: 1.0753 - sparse_categorical_accuracy: 0.5857


940/Unknown  252s 262ms/step - loss: 1.0751 - sparse_categorical_accuracy: 0.5857


941/Unknown  252s 262ms/step - loss: 1.0748 - sparse_categorical_accuracy: 0.5858


942/Unknown  252s 262ms/step - loss: 1.0746 - sparse_categorical_accuracy: 0.5859


943/Unknown  252s 262ms/step - loss: 1.0744 - sparse_categorical_accuracy: 0.5860


944/Unknown  253s 262ms/step - loss: 1.0741 - sparse_categorical_accuracy: 0.5860


945/Unknown  253s 262ms/step - loss: 1.0739 - sparse_categorical_accuracy: 0.5861


946/Unknown  253s 262ms/step - loss: 1.0736 - sparse_categorical_accuracy: 0.5862


947/Unknown  253s 262ms/step - loss: 1.0734 - sparse_categorical_accuracy: 0.5863


948/Unknown  254s 262ms/step - loss: 1.0732 - sparse_categorical_accuracy: 0.5863


949/Unknown  254s 262ms/step - loss: 1.0729 - sparse_categorical_accuracy: 0.5864


950/Unknown  254s 262ms/step - loss: 1.0727 - sparse_categorical_accuracy: 0.5865


951/Unknown  254s 262ms/step - loss: 1.0725 - sparse_categorical_accuracy: 0.5866


952/Unknown  255s 262ms/step - loss: 1.0722 - sparse_categorical_accuracy: 0.5866


953/Unknown  255s 262ms/step - loss: 1.0720 - sparse_categorical_accuracy: 0.5867


954/Unknown  255s 262ms/step - loss: 1.0718 - sparse_categorical_accuracy: 0.5868


955/Unknown  255s 262ms/step - loss: 1.0715 - sparse_categorical_accuracy: 0.5869


956/Unknown  256s 262ms/step - loss: 1.0713 - sparse_categorical_accuracy: 0.5869


957/Unknown  256s 262ms/step - loss: 1.0711 - sparse_categorical_accuracy: 0.5870


958/Unknown  256s 262ms/step - loss: 1.0708 - sparse_categorical_accuracy: 0.5871


959/Unknown  256s 262ms/step - loss: 1.0706 - sparse_categorical_accuracy: 0.5872


960/Unknown  257s 262ms/step - loss: 1.0704 - sparse_categorical_accuracy: 0.5872


961/Unknown  257s 262ms/step - loss: 1.0702 - sparse_categorical_accuracy: 0.5873


962/Unknown  257s 262ms/step - loss: 1.0699 - sparse_categorical_accuracy: 0.5874


963/Unknown  258s 262ms/step - loss: 1.0697 - sparse_categorical_accuracy: 0.5875


964/Unknown  258s 262ms/step - loss: 1.0695 - sparse_categorical_accuracy: 0.5875


965/Unknown  258s 262ms/step - loss: 1.0692 - sparse_categorical_accuracy: 0.5876


966/Unknown  259s 262ms/step - loss: 1.0690 - sparse_categorical_accuracy: 0.5877


967/Unknown  259s 262ms/step - loss: 1.0688 - sparse_categorical_accuracy: 0.5878


968/Unknown  259s 262ms/step - loss: 1.0685 - sparse_categorical_accuracy: 0.5878


969/Unknown  259s 262ms/step - loss: 1.0683 - sparse_categorical_accuracy: 0.5879


970/Unknown  260s 262ms/step - loss: 1.0681 - sparse_categorical_accuracy: 0.5880


971/Unknown  260s 262ms/step - loss: 1.0679 - sparse_categorical_accuracy: 0.5880


972/Unknown  260s 262ms/step - loss: 1.0676 - sparse_categorical_accuracy: 0.5881


973/Unknown  261s 262ms/step - loss: 1.0674 - sparse_categorical_accuracy: 0.5882


974/Unknown  261s 262ms/step - loss: 1.0672 - sparse_categorical_accuracy: 0.5883


975/Unknown  261s 262ms/step - loss: 1.0670 - sparse_categorical_accuracy: 0.5883


976/Unknown  261s 262ms/step - loss: 1.0667 - sparse_categorical_accuracy: 0.5884


977/Unknown  262s 262ms/step - loss: 1.0665 - sparse_categorical_accuracy: 0.5885


978/Unknown  262s 262ms/step - loss: 1.0663 - sparse_categorical_accuracy: 0.5886


979/Unknown  262s 262ms/step - loss: 1.0661 - sparse_categorical_accuracy: 0.5886


980/Unknown  263s 262ms/step - loss: 1.0658 - sparse_categorical_accuracy: 0.5887


981/Unknown  263s 262ms/step - loss: 1.0656 - sparse_categorical_accuracy: 0.5888


982/Unknown  263s 262ms/step - loss: 1.0654 - sparse_categorical_accuracy: 0.5888


983/Unknown  263s 262ms/step - loss: 1.0652 - sparse_categorical_accuracy: 0.5889


984/Unknown  264s 262ms/step - loss: 1.0649 - sparse_categorical_accuracy: 0.5890


985/Unknown  264s 262ms/step - loss: 1.0647 - sparse_categorical_accuracy: 0.5891


986/Unknown  264s 262ms/step - loss: 1.0645 - sparse_categorical_accuracy: 0.5891


987/Unknown  264s 262ms/step - loss: 1.0643 - sparse_categorical_accuracy: 0.5892


988/Unknown  265s 262ms/step - loss: 1.0641 - sparse_categorical_accuracy: 0.5893


989/Unknown  265s 262ms/step - loss: 1.0638 - sparse_categorical_accuracy: 0.5893


990/Unknown  265s 262ms/step - loss: 1.0636 - sparse_categorical_accuracy: 0.5894


991/Unknown  265s 262ms/step - loss: 1.0634 - sparse_categorical_accuracy: 0.5895


992/Unknown  266s 262ms/step - loss: 1.0632 - sparse_categorical_accuracy: 0.5896


993/Unknown  266s 262ms/step - loss: 1.0629 - sparse_categorical_accuracy: 0.5896


994/Unknown  266s 262ms/step - loss: 1.0627 - sparse_categorical_accuracy: 0.5897


995/Unknown  266s 262ms/step - loss: 1.0625 - sparse_categorical_accuracy: 0.5898


996/Unknown  267s 262ms/step - loss: 1.0623 - sparse_categorical_accuracy: 0.5898


997/Unknown  267s 262ms/step - loss: 1.0621 - sparse_categorical_accuracy: 0.5899


998/Unknown  267s 262ms/step - loss: 1.0618 - sparse_categorical_accuracy: 0.5900


999/Unknown  267s 262ms/step - loss: 1.0616 - sparse_categorical_accuracy: 0.5900

1000/未知 268 秒 262 毫秒/步 - loss: 1.0614 - sparse_categorical_accuracy: 0.5901

1001/未知 268 秒 262 毫秒/步 - loss: 1.0612 - sparse_categorical_accuracy: 0.5902

1002/未知 268 秒 262 毫秒/步 - loss: 1.0610 - sparse_categorical_accuracy: 0.5903

1003/未知 269 秒 262 毫秒/步 - loss: 1.0608 - sparse_categorical_accuracy: 0.5903

1004/未知 269 秒 262 毫秒/步 - loss: 1.0605 - sparse_categorical_accuracy: 0.5904

1005/未知 269 秒 262 毫秒/步 - loss: 1.0603 - sparse_categorical_accuracy: 0.5905

1006/未知 270 秒 263 毫秒/步 - loss: 1.0601 - sparse_categorical_accuracy: 0.5905

1007/未知 270 秒 263 毫秒/步 - loss: 1.0599 - sparse_categorical_accuracy: 0.5906

1008/未知 270 秒 263 毫秒/步 - loss: 1.0597 - sparse_categorical_accuracy: 0.5907

1009/未知 271 秒 263 毫秒/步 - loss: 1.0595 - sparse_categorical_accuracy: 0.5907

1010/未知 271 秒 263 毫秒/步 - loss: 1.0592 - sparse_categorical_accuracy: 0.5908

1011/未知 271 秒 263 毫秒/步 - loss: 1.0590 - sparse_categorical_accuracy: 0.5909

1012/未知 271 秒 263 毫秒/步 - loss: 1.0588 - sparse_categorical_accuracy: 0.5909

1013/未知 272 秒 263 毫秒/步 - loss: 1.0586 - sparse_categorical_accuracy: 0.5910

1014/未知 272 秒 263 毫秒/步 - loss: 1.0584 - sparse_categorical_accuracy: 0.5911

1015/未知 272 秒 263 毫秒/步 - loss: 1.0582 - sparse_categorical_accuracy: 0.5912

1016/未知 272 秒 263 毫秒/步 - loss: 1.0580 - sparse_categorical_accuracy: 0.5912

1017/未知 273 秒 263 毫秒/步 - loss: 1.0578 - sparse_categorical_accuracy: 0.5913

1018/未知 273 秒 263 毫秒/步 - loss: 1.0575 - sparse_categorical_accuracy: 0.5914

1019/未知 273 秒 263 毫秒/步 - loss: 1.0573 - sparse_categorical_accuracy: 0.5914

1020/未知 273 秒 263 毫秒/步 - loss: 1.0571 - sparse_categorical_accuracy: 0.5915

1021/未知 274 秒 263 毫秒/步 - loss: 1.0569 - sparse_categorical_accuracy: 0.5916

1022/未知 274 秒 263 毫秒/步 - loss: 1.0567 - sparse_categorical_accuracy: 0.5916

1023/未知 274 秒 263 毫秒/步 - loss: 1.0565 - sparse_categorical_accuracy: 0.5917

1024/未知 275 秒 263 毫秒/步 - loss: 1.0563 - sparse_categorical_accuracy: 0.5918

1025/未知 275 秒 263 毫秒/步 - loss: 1.0561 - sparse_categorical_accuracy: 0.5918

1026/未知 275 秒 263 毫秒/步 - loss: 1.0559 - sparse_categorical_accuracy: 0.5919

1027/未知 275 秒 263 毫秒/步 - loss: 1.0556 - sparse_categorical_accuracy: 0.5920

1028/未知 276 秒 263 毫秒/步 - loss: 1.0554 - sparse_categorical_accuracy: 0.5920

1029/未知 276 秒 263 毫秒/步 - loss: 1.0552 - sparse_categorical_accuracy: 0.5921

1030/未知 276 秒 263 毫秒/步 - loss: 1.0550 - sparse_categorical_accuracy: 0.5922

1031/未知 276 秒 263 毫秒/步 - loss: 1.0548 - sparse_categorical_accuracy: 0.5922

1032/未知 277 秒 263 毫秒/步 - loss: 1.0546 - sparse_categorical_accuracy: 0.5923

1033/未知 277 秒 263 毫秒/步 - loss: 1.0544 - sparse_categorical_accuracy: 0.5924

1034/未知 277 秒 263 毫秒/步 - loss: 1.0542 - sparse_categorical_accuracy: 0.5924

1035/未知 278 秒 263 毫秒/步 - loss: 1.0540 - sparse_categorical_accuracy: 0.5925

1036/未知 278 秒 263 毫秒/步 - loss: 1.0538 - sparse_categorical_accuracy: 0.5926

1037/未知 278 秒 263 毫秒/步 - loss: 1.0536 - sparse_categorical_accuracy: 0.5926

1038/未知 278 秒 263 毫秒/步 - loss: 1.0533 - sparse_categorical_accuracy: 0.5927

1039/未知 279 秒 263 毫秒/步 - loss: 1.0531 - sparse_categorical_accuracy: 0.5928

1040/未知 279 秒 263 毫秒/步 - loss: 1.0529 - sparse_categorical_accuracy: 0.5928

1041/未知 279 秒 263 毫秒/步 - loss: 1.0527 - sparse_categorical_accuracy: 0.5929

1042/未知 280 秒 263 毫秒/步 - loss: 1.0525 - sparse_categorical_accuracy: 0.5930

1043/未知 280 秒 263 毫秒/步 - loss: 1.0523 - sparse_categorical_accuracy: 0.5930

1044/未知 280 秒 263 毫秒/步 - loss: 1.0521 - sparse_categorical_accuracy: 0.5931

1045/未知 280 秒 263 毫秒/步 - loss: 1.0519 - sparse_categorical_accuracy: 0.5932

1046/未知 281 秒 263 毫秒/步 - loss: 1.0517 - sparse_categorical_accuracy: 0.5932

1047/未知 281 秒 263 毫秒/步 - loss: 1.0515 - sparse_categorical_accuracy: 0.5933

1048/未知 281 秒 263 毫秒/步 - loss: 1.0513 - sparse_categorical_accuracy: 0.5934

1049/未知 282 秒 263 毫秒/步 - loss: 1.0511 - sparse_categorical_accuracy: 0.5934

1050/未知 282 秒 263 毫秒/步 - loss: 1.0509 - sparse_categorical_accuracy: 0.5935

1051/未知 282 秒 263 毫秒/步 - loss: 1.0507 - sparse_categorical_accuracy: 0.5935

1052/未知 283 秒 263 毫秒/步 - loss: 1.0505 - sparse_categorical_accuracy: 0.5936

1053/未知 283 秒 263 毫秒/步 - loss: 1.0503 - sparse_categorical_accuracy: 0.5937

1054/未知 283 秒 263 毫秒/步 - loss: 1.0501 - sparse_categorical_accuracy: 0.5937

1055/未知 283 秒 263 毫秒/步 - loss: 1.0499 - sparse_categorical_accuracy: 0.5938

1056/未知 284 秒 263 毫秒/步 - loss: 1.0497 - sparse_categorical_accuracy: 0.5939

1057/未知 284 秒 263 毫秒/步 - loss: 1.0495 - sparse_categorical_accuracy: 0.5939

1058/未知 284 秒 263 毫秒/步 - loss: 1.0493 - sparse_categorical_accuracy: 0.5940

1059/未知 285 秒 263 毫秒/步 - loss: 1.0491 - sparse_categorical_accuracy: 0.5941

1060/未知 285 秒 263 毫秒/步 - loss: 1.0489 - sparse_categorical_accuracy: 0.5941

1061/未知 285 秒 263 毫秒/步 - loss: 1.0487 - sparse_categorical_accuracy: 0.5942

1062/未知 285 秒 263 毫秒/步 - loss: 1.0485 - sparse_categorical_accuracy: 0.5943

1063/未知 285 秒 263 毫秒/步 - loss: 1.0483 - sparse_categorical_accuracy: 0.5943

1064/未知 286 秒 263 毫秒/步 - loss: 1.0481 - sparse_categorical_accuracy: 0.5944

1065/未知 286 秒 263 毫秒/步 - loss: 1.0479 - sparse_categorical_accuracy: 0.5944

1066/未知 286 秒 263 毫秒/步 - loss: 1.0477 - sparse_categorical_accuracy: 0.5945

1067/未知 286 秒 263 毫秒/步 - loss: 1.0475 - sparse_categorical_accuracy: 0.5946

1068/未知 287 秒 263 毫秒/步 - loss: 1.0473 - sparse_categorical_accuracy: 0.5946

1069/未知 287 秒 263 毫秒/步 - loss: 1.0471 - sparse_categorical_accuracy: 0.5947

1070/未知 287 秒 263 毫秒/步 - loss: 1.0469 - sparse_categorical_accuracy: 0.5948

1071/未知 287 秒 263 毫秒/步 - loss: 1.0467 - sparse_categorical_accuracy: 0.5948

1072/未知 288 秒 263 毫秒/步 - loss: 1.0465 - sparse_categorical_accuracy: 0.5949

1073/未知 288 秒 263 毫秒/步 - loss: 1.0463 - sparse_categorical_accuracy: 0.5949

1074/未知 288 秒 263 毫秒/步 - loss: 1.0461 - sparse_categorical_accuracy: 0.5950

1075/未知 289 秒 263 毫秒/步 - loss: 1.0459 - sparse_categorical_accuracy: 0.5951

1076/未知 289 秒 263 毫秒/步 - loss: 1.0457 - sparse_categorical_accuracy: 0.5951

1077/未知 289 秒 263 毫秒/步 - loss: 1.0455 - sparse_categorical_accuracy: 0.5952

1078/未知 290 秒 264 毫秒/步 - loss: 1.0453 - sparse_categorical_accuracy: 0.5953

1079/未知 290 秒 264 毫秒/步 - loss: 1.0451 - sparse_categorical_accuracy: 0.5953

1080/未知 290 秒 264 毫秒/步 - loss: 1.0449 - sparse_categorical_accuracy: 0.5954

1081/未知 291 秒 264 毫秒/步 - loss: 1.0447 - sparse_categorical_accuracy: 0.5954

1082/未知 291 秒 264 毫秒/步 - loss: 1.0445 - sparse_categorical_accuracy: 0.5955

1083/未知 291 秒 264 毫秒/步 - loss: 1.0443 - sparse_categorical_accuracy: 0.5956

1084/未知 291 秒 264 毫秒/步 - loss: 1.0441 - sparse_categorical_accuracy: 0.5956

1085/未知 292 秒 264 毫秒/步 - loss: 1.0439 - sparse_categorical_accuracy: 0.5957

1086/未知 292 秒 264 毫秒/步 - loss: 1.0437 - sparse_categorical_accuracy: 0.5957

1087/未知 292 秒 264 毫秒/步 - loss: 1.0436 - sparse_categorical_accuracy: 0.5958

1088/未知 293 秒 264 毫秒/步 - loss: 1.0434 - sparse_categorical_accuracy: 0.5959

1089/未知 293 秒 264 毫秒/步 - loss: 1.0432 - sparse_categorical_accuracy: 0.5959

1090/未知 293 秒 264 毫秒/步 - loss: 1.0430 - sparse_categorical_accuracy: 0.5960

1091/未知 293 秒 264 毫秒/步 - loss: 1.0428 - sparse_categorical_accuracy: 0.5961

1092/未知 294 秒 264 毫秒/步 - loss: 1.0426 - sparse_categorical_accuracy: 0.5961

1093/未知 294 秒 264 毫秒/步 - loss: 1.0424 - sparse_categorical_accuracy: 0.5962

1094/未知 294 秒 264 毫秒/步 - loss: 1.0422 - sparse_categorical_accuracy: 0.5962

1095/未知 294 秒 264 毫秒/步 - loss: 1.0420 - sparse_categorical_accuracy: 0.5963

1096/未知 295 秒 264 毫秒/步 - loss: 1.0418 - sparse_categorical_accuracy: 0.5964

1097/未知 295 秒 264 毫秒/步 - loss: 1.0416 - sparse_categorical_accuracy: 0.5964

1098/未知 295 秒 264 毫秒/步 - loss: 1.0414 - sparse_categorical_accuracy: 0.5965

1099/未知 295 秒 264 毫秒/步 - loss: 1.0413 - sparse_categorical_accuracy: 0.5965

1100/未知 296 秒 264 毫秒/步 - loss: 1.0411 - sparse_categorical_accuracy: 0.5966

1101/未知 296 秒 264 毫秒/步 - loss: 1.0409 - sparse_categorical_accuracy: 0.5967

1102/未知 296 秒 264 毫秒/步 - loss: 1.0407 - sparse_categorical_accuracy: 0.5967

1103/未知 296 秒 264 毫秒/步 - loss: 1.0405 - sparse_categorical_accuracy: 0.5968

1104/未知 297 秒 264 毫秒/步 - loss: 1.0403 - sparse_categorical_accuracy: 0.5968

1105/未知 297 秒 264 毫秒/步 - loss: 1.0401 - sparse_categorical_accuracy: 0.5969

1106/未知 297 秒 264 毫秒/步 - loss: 1.0399 - sparse_categorical_accuracy: 0.5970

1107/未知 298 秒 264 毫秒/步 - loss: 1.0397 - sparse_categorical_accuracy: 0.5970

1108/未知 298 秒 264 毫秒/步 - loss: 1.0396 - sparse_categorical_accuracy: 0.5971

1109/未知 298 秒 264 毫秒/步 - loss: 1.0394 - sparse_categorical_accuracy: 0.5971

1110/未知 299 秒 264 毫秒/步 - loss: 1.0392 - sparse_categorical_accuracy: 0.5972

1111/未知 299 秒 264 毫秒/步 - loss: 1.0390 - sparse_categorical_accuracy: 0.5973

1112/未知 299 秒 264 毫秒/步 - loss: 1.0388 - sparse_categorical_accuracy: 0.5973

1113/未知 299 秒 264 毫秒/步 - loss: 1.0386 - sparse_categorical_accuracy: 0.5974

1114/未知 300 秒 264 毫秒/步 - loss: 1.0384 - sparse_categorical_accuracy: 0.5974

1115/未知 300 秒 264 毫秒/步 - loss: 1.0382 - sparse_categorical_accuracy: 0.5975

1116/未知 300 秒 264 毫秒/步 - loss: 1.0381 - sparse_categorical_accuracy: 0.5976

1117/未知 300 秒 264 毫秒/步 - loss: 1.0379 - sparse_categorical_accuracy: 0.5976

1118/未知 301 秒 264 毫秒/步 - loss: 1.0377 - sparse_categorical_accuracy: 0.5977

1119/未知 301 秒 264 毫秒/步 - loss: 1.0375 - sparse_categorical_accuracy: 0.5977

1120/未知 301 秒 264 毫秒/步 - loss: 1.0373 - sparse_categorical_accuracy: 0.5978

1121/未知 301 秒 264 毫秒/步 - loss: 1.0371 - sparse_categorical_accuracy: 0.5978

1122/未知 302 秒 264 毫秒/步 - loss: 1.0369 - sparse_categorical_accuracy: 0.5979

1123/未知 302 秒 264 毫秒/步 - loss: 1.0368 - sparse_categorical_accuracy: 0.5980

1124/未知 302 秒 264 毫秒/步 - loss: 1.0366 - sparse_categorical_accuracy: 0.5980

1125/未知 302 秒 264 毫秒/步 - loss: 1.0364 - sparse_categorical_accuracy: 0.5981

1126/未知 303 秒 264 毫秒/步 - loss: 1.0362 - sparse_categorical_accuracy: 0.5981

1127/未知 303 秒 264 毫秒/步 - loss: 1.0360 - sparse_categorical_accuracy: 0.5982

1128/未知 303 秒 264 毫秒/步 - loss: 1.0358 - sparse_categorical_accuracy: 0.5983

1129/未知 303 秒 264 毫秒/步 - loss: 1.0357 - sparse_categorical_accuracy: 0.5983

1130/未知 304 秒 264 毫秒/步 - loss: 1.0355 - sparse_categorical_accuracy: 0.5984

1131/未知 304 秒 264 毫秒/步 - loss: 1.0353 - sparse_categorical_accuracy: 0.5984

1132/未知 304 秒 264 毫秒/步 - loss: 1.0351 - sparse_categorical_accuracy: 0.5985

1133/未知 305 秒 264 毫秒/步 - loss: 1.0349 - sparse_categorical_accuracy: 0.5985

1134/未知 305 秒 264 毫秒/步 - loss: 1.0347 - sparse_categorical_accuracy: 0.5986

1135/未知 305 秒 264 毫秒/步 - loss: 1.0346 - sparse_categorical_accuracy: 0.5987

1136/未知 306 秒 264 毫秒/步 - loss: 1.0344 - sparse_categorical_accuracy: 0.5987

1137/未知 306 秒 264 毫秒/步 - loss: 1.0342 - sparse_categorical_accuracy: 0.5988

1138/未知 306 秒 264 毫秒/步 - loss: 1.0340 - sparse_categorical_accuracy: 0.5988

1139/未知 306 秒 264 毫秒/步 - loss: 1.0338 - sparse_categorical_accuracy: 0.5989

1140/未知 307 秒 264 毫秒/步 - loss: 1.0337 - sparse_categorical_accuracy: 0.5990

1141/未知 307 秒 264 毫秒/步 - loss: 1.0335 - sparse_categorical_accuracy: 0.5990

1142/未知 307 秒 264 毫秒/步 - loss: 1.0333 - sparse_categorical_accuracy: 0.5991

1143/未知 308 秒 264 毫秒/步 - loss: 1.0331 - sparse_categorical_accuracy: 0.5991

1144/未知 308 秒 264 毫秒/步 - loss: 1.0329 - sparse_categorical_accuracy: 0.5992

1145/未知 308 秒 264 毫秒/步 - loss: 1.0328 - sparse_categorical_accuracy: 0.5992

1146/未知 308 秒 264 毫秒/步 - loss: 1.0326 - sparse_categorical_accuracy: 0.5993

1147/未知 309 秒 264 毫秒/步 - loss: 1.0324 - sparse_categorical_accuracy: 0.5993

1148/未知 309 秒 264 毫秒/步 - loss: 1.0322 - sparse_categorical_accuracy: 0.5994

1149/未知 309 秒 264 毫秒/步 - loss: 1.0320 - sparse_categorical_accuracy: 0.5995

1150/未知 310 秒 264 毫秒/步 - loss: 1.0319 - sparse_categorical_accuracy: 0.5995

1151/未知 310 秒 264 毫秒/步 - loss: 1.0317 - sparse_categorical_accuracy: 0.5996

1152/未知 310 秒 264 毫秒/步 - loss: 1.0315 - sparse_categorical_accuracy: 0.5996

1153/未知 310 秒 264 毫秒/步 - loss: 1.0313 - sparse_categorical_accuracy: 0.5997

1154/未知 311 秒 264 毫秒/步 - loss: 1.0311 - sparse_categorical_accuracy: 0.5997

1155/未知 311 秒 264 毫秒/步 - loss: 1.0310 - sparse_categorical_accuracy: 0.5998

1156/未知 311 秒 264 毫秒/步 - loss: 1.0308 - sparse_categorical_accuracy: 0.5999

1157/未知 312 秒 265 毫秒/步 - loss: 1.0306 - sparse_categorical_accuracy: 0.5999

1158/未知 312 秒 265 毫秒/步 - loss: 1.0304 - sparse_categorical_accuracy: 0.6000

1159/未知 312 秒 265 毫秒/步 - loss: 1.0303 - sparse_categorical_accuracy: 0.6000

1160/未知 312 秒 265 毫秒/步 - loss: 1.0301 - sparse_categorical_accuracy: 0.6001

1161/未知 313 秒 265 毫秒/步 - loss: 1.0299 - sparse_categorical_accuracy: 0.6001

1162/未知 313 秒 265 毫秒/步 - loss: 1.0297 - sparse_categorical_accuracy: 0.6002

1163/未知 313 秒 265 毫秒/步 - loss: 1.0296 - sparse_categorical_accuracy: 0.6003

1164/未知 314 秒 265 毫秒/步 - loss: 1.0294 - sparse_categorical_accuracy: 0.6003

1165/未知 314 秒 265 毫秒/步 - loss: 1.0292 - sparse_categorical_accuracy: 0.6004

1166/未知 314 秒 265 毫秒/步 - loss: 1.0290 - sparse_categorical_accuracy: 0.6004

1167/未知 314 秒 265 毫秒/步 - loss: 1.0289 - sparse_categorical_accuracy: 0.6005

1168/未知 315 秒 265 毫秒/步 - loss: 1.0287 - sparse_categorical_accuracy: 0.6005

1169/未知 315 秒 265 毫秒/步 - loss: 1.0285 - sparse_categorical_accuracy: 0.6006

1170/未知 315 秒 265 毫秒/步 - loss: 1.0283 - sparse_categorical_accuracy: 0.6006

1171/未知 315 秒 265 毫秒/步 - loss: 1.0282 - sparse_categorical_accuracy: 0.6007

1172/未知 316 秒 264 毫秒/步 - loss: 1.0280 - sparse_categorical_accuracy: 0.6008

1173/未知 316 秒 264 毫秒/步 - loss: 1.0278 - sparse_categorical_accuracy: 0.6008

1174/未知 316 秒 264 毫秒/步 - loss: 1.0276 - sparse_categorical_accuracy: 0.6009

1175/未知 316 秒 264 毫秒/步 - loss: 1.0275 - sparse_categorical_accuracy: 0.6009

1176/未知 316 秒 264 毫秒/步 - loss: 1.0273 - sparse_categorical_accuracy: 0.6010

1177/未知 317 秒 264 毫秒/步 - loss: 1.0271 - sparse_categorical_accuracy: 0.6010

1178/未知 317 秒 264 毫秒/步 - loss: 1.0269 - sparse_categorical_accuracy: 0.6011

1179/未知 317 秒 264 毫秒/步 - loss: 1.0268 - sparse_categorical_accuracy: 0.6011

1180/未知 317 秒 264 毫秒/步 - loss: 1.0266 - sparse_categorical_accuracy: 0.6012

1181/未知 318 秒 264 毫秒/步 - loss: 1.0264 - sparse_categorical_accuracy: 0.6012

1182/未知 318 秒 264 毫秒/步 - loss: 1.0263 - sparse_categorical_accuracy: 0.6013

1183/未知 318 秒 264 毫秒/步 - loss: 1.0261 - sparse_categorical_accuracy: 0.6014

1184/未知 318 秒 264 毫秒/步 - loss: 1.0259 - sparse_categorical_accuracy: 0.6014

1185/未知 319 秒 264 毫秒/步 - loss: 1.0257 - sparse_categorical_accuracy: 0.6015

1186/未知 319 秒 264 毫秒/步 - loss: 1.0256 - sparse_categorical_accuracy: 0.6015

1187/未知 319 秒 264 毫秒/步 - loss: 1.0254 - sparse_categorical_accuracy: 0.6016

1188/未知 319 秒 264 毫秒/步 - loss: 1.0252 - sparse_categorical_accuracy: 0.6016

1189/未知 320 秒 264 毫秒/步 - loss: 1.0251 - sparse_categorical_accuracy: 0.6017

1190/未知 320 秒 264 毫秒/步 - loss: 1.0249 - sparse_categorical_accuracy: 0.6017

1191/未知 320 秒 264 毫秒/步 - loss: 1.0247 - sparse_categorical_accuracy: 0.6018

1192/未知 320 秒 264 毫秒/步 - loss: 1.0245 - sparse_categorical_accuracy: 0.6018

1193/未知 321 秒 264 毫秒/步 - loss: 1.0244 - sparse_categorical_accuracy: 0.6019

1194/未知 321 秒 264 毫秒/步 - loss: 1.0242 - sparse_categorical_accuracy: 0.6019

1195/未知 321 秒 264 毫秒/步 - loss: 1.0240 - sparse_categorical_accuracy: 0.6020

1196/未知 321 秒 264 毫秒/步 - loss: 1.0239 - sparse_categorical_accuracy: 0.6021

1197/未知 322 秒 264 毫秒/步 - loss: 1.0237 - sparse_categorical_accuracy: 0.6021

1198/未知 322 秒 264 毫秒/步 - loss: 1.0235 - sparse_categorical_accuracy: 0.6022

1199/未知 322 秒 264 毫秒/步 - loss: 1.0234 - sparse_categorical_accuracy: 0.6022

1200/未知 322 秒 264 毫秒/步 - loss: 1.0232 - sparse_categorical_accuracy: 0.6023

1201/未知 323 秒 264 毫秒/步 - loss: 1.0230 - sparse_categorical_accuracy: 0.6023

1202/未知 323 秒 264 毫秒/步 - loss: 1.0229 - sparse_categorical_accuracy: 0.6024

1203/未知 323 秒 264 毫秒/步 - loss: 1.0227 - sparse_categorical_accuracy: 0.6024

1204/未知 323 秒 264 毫秒/步 - loss: 1.0225 - sparse_categorical_accuracy: 0.6025

1205/未知 324 秒 264 毫秒/步 - loss: 1.0224 - sparse_categorical_accuracy: 0.6025

1206/未知 324 秒 264 毫秒/步 - loss: 1.0222 - sparse_categorical_accuracy: 0.6026

1207/未知 324 秒 264 毫秒/步 - loss: 1.0220 - sparse_categorical_accuracy: 0.6026

1208/未知 324 秒 264 毫秒/步 - loss: 1.0219 - sparse_categorical_accuracy: 0.6027

1209/未知 325 秒 264 毫秒/步 - loss: 1.0217 - sparse_categorical_accuracy: 0.6027

1210/未知 325 秒 264 毫秒/步 - loss: 1.0215 - sparse_categorical_accuracy: 0.6028

1211/未知 325 秒 264 毫秒/步 - loss: 1.0214 - sparse_categorical_accuracy: 0.6029

1212/未知 326 秒 264 毫秒/步 - loss: 1.0212 - sparse_categorical_accuracy: 0.6029

1213/未知 326 秒 264 毫秒/步 - loss: 1.0210 - sparse_categorical_accuracy: 0.6030

1214/未知 326 秒 264 毫秒/步 - loss: 1.0209 - sparse_categorical_accuracy: 0.6030

1215/未知 327 秒 264 毫秒/步 - loss: 1.0207 - sparse_categorical_accuracy: 0.6031

1216/未知 327 秒 264 毫秒/步 - loss: 1.0205 - sparse_categorical_accuracy: 0.6031

1217/未知 327 秒 264 毫秒/步 - loss: 1.0204 - sparse_categorical_accuracy: 0.6032

1218/未知 327 秒 264 毫秒/步 - loss: 1.0202 - sparse_categorical_accuracy: 0.6032

1219/未知 328 秒 264 毫秒/步 - loss: 1.0200 - sparse_categorical_accuracy: 0.6033

1220/未知 328 秒 264 毫秒/步 - loss: 1.0199 - sparse_categorical_accuracy: 0.6033

1221/未知 328 秒 264 毫秒/步 - loss: 1.0197 - sparse_categorical_accuracy: 0.6034

1222/未知 328 秒 264 毫秒/步 - loss: 1.0196 - sparse_categorical_accuracy: 0.6034

1223/未知 329 秒 264 毫秒/步 - loss: 1.0194 - sparse_categorical_accuracy: 0.6035

1224/未知 329 秒 264 毫秒/步 - loss: 1.0192 - sparse_categorical_accuracy: 0.6035

1225/未知 329 秒 264 毫秒/步 - loss: 1.0191 - sparse_categorical_accuracy: 0.6036

1226/未知 329 秒 264 毫秒/步 - loss: 1.0189 - sparse_categorical_accuracy: 0.6036

1227/未知 330 秒 264 毫秒/步 - loss: 1.0187 - sparse_categorical_accuracy: 0.6037

1228/未知 330 秒 264 毫秒/步 - loss: 1.0186 - sparse_categorical_accuracy: 0.6037

1229/未知 330 秒 264 毫秒/步 - loss: 1.0184 - sparse_categorical_accuracy: 0.6038

1230/未知 330 秒 264 毫秒/步 - loss: 1.0183 - sparse_categorical_accuracy: 0.6038

1231/未知 331 秒 264 毫秒/步 - loss: 1.0181 - sparse_categorical_accuracy: 0.6039

1232/未知 331 秒 264 毫秒/步 - loss: 1.0179 - sparse_categorical_accuracy: 0.6039

1233/未知 331 秒 264 毫秒/步 - loss: 1.0178 - sparse_categorical_accuracy: 0.6040

1234/未知 331 秒 264 毫秒/步 - loss: 1.0176 - sparse_categorical_accuracy: 0.6040

1235/未知 332 秒 264 毫秒/步 - loss: 1.0174 - sparse_categorical_accuracy: 0.6041

1236/未知 332 秒 264 毫秒/步 - loss: 1.0173 - sparse_categorical_accuracy: 0.6041

1237/未知 332 秒 264 毫秒/步 - loss: 1.0171 - sparse_categorical_accuracy: 0.6042

1238/未知 332 秒 264 毫秒/步 - loss: 1.0170 - sparse_categorical_accuracy: 0.6042

1239/未知 333 秒 264 毫秒/步 - loss: 1.0168 - sparse_categorical_accuracy: 0.6043

1240/未知 333 秒 264 毫秒/步 - loss: 1.0166 - sparse_categorical_accuracy: 0.6043

1241/未知 334 秒 264 毫秒/步 - loss: 1.0165 - sparse_categorical_accuracy: 0.6044

1242/未知 334 秒 264 毫秒/步 - loss: 1.0163 - sparse_categorical_accuracy: 0.6044

1243/未知 334 秒 264 毫秒/步 - loss: 1.0162 - sparse_categorical_accuracy: 0.6045

1244/未知 335 秒 265 毫秒/步 - loss: 1.0160 - sparse_categorical_accuracy: 0.6045

1245/未知 335 秒 265 毫秒/步 - loss: 1.0158 - sparse_categorical_accuracy: 0.6046

1246/未知 335 秒 265 毫秒/步 - loss: 1.0157 - sparse_categorical_accuracy: 0.6046

1247/未知 335 秒 265 毫秒/步 - loss: 1.0155 - sparse_categorical_accuracy: 0.6047

1248/未知 336 秒 265 毫秒/步 - loss: 1.0154 - sparse_categorical_accuracy: 0.6048

1249/未知 336 秒 265 毫秒/步 - loss: 1.0152 - sparse_categorical_accuracy: 0.6048

1250/未知 336 秒 265 毫秒/步 - loss: 1.0150 - sparse_categorical_accuracy: 0.6049

1251/未知 337 秒 265 毫秒/步 - loss: 1.0149 - sparse_categorical_accuracy: 0.6049

1252/未知 337 秒 265 毫秒/步 - loss: 1.0147 - sparse_categorical_accuracy: 0.6050

1253/未知 337 秒 265 毫秒/步 - loss: 1.0146 - sparse_categorical_accuracy: 0.6050

1254/未知 337 秒 265 毫秒/步 - loss: 1.0144 - sparse_categorical_accuracy: 0.6051

1255/未知 338 秒 265 毫秒/步 - loss: 1.0143 - sparse_categorical_accuracy: 0.6051

1256/未知 338 秒 265 毫秒/步 - loss: 1.0141 - sparse_categorical_accuracy: 0.6052

1257/未知 338 秒 264 毫秒/步 - loss: 1.0139 - sparse_categorical_accuracy: 0.6052

1258/未知 338 秒 264 毫秒/步 - loss: 1.0138 - sparse_categorical_accuracy: 0.6053

1259/未知 338 秒 264 毫秒/步 - loss: 1.0136 - sparse_categorical_accuracy: 0.6053

1260/未知 339 秒 264 毫秒/步 - loss: 1.0135 - sparse_categorical_accuracy: 0.6054

1261/未知 339 秒 264 毫秒/步 - loss: 1.0133 - sparse_categorical_accuracy: 0.6054

1262/未知 339 秒 264 毫秒/步 - loss: 1.0132 - sparse_categorical_accuracy: 0.6055

1263/未知 339 秒 264 毫秒/步 - loss: 1.0130 - sparse_categorical_accuracy: 0.6055

1264/未知 340 秒 264 毫秒/步 - loss: 1.0128 - sparse_categorical_accuracy: 0.6055

1265/未知 340 秒 264 毫秒/步 - loss: 1.0127 - sparse_categorical_accuracy: 0.6056

1266/未知 340 秒 264 毫秒/步 - loss: 1.0125 - sparse_categorical_accuracy: 0.6056

1267/未知 340 秒 264 毫秒/步 - loss: 1.0124 - sparse_categorical_accuracy: 0.6057

1268/未知 341 秒 264 毫秒/步 - loss: 1.0122 - sparse_categorical_accuracy: 0.6057

1269/未知 341 秒 264 毫秒/步 - loss: 1.0121 - sparse_categorical_accuracy: 0.6058

1270/未知 341 秒 264 毫秒/步 - loss: 1.0119 - sparse_categorical_accuracy: 0.6058

1271/未知 341 秒 264 毫秒/步 - loss: 1.0118 - sparse_categorical_accuracy: 0.6059

1272/未知 342 秒 264 毫秒/步 - loss: 1.0116 - sparse_categorical_accuracy: 0.6059

1273/未知 342 秒 264 毫秒/步 - loss: 1.0114 - sparse_categorical_accuracy: 0.6060

1274/未知 342 秒 264 毫秒/步 - loss: 1.0113 - sparse_categorical_accuracy: 0.6060

1275/未知 342 秒 264 毫秒/步 - loss: 1.0111 - sparse_categorical_accuracy: 0.6061

1276/未知 343 秒 264 毫秒/步 - loss: 1.0110 - sparse_categorical_accuracy: 0.6061

1277/未知 343 秒 264 毫秒/步 - loss: 1.0108 - sparse_categorical_accuracy: 0.6062

1278/未知 343 秒 264 毫秒/步 - loss: 1.0107 - sparse_categorical_accuracy: 0.6062

1279/未知 344 秒 264 毫秒/步 - loss: 1.0105 - sparse_categorical_accuracy: 0.6063

1280/未知 344 秒 264 毫秒/步 - loss: 1.0104 - sparse_categorical_accuracy: 0.6063

1281/未知 344 秒 264 毫秒/步 - loss: 1.0102 - sparse_categorical_accuracy: 0.6064

1282/未知 345 秒 264 毫秒/步 - loss: 1.0101 - sparse_categorical_accuracy: 0.6064

1283/未知 345 秒 264 毫秒/步 - loss: 1.0099 - sparse_categorical_accuracy: 0.6065

1284/未知 345 秒 264 毫秒/步 - loss: 1.0098 - sparse_categorical_accuracy: 0.6065

1285/未知 345 秒 265 毫秒/步 - loss: 1.0096 - sparse_categorical_accuracy: 0.6066

1286/未知 346 秒 265 毫秒/步 - loss: 1.0095 - sparse_categorical_accuracy: 0.6066

1287/未知 346 秒 265 毫秒/步 - loss: 1.0093 - sparse_categorical_accuracy: 0.6067

1288/未知 346 秒 265 毫秒/步 - loss: 1.0092 - sparse_categorical_accuracy: 0.6067

1289/未知 347 秒 265 毫秒/步 - loss: 1.0090 - sparse_categorical_accuracy: 0.6068

1290/未知 347 秒 265 毫秒/步 - loss: 1.0088 - sparse_categorical_accuracy: 0.6068

1291/未知 347 秒 265 毫秒/步 - loss: 1.0087 - sparse_categorical_accuracy: 0.6069

1292/未知 347 秒 265 毫秒/步 - loss: 1.0085 - sparse_categorical_accuracy: 0.6069

1293/未知 348 秒 265 毫秒/步 - loss: 1.0084 - sparse_categorical_accuracy: 0.6070

1294/未知 348 秒 265 毫秒/步 - loss: 1.0082 - sparse_categorical_accuracy: 0.6070

1295/未知 348 秒 265 毫秒/步 - loss: 1.0081 - sparse_categorical_accuracy: 0.6071

1296/未知 349 秒 265 毫秒/步 - loss: 1.0079 - sparse_categorical_accuracy: 0.6071

1297/未知 349 秒 265 毫秒/步 - loss: 1.0078 - sparse_categorical_accuracy: 0.6071

1298/未知 349 秒 265 毫秒/步 - loss: 1.0076 - sparse_categorical_accuracy: 0.6072

1299/未知 350 秒 265 毫秒/步 - loss: 1.0075 - sparse_categorical_accuracy: 0.6072

1300/未知 350 秒 265 毫秒/步 - loss: 1.0073 - sparse_categorical_accuracy: 0.6073

1301/未知 350 秒 265 毫秒/步 - loss: 1.0072 - sparse_categorical_accuracy: 0.6073

1302/未知 350 秒 265 毫秒/步 - loss: 1.0070 - sparse_categorical_accuracy: 0.6074

1303/未知 351 秒 265 毫秒/步 - loss: 1.0069 - sparse_categorical_accuracy: 0.6074

1304/未知 351 秒 265 毫秒/步 - loss: 1.0067 - sparse_categorical_accuracy: 0.6075

1305/未知 351 秒 265 毫秒/步 - loss: 1.0066 - sparse_categorical_accuracy: 0.6075

1306/未知 351 秒 265 毫秒/步 - loss: 1.0064 - sparse_categorical_accuracy: 0.6076

1307/未知 352 秒 265 毫秒/步 - loss: 1.0063 - sparse_categorical_accuracy: 0.6076

1308/未知 352 秒 265 毫秒/步 - loss: 1.0061 - sparse_categorical_accuracy: 0.6077

1309/未知 352 秒 265 毫秒/步 - loss: 1.0060 - sparse_categorical_accuracy: 0.6077

1310/未知 353 秒 265 毫秒/步 - loss: 1.0059 - sparse_categorical_accuracy: 0.6078

1311/未知 353 秒 265 毫秒/步 - loss: 1.0057 - sparse_categorical_accuracy: 0.6078

1312/未知 353 秒 265 毫秒/步 - loss: 1.0056 - sparse_categorical_accuracy: 0.6079

1313/未知 354 秒 265 毫秒/步 - loss: 1.0054 - sparse_categorical_accuracy: 0.6079

1314/未知 354 秒 265 毫秒/步 - loss: 1.0053 - sparse_categorical_accuracy: 0.6079

1315/未知 354 秒 265 毫秒/步 - loss: 1.0051 - sparse_categorical_accuracy: 0.6080

1316/未知 354 秒 265 毫秒/步 - loss: 1.0050 - sparse_categorical_accuracy: 0.6080

1317/未知 355 秒 265 毫秒/步 - loss: 1.0048 - sparse_categorical_accuracy: 0.6081

1318/未知 355 秒 265 毫秒/步 - loss: 1.0047 - sparse_categorical_accuracy: 0.6081

1319/未知 355 秒 265 毫秒/步 - loss: 1.0045 - sparse_categorical_accuracy: 0.6082

1320/未知 356 秒 265 毫秒/步 - loss: 1.0044 - sparse_categorical_accuracy: 0.6082

1321/未知 356 秒 265 毫秒/步 - loss: 1.0042 - sparse_categorical_accuracy: 0.6083

1322/未知 356 秒 265 毫秒/步 - loss: 1.0041 - sparse_categorical_accuracy: 0.6083

1323/未知 356 秒 265 毫秒/步 - loss: 1.0039 - sparse_categorical_accuracy: 0.6084

1324/未知 357 秒 265 毫秒/步 - loss: 1.0038 - sparse_categorical_accuracy: 0.6084

1325/未知 357 秒 265 毫秒/步 - loss: 1.0036 - sparse_categorical_accuracy: 0.6085

1326/未知 357 秒 265 毫秒/步 - loss: 1.0035 - sparse_categorical_accuracy: 0.6085

1327/未知 358 秒 265 毫秒/步 - loss: 1.0034 - sparse_categorical_accuracy: 0.6086

1328/未知 358 秒 265 毫秒/步 - loss: 1.0032 - sparse_categorical_accuracy: 0.6086

1329/未知 358 秒 265 毫秒/步 - loss: 1.0031 - sparse_categorical_accuracy: 0.6086

1330/未知 358 秒 265 毫秒/步 - loss: 1.0029 - sparse_categorical_accuracy: 0.6087

1331/未知 359 秒 265 毫秒/步 - loss: 1.0028 - sparse_categorical_accuracy: 0.6087

1332/未知 359 秒 265 毫秒/步 - loss: 1.0026 - sparse_categorical_accuracy: 0.6088

1333/未知 359 秒 265 毫秒/步 - loss: 1.0025 - sparse_categorical_accuracy: 0.6088

1334/未知 359 秒 265 毫秒/步 - loss: 1.0023 - sparse_categorical_accuracy: 0.6089

1335/未知 360 秒 265 毫秒/步 - loss: 1.0022 - sparse_categorical_accuracy: 0.6089

1336/未知 360 秒 265 毫秒/步 - loss: 1.0021 - sparse_categorical_accuracy: 0.6090

1337/未知 360 秒 265 毫秒/步 - loss: 1.0019 - sparse_categorical_accuracy: 0.6090

1338/未知 360 秒 265 毫秒/步 - loss: 1.0018 - sparse_categorical_accuracy: 0.6091

1339/未知 361 秒 265 毫秒/步 - loss: 1.0016 - sparse_categorical_accuracy: 0.6091

1340/未知 361 秒 265 毫秒/步 - loss: 1.0015 - sparse_categorical_accuracy: 0.6091

1341/未知 361 秒 265 毫秒/步 - loss: 1.0013 - sparse_categorical_accuracy: 0.6092

1342/未知 361 秒 265 毫秒/步 - loss: 1.0012 - sparse_categorical_accuracy: 0.6092

1343/未知 362 秒 265 毫秒/步 - loss: 1.0010 - sparse_categorical_accuracy: 0.6093

1344/未知 362 秒 265 毫秒/步 - loss: 1.0009 - sparse_categorical_accuracy: 0.6093

1345/未知 362 秒 265 毫秒/步 - loss: 1.0008 - sparse_categorical_accuracy: 0.6094

1346/未知 363 秒 265 毫秒/步 - loss: 1.0006 - sparse_categorical_accuracy: 0.6094

1347/未知 363 秒 265 毫秒/步 - loss: 1.0005 - sparse_categorical_accuracy: 0.6095

1348/未知 363 秒 265 毫秒/步 - loss: 1.0003 - sparse_categorical_accuracy: 0.6095

1349/未知 364 秒 265 毫秒/步 - loss: 1.0002 - sparse_categorical_accuracy: 0.6096

1350/未知 364 秒 265 毫秒/步 - loss: 1.0000 - sparse_categorical_accuracy: 0.6096

1351/未知 364 秒 265 毫秒/步 - loss: 0.9999 - sparse_categorical_accuracy: 0.6096

1352/未知 364 秒 265 毫秒/步 - loss: 0.9998 - sparse_categorical_accuracy: 0.6097

1353/未知 365 秒 265 毫秒/步 - loss: 0.9996 - sparse_categorical_accuracy: 0.6097

1354/未知 365 秒 265 毫秒/步 - loss: 0.9995 - sparse_categorical_accuracy: 0.6098

1355/未知 365 秒 265 毫秒/步 - loss: 0.9993 - sparse_categorical_accuracy: 0.6098

1356/未知 366 秒 265 毫秒/步 - loss: 0.9992 - sparse_categorical_accuracy: 0.6099

1357/未知 366 秒 266 毫秒/步 - loss: 0.9991 - sparse_categorical_accuracy: 0.6099

1358/未知 366 秒 266 毫秒/步 - loss: 0.9989 - sparse_categorical_accuracy: 0.6100

1359/未知 366 秒 266 毫秒/步 - loss: 0.9988 - sparse_categorical_accuracy: 0.6100

1360/未知 367 秒 266 毫秒/步 - loss: 0.9986 - sparse_categorical_accuracy: 0.6100

1361/未知 367 秒 266 毫秒/步 - loss: 0.9985 - sparse_categorical_accuracy: 0.6101

1362/未知 367 秒 265 毫秒/步 - loss: 0.9984 - sparse_categorical_accuracy: 0.6101

1363/未知 367 秒 265 毫秒/步 - loss: 0.9982 - sparse_categorical_accuracy: 0.6102

1364/未知 368 秒 266 毫秒/步 - loss: 0.9981 - sparse_categorical_accuracy: 0.6102

1365/未知 368 秒 266 毫秒/步 - loss: 0.9979 - sparse_categorical_accuracy: 0.6103

1366/未知 368 秒 266 毫秒/步 - loss: 0.9978 - sparse_categorical_accuracy: 0.6103

1367/未知 369 秒 266 毫秒/步 - loss: 0.9977 - sparse_categorical_accuracy: 0.6104

1368/未知 369 秒 266 毫秒/步 - loss: 0.9975 - sparse_categorical_accuracy: 0.6104

1369/未知 369 秒 266 毫秒/步 - loss: 0.9974 - sparse_categorical_accuracy: 0.6104

1370/未知 369 秒 266 毫秒/步 - loss: 0.9972 - sparse_categorical_accuracy: 0.6105

1371/未知 370 秒 266 毫秒/步 - loss: 0.9971 - sparse_categorical_accuracy: 0.6105

1372/未知 370 秒 266 毫秒/步 - loss: 0.9970 - sparse_categorical_accuracy: 0.6106

1373/未知 370 秒 266 毫秒/步 - loss: 0.9968 - sparse_categorical_accuracy: 0.6106

1374/未知 371 秒 266 毫秒/步 - loss: 0.9967 - sparse_categorical_accuracy: 0.6107

1375/未知 371 秒 266 毫秒/步 - loss: 0.9965 - sparse_categorical_accuracy: 0.6107

1376/未知 371 秒 266 毫秒/步 - loss: 0.9964 - sparse_categorical_accuracy: 0.6107

1377/未知 372 秒 266 毫秒/步 - loss: 0.9963 - sparse_categorical_accuracy: 0.6108

1378/未知 372 秒 266 毫秒/步 - loss: 0.9961 - sparse_categorical_accuracy: 0.6108

1379/未知 372 秒 266 毫秒/步 - loss: 0.9960 - sparse_categorical_accuracy: 0.6109

1380/未知 372 秒 266 毫秒/步 - loss: 0.9959 - sparse_categorical_accuracy: 0.6109

1381/未知 373 秒 266 毫秒/步 - loss: 0.9957 - sparse_categorical_accuracy: 0.6110

1382/未知 373 秒 266 毫秒/步 - loss: 0.9956 - sparse_categorical_accuracy: 0.6110

1383/未知 373 秒 266 毫秒/步 - loss: 0.9954 - sparse_categorical_accuracy: 0.6111

1384/未知 374 秒 266 毫秒/步 - loss: 0.9953 - sparse_categorical_accuracy: 0.6111

1385/未知 374 秒 266 毫秒/步 - loss: 0.9952 - sparse_categorical_accuracy: 0.6111

1386/未知 374 秒 266 毫秒/步 - loss: 0.9950 - sparse_categorical_accuracy: 0.6112

1387/未知 374 秒 266 毫秒/步 - loss: 0.9949 - sparse_categorical_accuracy: 0.6112

1388/未知 375 秒 266 毫秒/步 - loss: 0.9948 - sparse_categorical_accuracy: 0.6113

1389/未知 375 秒 266 毫秒/步 - loss: 0.9946 - sparse_categorical_accuracy: 0.6113

1390/未知 375 秒 266 毫秒/步 - loss: 0.9945 - sparse_categorical_accuracy: 0.6114

1391/未知 376 秒 266 毫秒/步 - loss: 0.9943 - sparse_categorical_accuracy: 0.6114

1392/未知 376 秒 266 毫秒/步 - loss: 0.9942 - sparse_categorical_accuracy: 0.6114

1393/未知 376 秒 266 毫秒/步 - loss: 0.9941 - sparse_categorical_accuracy: 0.6115

1394/未知 377 秒 266 毫秒/步 - loss: 0.9939 - sparse_categorical_accuracy: 0.6115

1395/未知 377 秒 266 毫秒/步 - loss: 0.9938 - sparse_categorical_accuracy: 0.6116

1396/未知 377 秒 266 毫秒/步 - loss: 0.9937 - sparse_categorical_accuracy: 0.6116

1397/未知 378 秒 266 毫秒/步 - loss: 0.9935 - sparse_categorical_accuracy: 0.6117

1398/未知 378 秒 266 毫秒/步 - loss: 0.9934 - sparse_categorical_accuracy: 0.6117

1399/未知 378 秒 266 毫秒/步 - loss: 0.9933 - sparse_categorical_accuracy: 0.6117

1400/未知 378 秒 266 毫秒/步 - loss: 0.9931 - sparse_categorical_accuracy: 0.6118

1401/未知 379 秒 266 毫秒/步 - loss: 0.9930 - sparse_categorical_accuracy: 0.6118

1402/未知 379 秒 266 毫秒/步 - loss: 0.9929 - sparse_categorical_accuracy: 0.6119

1403/未知 379 秒 266 毫秒/步 - loss: 0.9927 - sparse_categorical_accuracy: 0.6119

1404/未知 379 秒 266 毫秒/步 - loss: 0.9926 - sparse_categorical_accuracy: 0.6120

1405/未知 380 秒 266 毫秒/步 - loss: 0.9925 - sparse_categorical_accuracy: 0.6120

1406/未知 380 秒 266 毫秒/步 - loss: 0.9923 - sparse_categorical_accuracy: 0.6120

1407/未知 380 秒 266 毫秒/步 - loss: 0.9922 - sparse_categorical_accuracy: 0.6121

1408/未知 380 秒 266 毫秒/步 - loss: 0.9921 - sparse_categorical_accuracy: 0.6121

1409/未知 381 秒 266 毫秒/步 - loss: 0.9919 - sparse_categorical_accuracy: 0.6122

1410/未知 381 秒 266 毫秒/步 - loss: 0.9918 - sparse_categorical_accuracy: 0.6122

1411/未知 381 秒 266 毫秒/步 - loss: 0.9917 - sparse_categorical_accuracy: 0.6122

1412/未知 382 秒 266 毫秒/步 - loss: 0.9915 - sparse_categorical_accuracy: 0.6123

1413/未知 382 秒 266 毫秒/步 - loss: 0.9914 - sparse_categorical_accuracy: 0.6123

1414/未知 382 秒 266 毫秒/步 - loss: 0.9913 - sparse_categorical_accuracy: 0.6124

1415/未知 382 秒 266 毫秒/步 - loss: 0.9911 - sparse_categorical_accuracy: 0.6124

1416/未知 383 秒 266 毫秒/步 - loss: 0.9910 - sparse_categorical_accuracy: 0.6125

1417/未知 383 秒 266 毫秒/步 - loss: 0.9909 - sparse_categorical_accuracy: 0.6125

1418/未知 383 秒 266 毫秒/步 - loss: 0.9907 - sparse_categorical_accuracy: 0.6125

1419/未知 384 秒 266 毫秒/步 - loss: 0.9906 - sparse_categorical_accuracy: 0.6126

1420/未知 384 秒 267 毫秒/步 - loss: 0.9905 - sparse_categorical_accuracy: 0.6126

1421/未知 384 秒 267 毫秒/步 - loss: 0.9903 - sparse_categorical_accuracy: 0.6127

1422/未知 385 秒 267 毫秒/步 - loss: 0.9902 - sparse_categorical_accuracy: 0.6127

1423/未知 385 秒 267 毫秒/步 - loss: 0.9901 - sparse_categorical_accuracy: 0.6127

1424/未知 386 秒 267 毫秒/步 - loss: 0.9899 - sparse_categorical_accuracy: 0.6128

1425/未知 386 秒 267 毫秒/步 - loss: 0.9898 - sparse_categorical_accuracy: 0.6128

1426/未知 386 秒 267 毫秒/步 - loss: 0.9897 - sparse_categorical_accuracy: 0.6129

1427/未知 386 秒 267 毫秒/步 - loss: 0.9895 - sparse_categorical_accuracy: 0.6129

1428/未知 387 秒 267 毫秒/步 - loss: 0.9894 - sparse_categorical_accuracy: 0.6130

1429/未知 387 秒 267 毫秒/步 - loss: 0.9893 - sparse_categorical_accuracy: 0.6130

1430/未知 387 秒 267 毫秒/步 - loss: 0.9891 - sparse_categorical_accuracy: 0.6130

1431/未知 388 秒 267 毫秒/步 - loss: 0.9890 - sparse_categorical_accuracy: 0.6131

1432/未知 388 秒 267 毫秒/步 - loss: 0.9889 - sparse_categorical_accuracy: 0.6131

1433/未知 388 秒 267 毫秒/步 - loss: 0.9888 - sparse_categorical_accuracy: 0.6132

1434/未知 388 秒 267 毫秒/步 - loss: 0.9886 - sparse_categorical_accuracy: 0.6132

1435/未知 389 秒 267 毫秒/步 - loss: 0.9885 - sparse_categorical_accuracy: 0.6132

1436/未知 389 秒 267 毫秒/步 - loss: 0.9884 - sparse_categorical_accuracy: 0.6133

1437/未知 389 秒 267 毫秒/步 - loss: 0.9882 - sparse_categorical_accuracy: 0.6133

1438/未知 390 秒 267 毫秒/步 - loss: 0.9881 - sparse_categorical_accuracy: 0.6134

1439/未知 390 秒 267 毫秒/步 - loss: 0.9880 - sparse_categorical_accuracy: 0.6134

1440/未知 390 秒 267 毫秒/步 - loss: 0.9878 - sparse_categorical_accuracy: 0.6134

1441/未知 391 秒 267 毫秒/步 - loss: 0.9877 - sparse_categorical_accuracy: 0.6135

1442/未知 391 秒 267 毫秒/步 - loss: 0.9876 - sparse_categorical_accuracy: 0.6135

1443/未知 391 秒 267 毫秒/步 - loss: 0.9875 - sparse_categorical_accuracy: 0.6136

1444/未知 391 秒 267 毫秒/步 - loss: 0.9873 - sparse_categorical_accuracy: 0.6136

1445/未知 392 秒 267 毫秒/步 - loss: 0.9872 - sparse_categorical_accuracy: 0.6137

1446/未知 392 秒 267 毫秒/步 - loss: 0.9871 - sparse_categorical_accuracy: 0.6137

1447/未知 392 秒 267 毫秒/步 - loss: 0.9869 - sparse_categorical_accuracy: 0.6137

1448/未知 393 秒 267 毫秒/步 - loss: 0.9868 - sparse_categorical_accuracy: 0.6138

1449/未知 393 秒 268 毫秒/步 - loss: 0.9867 - sparse_categorical_accuracy: 0.6138

1450/未知 394 秒 268 毫秒/步 - loss: 0.9866 - sparse_categorical_accuracy: 0.6139

1451/未知 394 秒 268 毫秒/步 - loss: 0.9864 - sparse_categorical_accuracy: 0.6139

1452/未知 394 秒 268 毫秒/步 - loss: 0.9863 - sparse_categorical_accuracy: 0.6139

1453/未知 395 秒 268 毫秒/步 - loss: 0.9862 - sparse_categorical_accuracy: 0.6140

1454/未知 395 秒 268 毫秒/步 - loss: 0.9861 - sparse_categorical_accuracy: 0.6140

1455/未知 395 秒 268 毫秒/步 - loss: 0.9859 - sparse_categorical_accuracy: 0.6141

1456/未知 396 秒 268 毫秒/步 - loss: 0.9858 - sparse_categorical_accuracy: 0.6141

1457/未知 396 秒 268 毫秒/步 - loss: 0.9857 - sparse_categorical_accuracy: 0.6141

1458/未知 396 秒 268 毫秒/步 - loss: 0.9855 - sparse_categorical_accuracy: 0.6142

1459/未知 396 秒 268 毫秒/步 - loss: 0.9854 - sparse_categorical_accuracy: 0.6142

1460/未知 397 秒 268 毫秒/步 - loss: 0.9853 - sparse_categorical_accuracy: 0.6143

1461/未知 397 秒 268 毫秒/步 - loss: 0.9852 - sparse_categorical_accuracy: 0.6143

1462/未知 397 秒 268 毫秒/步 - loss: 0.9850 - sparse_categorical_accuracy: 0.6143

1463/未知 397 秒 268 毫秒/步 - loss: 0.9849 - sparse_categorical_accuracy: 0.6144

1464/未知 398 秒 268 毫秒/步 - loss: 0.9848 - sparse_categorical_accuracy: 0.6144

1465/未知 398 秒 268 毫秒/步 - loss: 0.9847 - sparse_categorical_accuracy: 0.6145

1466/未知 398 秒 268 毫秒/步 - loss: 0.9845 - sparse_categorical_accuracy: 0.6145

1467/未知 399 秒 268 毫秒/步 - loss: 0.9844 - sparse_categorical_accuracy: 0.6145

1468/未知 399 秒 268 毫秒/步 - loss: 0.9843 - sparse_categorical_accuracy: 0.6146

1469/未知 399 秒 268 毫秒/步 - loss: 0.9842 - sparse_categorical_accuracy: 0.6146

1470/未知 399 秒 268 毫秒/步 - loss: 0.9840 - sparse_categorical_accuracy: 0.6147

1471/未知 400 秒 268 毫秒/步 - loss: 0.9839 - sparse_categorical_accuracy: 0.6147

1472/未知 400 秒 268 毫秒/步 - loss: 0.9838 - sparse_categorical_accuracy: 0.6147

1473/未知 400 秒 268 毫秒/步 - loss: 0.9837 - sparse_categorical_accuracy: 0.6148

1474/未知 401 秒 268 毫秒/步 - loss: 0.9835 - sparse_categorical_accuracy: 0.6148

1475/未知 401 秒 268 毫秒/步 - loss: 0.9834 - sparse_categorical_accuracy: 0.6149

1476/未知 401 秒 268 毫秒/步 - loss: 0.9833 - sparse_categorical_accuracy: 0.6149

1477/未知 401 秒 268 毫秒/步 - loss: 0.9832 - sparse_categorical_accuracy: 0.6149

1478/未知 402 秒 268 毫秒/步 - loss: 0.9830 - sparse_categorical_accuracy: 0.6150

1479/未知 402 秒 268 毫秒/步 - loss: 0.9829 - sparse_categorical_accuracy: 0.6150

1480/未知 402 秒 268 毫秒/步 - loss: 0.9828 - sparse_categorical_accuracy: 0.6150

1481/未知 403 秒 268 毫秒/步 - loss: 0.9827 - sparse_categorical_accuracy: 0.6151

1482/未知 403 秒 268 毫秒/步 - loss: 0.9825 - sparse_categorical_accuracy: 0.6151

1483/未知 403 秒 268 毫秒/步 - loss: 0.9824 - sparse_categorical_accuracy: 0.6152

1484/未知 404 秒 268 毫秒/步 - loss: 0.9823 - sparse_categorical_accuracy: 0.6152

1485/未知 404 秒 268 毫秒/步 - loss: 0.9822 - sparse_categorical_accuracy: 0.6152

1486/未知 404 秒 268 毫秒/步 - loss: 0.9820 - sparse_categorical_accuracy: 0.6153

1487/未知 404 秒 268 毫秒/步 - loss: 0.9819 - sparse_categorical_accuracy: 0.6153

1488/未知 405 秒 268 毫秒/步 - loss: 0.9818 - sparse_categorical_accuracy: 0.6154

1489/未知 405 秒 268 毫秒/步 - loss: 0.9817 - sparse_categorical_accuracy: 0.6154

1490/未知 405 秒 268 毫秒/步 - loss: 0.9815 - sparse_categorical_accuracy: 0.6154

1491/未知 406 秒 268 毫秒/步 - loss: 0.9814 - sparse_categorical_accuracy: 0.6155

1492/未知 406 秒 268 毫秒/步 - loss: 0.9813 - sparse_categorical_accuracy: 0.6155

1493/未知 406 秒 268 毫秒/步 - loss: 0.9812 - sparse_categorical_accuracy: 0.6156

1494/未知 406 秒 268 毫秒/步 - loss: 0.9810 - sparse_categorical_accuracy: 0.6156

1495/未知 407 秒 268 毫秒/步 - loss: 0.9809 - sparse_categorical_accuracy: 0.6156

1496/未知 407 秒 268 毫秒/步 - loss: 0.9808 - sparse_categorical_accuracy: 0.6157

1497/未知 407 秒 268 毫秒/步 - loss: 0.9807 - sparse_categorical_accuracy: 0.6157

1498/未知 408 秒 268 毫秒/步 - loss: 0.9806 - sparse_categorical_accuracy: 0.6157

1499/未知 408 秒 268 毫秒/步 - loss: 0.9804 - sparse_categorical_accuracy: 0.6158

1500/未知 408 秒 268 毫秒/步 - loss: 0.9803 - sparse_categorical_accuracy: 0.6158

1501/未知 408 秒 268 毫秒/步 - loss: 0.9802 - sparse_categorical_accuracy: 0.6159

1502/未知 409 秒 268 毫秒/步 - loss: 0.9801 - sparse_categorical_accuracy: 0.6159

1503/未知 409 秒 268 毫秒/步 - loss: 0.9800 - sparse_categorical_accuracy: 0.6159

1504/未知 409 秒 268 毫秒/步 - loss: 0.9798 - sparse_categorical_accuracy: 0.6160

1505/未知 410 秒 268 毫秒/步 - loss: 0.9797 - sparse_categorical_accuracy: 0.6160

1506/未知 410 秒 269 毫秒/步 - loss: 0.9796 - sparse_categorical_accuracy: 0.6161

1507/未知 410 秒 269 毫秒/步 - loss: 0.9795 - sparse_categorical_accuracy: 0.6161

1508/未知 411 秒 269 毫秒/步 - loss: 0.9793 - sparse_categorical_accuracy: 0.6161

1509/未知 411 秒 269 毫秒/步 - loss: 0.9792 - sparse_categorical_accuracy: 0.6162

1510/未知 411 秒 269 毫秒/步 - loss: 0.9791 - sparse_categorical_accuracy: 0.6162

1511/未知 411 秒 269 毫秒/步 - loss: 0.9790 - sparse_categorical_accuracy: 0.6162

1512/未知 412 秒 269 毫秒/步 - loss: 0.9789 - sparse_categorical_accuracy: 0.6163

1513/未知 412 秒 269 毫秒/步 - loss: 0.9787 - sparse_categorical_accuracy: 0.6163

1514/未知 412 秒 269 毫秒/步 - loss: 0.9786 - sparse_categorical_accuracy: 0.6164

1515/未知 413 秒 269 毫秒/步 - loss: 0.9785 - sparse_categorical_accuracy: 0.6164

1516/未知 413 秒 269 毫秒/步 - loss: 0.9784 - sparse_categorical_accuracy: 0.6164

1517/未知 413 秒 269 毫秒/步 - loss: 0.9783 - sparse_categorical_accuracy: 0.6165

1518/未知 413 秒 269 毫秒/步 - loss: 0.9781 - sparse_categorical_accuracy: 0.6165

1519/未知 414 秒 269 毫秒/步 - loss: 0.9780 - sparse_categorical_accuracy: 0.6166

1520/未知 414 秒 269 毫秒/步 - loss: 0.9779 - sparse_categorical_accuracy: 0.6166

1521/未知 414 秒 269 毫秒/步 - loss: 0.9778 - sparse_categorical_accuracy: 0.6166

1522/未知 415 秒 269 毫秒/步 - loss: 0.9777 - sparse_categorical_accuracy: 0.6167

1523/未知 415 秒 269 毫秒/步 - loss: 0.9775 - sparse_categorical_accuracy: 0.6167

1524/未知 415 秒 269 毫秒/步 - loss: 0.9774 - sparse_categorical_accuracy: 0.6167

1525/未知 415 秒 269 毫秒/步 - loss: 0.9773 - sparse_categorical_accuracy: 0.6168

1526/未知 416 秒 269 毫秒/步 - loss: 0.9772 - sparse_categorical_accuracy: 0.6168

1527/未知 416 秒 269 毫秒/步 - loss: 0.9771 - sparse_categorical_accuracy: 0.6169

1528/未知 416 秒 269 毫秒/步 - loss: 0.9769 - sparse_categorical_accuracy: 0.6169

1529/未知 417 秒 269 毫秒/步 - loss: 0.9768 - sparse_categorical_accuracy: 0.6169

1530/未知 417 秒 269 毫秒/步 - loss: 0.9767 - sparse_categorical_accuracy: 0.6170

1531/未知 417 秒 269 毫秒/步 - loss: 0.9766 - sparse_categorical_accuracy: 0.6170

1532/未知 417 秒 269 毫秒/步 - loss: 0.9765 - sparse_categorical_accuracy: 0.6170

1533/未知 418 秒 269 毫秒/步 - loss: 0.9764 - sparse_categorical_accuracy: 0.6171

1534/未知 418 秒 269 毫秒/步 - loss: 0.9762 - sparse_categorical_accuracy: 0.6171

1535/未知 418 秒 269 毫秒/步 - loss: 0.9761 - sparse_categorical_accuracy: 0.6172

1536/未知 418 秒 269 毫秒/步 - loss: 0.9760 - sparse_categorical_accuracy: 0.6172

1537/未知 419 秒 269 毫秒/步 - loss: 0.9759 - sparse_categorical_accuracy: 0.6172

1538/未知 419 秒 269 毫秒/步 - loss: 0.9758 - sparse_categorical_accuracy: 0.6173

1539/未知 419 秒 269 毫秒/步 - loss: 0.9756 - sparse_categorical_accuracy: 0.6173

1540/未知 420 秒 269 毫秒/步 - loss: 0.9755 - sparse_categorical_accuracy: 0.6173

1541/未知 420 秒 269 毫秒/步 - loss: 0.9754 - sparse_categorical_accuracy: 0.6174

1542/未知 420 秒 269 毫秒/步 - loss: 0.9753 - sparse_categorical_accuracy: 0.6174

1543/未知 420 秒 269 毫秒/步 - loss: 0.9752 - sparse_categorical_accuracy: 0.6174

1544/未知 421 秒 269 毫秒/步 - loss: 0.9751 - sparse_categorical_accuracy: 0.6175

1545/未知 421 秒 269 毫秒/步 - loss: 0.9749 - sparse_categorical_accuracy: 0.6175

1546/未知 421 秒 269 毫秒/步 - loss: 0.9748 - sparse_categorical_accuracy: 0.6176

1547/未知 422 秒 269 毫秒/步 - loss: 0.9747 - sparse_categorical_accuracy: 0.6176

1548/未知 422 秒 269 毫秒/步 - loss: 0.9746 - sparse_categorical_accuracy: 0.6176

1549/未知 422 秒 269 毫秒/步 - loss: 0.9745 - sparse_categorical_accuracy: 0.6177

1550/未知 422 秒 269 毫秒/步 - loss: 0.9744 - sparse_categorical_accuracy: 0.6177

1551/未知 423 秒 269 毫秒/步 - loss: 0.9742 - sparse_categorical_accuracy: 0.6177

1552/未知 423 秒 269 毫秒/步 - loss: 0.9741 - sparse_categorical_accuracy: 0.6178

1553/未知 423 秒 269 毫秒/步 - loss: 0.9740 - sparse_categorical_accuracy: 0.6178

1554/未知 424 秒 269 毫秒/步 - loss: 0.9739 - sparse_categorical_accuracy: 0.6179

1555/未知 424 秒 269 毫秒/步 - loss: 0.9738 - sparse_categorical_accuracy: 0.6179

1556/未知 424 秒 269 毫秒/步 - loss: 0.9737 - sparse_categorical_accuracy: 0.6179

1557/未知 424 秒 269 毫秒/步 - loss: 0.9736 - sparse_categorical_accuracy: 0.6180

1558/未知 425 秒 269 毫秒/步 - loss: 0.9734 - sparse_categorical_accuracy: 0.6180

1559/未知 425 秒 269 毫秒/步 - loss: 0.9733 - sparse_categorical_accuracy: 0.6180

1560/未知 425 秒 269 毫秒/步 - loss: 0.9732 - sparse_categorical_accuracy: 0.6181

1561/未知 426 秒 269 毫秒/步 - loss: 0.9731 - sparse_categorical_accuracy: 0.6181

1562/未知 426 秒 269 毫秒/步 - loss: 0.9730 - sparse_categorical_accuracy: 0.6181

1563/未知 426 秒 269 毫秒/步 - loss: 0.9729 - sparse_categorical_accuracy: 0.6182

1564/未知 427 秒 269 毫秒/步 - loss: 0.9727 - sparse_categorical_accuracy: 0.6182

1565/未知 427 秒 269 毫秒/步 - loss: 0.9726 - sparse_categorical_accuracy: 0.6182

1566/未知 427 秒 269 毫秒/步 - loss: 0.9725 - sparse_categorical_accuracy: 0.6183

1567/未知 427 秒 269 毫秒/步 - loss: 0.9724 - sparse_categorical_accuracy: 0.6183

1568/未知 428 秒 269 毫秒/步 - loss: 0.9723 - sparse_categorical_accuracy: 0.6184

1569/未知 428 秒 269 毫秒/步 - loss: 0.9722 - sparse_categorical_accuracy: 0.6184

1570/未知 428 秒 269 毫秒/步 - loss: 0.9721 - sparse_categorical_accuracy: 0.6184

1571/未知 428 秒 269 毫秒/步 - loss: 0.9719 - sparse_categorical_accuracy: 0.6185

1572/未知 429 秒 269 毫秒/步 - loss: 0.9718 - sparse_categorical_accuracy: 0.6185

1573/未知 429 秒 269 毫秒/步 - loss: 0.9717 - sparse_categorical_accuracy: 0.6185

1574/未知 429 秒 269 毫秒/步 - loss: 0.9716 - sparse_categorical_accuracy: 0.6186

1575/未知 430 秒 269 毫秒/步 - loss: 0.9715 - sparse_categorical_accuracy: 0.6186

1576/未知 430 秒 269 毫秒/步 - loss: 0.9714 - sparse_categorical_accuracy: 0.6186

1577/未知 430 秒 269 毫秒/步 - loss: 0.9713 - sparse_categorical_accuracy: 0.6187

1578/未知 430 秒 269 毫秒/步 - loss: 0.9712 - sparse_categorical_accuracy: 0.6187

1579/未知 431 秒 269 毫秒/步 - loss: 0.9710 - sparse_categorical_accuracy: 0.6188

1580/未知 431 秒 269 毫秒/步 - loss: 0.9709 - sparse_categorical_accuracy: 0.6188

1581/未知 431 秒 269 毫秒/步 - loss: 0.9708 - sparse_categorical_accuracy: 0.6188

1582/未知 432 秒 269 毫秒/步 - loss: 0.9707 - sparse_categorical_accuracy: 0.6189

1583/未知 432 秒 269 毫秒/步 - loss: 0.9706 - sparse_categorical_accuracy: 0.6189

1584/未知 432 秒 269 毫秒/步 - loss: 0.9705 - sparse_categorical_accuracy: 0.6189

1585/未知 433 秒 269 毫秒/步 - loss: 0.9704 - sparse_categorical_accuracy: 0.6190

1586/未知 433 秒 269 毫秒/步 - loss: 0.9702 - sparse_categorical_accuracy: 0.6190

1587/未知 433 秒 269 毫秒/步 - loss: 0.9701 - sparse_categorical_accuracy: 0.6190

1588/未知 433 秒 269 毫秒/步 - loss: 0.9700 - sparse_categorical_accuracy: 0.6191

1589/未知 434 秒 269 毫秒/步 - loss: 0.9699 - sparse_categorical_accuracy: 0.6191

1590/未知 434 秒 269 毫秒/步 - loss: 0.9698 - sparse_categorical_accuracy: 0.6191

1591/未知 434 秒 269 毫秒/步 - loss: 0.9697 - sparse_categorical_accuracy: 0.6192

1592/未知 435 秒 270 毫秒/步 - loss: 0.9696 - sparse_categorical_accuracy: 0.6192

1593/未知 435 秒 270 毫秒/步 - loss: 0.9695 - sparse_categorical_accuracy: 0.6192

1594/未知 435 秒 270 毫秒/步 - loss: 0.9694 - sparse_categorical_accuracy: 0.6193

1595/未知 435 秒 270 毫秒/步 - loss: 0.9692 - sparse_categorical_accuracy: 0.6193

1596/未知 436 秒 270 毫秒/步 - loss: 0.9691 - sparse_categorical_accuracy: 0.6194

1597/未知 436 秒 270 毫秒/步 - loss: 0.9690 - sparse_categorical_accuracy: 0.6194

1598/未知 436 秒 270 毫秒/步 - loss: 0.9689 - sparse_categorical_accuracy: 0.6194

1599/未知 437 秒 270 毫秒/步 - loss: 0.9688 - sparse_categorical_accuracy: 0.6195

1600/未知 437 秒 270 毫秒/步 - loss: 0.9687 - sparse_categorical_accuracy: 0.6195

1601/未知 437 秒 270 毫秒/步 - loss: 0.9686 - sparse_categorical_accuracy: 0.6195

1602/未知 437 秒 270 毫秒/步 - loss: 0.9685 - sparse_categorical_accuracy: 0.6196

1603/未知 438 秒 270 毫秒/步 - loss: 0.9684 - sparse_categorical_accuracy: 0.6196

1604/未知 438 秒 270 毫秒/步 - loss: 0.9682 - sparse_categorical_accuracy: 0.6196

1605/未知 438 秒 270 毫秒/步 - loss: 0.9681 - sparse_categorical_accuracy: 0.6197

1606/未知 439 秒 270 毫秒/步 - loss: 0.9680 - sparse_categorical_accuracy: 0.6197

1607/未知 439 秒 270 毫秒/步 - loss: 0.9679 - sparse_categorical_accuracy: 0.6197

1608/未知 439 秒 270 毫秒/步 - loss: 0.9678 - sparse_categorical_accuracy: 0.6198

1609/未知 439 秒 270 毫秒/步 - loss: 0.9677 - sparse_categorical_accuracy: 0.6198

1610/未知 440 秒 270 毫秒/步 - loss: 0.9676 - sparse_categorical_accuracy: 0.6198

1611/未知 440 秒 270 毫秒/步 - loss: 0.9675 - sparse_categorical_accuracy: 0.6199

1612/未知 440 秒 270 毫秒/步 - loss: 0.9674 - sparse_categorical_accuracy: 0.6199

1613/未知 441 秒 270 毫秒/步 - loss: 0.9673 - sparse_categorical_accuracy: 0.6199

1614/未知 441 秒 270 毫秒/步 - loss: 0.9671 - sparse_categorical_accuracy: 0.6200

1615/未知 441 秒 270 毫秒/步 - loss: 0.9670 - sparse_categorical_accuracy: 0.6200

1616/未知 442 秒 270 毫秒/步 - loss: 0.9669 - sparse_categorical_accuracy: 0.6200

1617/未知 442 秒 270 毫秒/步 - loss: 0.9668 - sparse_categorical_accuracy: 0.6201

1618/未知 442 秒 270 毫秒/步 - loss: 0.9667 - sparse_categorical_accuracy: 0.6201

1619/未知 442 秒 270 毫秒/步 - loss: 0.9666 - sparse_categorical_accuracy: 0.6202

1620/未知 443 秒 270 毫秒/步 - loss: 0.9665 - sparse_categorical_accuracy: 0.6202

1621/未知 443 秒 270 毫秒/步 - loss: 0.9664 - sparse_categorical_accuracy: 0.6202

1622/未知 443 秒 270 毫秒/步 - loss: 0.9663 - sparse_categorical_accuracy: 0.6203

1623/未知 444 秒 270 毫秒/步 - loss: 0.9662 - sparse_categorical_accuracy: 0.6203

1624/未知 444 秒 270 毫秒/步 - loss: 0.9661 - sparse_categorical_accuracy: 0.6203

1625/未知 444 秒 270 毫秒/步 - loss: 0.9659 - sparse_categorical_accuracy: 0.6204

1626/未知 445 秒 270 毫秒/步 - loss: 0.9658 - sparse_categorical_accuracy: 0.6204

1627/未知 445 秒 270 毫秒/步 - loss: 0.9657 - sparse_categorical_accuracy: 0.6204

1628/未知 445 秒 270 毫秒/步 - loss: 0.9656 - sparse_categorical_accuracy: 0.6205

1629/未知 446 秒 270 毫秒/步 - loss: 0.9655 - sparse_categorical_accuracy: 0.6205

1630/未知 446 秒 270 毫秒/步 - loss: 0.9654 - sparse_categorical_accuracy: 0.6205

1631/未知 446 秒 270 毫秒/步 - loss: 0.9653 - sparse_categorical_accuracy: 0.6206

1632/未知 447 秒 270 毫秒/步 - loss: 0.9652 - sparse_categorical_accuracy: 0.6206

1633/未知 447 秒 270 毫秒/步 - loss: 0.9651 - sparse_categorical_accuracy: 0.6206

1634/未知 447 秒 270 毫秒/步 - loss: 0.9650 - sparse_categorical_accuracy: 0.6207

1635/未知 448 秒 271 毫秒/步 - loss: 0.9649 - sparse_categorical_accuracy: 0.6207

1636/未知 448 秒 271 毫秒/步 - loss: 0.9648 - sparse_categorical_accuracy: 0.6207

1637/未知 448 秒 271 毫秒/步 - loss: 0.9646 - sparse_categorical_accuracy: 0.6208

1638/未知 449 秒 271 毫秒/步 - loss: 0.9645 - sparse_categorical_accuracy: 0.6208

1639/未知 449 秒 271 毫秒/步 - loss: 0.9644 - sparse_categorical_accuracy: 0.6208

1640/未知 449 秒 271 毫秒/步 - loss: 0.9643 - sparse_categorical_accuracy: 0.6209

1641/未知 450 秒 271 毫秒/步 - loss: 0.9642 - sparse_categorical_accuracy: 0.6209

1642/未知 450 秒 271 毫秒/步 - loss: 0.9641 - sparse_categorical_accuracy: 0.6209

1643/未知 450 秒 271 毫秒/步 - loss: 0.9640 - sparse_categorical_accuracy: 0.6210

1644/未知 450 秒 271 毫秒/步 - loss: 0.9639 - sparse_categorical_accuracy: 0.6210

1645/未知 451 秒 271 毫秒/步 - loss: 0.9638 - sparse_categorical_accuracy: 0.6210

1646/未知 451 秒 271 毫秒/步 - loss: 0.9637 - sparse_categorical_accuracy: 0.6211

1647/未知 451 秒 271 毫秒/步 - loss: 0.9636 - sparse_categorical_accuracy: 0.6211

1648/未知 452 秒 271 毫秒/步 - loss: 0.9635 - sparse_categorical_accuracy: 0.6211

1649/未知 452 秒 271 毫秒/步 - loss: 0.9634 - sparse_categorical_accuracy: 0.6212

1650/未知 452 秒 271 毫秒/步 - loss: 0.9633 - sparse_categorical_accuracy: 0.6212

1651/未知 452 秒 271 毫秒/步 - loss: 0.9632 - sparse_categorical_accuracy: 0.6212

1652/未知 453 秒 271毫秒/步 - loss: 0.9631 - sparse_categorical_accuracy: 0.6213

1653/未知 453 秒 271毫秒/步 - loss: 0.9629 - sparse_categorical_accuracy: 0.6213

1654/未知 453 秒 271毫秒/步 - loss: 0.9628 - sparse_categorical_accuracy: 0.6213

1655/未知 454 秒 271毫秒/步 - loss: 0.9627 - sparse_categorical_accuracy: 0.6214

1656/未知 454 秒 271毫秒/步 - loss: 0.9626 - sparse_categorical_accuracy: 0.6214

1657/未知 454 秒 271毫秒/步 - loss: 0.9625 - sparse_categorical_accuracy: 0.6214

1658/未知 455 秒 271毫秒/步 - loss: 0.9624 - sparse_categorical_accuracy: 0.6215

1659/未知 455 秒 271毫秒/步 - loss: 0.9623 - sparse_categorical_accuracy: 0.6215

1660/未知 455 秒 271毫秒/步 - loss: 0.9622 - sparse_categorical_accuracy: 0.6215

1661/未知 455 秒 271毫秒/步 - loss: 0.9621 - sparse_categorical_accuracy: 0.6216

1662/未知 456 秒 271毫秒/步 - loss: 0.9620 - sparse_categorical_accuracy: 0.6216

1663/未知 456 秒 271毫秒/步 - loss: 0.9619 - sparse_categorical_accuracy: 0.6216

1664/未知 456 秒 271毫秒/步 - loss: 0.9618 - sparse_categorical_accuracy: 0.6217

1665/未知 457 秒 271毫秒/步 - loss: 0.9617 - sparse_categorical_accuracy: 0.6217

1666/未知 457 秒 271毫秒/步 - loss: 0.9616 - sparse_categorical_accuracy: 0.6217

1667/未知 457 秒 271毫秒/步 - loss: 0.9615 - sparse_categorical_accuracy: 0.6218

1668/未知 457 秒 271毫秒/步 - loss: 0.9614 - sparse_categorical_accuracy: 0.6218

1669/未知 458 秒 271毫秒/步 - loss: 0.9613 - sparse_categorical_accuracy: 0.6218

1670/未知 458 秒 271毫秒/步 - loss: 0.9612 - sparse_categorical_accuracy: 0.6219

1671/未知 458 秒 271毫秒/步 - loss: 0.9611 - sparse_categorical_accuracy: 0.6219

1672/未知 459 秒 271毫秒/步 - loss: 0.9610 - sparse_categorical_accuracy: 0.6219

1673/未知 459 秒 271毫秒/步 - loss: 0.9609 - sparse_categorical_accuracy: 0.6220

1674/未知 459 秒 271毫秒/步 - loss: 0.9607 - sparse_categorical_accuracy: 0.6220

1675/未知 460 秒 271毫秒/步 - loss: 0.9606 - sparse_categorical_accuracy: 0.6220

1676/未知 460 秒 271毫秒/步 - loss: 0.9605 - sparse_categorical_accuracy: 0.6221

1677/未知 460 秒 271毫秒/步 - loss: 0.9604 - sparse_categorical_accuracy: 0.6221

1678/未知 460 秒 271毫秒/步 - loss: 0.9603 - sparse_categorical_accuracy: 0.6221

1679/未知 461 秒 271毫秒/步 - loss: 0.9602 - sparse_categorical_accuracy: 0.6222

1680/未知 461 秒 271毫秒/步 - loss: 0.9601 - sparse_categorical_accuracy: 0.6222

1681/未知 461 秒 271毫秒/步 - loss: 0.9600 - sparse_categorical_accuracy: 0.6222

1682/未知 462 秒 271毫秒/步 - loss: 0.9599 - sparse_categorical_accuracy: 0.6223

1683/未知 462 秒 271毫秒/步 - loss: 0.9598 - sparse_categorical_accuracy: 0.6223

1684/未知 462 秒 271毫秒/步 - loss: 0.9597 - sparse_categorical_accuracy: 0.6223

1685/未知 462 秒 271毫秒/步 - loss: 0.9596 - sparse_categorical_accuracy: 0.6224

1686/未知 463 秒 271毫秒/步 - loss: 0.9595 - sparse_categorical_accuracy: 0.6224

1687/未知 463 秒 271毫秒/步 - loss: 0.9594 - sparse_categorical_accuracy: 0.6224

1688/未知 463 秒 271毫秒/步 - loss: 0.9593 - sparse_categorical_accuracy: 0.6224

1689/未知 463 秒 271毫秒/步 - loss: 0.9592 - sparse_categorical_accuracy: 0.6225

1690/未知 464 秒 271毫秒/步 - loss: 0.9591 - sparse_categorical_accuracy: 0.6225

1691/未知 464 秒 271毫秒/步 - loss: 0.9590 - sparse_categorical_accuracy: 0.6225

1692/未知 464 秒 271毫秒/步 - loss: 0.9589 - sparse_categorical_accuracy: 0.6226

1693/未知 464 秒 271毫秒/步 - loss: 0.9588 - sparse_categorical_accuracy: 0.6226

1694/未知 465 秒 271毫秒/步 - loss: 0.9587 - sparse_categorical_accuracy: 0.6226

1695/未知 465 秒 271毫秒/步 - loss: 0.9586 - sparse_categorical_accuracy: 0.6227

1696/未知 465 秒 271毫秒/步 - loss: 0.9585 - sparse_categorical_accuracy: 0.6227

1697/未知 465 秒 271毫秒/步 - loss: 0.9584 - sparse_categorical_accuracy: 0.6227

1698/未知 466 秒 271毫秒/步 - loss: 0.9583 - sparse_categorical_accuracy: 0.6228

1699/未知 466 秒 271毫秒/步 - loss: 0.9582 - sparse_categorical_accuracy: 0.6228

1700/未知 466 秒 271毫秒/步 - loss: 0.9581 - sparse_categorical_accuracy: 0.6228

1701/未知 466 秒 271毫秒/步 - loss: 0.9580 - sparse_categorical_accuracy: 0.6229

1702/未知 467 秒 271毫秒/步 - loss: 0.9579 - sparse_categorical_accuracy: 0.6229

1703/未知 467 秒 271毫秒/步 - loss: 0.9578 - sparse_categorical_accuracy: 0.6229

1704/未知 467 秒 271毫秒/步 - loss: 0.9577 - sparse_categorical_accuracy: 0.6230

1705/未知 468 秒 271毫秒/步 - loss: 0.9576 - sparse_categorical_accuracy: 0.6230

1706/未知 468 秒 271毫秒/步 - loss: 0.9575 - sparse_categorical_accuracy: 0.6230

1707/未知 468 秒 271毫秒/步 - loss: 0.9574 - sparse_categorical_accuracy: 0.6231

1708/未知 469 秒 271毫秒/步 - loss: 0.9573 - sparse_categorical_accuracy: 0.6231

1709/未知 469 秒 271毫秒/步 - loss: 0.9572 - sparse_categorical_accuracy: 0.6231

1710/未知 469 秒 271毫秒/步 - loss: 0.9571 - sparse_categorical_accuracy: 0.6232

1711/未知 470 秒 271毫秒/步 - loss: 0.9570 - sparse_categorical_accuracy: 0.6232

1712/未知 470 秒 271毫秒/步 - loss: 0.9569 - sparse_categorical_accuracy: 0.6232

1713/未知 470 秒 271毫秒/步 - loss: 0.9568 - sparse_categorical_accuracy: 0.6232

1714/未知 470 秒 271毫秒/步 - loss: 0.9567 - sparse_categorical_accuracy: 0.6233

1715/未知 471 秒 271毫秒/步 - loss: 0.9566 - sparse_categorical_accuracy: 0.6233

1716/未知 471 秒 271毫秒/步 - loss: 0.9565 - sparse_categorical_accuracy: 0.6233

1717/未知 471 秒 271毫秒/步 - loss: 0.9564 - sparse_categorical_accuracy: 0.6234

1718/未知 471 秒 271毫秒/步 - loss: 0.9563 - sparse_categorical_accuracy: 0.6234

1719/未知 472 秒 271毫秒/步 - loss: 0.9562 - sparse_categorical_accuracy: 0.6234

1720/未知 472 秒 271毫秒/步 - loss: 0.9561 - sparse_categorical_accuracy: 0.6235

1721/未知 472 秒 271毫秒/步 - loss: 0.9560 - sparse_categorical_accuracy: 0.6235

1722/未知 472 秒 271毫秒/步 - loss: 0.9559 - sparse_categorical_accuracy: 0.6235

1723/未知 473 秒 271毫秒/步 - loss: 0.9558 - sparse_categorical_accuracy: 0.6236

1724/未知 473 秒 271毫秒/步 - loss: 0.9557 - sparse_categorical_accuracy: 0.6236

1725/未知 473 秒 271毫秒/步 - loss: 0.9556 - sparse_categorical_accuracy: 0.6236

1726/未知 473 秒 271毫秒/步 - loss: 0.9555 - sparse_categorical_accuracy: 0.6237

1727/未知 474 秒 271毫秒/步 - loss: 0.9554 - sparse_categorical_accuracy: 0.6237

1728/未知 474 秒 271毫秒/步 - loss: 0.9553 - sparse_categorical_accuracy: 0.6237

1729/未知 474 秒 271毫秒/步 - loss: 0.9552 - sparse_categorical_accuracy: 0.6237

1730/未知 474 秒 271毫秒/步 - loss: 0.9551 - sparse_categorical_accuracy: 0.6238

1731/未知 475 秒 271毫秒/步 - loss: 0.9550 - sparse_categorical_accuracy: 0.6238

1732/未知 475 秒 271毫秒/步 - loss: 0.9549 - sparse_categorical_accuracy: 0.6238

1733/未知 476 秒 271毫秒/步 - loss: 0.9548 - sparse_categorical_accuracy: 0.6239

1734/未知 476 秒 271毫秒/步 - loss: 0.9547 - sparse_categorical_accuracy: 0.6239

1735/未知 476 秒 271毫秒/步 - loss: 0.9546 - sparse_categorical_accuracy: 0.6239

1736/未知 477 秒 271毫秒/步 - loss: 0.9545 - sparse_categorical_accuracy: 0.6240

1737/未知 477 秒 271毫秒/步 - loss: 0.9544 - sparse_categorical_accuracy: 0.6240

1738/未知 477 秒 271毫秒/步 - loss: 0.9543 - sparse_categorical_accuracy: 0.6240

1739/未知 478 秒 272毫秒/步 - loss: 0.9542 - sparse_categorical_accuracy: 0.6241

1740/未知 478 秒 272毫秒/步 - loss: 0.9541 - sparse_categorical_accuracy: 0.6241

1741/未知 478 秒 272毫秒/步 - loss: 0.9540 - sparse_categorical_accuracy: 0.6241

1742/未知 479 秒 272毫秒/步 - loss: 0.9539 - sparse_categorical_accuracy: 0.6242

1743/未知 479 秒 272毫秒/步 - loss: 0.9538 - sparse_categorical_accuracy: 0.6242

1744/未知 479 秒 272毫秒/步 - loss: 0.9537 - sparse_categorical_accuracy: 0.6242

1745/未知 480 秒 272毫秒/步 - loss: 0.9536 - sparse_categorical_accuracy: 0.6242

1746/未知 480 秒 272毫秒/步 - loss: 0.9535 - sparse_categorical_accuracy: 0.6243

1747/未知 480 秒 272毫秒/步 - loss: 0.9534 - sparse_categorical_accuracy: 0.6243

1748/未知 481 秒 272毫秒/步 - loss: 0.9533 - sparse_categorical_accuracy: 0.6243

1749/未知 481 秒 272毫秒/步 - loss: 0.9532 - sparse_categorical_accuracy: 0.6244

1750/未知 481 秒 272毫秒/步 - loss: 0.9531 - sparse_categorical_accuracy: 0.6244

1751/未知 481 秒 272毫秒/步 - loss: 0.9530 - sparse_categorical_accuracy: 0.6244

1752/未知 482 秒 272毫秒/步 - loss: 0.9529 - sparse_categorical_accuracy: 0.6245

1753/未知 482 秒 272毫秒/步 - loss: 0.9528 - sparse_categorical_accuracy: 0.6245

1754/未知 482 秒 272毫秒/步 - loss: 0.9527 - sparse_categorical_accuracy: 0.6245

1755/未知 483 秒 272毫秒/步 - loss: 0.9526 - sparse_categorical_accuracy: 0.6246

1756/未知 483 秒 272毫秒/步 - loss: 0.9525 - sparse_categorical_accuracy: 0.6246

1757/未知 483 秒 272毫秒/步 - loss: 0.9524 - sparse_categorical_accuracy: 0.6246

1758/未知 484 秒 272毫秒/步 - loss: 0.9523 - sparse_categorical_accuracy: 0.6246

1759/未知 484 秒 272毫秒/步 - loss: 0.9522 - sparse_categorical_accuracy: 0.6247

1760/未知 484 秒 272毫秒/步 - loss: 0.9521 - sparse_categorical_accuracy: 0.6247

1761/未知 484 秒 272毫秒/步 - loss: 0.9520 - sparse_categorical_accuracy: 0.6247

1762/未知 485 秒 272毫秒/步 - loss: 0.9519 - sparse_categorical_accuracy: 0.6248

1763/未知 485 秒 272毫秒/步 - loss: 0.9519 - sparse_categorical_accuracy: 0.6248

1764/未知 485 秒 272毫秒/步 - loss: 0.9518 - sparse_categorical_accuracy: 0.6248

1765/未知 486 秒 272毫秒/步 - loss: 0.9517 - sparse_categorical_accuracy: 0.6249

1766/未知 486 秒 272毫秒/步 - loss: 0.9516 - sparse_categorical_accuracy: 0.6249

1767/未知 486 秒 272毫秒/步 - loss: 0.9515 - sparse_categorical_accuracy: 0.6249

1768/未知 487 秒 272毫秒/步 - loss: 0.9514 - sparse_categorical_accuracy: 0.6249

1769/未知 487 秒 272毫秒/步 - loss: 0.9513 - sparse_categorical_accuracy: 0.6250

1770/未知 488 秒 272毫秒/步 - loss: 0.9512 - sparse_categorical_accuracy: 0.6250

1771/未知 488 秒 272毫秒/步 - loss: 0.9511 - sparse_categorical_accuracy: 0.6250

1772/未知 488 秒 272毫秒/步 - loss: 0.9510 - sparse_categorical_accuracy: 0.6251

1773/未知 489 秒 272毫秒/步 - loss: 0.9509 - sparse_categorical_accuracy: 0.6251

1774/未知 489 秒 273毫秒/步 - loss: 0.9508 - sparse_categorical_accuracy: 0.6251

1775/未知 489 秒 273毫秒/步 - loss: 0.9507 - sparse_categorical_accuracy: 0.6252

1776/未知 490 秒 273毫秒/步 - loss: 0.9506 - sparse_categorical_accuracy: 0.6252

1777/未知 490 秒 273毫秒/步 - loss: 0.9505 - sparse_categorical_accuracy: 0.6252

1778/未知 490 秒 273毫秒/步 - loss: 0.9504 - sparse_categorical_accuracy: 0.6252

1779/未知 491 秒 273毫秒/步 - loss: 0.9503 - sparse_categorical_accuracy: 0.6253

1780/未知 491 秒 273毫秒/步 - loss: 0.9502 - sparse_categorical_accuracy: 0.6253

1781/未知 492 秒 273毫秒/步 - loss: 0.9501 - sparse_categorical_accuracy: 0.6253

1782/未知 492 秒 273毫秒/步 - loss: 0.9500 - sparse_categorical_accuracy: 0.6254

1783/未知 492 秒 273毫秒/步 - loss: 0.9499 - sparse_categorical_accuracy: 0.6254

1784/未知 493 秒 273毫秒/步 - loss: 0.9498 - sparse_categorical_accuracy: 0.6254

1785/未知 493 秒 273毫秒/步 - loss: 0.9498 - sparse_categorical_accuracy: 0.6255

1786/未知 493 秒 273毫秒/步 - loss: 0.9497 - sparse_categorical_accuracy: 0.6255

1787/未知 494 秒 273毫秒/步 - loss: 0.9496 - sparse_categorical_accuracy: 0.6255

1788/未知 494 秒 273毫秒/步 - loss: 0.9495 - sparse_categorical_accuracy: 0.6255

1789/未知 494 秒 273毫秒/步 - loss: 0.9494 - sparse_categorical_accuracy: 0.6256

1790/未知 495 秒 273毫秒/步 - loss: 0.9493 - sparse_categorical_accuracy: 0.6256

1791/未知 495 秒 273毫秒/步 - loss: 0.9492 - sparse_categorical_accuracy: 0.6256

1792/未知 495 秒 273毫秒/步 - loss: 0.9491 - sparse_categorical_accuracy: 0.6257

1793/未知 496 秒 273毫秒/步 - loss: 0.9490 - sparse_categorical_accuracy: 0.6257

1794/未知 496 秒 273毫秒/步 - loss: 0.9489 - sparse_categorical_accuracy: 0.6257

1795/未知 496 秒 273毫秒/步 - loss: 0.9488 - sparse_categorical_accuracy: 0.6258

1796/未知 497 秒 273毫秒/步 - loss: 0.9487 - sparse_categorical_accuracy: 0.6258

1797/未知 497 秒 274毫秒/步 - loss: 0.9486 - sparse_categorical_accuracy: 0.6258

1798/未知 497 秒 274毫秒/步 - loss: 0.9485 - sparse_categorical_accuracy: 0.6258

1799/未知 498 秒 274毫秒/步 - loss: 0.9484 - sparse_categorical_accuracy: 0.6259

1800/未知 498 秒 274毫秒/步 - loss: 0.9483 - sparse_categorical_accuracy: 0.6259

1801/未知 498 秒 274毫秒/步 - loss: 0.9482 - sparse_categorical_accuracy: 0.6259

1802/未知 499 秒 274毫秒/步 - loss: 0.9482 - sparse_categorical_accuracy: 0.6260

1803/未知 499 秒 274毫秒/步 - loss: 0.9481 - sparse_categorical_accuracy: 0.6260

1804/未知 499 秒 274毫秒/步 - loss: 0.9480 - sparse_categorical_accuracy: 0.6260

1805/未知 500 秒 274毫秒/步 - loss: 0.9479 - sparse_categorical_accuracy: 0.6260

1806/未知 500 秒 274毫秒/步 - loss: 0.9478 - sparse_categorical_accuracy: 0.6261

1807/未知 500 秒 274毫秒/步 - loss: 0.9477 - sparse_categorical_accuracy: 0.6261

1808/未知 501 秒 274毫秒/步 - loss: 0.9476 - sparse_categorical_accuracy: 0.6261

1809/未知 501 秒 274毫秒/步 - loss: 0.9475 - sparse_categorical_accuracy: 0.6262

1810/未知 501 秒 274毫秒/步 - loss: 0.9474 - sparse_categorical_accuracy: 0.6262

1811/未知 502 秒 274毫秒/步 - loss: 0.9473 - sparse_categorical_accuracy: 0.6262

1812/未知 502 秒 274毫秒/步 - loss: 0.9472 - sparse_categorical_accuracy: 0.6263

1813/未知 502 秒 274毫秒/步 - loss: 0.9471 - sparse_categorical_accuracy: 0.6263

1814/未知 503 秒 274毫秒/步 - loss: 0.9470 - sparse_categorical_accuracy: 0.6263

1815/未知 503 秒 274毫秒/步 - loss: 0.9469 - sparse_categorical_accuracy: 0.6263

1816/未知 503 秒 274毫秒/步 - loss: 0.9469 - sparse_categorical_accuracy: 0.6264

1817/未知 504 秒 274毫秒/步 - loss: 0.9468 - sparse_categorical_accuracy: 0.6264

1818/未知 504 秒 274毫秒/步 - loss: 0.9467 - sparse_categorical_accuracy: 0.6264

1819/未知 504 秒 274毫秒/步 - loss: 0.9466 - sparse_categorical_accuracy: 0.6265

1820/未知 505 秒 274毫秒/步 - loss: 0.9465 - sparse_categorical_accuracy: 0.6265

1821/未知 505 秒 274毫秒/步 - loss: 0.9464 - sparse_categorical_accuracy: 0.6265

1822/未知 505 秒 274毫秒/步 - loss: 0.9463 - sparse_categorical_accuracy: 0.6265

1823/未知 505 秒 274毫秒/步 - loss: 0.9462 - sparse_categorical_accuracy: 0.6266

1824/未知 506 秒 274毫秒/步 - loss: 0.9461 - sparse_categorical_accuracy: 0.6266

1825/未知 506 秒 274毫秒/步 - loss: 0.9460 - sparse_categorical_accuracy: 0.6266

1826/未知 506 秒 274毫秒/步 - loss: 0.9459 - sparse_categorical_accuracy: 0.6267

1827/未知 507 秒 274毫秒/步 - loss: 0.9458 - sparse_categorical_accuracy: 0.6267

1828/未知 507 秒 274毫秒/步 - loss: 0.9458 - sparse_categorical_accuracy: 0.6267

1829/未知 507 秒 274毫秒/步 - loss: 0.9457 - sparse_categorical_accuracy: 0.6267

1830/未知 508 秒 274毫秒/步 - loss: 0.9456 - sparse_categorical_accuracy: 0.6268

1831/未知 508 秒 274毫秒/步 - loss: 0.9455 - sparse_categorical_accuracy: 0.6268

1832/未知 508 秒 274毫秒/步 - loss: 0.9454 - sparse_categorical_accuracy: 0.6268

1833/未知 509 秒 274毫秒/步 - loss: 0.9453 - sparse_categorical_accuracy: 0.6269

1834/未知 509 秒 274毫秒/步 - loss: 0.9452 - sparse_categorical_accuracy: 0.6269

1835/未知 509 秒 275毫秒/步 - loss: 0.9451 - sparse_categorical_accuracy: 0.6269

1836/未知 510 秒 275毫秒/步 - loss: 0.9450 - sparse_categorical_accuracy: 0.6269

1837/未知 510 秒 275毫秒/步 - loss: 0.9449 - sparse_categorical_accuracy: 0.6270

1838/未知 510 秒 275毫秒/步 - loss: 0.9448 - sparse_categorical_accuracy: 0.6270

1839/未知 511 秒 275毫秒/步 - loss: 0.9448 - sparse_categorical_accuracy: 0.6270

1840/未知 511 秒 275毫秒/步 - loss: 0.9447 - sparse_categorical_accuracy: 0.6271

1841/未知 511 秒 275毫秒/步 - loss: 0.9446 - sparse_categorical_accuracy: 0.6271

1842/未知 512 秒 275毫秒/步 - loss: 0.9445 - sparse_categorical_accuracy: 0.6271

1843/未知 512 秒 275毫秒/步 - loss: 0.9444 - sparse_categorical_accuracy: 0.6271

1844/未知 513 秒 275毫秒/步 - loss: 0.9443 - sparse_categorical_accuracy: 0.6272

1845/未知 513 秒 275毫秒/步 - loss: 0.9442 - sparse_categorical_accuracy: 0.6272

1846/未知 513 秒 275毫秒/步 - loss: 0.9441 - sparse_categorical_accuracy: 0.6272

1847/未知 513 秒 275毫秒/步 - loss: 0.9440 - sparse_categorical_accuracy: 0.6273

1848/未知 514 秒 275毫秒/步 - loss: 0.9439 - sparse_categorical_accuracy: 0.6273

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1850/未知 514 秒 275毫秒/步 - loss: 0.9438 - sparse_categorical_accuracy: 0.6273

1851/未知 514 秒 275毫秒/步 - loss: 0.9437 - sparse_categorical_accuracy: 0.6274

1852/未知 515 秒 275毫秒/步 - loss: 0.9436 - sparse_categorical_accuracy: 0.6274

1853/未知 515 秒 275毫秒/步 - loss: 0.9435 - sparse_categorical_accuracy: 0.6274

1854/未知 515 秒 275毫秒/步 - loss: 0.9434 - sparse_categorical_accuracy: 0.6275

1855/未知 516 秒 275毫秒/步 - loss: 0.9433 - sparse_categorical_accuracy: 0.6275

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1865/1865 ━━━━━━━━━━━━━━━━━━━━ 519 秒 275毫秒/步 - loss: 0.9423 - sparse_categorical_accuracy: 0.6278

Model training finished

/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/trainers/epoch_iterator.py:151: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.
  self._interrupted_warning()

Test accuracy: 71.32%

基线线性模型达到了约 76% 的测试准确率。

实验 2：Wide & Deep 模型

在第二个实验中，我们创建了一个 Wide & Deep 模型。该模型的宽部分是一个线性模型，而深度部分是一个多层前馈网络。

在模型的宽部分使用输入特征的稀疏表示，在深度部分使用输入特征的密集表示。

请注意，每个输入特征都会以不同的表示形式贡献给模型的两个部分。

def create_wide_and_deep_model():
    inputs = create_model_inputs()
    wide = encode_inputs(inputs)
    wide = layers.BatchNormalization()(wide)

    deep = encode_inputs(inputs, use_embedding=True)
    for units in hidden_units:
        deep = layers.Dense(units)(deep)
        deep = layers.BatchNormalization()(deep)
        deep = layers.ReLU()(deep)
        deep = layers.Dropout(dropout_rate)(deep)

    merged = layers.concatenate([wide, deep])
    outputs = layers.Dense(units=NUM_CLASSES, activation="softmax")(merged)
    model = keras.Model(inputs=inputs, outputs=outputs)
    return model


wide_and_deep_model = create_wide_and_deep_model()
keras.utils.plot_model(wide_and_deep_model, show_shapes=True, rankdir="LR")

png

让我们开始运行

run_experiment(wide_and_deep_model)

Start training the model...

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948/Unknown  392s 411ms/step - loss: 1.0002 - sparse_categorical_accuracy: 0.6048


949/Unknown  393s 411ms/step - loss: 0.9999 - sparse_categorical_accuracy: 0.6049


950/Unknown  393s 411ms/step - loss: 0.9997 - sparse_categorical_accuracy: 0.6049


951/Unknown  393s 411ms/step - loss: 0.9995 - sparse_categorical_accuracy: 0.6050


952/Unknown  394s 411ms/step - loss: 0.9992 - sparse_categorical_accuracy: 0.6051


953/Unknown  394s 411ms/step - loss: 0.9990 - sparse_categorical_accuracy: 0.6052


954/Unknown  394s 411ms/step - loss: 0.9988 - sparse_categorical_accuracy: 0.6053


955/Unknown  395s 410ms/step - loss: 0.9985 - sparse_categorical_accuracy: 0.6053


956/Unknown  395s 410ms/step - loss: 0.9983 - sparse_categorical_accuracy: 0.6054


957/Unknown  395s 410ms/step - loss: 0.9980 - sparse_categorical_accuracy: 0.6055


958/Unknown  396s 410ms/step - loss: 0.9978 - sparse_categorical_accuracy: 0.6056


959/Unknown  396s 410ms/step - loss: 0.9976 - sparse_categorical_accuracy: 0.6057


960/Unknown  397s 410ms/step - loss: 0.9973 - sparse_categorical_accuracy: 0.6057


961/Unknown  397s 410ms/step - loss: 0.9971 - sparse_categorical_accuracy: 0.6058


962/Unknown  397s 410ms/step - loss: 0.9969 - sparse_categorical_accuracy: 0.6059


963/Unknown  398s 410ms/step - loss: 0.9966 - sparse_categorical_accuracy: 0.6060


964/Unknown  398s 410ms/step - loss: 0.9964 - sparse_categorical_accuracy: 0.6060


965/Unknown  398s 410ms/step - loss: 0.9962 - sparse_categorical_accuracy: 0.6061


966/Unknown  399s 410ms/step - loss: 0.9959 - sparse_categorical_accuracy: 0.6062


967/Unknown  399s 410ms/step - loss: 0.9957 - sparse_categorical_accuracy: 0.6063


968/Unknown  400s 410ms/step - loss: 0.9955 - sparse_categorical_accuracy: 0.6064


969/Unknown  400s 410ms/step - loss: 0.9952 - sparse_categorical_accuracy: 0.6064


970/Unknown  401s 410ms/step - loss: 0.9950 - sparse_categorical_accuracy: 0.6065


971/Unknown  401s 410ms/step - loss: 0.9948 - sparse_categorical_accuracy: 0.6066


972/Unknown  402s 410ms/step - loss: 0.9945 - sparse_categorical_accuracy: 0.6067


973/Unknown  402s 410ms/step - loss: 0.9943 - sparse_categorical_accuracy: 0.6067


974/Unknown  402s 410ms/step - loss: 0.9941 - sparse_categorical_accuracy: 0.6068


975/Unknown  403s 410ms/step - loss: 0.9938 - sparse_categorical_accuracy: 0.6069


976/Unknown  403s 410ms/step - loss: 0.9936 - sparse_categorical_accuracy: 0.6070


977/Unknown  404s 410ms/step - loss: 0.9934 - sparse_categorical_accuracy: 0.6070


978/Unknown  404s 410ms/step - loss: 0.9931 - sparse_categorical_accuracy: 0.6071


979/Unknown  404s 410ms/step - loss: 0.9929 - sparse_categorical_accuracy: 0.6072


980/Unknown  405s 410ms/step - loss: 0.9927 - sparse_categorical_accuracy: 0.6073


981/Unknown  405s 410ms/step - loss: 0.9925 - sparse_categorical_accuracy: 0.6073


982/Unknown  405s 410ms/step - loss: 0.9922 - sparse_categorical_accuracy: 0.6074


983/Unknown  406s 410ms/step - loss: 0.9920 - sparse_categorical_accuracy: 0.6075


984/Unknown  406s 410ms/step - loss: 0.9918 - sparse_categorical_accuracy: 0.6076


985/Unknown  406s 410ms/step - loss: 0.9915 - sparse_categorical_accuracy: 0.6076


986/Unknown  407s 410ms/step - loss: 0.9913 - sparse_categorical_accuracy: 0.6077


987/Unknown  407s 410ms/step - loss: 0.9911 - sparse_categorical_accuracy: 0.6078


988/Unknown  408s 410ms/step - loss: 0.9909 - sparse_categorical_accuracy: 0.6079


989/Unknown  408s 410ms/step - loss: 0.9906 - sparse_categorical_accuracy: 0.6079


990/Unknown  408s 410ms/step - loss: 0.9904 - sparse_categorical_accuracy: 0.6080


991/Unknown  409s 410ms/step - loss: 0.9902 - sparse_categorical_accuracy: 0.6081


992/Unknown  409s 410ms/step - loss: 0.9900 - sparse_categorical_accuracy: 0.6082


993/Unknown  410s 410ms/step - loss: 0.9897 - sparse_categorical_accuracy: 0.6082


994/Unknown  410s 410ms/step - loss: 0.9895 - sparse_categorical_accuracy: 0.6083


995/Unknown  411s 410ms/step - loss: 0.9893 - sparse_categorical_accuracy: 0.6084


996/Unknown  411s 410ms/step - loss: 0.9891 - sparse_categorical_accuracy: 0.6085


997/Unknown  411s 410ms/step - loss: 0.9888 - sparse_categorical_accuracy: 0.6085


998/Unknown  412s 410ms/step - loss: 0.9886 - sparse_categorical_accuracy: 0.6086


999/Unknown  412s 410ms/step - loss: 0.9884 - sparse_categorical_accuracy: 0.6087

1000/未知 413 秒 410毫秒/步 - loss: 0.9882 - sparse_categorical_accuracy: 0.6088

1001/未知 413 秒 410毫秒/步 - loss: 0.9880 - sparse_categorical_accuracy: 0.6088

1002/未知 414 秒 410毫秒/步 - loss: 0.9877 - sparse_categorical_accuracy: 0.6089

1003/未知 414 秒 410毫秒/步 - loss: 0.9875 - sparse_categorical_accuracy: 0.6090

1004/未知 414 秒 410毫秒/步 - loss: 0.9873 - sparse_categorical_accuracy: 0.6091

1005/未知 415 秒 410毫秒/步 - loss: 0.9871 - sparse_categorical_accuracy: 0.6091

1006/未知 415 秒 410毫秒/步 - loss: 0.9868 - sparse_categorical_accuracy: 0.6092

1007/未知 416 秒 410毫秒/步 - loss: 0.9866 - sparse_categorical_accuracy: 0.6093

1008/未知 416 秒 410毫秒/步 - loss: 0.9864 - sparse_categorical_accuracy: 0.6093

1009/未知 416 秒 410毫秒/步 - loss: 0.9862 - sparse_categorical_accuracy: 0.6094

1010/未知 416 秒 410毫秒/步 - loss: 0.9860 - sparse_categorical_accuracy: 0.6095

1011/未知 417 秒 410毫秒/步 - loss: 0.9857 - sparse_categorical_accuracy: 0.6096

1012/未知 417 秒 409毫秒/步 - loss: 0.9855 - sparse_categorical_accuracy: 0.6096

1013/未知 417 秒 409毫秒/步 - loss: 0.9853 - sparse_categorical_accuracy: 0.6097

1014/未知 418 秒 409毫秒/步 - loss: 0.9851 - sparse_categorical_accuracy: 0.6098

1015/未知 418 秒 409毫秒/步 - loss: 0.9849 - sparse_categorical_accuracy: 0.6099

1016/未知 418 秒 409毫秒/步 - loss: 0.9847 - sparse_categorical_accuracy: 0.6099

1017/未知 419 秒 409毫秒/步 - loss: 0.9844 - sparse_categorical_accuracy: 0.6100

1018/未知 419 秒 409毫秒/步 - loss: 0.9842 - sparse_categorical_accuracy: 0.6101

1019/未知 419 秒 409毫秒/步 - loss: 0.9840 - sparse_categorical_accuracy: 0.6101

1020/未知 420 秒 409毫秒/步 - loss: 0.9838 - sparse_categorical_accuracy: 0.6102

1021/未知 420 秒 409毫秒/步 - loss: 0.9836 - sparse_categorical_accuracy: 0.6103

1022/未知 421 秒 409毫秒/步 - loss: 0.9834 - sparse_categorical_accuracy: 0.6104

1023/未知 421 秒 409毫秒/步 - loss: 0.9831 - sparse_categorical_accuracy: 0.6104

1024/未知 422 秒 409毫秒/步 - loss: 0.9829 - sparse_categorical_accuracy: 0.6105

1025/未知 422 秒 409毫秒/步 - loss: 0.9827 - sparse_categorical_accuracy: 0.6106

1026/未知 423 秒 409毫秒/步 - loss: 0.9825 - sparse_categorical_accuracy: 0.6106

1027/未知 423 秒 409毫秒/步 - loss: 0.9823 - sparse_categorical_accuracy: 0.6107

1028/未知 423 秒 409毫秒/步 - loss: 0.9821 - sparse_categorical_accuracy: 0.6108

1029/未知 424 秒 409毫秒/步 - loss: 0.9819 - sparse_categorical_accuracy: 0.6109

1030/未知 424 秒 409毫秒/步 - loss: 0.9816 - sparse_categorical_accuracy: 0.6109

1031/未知 425 秒 409毫秒/步 - loss: 0.9814 - sparse_categorical_accuracy: 0.6110

1032/未知 425 秒 409毫秒/步 - loss: 0.9812 - sparse_categorical_accuracy: 0.6111

1033/未知 425 秒 409毫秒/步 - loss: 0.9810 - sparse_categorical_accuracy: 0.6111

1034/未知 426 秒 409毫秒/步 - loss: 0.9808 - sparse_categorical_accuracy: 0.6112

1035/未知 426 秒 409毫秒/步 - loss: 0.9806 - sparse_categorical_accuracy: 0.6113

1036/未知 427 秒 409毫秒/步 - loss: 0.9804 - sparse_categorical_accuracy: 0.6113

1037/未知 427 秒 409毫秒/步 - loss: 0.9802 - sparse_categorical_accuracy: 0.6114

1038/未知 427 秒 409毫秒/步 - loss: 0.9799 - sparse_categorical_accuracy: 0.6115

1039/未知 428 秒 409毫秒/步 - loss: 0.9797 - sparse_categorical_accuracy: 0.6116

1040/未知 428 秒 409毫秒/步 - loss: 0.9795 - sparse_categorical_accuracy: 0.6116

1041/未知 428 秒 409毫秒/步 - loss: 0.9793 - sparse_categorical_accuracy: 0.6117

1042/未知 429 秒 409毫秒/步 - loss: 0.9791 - sparse_categorical_accuracy: 0.6118

1043/未知 429 秒 409毫秒/步 - loss: 0.9789 - sparse_categorical_accuracy: 0.6118

1044/未知 430 秒 409毫秒/步 - loss: 0.9787 - sparse_categorical_accuracy: 0.6119

1045/未知 430 秒 409毫秒/步 - loss: 0.9785 - sparse_categorical_accuracy: 0.6120

1046/未知 430 秒 409毫秒/步 - loss: 0.9783 - sparse_categorical_accuracy: 0.6120

1047/未知 431 秒 409毫秒/步 - loss: 0.9781 - sparse_categorical_accuracy: 0.6121

1048/未知 431 秒 409毫秒/步 - loss: 0.9779 - sparse_categorical_accuracy: 0.6122

1049/未知 432 秒 409毫秒/步 - loss: 0.9777 - sparse_categorical_accuracy: 0.6122

1050/未知 432 秒 409毫秒/步 - loss: 0.9774 - sparse_categorical_accuracy: 0.6123

1051/未知 433 秒 409毫秒/步 - loss: 0.9772 - sparse_categorical_accuracy: 0.6124

1052/未知 433 秒 409毫秒/步 - loss: 0.9770 - sparse_categorical_accuracy: 0.6125

1053/未知 433 秒 409毫秒/步 - loss: 0.9768 - sparse_categorical_accuracy: 0.6125

1054/未知 434 秒 409毫秒/步 - loss: 0.9766 - sparse_categorical_accuracy: 0.6126

1055/未知 434 秒 409毫秒/步 - loss: 0.9764 - sparse_categorical_accuracy: 0.6127

1056/未知 435 秒 409毫秒/步 - loss: 0.9762 - sparse_categorical_accuracy: 0.6127

1057/未知 435 秒 409毫秒/步 - loss: 0.9760 - sparse_categorical_accuracy: 0.6128

1058/未知 435 秒 409毫秒/步 - loss: 0.9758 - sparse_categorical_accuracy: 0.6129

1059/未知 436 秒 409毫秒/步 - loss: 0.9756 - sparse_categorical_accuracy: 0.6129

1060/未知 436 秒 409毫秒/步 - loss: 0.9754 - sparse_categorical_accuracy: 0.6130

1061/未知 436 秒 409毫秒/步 - loss: 0.9752 - sparse_categorical_accuracy: 0.6131

1062/未知 437 秒 409毫秒/步 - loss: 0.9750 - sparse_categorical_accuracy: 0.6131

1063/未知 437 秒 409毫秒/步 - loss: 0.9748 - sparse_categorical_accuracy: 0.6132

1064/未知 438 秒 409毫秒/步 - loss: 0.9746 - sparse_categorical_accuracy: 0.6133

1065/未知 438 秒 409毫秒/步 - loss: 0.9744 - sparse_categorical_accuracy: 0.6133

1066/未知 439 秒 409毫秒/步 - loss: 0.9742 - sparse_categorical_accuracy: 0.6134

1067/未知 439 秒 409毫秒/步 - loss: 0.9740 - sparse_categorical_accuracy: 0.6135

1068/未知 440 秒 409毫秒/步 - loss: 0.9738 - sparse_categorical_accuracy: 0.6135

1069/未知 440 秒 409毫秒/步 - loss: 0.9736 - sparse_categorical_accuracy: 0.6136

1070/未知 441 秒 409毫秒/步 - loss: 0.9734 - sparse_categorical_accuracy: 0.6137

1071/未知 441 秒 409毫秒/步 - loss: 0.9732 - sparse_categorical_accuracy: 0.6137

1072/未知 442 秒 409毫秒/步 - loss: 0.9730 - sparse_categorical_accuracy: 0.6138

1073/未知 442 秒 409毫秒/步 - loss: 0.9728 - sparse_categorical_accuracy: 0.6139

1074/未知 443 秒 410毫秒/步 - loss: 0.9726 - sparse_categorical_accuracy: 0.6139

1075/未知 443 秒 410毫秒/步 - loss: 0.9723 - sparse_categorical_accuracy: 0.6140

1076/未知 444 秒 410毫秒/步 - loss: 0.9721 - sparse_categorical_accuracy: 0.6141

1077/未知 444 秒 410毫秒/步 - loss: 0.9719 - sparse_categorical_accuracy: 0.6141

1078/未知 445 秒 410毫秒/步 - loss: 0.9717 - sparse_categorical_accuracy: 0.6142

1079/未知 445 秒 410毫秒/步 - loss: 0.9716 - sparse_categorical_accuracy: 0.6143

1080/未知 445 秒 410毫秒/步 - loss: 0.9714 - sparse_categorical_accuracy: 0.6143

1081/未知 446 秒 410毫秒/步 - loss: 0.9712 - sparse_categorical_accuracy: 0.6144

1082/未知 446 秒 410毫秒/步 - loss: 0.9710 - sparse_categorical_accuracy: 0.6145

1083/未知 447 秒 410毫秒/步 - loss: 0.9708 - sparse_categorical_accuracy: 0.6145

1084/未知 447 秒 410毫秒/步 - loss: 0.9706 - sparse_categorical_accuracy: 0.6146

1085/未知 448 秒 410毫秒/步 - loss: 0.9704 - sparse_categorical_accuracy: 0.6147

1086/未知 448 秒 410毫秒/步 - loss: 0.9702 - sparse_categorical_accuracy: 0.6147

1087/未知 449 秒 410毫秒/步 - loss: 0.9700 - sparse_categorical_accuracy: 0.6148

1088/未知 449 秒 410毫秒/步 - loss: 0.9698 - sparse_categorical_accuracy: 0.6149

1089/未知 449 秒 410毫秒/步 - loss: 0.9696 - sparse_categorical_accuracy: 0.6149

1090/未知 450 秒 410毫秒/步 - loss: 0.9694 - sparse_categorical_accuracy: 0.6150

1091/未知 450 秒 410毫秒/步 - loss: 0.9692 - sparse_categorical_accuracy: 0.6150

1092/未知 451 秒 410毫秒/步 - loss: 0.9690 - sparse_categorical_accuracy: 0.6151

1093/未知 451 秒 411毫秒/步 - loss: 0.9688 - sparse_categorical_accuracy: 0.6152

1094/未知 452 秒 411毫秒/步 - loss: 0.9686 - sparse_categorical_accuracy: 0.6152

1095/未知 452 秒 411毫秒/步 - loss: 0.9684 - sparse_categorical_accuracy: 0.6153

1096/未知 453 秒 411毫秒/步 - loss: 0.9682 - sparse_categorical_accuracy: 0.6154

1097/未知 453 秒 411毫秒/步 - loss: 0.9680 - sparse_categorical_accuracy: 0.6154

1098/未知 454 秒 411毫秒/步 - loss: 0.9678 - sparse_categorical_accuracy: 0.6155

1099/未知 454 秒 411毫秒/步 - loss: 0.9676 - sparse_categorical_accuracy: 0.6156

1100/未知 455 秒 411毫秒/步 - loss: 0.9674 - sparse_categorical_accuracy: 0.6156

1101/未知 455 秒 411毫秒/步 - loss: 0.9672 - sparse_categorical_accuracy: 0.6157

1102/未知 456 秒 411毫秒/步 - loss: 0.9670 - sparse_categorical_accuracy: 0.6158

1103/未知 456 秒 411毫秒/步 - loss: 0.9668 - sparse_categorical_accuracy: 0.6158

1104/未知 457 秒 411毫秒/步 - loss: 0.9667 - sparse_categorical_accuracy: 0.6159

1105/未知 457 秒 411毫秒/步 - loss: 0.9665 - sparse_categorical_accuracy: 0.6159

1106/未知 457 秒 411毫秒/步 - loss: 0.9663 - sparse_categorical_accuracy: 0.6160

1107/未知 458 秒 411毫秒/步 - loss: 0.9661 - sparse_categorical_accuracy: 0.6161

1108/未知 458 秒 411毫秒/步 - loss: 0.9659 - sparse_categorical_accuracy: 0.6161

1109/未知 459 秒 411毫秒/步 - loss: 0.9657 - sparse_categorical_accuracy: 0.6162

1110/未知 459 秒 411毫秒/步 - loss: 0.9655 - sparse_categorical_accuracy: 0.6163

1111/未知 459 秒 411毫秒/步 - loss: 0.9653 - sparse_categorical_accuracy: 0.6163

1112/未知 460 秒 411毫秒/步 - loss: 0.9651 - sparse_categorical_accuracy: 0.6164

1113/未知 460 秒 411毫秒/步 - loss: 0.9649 - sparse_categorical_accuracy: 0.6165

1114/未知 461 秒 411毫秒/步 - loss: 0.9647 - sparse_categorical_accuracy: 0.6165

1115/未知 461 秒 411毫秒/步 - loss: 0.9645 - sparse_categorical_accuracy: 0.6166

1116/未知 462 秒 411毫秒/步 - loss: 0.9644 - sparse_categorical_accuracy: 0.6166

1117/未知 462 秒 412毫秒/步 - loss: 0.9642 - sparse_categorical_accuracy: 0.6167

1118/未知 463 秒 412毫秒/步 - loss: 0.9640 - sparse_categorical_accuracy: 0.6168

1119/未知 463 秒 412毫秒/步 - loss: 0.9638 - sparse_categorical_accuracy: 0.6168

1120/未知 464 秒 412毫秒/步 - loss: 0.9636 - sparse_categorical_accuracy: 0.6169

1121/未知 464 秒 412毫秒/步 - loss: 0.9634 - sparse_categorical_accuracy: 0.6170

1122/未知 465 秒 412毫秒/步 - loss: 0.9632 - sparse_categorical_accuracy: 0.6170

1123/未知 465 秒 412毫秒/步 - loss: 0.9630 - sparse_categorical_accuracy: 0.6171

1124/未知 466 秒 412毫秒/步 - loss: 0.9628 - sparse_categorical_accuracy: 0.6171

1125/未知 466 秒 412毫秒/步 - loss: 0.9627 - sparse_categorical_accuracy: 0.6172

1126/未知 467 秒 412毫秒/步 - loss: 0.9625 - sparse_categorical_accuracy: 0.6173

1127/未知 467 秒 412毫秒/步 - loss: 0.9623 - sparse_categorical_accuracy: 0.6173

1128/未知 468 秒 412毫秒/步 - loss: 0.9621 - sparse_categorical_accuracy: 0.6174

1129/未知 468 秒 412毫秒/步 - loss: 0.9619 - sparse_categorical_accuracy: 0.6174

1130/未知 469 秒 412毫秒/步 - loss: 0.9617 - sparse_categorical_accuracy: 0.6175

1131/未知 469 秒 412毫秒/步 - loss: 0.9615 - sparse_categorical_accuracy: 0.6176

1132/未知 470 秒 412毫秒/步 - loss: 0.9614 - sparse_categorical_accuracy: 0.6176

1133/未知 470 秒 412毫秒/步 - loss: 0.9612 - sparse_categorical_accuracy: 0.6177

1134/未知 471 秒 413毫秒/步 - loss: 0.9610 - sparse_categorical_accuracy: 0.6178

1135/未知 471 秒 413毫秒/步 - loss: 0.9608 - sparse_categorical_accuracy: 0.6178

1136/未知 471 秒 413毫秒/步 - loss: 0.9606 - sparse_categorical_accuracy: 0.6179

1137/未知 472 秒 413毫秒/步 - loss: 0.9604 - sparse_categorical_accuracy: 0.6179

1138/未知 472 秒 413毫秒/步 - loss: 0.9602 - sparse_categorical_accuracy: 0.6180

1139/未知 473 秒 413毫秒/步 - loss: 0.9601 - sparse_categorical_accuracy: 0.6181

1140/未知 473 秒 413毫秒/步 - loss: 0.9599 - sparse_categorical_accuracy: 0.6181

1141/未知 474 秒 413毫秒/步 - loss: 0.9597 - sparse_categorical_accuracy: 0.6182

1142/未知 474 秒 413毫秒/步 - loss: 0.9595 - sparse_categorical_accuracy: 0.6182

1143/未知 475 秒 413毫秒/步 - loss: 0.9593 - sparse_categorical_accuracy: 0.6183

1144/未知 475 秒 413毫秒/步 - loss: 0.9591 - sparse_categorical_accuracy: 0.6184

1145/未知 476 秒 413毫秒/步 - loss: 0.9590 - sparse_categorical_accuracy: 0.6184

1146/未知 476 秒 413毫秒/步 - loss: 0.9588 - sparse_categorical_accuracy: 0.6185

1147/未知 477 秒 413毫秒/步 - loss: 0.9586 - sparse_categorical_accuracy: 0.6185

1148/未知 477 秒 413毫秒/步 - loss: 0.9584 - sparse_categorical_accuracy: 0.6186

1149/未知 478 秒 413毫秒/步 - loss: 0.9582 - sparse_categorical_accuracy: 0.6187

1150/未知 478 秒 413毫秒/步 - loss: 0.9580 - sparse_categorical_accuracy: 0.6187

1151/未知 479 秒 413毫秒/步 - loss: 0.9579 - sparse_categorical_accuracy: 0.6188

1152/未知 479 秒 413毫秒/步 - loss: 0.9577 - sparse_categorical_accuracy: 0.6188

1153/未知 479 秒 413毫秒/步 - loss: 0.9575 - sparse_categorical_accuracy: 0.6189

1154/未知 480 秒 413毫秒/步 - loss: 0.9573 - sparse_categorical_accuracy: 0.6190

1155/未知 480 秒 413毫秒/步 - loss: 0.9571 - sparse_categorical_accuracy: 0.6190

1156/未知 480 秒 413毫秒/步 - loss: 0.9570 - sparse_categorical_accuracy: 0.6191

1157/未知 481 秒 413毫秒/步 - loss: 0.9568 - sparse_categorical_accuracy: 0.6191

1158/未知 481 秒 413毫秒/步 - loss: 0.9566 - sparse_categorical_accuracy: 0.6192

1159/未知 482 秒 413毫秒/步 - loss: 0.9564 - sparse_categorical_accuracy: 0.6193

1160/未知 482 秒 413毫秒/步 - loss: 0.9562 - sparse_categorical_accuracy: 0.6193

1161/未知 482 秒 413毫秒/步 - loss: 0.9561 - sparse_categorical_accuracy: 0.6194

1162/未知 483 秒 413毫秒/步 - loss: 0.9559 - sparse_categorical_accuracy: 0.6194

1163/未知 483 秒 413毫秒/步 - loss: 0.9557 - sparse_categorical_accuracy: 0.6195

1164/未知 484 秒 413毫秒/步 - loss: 0.9555 - sparse_categorical_accuracy: 0.6196

1165/未知 484 秒 413毫秒/步 - loss: 0.9554 - sparse_categorical_accuracy: 0.6196

1166/未知 484 秒 413毫秒/步 - loss: 0.9552 - sparse_categorical_accuracy: 0.6197

1167/未知 485 秒 413毫秒/步 - loss: 0.9550 - sparse_categorical_accuracy: 0.6197

1168/未知 485 秒 413毫秒/步 - loss: 0.9548 - sparse_categorical_accuracy: 0.6198

1169/未知 486 秒 413毫秒/步 - loss: 0.9546 - sparse_categorical_accuracy: 0.6199

1170/未知 486 秒 413毫秒/步 - loss: 0.9545 - sparse_categorical_accuracy: 0.6199

1171/未知 487 秒 413毫秒/步 - loss: 0.9543 - sparse_categorical_accuracy: 0.6200

1172/未知 487 秒 413毫秒/步 - loss: 0.9541 - sparse_categorical_accuracy: 0.6200

1173/未知 488 秒 413毫秒/步 - loss: 0.9539 - sparse_categorical_accuracy: 0.6201

1174/未知 488 秒 413毫秒/步 - loss: 0.9538 - sparse_categorical_accuracy: 0.6201

1175/未知 489 秒 413毫秒/步 - loss: 0.9536 - sparse_categorical_accuracy: 0.6202

1176/未知 489 秒 413毫秒/步 - loss: 0.9534 - sparse_categorical_accuracy: 0.6203

1177/未知 489 秒 413毫秒/步 - loss: 0.9532 - sparse_categorical_accuracy: 0.6203

1178/未知 490 秒 413毫秒/步 - loss: 0.9531 - sparse_categorical_accuracy: 0.6204

1179/未知 490 秒 413毫秒/步 - loss: 0.9529 - sparse_categorical_accuracy: 0.6204

1180/未知 491 秒 414毫秒/步 - loss: 0.9527 - sparse_categorical_accuracy: 0.6205

1181/未知 491 秒 414毫秒/步 - loss: 0.9525 - sparse_categorical_accuracy: 0.6206

1182/未知 492 秒 414毫秒/步 - loss: 0.9524 - sparse_categorical_accuracy: 0.6206

1183/未知 492 秒 414毫秒/步 - loss: 0.9522 - sparse_categorical_accuracy: 0.6207

1184/未知 492 秒 414毫秒/步 - loss: 0.9520 - sparse_categorical_accuracy: 0.6207

1185/未知 493 秒 414毫秒/步 - loss: 0.9518 - sparse_categorical_accuracy: 0.6208

1186/未知 493 秒 413毫秒/步 - loss: 0.9517 - sparse_categorical_accuracy: 0.6208

1187/未知 493 秒 413毫秒/步 - loss: 0.9515 - sparse_categorical_accuracy: 0.6209

1188/未知 494 秒 413毫秒/步 - loss: 0.9513 - sparse_categorical_accuracy: 0.6210

1189/未知 494 秒 413毫秒/步 - loss: 0.9511 - sparse_categorical_accuracy: 0.6210

1190/未知 495 秒 413毫秒/步 - loss: 0.9510 - sparse_categorical_accuracy: 0.6211

1191/未知 495 秒 413毫秒/步 - loss: 0.9508 - sparse_categorical_accuracy: 0.6211

1192/未知 495 秒 413毫秒/步 - loss: 0.9506 - sparse_categorical_accuracy: 0.6212

1193/未知 496 秒 413毫秒/步 - loss: 0.9504 - sparse_categorical_accuracy: 0.6212

1194/未知 496 秒 413毫秒/步 - loss: 0.9503 - sparse_categorical_accuracy: 0.6213

1195/未知 496 秒 413毫秒/步 - loss: 0.9501 - sparse_categorical_accuracy: 0.6214

1196/未知 497 秒 413毫秒/步 - loss: 0.9499 - sparse_categorical_accuracy: 0.6214

1197/未知 497 秒 413毫秒/步 - loss: 0.9498 - sparse_categorical_accuracy: 0.6215

1198/未知 498 秒 413毫秒/步 - loss: 0.9496 - sparse_categorical_accuracy: 0.6215

1199/未知 498 秒 413毫秒/步 - loss: 0.9494 - sparse_categorical_accuracy: 0.6216

1200/未知 499 秒 413毫秒/步 - loss: 0.9492 - sparse_categorical_accuracy: 0.6216

1201/未知 499 秒 413毫秒/步 - loss: 0.9491 - sparse_categorical_accuracy: 0.6217

1202/未知 500 秒 413毫秒/步 - loss: 0.9489 - sparse_categorical_accuracy: 0.6218

1203/未知 500 秒 413毫秒/步 - loss: 0.9487 - sparse_categorical_accuracy: 0.6218

1204/未知 500 秒 413毫秒/步 - loss: 0.9486 - sparse_categorical_accuracy: 0.6219

1205/未知 501 秒 413毫秒/步 - loss: 0.9484 - sparse_categorical_accuracy: 0.6219

1206/未知 501 秒 413毫秒/步 - loss: 0.9482 - sparse_categorical_accuracy: 0.6220

1207/未知 501 秒 413毫秒/步 - loss: 0.9481 - sparse_categorical_accuracy: 0.6220

1208/未知 502 秒 413毫秒/步 - loss: 0.9479 - sparse_categorical_accuracy: 0.6221

1209/未知 502 秒 413毫秒/步 - loss: 0.9477 - sparse_categorical_accuracy: 0.6221

1210/未知 503 秒 413毫秒/步 - loss: 0.9476 - sparse_categorical_accuracy: 0.6222

1211/未知 503 秒 413毫秒/步 - loss: 0.9474 - sparse_categorical_accuracy: 0.6223

1212/未知 503 秒 413毫秒/步 - loss: 0.9472 - sparse_categorical_accuracy: 0.6223

1213/未知 504 秒 413毫秒/步 - loss: 0.9470 - sparse_categorical_accuracy: 0.6224

1214/未知 504 秒 413毫秒/步 - loss: 0.9469 - sparse_categorical_accuracy: 0.6224

1215/未知 505 秒 413毫秒/步 - loss: 0.9467 - sparse_categorical_accuracy: 0.6225

1216/未知 505 秒 413毫秒/步 - loss: 0.9465 - sparse_categorical_accuracy: 0.6225

1217/未知 506 秒 413毫秒/步 - loss: 0.9464 - sparse_categorical_accuracy: 0.6226

1218/未知 506 秒 413毫秒/步 - loss: 0.9462 - sparse_categorical_accuracy: 0.6226

1219/未知 506 秒 413毫秒/步 - loss: 0.9460 - sparse_categorical_accuracy: 0.6227

1220/未知 507 秒 413毫秒/步 - loss: 0.9459 - sparse_categorical_accuracy: 0.6228

1221/未知 507 秒 413毫秒/步 - loss: 0.9457 - sparse_categorical_accuracy: 0.6228

1222/未知 508 秒 413毫秒/步 - loss: 0.9455 - sparse_categorical_accuracy: 0.6229

1223/未知 508 秒 413毫秒/步 - loss: 0.9454 - sparse_categorical_accuracy: 0.6229

1224/未知 509 秒 413毫秒/步 - loss: 0.9452 - sparse_categorical_accuracy: 0.6230

1225/未知 509 秒 413毫秒/步 - loss: 0.9450 - sparse_categorical_accuracy: 0.6230

1226/未知 509 秒 413毫秒/步 - loss: 0.9449 - sparse_categorical_accuracy: 0.6231

1227/未知 510 秒 413毫秒/步 - loss: 0.9447 - sparse_categorical_accuracy: 0.6231

1228/未知 510 秒 413毫秒/步 - loss: 0.9446 - sparse_categorical_accuracy: 0.6232

1229/未知 511 秒 413毫秒/步 - loss: 0.9444 - sparse_categorical_accuracy: 0.6233

1230/未知 511 秒 413毫秒/步 - loss: 0.9442 - sparse_categorical_accuracy: 0.6233

1231/未知 512 秒 413毫秒/步 - loss: 0.9441 - sparse_categorical_accuracy: 0.6234

1232/未知 512 秒 414毫秒/步 - loss: 0.9439 - sparse_categorical_accuracy: 0.6234

1233/未知 513 秒 414毫秒/步 - loss: 0.9437 - sparse_categorical_accuracy: 0.6235

1234/未知 513 秒 414毫秒/步 - loss: 0.9436 - sparse_categorical_accuracy: 0.6235

1235/未知 513 秒 414毫秒/步 - loss: 0.9434 - sparse_categorical_accuracy: 0.6236

1236/未知 514 秒 414毫秒/步 - loss: 0.9432 - sparse_categorical_accuracy: 0.6236

1237/未知 514 秒 414毫秒/步 - loss: 0.9431 - sparse_categorical_accuracy: 0.6237

1238/未知 515 秒 414毫秒/步 - loss: 0.9429 - sparse_categorical_accuracy: 0.6237

1239/未知 515 秒 414毫秒/步 - loss: 0.9427 - sparse_categorical_accuracy: 0.6238

1240/未知 516 秒 414毫秒/步 - loss: 0.9426 - sparse_categorical_accuracy: 0.6239

1241/未知 516 秒 414毫秒/步 - loss: 0.9424 - sparse_categorical_accuracy: 0.6239

1242/未知 517 秒 414毫秒/步 - loss: 0.9423 - sparse_categorical_accuracy: 0.6240

1243/未知 517 秒 414毫秒/步 - loss: 0.9421 - sparse_categorical_accuracy: 0.6240

1244/未知 518 秒 414毫秒/步 - loss: 0.9419 - sparse_categorical_accuracy: 0.6241

1245/未知 518 秒 414毫秒/步 - loss: 0.9418 - sparse_categorical_accuracy: 0.6241

1246/未知 519 秒 414毫秒/步 - loss: 0.9416 - sparse_categorical_accuracy: 0.6242

1247/未知 519 秒 414毫秒/步 - loss: 0.9415 - sparse_categorical_accuracy: 0.6242

1248/未知 519 秒 414毫秒/步 - loss: 0.9413 - sparse_categorical_accuracy: 0.6243

1249/未知 520 秒 414毫秒/步 - loss: 0.9411 - sparse_categorical_accuracy: 0.6243

1250/未知 520 秒 414毫秒/步 - loss: 0.9410 - sparse_categorical_accuracy: 0.6244

1251/未知 521 秒 414毫秒/步 - loss: 0.9408 - sparse_categorical_accuracy: 0.6244

1252/未知 521 秒 414毫秒/步 - loss: 0.9406 - sparse_categorical_accuracy: 0.6245

1253/未知 521 秒 414毫秒/步 - loss: 0.9405 - sparse_categorical_accuracy: 0.6245

1254/未知 522 秒 414毫秒/步 - loss: 0.9403 - sparse_categorical_accuracy: 0.6246

1255/未知 522 秒 414毫秒/步 - loss: 0.9402 - sparse_categorical_accuracy: 0.6247

1256/未知 522 秒 414毫秒/步 - loss: 0.9400 - sparse_categorical_accuracy: 0.6247

1257/未知 523 秒 414毫秒/步 - loss: 0.9398 - sparse_categorical_accuracy: 0.6248

1258/未知 523 秒 414毫秒/步 - loss: 0.9397 - sparse_categorical_accuracy: 0.6248

1259/未知 524 秒 414毫秒/步 - loss: 0.9395 - sparse_categorical_accuracy: 0.6249

1260/未知 524 秒 414毫秒/步 - loss: 0.9394 - sparse_categorical_accuracy: 0.6249

1261/未知 524 秒 414毫秒/步 - loss: 0.9392 - sparse_categorical_accuracy: 0.6250

1262/未知 525 秒 414毫秒/步 - loss: 0.9391 - sparse_categorical_accuracy: 0.6250

1263/未知 525 秒 414毫秒/步 - loss: 0.9389 - sparse_categorical_accuracy: 0.6251

1264/未知 526 秒 414毫秒/步 - loss: 0.9387 - sparse_categorical_accuracy: 0.6251

1265/未知 526 秒 414毫秒/步 - loss: 0.9386 - sparse_categorical_accuracy: 0.6252

1266/未知 527 秒 414毫秒/步 - loss: 0.9384 - sparse_categorical_accuracy: 0.6252

1267/未知 527 秒 414毫秒/步 - loss: 0.9383 - sparse_categorical_accuracy: 0.6253

1268/未知 527 秒 414毫秒/步 - loss: 0.9381 - sparse_categorical_accuracy: 0.6253

1269/未知 528 秒 414毫秒/步 - loss: 0.9380 - sparse_categorical_accuracy: 0.6254

1270/未知 528 秒 414毫秒/步 - loss: 0.9378 - sparse_categorical_accuracy: 0.6254

1271/未知 529 秒 414毫秒/步 - loss: 0.9376 - sparse_categorical_accuracy: 0.6255

1272/未知 529 秒 414毫秒/步 - loss: 0.9375 - sparse_categorical_accuracy: 0.6255

1273/未知 530 秒 414毫秒/步 - loss: 0.9373 - sparse_categorical_accuracy: 0.6256

1274/未知 530 秒 414毫秒/步 - loss: 0.9372 - sparse_categorical_accuracy: 0.6256

1275/未知 531 秒 414毫秒/步 - loss: 0.9370 - sparse_categorical_accuracy: 0.6257

1276/未知 531 秒 414毫秒/步 - loss: 0.9369 - sparse_categorical_accuracy: 0.6257

1277/未知 532 秒 414毫秒/步 - loss: 0.9367 - sparse_categorical_accuracy: 0.6258

1278/未知 532 秒 414毫秒/步 - loss: 0.9365 - sparse_categorical_accuracy: 0.6259

1279/未知 532 秒 414毫秒/步 - loss: 0.9364 - sparse_categorical_accuracy: 0.6259

1280/未知 533 秒 414毫秒/步 - loss: 0.9362 - sparse_categorical_accuracy: 0.6260

1281/未知 533 秒 414毫秒/步 - loss: 0.9361 - sparse_categorical_accuracy: 0.6260

1282/未知 534 秒 414毫秒/步 - loss: 0.9359 - sparse_categorical_accuracy: 0.6261

1283/未知 534 秒 414毫秒/步 - loss: 0.9358 - sparse_categorical_accuracy: 0.6261

1284/未知 535 秒 414毫秒/步 - loss: 0.9356 - sparse_categorical_accuracy: 0.6262

1285/未知 535 秒 414毫秒/步 - loss: 0.9355 - sparse_categorical_accuracy: 0.6262

1286/未知 535 秒 414毫秒/步 - loss: 0.9353 - sparse_categorical_accuracy: 0.6263

1287/未知 536 秒 414毫秒/步 - loss: 0.9352 - sparse_categorical_accuracy: 0.6263

1288/未知 536 秒 414毫秒/步 - loss: 0.9350 - sparse_categorical_accuracy: 0.6264

1289/未知 537 秒 414毫秒/步 - loss: 0.9348 - sparse_categorical_accuracy: 0.6264

1290/未知 537 秒 414毫秒/步 - loss: 0.9347 - sparse_categorical_accuracy: 0.6265

1291/未知 537 秒 414毫秒/步 - loss: 0.9345 - sparse_categorical_accuracy: 0.6265

1292/未知 538 秒 414毫秒/步 - loss: 0.9344 - sparse_categorical_accuracy: 0.6266

1293/未知 538 秒 414毫秒/步 - loss: 0.9342 - sparse_categorical_accuracy: 0.6266

1294/未知 539 秒 414毫秒/步 - loss: 0.9341 - sparse_categorical_accuracy: 0.6267

1295/未知 539 秒 414毫秒/步 - loss: 0.9339 - sparse_categorical_accuracy: 0.6267

1296/未知 539 秒 414毫秒/步 - loss: 0.9338 - sparse_categorical_accuracy: 0.6268

1297/未知 540 秒 414毫秒/步 - loss: 0.9336 - sparse_categorical_accuracy: 0.6268

1298/未知 540 秒 414毫秒/步 - loss: 0.9335 - sparse_categorical_accuracy: 0.6269

1299/未知 540 秒 414毫秒/步 - loss: 0.9333 - sparse_categorical_accuracy: 0.6269

1300/未知 541 秒 414毫秒/步 - loss: 0.9332 - sparse_categorical_accuracy: 0.6270

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1302/未知 542 秒 414毫秒/步 - loss: 0.9329 - sparse_categorical_accuracy: 0.6271

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1304/未知 542 秒 414毫秒/步 - loss: 0.9326 - sparse_categorical_accuracy: 0.6272

1305/未知 543 秒 414毫秒/步 - loss: 0.9324 - sparse_categorical_accuracy: 0.6272

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1308/未知 544 秒 414毫秒/步 - loss: 0.9320 - sparse_categorical_accuracy: 0.6274

1309/未知 544 秒 414毫秒/步 - loss: 0.9318 - sparse_categorical_accuracy: 0.6274

1310/未知 545 秒 414毫秒/步 - loss: 0.9317 - sparse_categorical_accuracy: 0.6275

1311/未知 545 秒 414毫秒/步 - loss: 0.9315 - sparse_categorical_accuracy: 0.6275

1312/未知 546 秒 414毫秒/步 - loss: 0.9314 - sparse_categorical_accuracy: 0.6276

1313/未知 546 秒 414毫秒/步 - loss: 0.9312 - sparse_categorical_accuracy: 0.6276

1314/未知 547 秒 414毫秒/步 - loss: 0.9311 - sparse_categorical_accuracy: 0.6277

1315/未知 547 秒 414毫秒/步 - loss: 0.9309 - sparse_categorical_accuracy: 0.6277

1316/未知 548 秒 414毫秒/步 - loss: 0.9308 - sparse_categorical_accuracy: 0.6278

1317/未知 548 秒 414毫秒/步 - loss: 0.9306 - sparse_categorical_accuracy: 0.6278

1318/未知 549 秒 414毫秒/步 - loss: 0.9305 - sparse_categorical_accuracy: 0.6279

1319/未知 549 秒 414毫秒/步 - loss: 0.9303 - sparse_categorical_accuracy: 0.6279

1320/未知 550 秒 414毫秒/步 - loss: 0.9302 - sparse_categorical_accuracy: 0.6280

1321/未知 550 秒 414毫秒/步 - loss: 0.9300 - sparse_categorical_accuracy: 0.6280

1322/未知 551 秒 414毫秒/步 - loss: 0.9299 - sparse_categorical_accuracy: 0.6281

1323/未知 551 秒 415毫秒/步 - loss: 0.9297 - sparse_categorical_accuracy: 0.6281

1324/未知 552 秒 415毫秒/步 - loss: 0.9296 - sparse_categorical_accuracy: 0.6282

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1326/未知 553 秒 415毫秒/步 - loss: 0.9293 - sparse_categorical_accuracy: 0.6283

1327/未知 553 秒 415毫秒/步 - loss: 0.9291 - sparse_categorical_accuracy: 0.6283

1328/未知 553 秒 415毫秒/步 - loss: 0.9290 - sparse_categorical_accuracy: 0.6284

1329/未知 554 秒 415毫秒/步 - loss: 0.9288 - sparse_categorical_accuracy: 0.6284

1330/未知 554 秒 415毫秒/步 - loss: 0.9287 - sparse_categorical_accuracy: 0.6285

1331/未知 555 秒 415毫秒/步 - loss: 0.9285 - sparse_categorical_accuracy: 0.6285

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1333/未知 556 秒 415毫秒/步 - loss: 0.9283 - sparse_categorical_accuracy: 0.6286

1334/未知 556 秒 415毫秒/步 - loss: 0.9281 - sparse_categorical_accuracy: 0.6286

1335/未知 556 秒 415毫秒/步 - loss: 0.9280 - sparse_categorical_accuracy: 0.6287

1336/未知 557 秒 415毫秒/步 - loss: 0.9278 - sparse_categorical_accuracy: 0.6287

1337/未知 557 秒 415毫秒/步 - loss: 0.9277 - sparse_categorical_accuracy: 0.6288

1338/未知 558 秒 415毫秒/步 - loss: 0.9275 - sparse_categorical_accuracy: 0.6288

1339/未知 558 秒 415毫秒/步 - loss: 0.9274 - sparse_categorical_accuracy: 0.6289

1340/未知 559 秒 415毫秒/步 - loss: 0.9272 - sparse_categorical_accuracy: 0.6289

1341/未知 559 秒 415毫秒/步 - loss: 0.9271 - sparse_categorical_accuracy: 0.6290

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1351/未知 564 秒 415毫秒/步 - loss: 0.9256 - sparse_categorical_accuracy: 0.6295

1352/未知 564 秒 415毫秒/步 - loss: 0.9255 - sparse_categorical_accuracy: 0.6295

1353/未知 564 秒 415毫秒/步 - loss: 0.9254 - sparse_categorical_accuracy: 0.6296

1354/未知 565 秒 415毫秒/步 - loss: 0.9252 - sparse_categorical_accuracy: 0.6296

1355/未知 565 秒 415毫秒/步 - loss: 0.9251 - sparse_categorical_accuracy: 0.6296

1356/未知 565 秒 415毫秒/步 - loss: 0.9249 - sparse_categorical_accuracy: 0.6297

1357/未知 566 秒 415毫秒/步 - loss: 0.9248 - sparse_categorical_accuracy: 0.6297

1358/未知 566 秒 415毫秒/步 - loss: 0.9246 - sparse_categorical_accuracy: 0.6298

1359/未知 566 秒 415毫秒/步 - loss: 0.9245 - sparse_categorical_accuracy: 0.6298

1360/未知 567 秒 415毫秒/步 - loss: 0.9244 - sparse_categorical_accuracy: 0.6299

1361/未知 567 秒 415毫秒/步 - loss: 0.9242 - sparse_categorical_accuracy: 0.6299

1362/未知 568 秒 415毫秒/步 - loss: 0.9241 - sparse_categorical_accuracy: 0.6300

1363/未知 568 秒 415毫秒/步 - loss: 0.9239 - sparse_categorical_accuracy: 0.6300

1364/未知 568 秒 415毫秒/步 - loss: 0.9238 - sparse_categorical_accuracy: 0.6301

1365/未知 569 秒 415毫秒/步 - loss: 0.9237 - sparse_categorical_accuracy: 0.6301

1366/未知 569 秒 415毫秒/步 - loss: 0.9235 - sparse_categorical_accuracy: 0.6302

1367/未知 570 秒 415毫秒/步 - loss: 0.9234 - sparse_categorical_accuracy: 0.6302

1368/未知 570 秒 415毫秒/步 - loss: 0.9232 - sparse_categorical_accuracy: 0.6303

1369/未知 571 秒 415毫秒/步 - loss: 0.9231 - sparse_categorical_accuracy: 0.6303

1370/未知 571 秒 415毫秒/步 - loss: 0.9229 - sparse_categorical_accuracy: 0.6304

1371/未知 572 秒 415毫秒/步 - loss: 0.9228 - sparse_categorical_accuracy: 0.6304

1372/未知 572 秒 415毫秒/步 - loss: 0.9227 - sparse_categorical_accuracy: 0.6304

1373/未知 573 秒 415毫秒/步 - loss: 0.9225 - sparse_categorical_accuracy: 0.6305

1374/未知 573 秒 415毫秒/步 - loss: 0.9224 - sparse_categorical_accuracy: 0.6305

1375/未知 574 秒 415毫秒/步 - loss: 0.9222 - sparse_categorical_accuracy: 0.6306

1376/未知 574 秒 415毫秒/步 - loss: 0.9221 - sparse_categorical_accuracy: 0.6306

1377/未知 574 秒 415毫秒/步 - loss: 0.9220 - sparse_categorical_accuracy: 0.6307

1378/未知 575 秒 415毫秒/步 - loss: 0.9218 - sparse_categorical_accuracy: 0.6307

1379/未知 575 秒 415毫秒/步 - loss: 0.9217 - sparse_categorical_accuracy: 0.6308

1380/未知 575 秒 415毫秒/步 - loss: 0.9215 - sparse_categorical_accuracy: 0.6308

1381/未知 576 秒 415毫秒/步 - loss: 0.9214 - sparse_categorical_accuracy: 0.6309

1382/未知 576 秒 415毫秒/步 - loss: 0.9213 - sparse_categorical_accuracy: 0.6309

1383/未知 576 秒 415毫秒/步 - loss: 0.9211 - sparse_categorical_accuracy: 0.6309

1384/未知 577 秒 415毫秒/步 - loss: 0.9210 - sparse_categorical_accuracy: 0.6310

1385/未知 577 秒 415毫秒/步 - loss: 0.9209 - sparse_categorical_accuracy: 0.6310

1386/未知 578 秒 415毫秒/步 - loss: 0.9207 - sparse_categorical_accuracy: 0.6311

1387/未知 578 秒 415毫秒/步 - loss: 0.9206 - sparse_categorical_accuracy: 0.6311

1388/未知 578 秒 415毫秒/步 - loss: 0.9204 - sparse_categorical_accuracy: 0.6312

1389/未知 579 秒 415毫秒/步 - loss: 0.9203 - sparse_categorical_accuracy: 0.6312

1390/未知 579 秒 415毫秒/步 - loss: 0.9202 - sparse_categorical_accuracy: 0.6313

1391/未知 580 秒 415毫秒/步 - loss: 0.9200 - sparse_categorical_accuracy: 0.6313

1392/未知 580 秒 415毫秒/步 - loss: 0.9199 - sparse_categorical_accuracy: 0.6314

1393/未知 580 秒 415毫秒/步 - loss: 0.9198 - sparse_categorical_accuracy: 0.6314

1394/未知 581 秒 415毫秒/步 - loss: 0.9196 - sparse_categorical_accuracy: 0.6315

1395/未知 581 秒 415毫秒/步 - loss: 0.9195 - sparse_categorical_accuracy: 0.6315

1396/未知 582 秒 415毫秒/步 - loss: 0.9193 - sparse_categorical_accuracy: 0.6315

1397/未知 582 秒 415毫秒/步 - loss: 0.9192 - sparse_categorical_accuracy: 0.6316

1398/未知 583 秒 415毫秒/步 - loss: 0.9191 - sparse_categorical_accuracy: 0.6316

1399/未知 583 秒 415毫秒/步 - loss: 0.9189 - sparse_categorical_accuracy: 0.6317

1400/未知 583 秒 415毫秒/步 - loss: 0.9188 - sparse_categorical_accuracy: 0.6317

1401/未知 584 秒 415毫秒/步 - loss: 0.9187 - sparse_categorical_accuracy: 0.6318

1402/未知 584 秒 415毫秒/步 - loss: 0.9185 - sparse_categorical_accuracy: 0.6318

1403/未知 585 秒 415毫秒/步 - loss: 0.9184 - sparse_categorical_accuracy: 0.6319

1404/未知 585 秒 415毫秒/步 - loss: 0.9183 - sparse_categorical_accuracy: 0.6319

1405/未知 586 秒 415毫秒/步 - loss: 0.9181 - sparse_categorical_accuracy: 0.6319

1406/未知 586 秒 415毫秒/步 - loss: 0.9180 - sparse_categorical_accuracy: 0.6320

1407/未知 587 秒 415毫秒/步 - loss: 0.9178 - sparse_categorical_accuracy: 0.6320

1408/未知 587 秒 415毫秒/步 - loss: 0.9177 - sparse_categorical_accuracy: 0.6321

1409/未知 588 秒 415毫秒/步 - loss: 0.9176 - sparse_categorical_accuracy: 0.6321

1410/未知 588 秒 415毫秒/步 - loss: 0.9174 - sparse_categorical_accuracy: 0.6322

1411/未知 589 秒 415毫秒/步 - loss: 0.9173 - sparse_categorical_accuracy: 0.6322

1412/未知 589 秒 415毫秒/步 - loss: 0.9172 - sparse_categorical_accuracy: 0.6323

1413/未知 590 秒 415毫秒/步 - loss: 0.9170 - sparse_categorical_accuracy: 0.6323

1414/未知 590 秒 415毫秒/步 - loss: 0.9169 - sparse_categorical_accuracy: 0.6323

1415/未知 591 秒 415毫秒/步 - loss: 0.9168 - sparse_categorical_accuracy: 0.6324

1416/未知 591 秒 415毫秒/步 - loss: 0.9166 - sparse_categorical_accuracy: 0.6324

1417/未知 591 秒 415毫秒/步 - loss: 0.9165 - sparse_categorical_accuracy: 0.6325

1418/未知 592 秒 415毫秒/步 - loss: 0.9164 - sparse_categorical_accuracy: 0.6325

1419/未知 592 秒 415毫秒/步 - loss: 0.9162 - sparse_categorical_accuracy: 0.6326

1420/未知 592 秒 415毫秒/步 - loss: 0.9161 - sparse_categorical_accuracy: 0.6326

1421/未知 593 秒 415毫秒/步 - loss: 0.9160 - sparse_categorical_accuracy: 0.6327

1422/未知 593 秒 415毫秒/步 - loss: 0.9158 - sparse_categorical_accuracy: 0.6327

1423/未知 594 秒 415毫秒/步 - loss: 0.9157 - sparse_categorical_accuracy: 0.6327

1424/未知 594 秒 415毫秒/步 - loss: 0.9156 - sparse_categorical_accuracy: 0.6328

1425/未知 594 秒 415毫秒/步 - loss: 0.9154 - sparse_categorical_accuracy: 0.6328

1426/未知 595 秒 415毫秒/步 - loss: 0.9153 - sparse_categorical_accuracy: 0.6329

1427/未知 595 秒 415毫秒/步 - loss: 0.9152 - sparse_categorical_accuracy: 0.6329

1428/未知 596 秒 415毫秒/步 - loss: 0.9150 - sparse_categorical_accuracy: 0.6330

1429/未知 596 秒 415毫秒/步 - loss: 0.9149 - sparse_categorical_accuracy: 0.6330

1430/未知 596 秒 415毫秒/步 - loss: 0.9148 - sparse_categorical_accuracy: 0.6331

1431/未知 597 秒 415毫秒/步 - loss: 0.9146 - sparse_categorical_accuracy: 0.6331

1432/未知 597 秒 415毫秒/步 - loss: 0.9145 - sparse_categorical_accuracy: 0.6331

1433/未知 598 秒 415毫秒/步 - loss: 0.9144 - sparse_categorical_accuracy: 0.6332

1434/未知 598 秒 415毫秒/步 - loss: 0.9142 - sparse_categorical_accuracy: 0.6332

1435/未知 599 秒 415毫秒/步 - loss: 0.9141 - sparse_categorical_accuracy: 0.6333

1436/未知 599 秒 415毫秒/步 - loss: 0.9140 - sparse_categorical_accuracy: 0.6333

1437/未知 599 秒 415毫秒/步 - loss: 0.9139 - sparse_categorical_accuracy: 0.6334

1438/未知 600 秒 415毫秒/步 - loss: 0.9137 - sparse_categorical_accuracy: 0.6334

1439/未知 600 秒 415毫秒/步 - loss: 0.9136 - sparse_categorical_accuracy: 0.6334

1440/未知 601 秒 415毫秒/步 - loss: 0.9135 - sparse_categorical_accuracy: 0.6335

1441/未知 601 秒 415毫秒/步 - loss: 0.9133 - sparse_categorical_accuracy: 0.6335

1442/未知 602 秒 416毫秒/步 - loss: 0.9132 - sparse_categorical_accuracy: 0.6336

1443/未知 602 秒 416毫秒/步 - loss: 0.9131 - sparse_categorical_accuracy: 0.6336

1444/未知 603 秒 416毫秒/步 - loss: 0.9129 - sparse_categorical_accuracy: 0.6337

1445/未知 603 秒 416毫秒/步 - loss: 0.9128 - sparse_categorical_accuracy: 0.6337

1446/未知 604 秒 416毫秒/步 - loss: 0.9127 - sparse_categorical_accuracy: 0.6337

1447/未知 604 秒 416毫秒/步 - loss: 0.9126 - sparse_categorical_accuracy: 0.6338

1448/未知 605 秒 416毫秒/步 - loss: 0.9124 - sparse_categorical_accuracy: 0.6338

1449/未知 605 秒 416毫秒/步 - loss: 0.9123 - sparse_categorical_accuracy: 0.6339

1450/未知 606 秒 416毫秒/步 - loss: 0.9122 - sparse_categorical_accuracy: 0.6339

1451/未知 606 秒 416毫秒/步 - loss: 0.9120 - sparse_categorical_accuracy: 0.6340

1452/未知 606 秒 416毫秒/步 - loss: 0.9119 - sparse_categorical_accuracy: 0.6340

1453/未知 607 秒 416毫秒/步 - loss: 0.9118 - sparse_categorical_accuracy: 0.6340

1454/未知 607 秒 416毫秒/步 - loss: 0.9116 - sparse_categorical_accuracy: 0.6341

1455/未知 608 秒 416毫秒/步 - loss: 0.9115 - sparse_categorical_accuracy: 0.6341

1456/未知 608 秒 416毫秒/步 - loss: 0.9114 - sparse_categorical_accuracy: 0.6342

1457/未知 609 秒 416毫秒/步 - loss: 0.9113 - sparse_categorical_accuracy: 0.6342

1458/未知 609 秒 416毫秒/步 - loss: 0.9111 - sparse_categorical_accuracy: 0.6343

1459/未知 610 秒 416毫秒/步 - loss: 0.9110 - sparse_categorical_accuracy: 0.6343

1460/未知 610 秒 416毫秒/步 - loss: 0.9109 - sparse_categorical_accuracy: 0.6343

1461/未知 610 秒 416毫秒/步 - loss: 0.9108 - sparse_categorical_accuracy: 0.6344

1462/未知 611 秒 416毫秒/步 - loss: 0.9106 - sparse_categorical_accuracy: 0.6344

1463/未知 611 秒 416毫秒/步 - loss: 0.9105 - sparse_categorical_accuracy: 0.6345

1464/未知 612 秒 416毫秒/步 - loss: 0.9104 - sparse_categorical_accuracy: 0.6345

1465/未知 612 秒 416毫秒/步 - loss: 0.9102 - sparse_categorical_accuracy: 0.6345

1466/未知 613 秒 416毫秒/步 - loss: 0.9101 - sparse_categorical_accuracy: 0.6346

1467/未知 613 秒 416毫秒/步 - loss: 0.9100 - sparse_categorical_accuracy: 0.6346

1468/未知 613 秒 416毫秒/步 - loss: 0.9099 - sparse_categorical_accuracy: 0.6347

1469/未知 614 秒 416毫秒/步 - loss: 0.9097 - sparse_categorical_accuracy: 0.6347

1470/未知 614 秒 416毫秒/步 - loss: 0.9096 - sparse_categorical_accuracy: 0.6348

1471/未知 614 秒 416毫秒/步 - loss: 0.9095 - sparse_categorical_accuracy: 0.6348

1472/未知 615 秒 416毫秒/步 - loss: 0.9094 - sparse_categorical_accuracy: 0.6348

1473/未知 615 秒 416毫秒/步 - loss: 0.9092 - sparse_categorical_accuracy: 0.6349

1474/未知 615 秒 416毫秒/步 - loss: 0.9091 - sparse_categorical_accuracy: 0.6349

1475/未知 616 秒 416毫秒/步 - loss: 0.9090 - sparse_categorical_accuracy: 0.6350

1476/未知 616 秒 416毫秒/步 - loss: 0.9089 - sparse_categorical_accuracy: 0.6350

1477/未知 616 秒 416毫秒/步 - loss: 0.9087 - sparse_categorical_accuracy: 0.6350

1478/未知 617 秒 415毫秒/步 - loss: 0.9086 - sparse_categorical_accuracy: 0.6351

1479/未知 617 秒 415毫秒/步 - loss: 0.9085 - sparse_categorical_accuracy: 0.6351

1480/未知 617 秒 415毫秒/步 - loss: 0.9083 - sparse_categorical_accuracy: 0.6352

1481/未知 618 秒 415毫秒/步 - loss: 0.9082 - sparse_categorical_accuracy: 0.6352

1482/未知 618 秒 415毫秒/步 - loss: 0.9081 - sparse_categorical_accuracy: 0.6353

1483/未知 619 秒 415毫秒/步 - loss: 0.9080 - sparse_categorical_accuracy: 0.6353

1484/未知 619 秒 415毫秒/步 - loss: 0.9078 - sparse_categorical_accuracy: 0.6353

1485/未知 620 秒 415毫秒/步 - loss: 0.9077 - sparse_categorical_accuracy: 0.6354

1486/未知 620 秒 415毫秒/步 - loss: 0.9076 - sparse_categorical_accuracy: 0.6354

1487/未知 620 秒 415毫秒/步 - loss: 0.9075 - sparse_categorical_accuracy: 0.6355

1488/未知 621 秒 416毫秒/步 - loss: 0.9073 - sparse_categorical_accuracy: 0.6355

1489/未知 621 秒 416毫秒/步 - loss: 0.9072 - sparse_categorical_accuracy: 0.6355

1490/未知 622 秒 416毫秒/步 - loss: 0.9071 - sparse_categorical_accuracy: 0.6356

1491/未知 622 秒 416毫秒/步 - loss: 0.9070 - sparse_categorical_accuracy: 0.6356

1492/未知 623 秒 416毫秒/步 - loss: 0.9069 - sparse_categorical_accuracy: 0.6357

1493/未知 623 秒 416毫秒/步 - loss: 0.9067 - sparse_categorical_accuracy: 0.6357

1494/未知 624 秒 416毫秒/步 - loss: 0.9066 - sparse_categorical_accuracy: 0.6358

1495/未知 624 秒 416毫秒/步 - loss: 0.9065 - sparse_categorical_accuracy: 0.6358

1496/未知 624 秒 416毫秒/步 - loss: 0.9064 - sparse_categorical_accuracy: 0.6358

1497/未知 625 秒 416毫秒/步 - loss: 0.9062 - sparse_categorical_accuracy: 0.6359

1498/未知 625 秒 416毫秒/步 - loss: 0.9061 - sparse_categorical_accuracy: 0.6359

1499/未知 626 秒 416毫秒/步 - loss: 0.9060 - sparse_categorical_accuracy: 0.6360

1500/未知 626 秒 416毫秒/步 - loss: 0.9059 - sparse_categorical_accuracy: 0.6360

1501/未知 627 秒 416毫秒/步 - loss: 0.9057 - sparse_categorical_accuracy: 0.6360

1502/未知 627 秒 416毫秒/步 - loss: 0.9056 - sparse_categorical_accuracy: 0.6361

1503/未知 628 秒 416毫秒/步 - loss: 0.9055 - sparse_categorical_accuracy: 0.6361

1504/未知 628 秒 416毫秒/步 - loss: 0.9054 - sparse_categorical_accuracy: 0.6362

1505/未知 628 秒 416毫秒/步 - loss: 0.9053 - sparse_categorical_accuracy: 0.6362

1506/未知 629 秒 416毫秒/步 - loss: 0.9051 - sparse_categorical_accuracy: 0.6362

1507/未知 629 秒 416毫秒/步 - loss: 0.9050 - sparse_categorical_accuracy: 0.6363

1508/未知 630 秒 416毫秒/步 - loss: 0.9049 - sparse_categorical_accuracy: 0.6363

1509/未知 630 秒 416毫秒/步 - loss: 0.9048 - sparse_categorical_accuracy: 0.6364

1510/未知 631 秒 416毫秒/步 - loss: 0.9046 - sparse_categorical_accuracy: 0.6364

1511/未知 631 秒 416毫秒/步 - loss: 0.9045 - sparse_categorical_accuracy: 0.6364

1512/未知 631 秒 416毫秒/步 - loss: 0.9044 - sparse_categorical_accuracy: 0.6365

1513/未知 632 秒 416毫秒/步 - loss: 0.9043 - sparse_categorical_accuracy: 0.6365

1514/未知 632 秒 416毫秒/步 - loss: 0.9042 - sparse_categorical_accuracy: 0.6366

1515/未知 632 秒 416毫秒/步 - loss: 0.9040 - sparse_categorical_accuracy: 0.6366

1516/未知 633 秒 416毫秒/步 - loss: 0.9039 - sparse_categorical_accuracy: 0.6366

1517/未知 633 秒 416毫秒/步 - loss: 0.9038 - sparse_categorical_accuracy: 0.6367

1518/未知 634 秒 416毫秒/步 - loss: 0.9037 - sparse_categorical_accuracy: 0.6367

1519/未知 634 秒 416毫秒/步 - loss: 0.9036 - sparse_categorical_accuracy: 0.6368

1520/未知 634 秒 415毫秒/步 - loss: 0.9034 - sparse_categorical_accuracy: 0.6368

1521/未知 635 秒 415毫秒/步 - loss: 0.9033 - sparse_categorical_accuracy: 0.6368

1522/未知 635 秒 415毫秒/步 - loss: 0.9032 - sparse_categorical_accuracy: 0.6369

1523/未知 635 秒 415毫秒/步 - loss: 0.9031 - sparse_categorical_accuracy: 0.6369

1524/未知 636 秒 415毫秒/步 - loss: 0.9029 - sparse_categorical_accuracy: 0.6370

1525/未知 636 秒 415毫秒/步 - loss: 0.9028 - sparse_categorical_accuracy: 0.6370

1526/未知 637 秒 415毫秒/步 - loss: 0.9027 - sparse_categorical_accuracy: 0.6370

1527/未知 637 秒 415毫秒/步 - loss: 0.9026 - sparse_categorical_accuracy: 0.6371

1528/未知 638 秒 416毫秒/步 - loss: 0.9025 - sparse_categorical_accuracy: 0.6371

1529/未知 638 秒 416毫秒/步 - loss: 0.9023 - sparse_categorical_accuracy: 0.6372

1530/未知 639 秒 416毫秒/步 - loss: 0.9022 - sparse_categorical_accuracy: 0.6372

1531/未知 639 秒 416毫秒/步 - loss: 0.9021 - sparse_categorical_accuracy: 0.6372

1532/未知 640 秒 416毫秒/步 - loss: 0.9020 - sparse_categorical_accuracy: 0.6373

1533/未知 640 秒 416毫秒/步 - loss: 0.9019 - sparse_categorical_accuracy: 0.6373

1534/未知 641 秒 416毫秒/步 - loss: 0.9018 - sparse_categorical_accuracy: 0.6374

1535/未知 641 秒 416毫秒/步 - loss: 0.9016 - sparse_categorical_accuracy: 0.6374

1536/未知 641 秒 416毫秒/步 - loss: 0.9015 - sparse_categorical_accuracy: 0.6374

1537/未知 642 秒 416毫秒/步 - loss: 0.9014 - sparse_categorical_accuracy: 0.6375

1538/未知 642 秒 416毫秒/步 - loss: 0.9013 - sparse_categorical_accuracy: 0.6375

1539/未知 643 秒 416毫秒/步 - loss: 0.9012 - sparse_categorical_accuracy: 0.6376

1540/未知 643 秒 416毫秒/步 - loss: 0.9010 - sparse_categorical_accuracy: 0.6376

1541/未知 644 秒 416毫秒/步 - loss: 0.9009 - sparse_categorical_accuracy: 0.6376

1542/未知 644 秒 416毫秒/步 - loss: 0.9008 - sparse_categorical_accuracy: 0.6377

1543/未知 645 秒 416毫秒/步 - loss: 0.9007 - sparse_categorical_accuracy: 0.6377

1544/未知 645 秒 416毫秒/步 - loss: 0.9006 - sparse_categorical_accuracy: 0.6378

1545/未知 645 秒 416毫秒/步 - loss: 0.9004 - sparse_categorical_accuracy: 0.6378

1546/未知 646 秒 416毫秒/步 - loss: 0.9003 - sparse_categorical_accuracy: 0.6378

1547/未知 646 秒 416毫秒/步 - loss: 0.9002 - sparse_categorical_accuracy: 0.6379

1548/未知 646 秒 416毫秒/步 - loss: 0.9001 - sparse_categorical_accuracy: 0.6379

1549/未知 647 秒 416毫秒/步 - loss: 0.9000 - sparse_categorical_accuracy: 0.6379

1550/未知 647 秒 416毫秒/步 - loss: 0.8999 - sparse_categorical_accuracy: 0.6380

1551/未知 648 秒 416毫秒/步 - loss: 0.8997 - sparse_categorical_accuracy: 0.6380

1552/未知 648 秒 416毫秒/步 - loss: 0.8996 - sparse_categorical_accuracy: 0.6381

1553/未知 648 秒 416毫秒/步 - loss: 0.8995 - sparse_categorical_accuracy: 0.6381

1554/未知 649 秒 416毫秒/步 - loss: 0.8994 - sparse_categorical_accuracy: 0.6381

1555/未知 649 秒 416毫秒/步 - loss: 0.8993 - sparse_categorical_accuracy: 0.6382

1556/未知 650 秒 416毫秒/步 - loss: 0.8992 - sparse_categorical_accuracy: 0.6382

1557/未知 650 秒 416毫秒/步 - loss: 0.8990 - sparse_categorical_accuracy: 0.6383

1558/未知 650 秒 416毫秒/步 - loss: 0.8989 - sparse_categorical_accuracy: 0.6383

1559/未知 651 秒 416毫秒/步 - loss: 0.8988 - sparse_categorical_accuracy: 0.6383

1560/未知 651 秒 416毫秒/步 - loss: 0.8987 - sparse_categorical_accuracy: 0.6384

1561/未知 652 秒 416毫秒/步 - loss: 0.8986 - sparse_categorical_accuracy: 0.6384

1562/未知 652 秒 416毫秒/步 - loss: 0.8985 - sparse_categorical_accuracy: 0.6385

1563/未知 653 秒 416毫秒/步 - loss: 0.8983 - sparse_categorical_accuracy: 0.6385

1564/未知 653 秒 416毫秒/步 - loss: 0.8982 - sparse_categorical_accuracy: 0.6385

1565/未知 654 秒 416毫秒/步 - loss: 0.8981 - sparse_categorical_accuracy: 0.6386

1566/未知 654 秒 416毫秒/步 - loss: 0.8980 - sparse_categorical_accuracy: 0.6386

1567/未知 655 秒 416毫秒/步 - loss: 0.8979 - sparse_categorical_accuracy: 0.6386

1568/未知 655 秒 416毫秒/步 - loss: 0.8978 - sparse_categorical_accuracy: 0.6387

1569/未知 656 秒 416毫秒/步 - loss: 0.8977 - sparse_categorical_accuracy: 0.6387

1570/未知 656 秒 416毫秒/步 - loss: 0.8975 - sparse_categorical_accuracy: 0.6388

1571/未知 656 秒 416毫秒/步 - loss: 0.8974 - sparse_categorical_accuracy: 0.6388

1572/未知 657 秒 416毫秒/步 - loss: 0.8973 - sparse_categorical_accuracy: 0.6388

1573/未知 657 秒 416毫秒/步 - loss: 0.8972 - sparse_categorical_accuracy: 0.6389

1574/未知 658 秒 416毫秒/步 - loss: 0.8971 - sparse_categorical_accuracy: 0.6389

1575/未知 658 秒 416毫秒/步 - loss: 0.8970 - sparse_categorical_accuracy: 0.6389

1576/未知 659 秒 416毫秒/步 - loss: 0.8969 - sparse_categorical_accuracy: 0.6390

1577/未知 659 秒 416毫秒/步 - loss: 0.8967 - sparse_categorical_accuracy: 0.6390

1578/未知 660 秒 416毫秒/步 - loss: 0.8966 - sparse_categorical_accuracy: 0.6391

1579/未知 660 秒 416毫秒/步 - loss: 0.8965 - sparse_categorical_accuracy: 0.6391

1580/未知 661 秒 416毫秒/步 - loss: 0.8964 - sparse_categorical_accuracy: 0.6391

1581/未知 661 秒 416毫秒/步 - loss: 0.8963 - sparse_categorical_accuracy: 0.6392

1582/未知 662 秒 416毫秒/步 - loss: 0.8962 - sparse_categorical_accuracy: 0.6392

1583/未知 662 秒 417毫秒/步 - loss: 0.8961 - sparse_categorical_accuracy: 0.6392

1584/未知 662 秒 417毫秒/步 - loss: 0.8959 - sparse_categorical_accuracy: 0.6393

1585/未知 663 秒 417毫秒/步 - loss: 0.8958 - sparse_categorical_accuracy: 0.6393

1586/未知 663 秒 417毫秒/步 - loss: 0.8957 - sparse_categorical_accuracy: 0.6394

1587/未知 664 秒 417毫秒/步 - loss: 0.8956 - sparse_categorical_accuracy: 0.6394

1588/未知 664 秒 417毫秒/步 - loss: 0.8955 - sparse_categorical_accuracy: 0.6394

1589/未知 665 秒 417毫秒/步 - loss: 0.8954 - sparse_categorical_accuracy: 0.6395

1590/未知 665 秒 417毫秒/步 - loss: 0.8953 - sparse_categorical_accuracy: 0.6395

1591/未知 666 秒 417毫秒/步 - loss: 0.8952 - sparse_categorical_accuracy: 0.6395

1592/未知 666 秒 417毫秒/步 - loss: 0.8950 - sparse_categorical_accuracy: 0.6396

1593/未知 666 秒 417毫秒/步 - loss: 0.8949 - sparse_categorical_accuracy: 0.6396

1594/未知 667 秒 417毫秒/步 - loss: 0.8948 - sparse_categorical_accuracy: 0.6397

1595/未知 667 秒 417毫秒/步 - loss: 0.8947 - sparse_categorical_accuracy: 0.6397

1596/未知 668 秒 417毫秒/步 - loss: 0.8946 - sparse_categorical_accuracy: 0.6397

1597/未知 668 秒 417毫秒/步 - loss: 0.8945 - sparse_categorical_accuracy: 0.6398

1598/未知 669 秒 417毫秒/步 - loss: 0.8944 - sparse_categorical_accuracy: 0.6398

1599/未知 669 秒 417毫秒/步 - loss: 0.8943 - sparse_categorical_accuracy: 0.6398

1600/未知 669 秒 417毫秒/步 - loss: 0.8941 - sparse_categorical_accuracy: 0.6399

1601/未知 670 秒 417毫秒/步 - loss: 0.8940 - sparse_categorical_accuracy: 0.6399

1602/未知 670 秒 417毫秒/步 - loss: 0.8939 - sparse_categorical_accuracy: 0.6400

1603/未知 671 秒 417毫秒/步 - loss: 0.8938 - sparse_categorical_accuracy: 0.6400

1604/未知 671 秒 417毫秒/步 - loss: 0.8937 - sparse_categorical_accuracy: 0.6400

1605/未知 672 秒 417毫秒/步 - loss: 0.8936 - sparse_categorical_accuracy: 0.6401

1606/未知 672 秒 417毫秒/步 - loss: 0.8935 - sparse_categorical_accuracy: 0.6401

1607/未知 673 秒 417毫秒/步 - loss: 0.8934 - sparse_categorical_accuracy: 0.6401

1608/未知 673 秒 417毫秒/步 - loss: 0.8933 - sparse_categorical_accuracy: 0.6402

1609/未知 673 秒 417毫秒/步 - loss: 0.8931 - sparse_categorical_accuracy: 0.6402

1610/未知 674 秒 417毫秒/步 - loss: 0.8930 - sparse_categorical_accuracy: 0.6403

1611/未知 674 秒 417毫秒/步 - loss: 0.8929 - sparse_categorical_accuracy: 0.6403

1612/未知 675 秒 417毫秒/步 - loss: 0.8928 - sparse_categorical_accuracy: 0.6403

1613/未知 675 秒 417毫秒/步 - loss: 0.8927 - sparse_categorical_accuracy: 0.6404

1614/未知 675 秒 417毫秒/步 - loss: 0.8926 - sparse_categorical_accuracy: 0.6404

1615/未知 676 秒 417毫秒/步 - loss: 0.8925 - sparse_categorical_accuracy: 0.6404

1616/未知 676 秒 417毫秒/步 - loss: 0.8924 - sparse_categorical_accuracy: 0.6405

1617/未知 677 秒 417毫秒/步 - loss: 0.8923 - sparse_categorical_accuracy: 0.6405

1618/未知 677 秒 417毫秒/步 - loss: 0.8922 - sparse_categorical_accuracy: 0.6405

1619/未知 677毫秒/步 - loss: 0.8920 - sparse_categorical_accuracy: 0.6406

1620/未知 678毫秒/步 - loss: 0.8919 - sparse_categorical_accuracy: 0.6406

1621/未知 678毫秒/步 - loss: 0.8918 - sparse_categorical_accuracy: 0.6407

1622/未知 678毫秒/步 - loss: 0.8917 - sparse_categorical_accuracy: 0.6407

1623/未知 679毫秒/步 - loss: 0.8916 - sparse_categorical_accuracy: 0.6407

1624/未知 679毫秒/步 - loss: 0.8915 - sparse_categorical_accuracy: 0.6408

1625/未知 679毫秒/步 - loss: 0.8914 - sparse_categorical_accuracy: 0.6408

1626/未知 680毫秒/步 - loss: 0.8913 - sparse_categorical_accuracy: 0.6408

1627/未知 680毫秒/步 - loss: 0.8912 - sparse_categorical_accuracy: 0.6409

1628/未知 681毫秒/步 - loss: 0.8911 - sparse_categorical_accuracy: 0.6409

1629/未知 681毫秒/步 - loss: 0.8909 - sparse_categorical_accuracy: 0.6409

1630/未知 682毫秒/步 - loss: 0.8908 - sparse_categorical_accuracy: 0.6410

1631/未知 682毫秒/步 - loss: 0.8907 - sparse_categorical_accuracy: 0.6410

1632/未知 683毫秒/步 - loss: 0.8906 - sparse_categorical_accuracy: 0.6411

1633/未知 683毫秒/步 - loss: 0.8905 - sparse_categorical_accuracy: 0.6411

1634/未知 684毫秒/步 - loss: 0.8904 - sparse_categorical_accuracy: 0.6411

1635/未知 684毫秒/步 - loss: 0.8903 - sparse_categorical_accuracy: 0.6412

1636/未知 685毫秒/步 - loss: 0.8902 - sparse_categorical_accuracy: 0.6412

1637/未知 685毫秒/步 - loss: 0.8901 - sparse_categorical_accuracy: 0.6412

1638/未知 686毫秒/步 - loss: 0.8900 - sparse_categorical_accuracy: 0.6413

1639/未知 686毫秒/步 - loss: 0.8899 - sparse_categorical_accuracy: 0.6413

1640/未知 686毫秒/步 - loss: 0.8898 - sparse_categorical_accuracy: 0.6413

1641/未知 687毫秒/步 - loss: 0.8897 - sparse_categorical_accuracy: 0.6414

1642/未知 687毫秒/步 - loss: 0.8895 - sparse_categorical_accuracy: 0.6414

1643/未知 688毫秒/步 - loss: 0.8894 - sparse_categorical_accuracy: 0.6414

1644/未知 688毫秒/步 - loss: 0.8893 - sparse_categorical_accuracy: 0.6415

1645/未知 689毫秒/步 - loss: 0.8892 - sparse_categorical_accuracy: 0.6415

1646/未知 689毫秒/步 - loss: 0.8891 - sparse_categorical_accuracy: 0.6416

1647/未知 690毫秒/步 - loss: 0.8890 - sparse_categorical_accuracy: 0.6416

1648/未知 690毫秒/步 - loss: 0.8889 - sparse_categorical_accuracy: 0.6416

1649/未知 690毫秒/步 - loss: 0.8888 - sparse_categorical_accuracy: 0.6417

1650/未知 691毫秒/步 - loss: 0.8887 - sparse_categorical_accuracy: 0.6417

1651/未知 691毫秒/步 - loss: 0.8886 - sparse_categorical_accuracy: 0.6417

1652/未知 692毫秒/步 - loss: 0.8885 - sparse_categorical_accuracy: 0.6418

1653/未知 692毫秒/步 - loss: 0.8884 - sparse_categorical_accuracy: 0.6418

1654/未知 693毫秒/步 - loss: 0.8883 - sparse_categorical_accuracy: 0.6418

1655/未知 693毫秒/步 - loss: 0.8882 - sparse_categorical_accuracy: 0.6419

1656/未知 693毫秒/步 - loss: 0.8880 - sparse_categorical_accuracy: 0.6419

1657/未知 694毫秒/步 - loss: 0.8879 - sparse_categorical_accuracy: 0.6419

1658/未知 694毫秒/步 - loss: 0.8878 - sparse_categorical_accuracy: 0.6420

1659/未知 695毫秒/步 - loss: 0.8877 - sparse_categorical_accuracy: 0.6420

1660/未知 695毫秒/步 - loss: 0.8876 - sparse_categorical_accuracy: 0.6420

1661/未知 695毫秒/步 - loss: 0.8875 - sparse_categorical_accuracy: 0.6421

1662/未知 696毫秒/步 - loss: 0.8874 - sparse_categorical_accuracy: 0.6421

1663/未知 696毫秒/步 - loss: 0.8873 - sparse_categorical_accuracy: 0.6422

1664/未知 696毫秒/步 - loss: 0.8872 - sparse_categorical_accuracy: 0.6422

1665/未知 697毫秒/步 - loss: 0.8871 - sparse_categorical_accuracy: 0.6422

1666/未知 697毫秒/步 - loss: 0.8870 - sparse_categorical_accuracy: 0.6423

1667/未知 698毫秒/步 - loss: 0.8869 - sparse_categorical_accuracy: 0.6423

1668/未知 698毫秒/步 - loss: 0.8868 - sparse_categorical_accuracy: 0.6423

1669/未知 698毫秒/步 - loss: 0.8867 - sparse_categorical_accuracy: 0.6424

1670/未知 699毫秒/步 - loss: 0.8866 - sparse_categorical_accuracy: 0.6424

1671/未知 699毫秒/步 - loss: 0.8865 - sparse_categorical_accuracy: 0.6424

1672/未知 700毫秒/步 - loss: 0.8864 - sparse_categorical_accuracy: 0.6425

1673/未知 700毫秒/步 - loss: 0.8863 - sparse_categorical_accuracy: 0.6425

1674/未知 700毫秒/步 - loss: 0.8862 - sparse_categorical_accuracy: 0.6425

1675/未知 701毫秒/步 - loss: 0.8861 - sparse_categorical_accuracy: 0.6426

1676/未知 701毫秒/步 - loss: 0.8859 - sparse_categorical_accuracy: 0.6426

1677/未知 702毫秒/步 - loss: 0.8858 - sparse_categorical_accuracy: 0.6426

1678/未知 702毫秒/步 - loss: 0.8857 - sparse_categorical_accuracy: 0.6427

1679/未知 703毫秒/步 - loss: 0.8856 - sparse_categorical_accuracy: 0.6427

1680/未知 703毫秒/步 - loss: 0.8855 - sparse_categorical_accuracy: 0.6427

1681/未知 704毫秒/步 - loss: 0.8854 - sparse_categorical_accuracy: 0.6428

1682/未知 704毫秒/步 - loss: 0.8853 - sparse_categorical_accuracy: 0.6428

1683/未知 705毫秒/步 - loss: 0.8852 - sparse_categorical_accuracy: 0.6428

1684/未知 705毫秒/步 - loss: 0.8851 - sparse_categorical_accuracy: 0.6429

1685/未知 706毫秒/步 - loss: 0.8850 - sparse_categorical_accuracy: 0.6429

1686/未知 706毫秒/步 - loss: 0.8849 - sparse_categorical_accuracy: 0.6429

1687/未知 706毫秒/步 - loss: 0.8848 - sparse_categorical_accuracy: 0.6430

1688/未知 707毫秒/步 - loss: 0.8847 - sparse_categorical_accuracy: 0.6430

1689/未知 707毫秒/步 - loss: 0.8846 - sparse_categorical_accuracy: 0.6431

1690/未知 708毫秒/步 - loss: 0.8845 - sparse_categorical_accuracy: 0.6431

1691/未知 708毫秒/步 - loss: 0.8844 - sparse_categorical_accuracy: 0.6431

1692/未知 709毫秒/步 - loss: 0.8843 - sparse_categorical_accuracy: 0.6432

1693/未知 709毫秒/步 - loss: 0.8842 - sparse_categorical_accuracy: 0.6432

1694/未知 709毫秒/步 - loss: 0.8841 - sparse_categorical_accuracy: 0.6432

1695/未知 710毫秒/步 - loss: 0.8840 - sparse_categorical_accuracy: 0.6433

1696/未知 710毫秒/步 - loss: 0.8839 - sparse_categorical_accuracy: 0.6433

1697/未知 711毫秒/步 - loss: 0.8838 - sparse_categorical_accuracy: 0.6433

1698/未知 711毫秒/步 - loss: 0.8837 - sparse_categorical_accuracy: 0.6434

1699/未知 711毫秒/步 - loss: 0.8836 - sparse_categorical_accuracy: 0.6434

1700/未知 712毫秒/步 - loss: 0.8835 - sparse_categorical_accuracy: 0.6434

1701/未知 712毫秒/步 - loss: 0.8834 - sparse_categorical_accuracy: 0.6435

1702/未知 713毫秒/步 - loss: 0.8833 - sparse_categorical_accuracy: 0.6435

1703/未知 713毫秒/步 - loss: 0.8832 - sparse_categorical_accuracy: 0.6435

1704/未知 713毫秒/步 - loss: 0.8831 - sparse_categorical_accuracy: 0.6436

1705/未知 714毫秒/步 - loss: 0.8830 - sparse_categorical_accuracy: 0.6436

1706/未知 714毫秒/步 - loss: 0.8829 - sparse_categorical_accuracy: 0.6436

1707/未知 714毫秒/步 - loss: 0.8828 - sparse_categorical_accuracy: 0.6437

1708/未知 715毫秒/步 - loss: 0.8827 - sparse_categorical_accuracy: 0.6437

1709/未知 715毫秒/步 - loss: 0.8826 - sparse_categorical_accuracy: 0.6437

1710/未知 716毫秒/步 - loss: 0.8825 - sparse_categorical_accuracy: 0.6438

1711/未知 716毫秒/步 - loss: 0.8824 - sparse_categorical_accuracy: 0.6438

1712/未知 717毫秒/步 - loss: 0.8823 - sparse_categorical_accuracy: 0.6438

1713/未知 717毫秒/步 - loss: 0.8822 - sparse_categorical_accuracy: 0.6439

1714/未知 718毫秒/步 - loss: 0.8821 - sparse_categorical_accuracy: 0.6439

1715/未知 718毫秒/步 - loss: 0.8820 - sparse_categorical_accuracy: 0.6439

1716/未知 719毫秒/步 - loss: 0.8818 - sparse_categorical_accuracy: 0.6440

1717/未知 719毫秒/步 - loss: 0.8817 - sparse_categorical_accuracy: 0.6440

1718/未知 719毫秒/步 - loss: 0.8816 - sparse_categorical_accuracy: 0.6440

1719/未知 720毫秒/步 - loss: 0.8815 - sparse_categorical_accuracy: 0.6441

1720/未知 720毫秒/步 - loss: 0.8814 - sparse_categorical_accuracy: 0.6441

1721/未知 720毫秒/步 - loss: 0.8813 - sparse_categorical_accuracy: 0.6441

1722/未知 721毫秒/步 - loss: 0.8812 - sparse_categorical_accuracy: 0.6442

1723/未知 721毫秒/步 - loss: 0.8811 - sparse_categorical_accuracy: 0.6442

1724/未知 722毫秒/步 - loss: 0.8810 - sparse_categorical_accuracy: 0.6442

1725/未知 722毫秒/步 - loss: 0.8809 - sparse_categorical_accuracy: 0.6443

1726/未知 722毫秒/步 - loss: 0.8808 - sparse_categorical_accuracy: 0.6443

1727/未知 723毫秒/步 - loss: 0.8807 - sparse_categorical_accuracy: 0.6443

1728/未知 723毫秒/步 - loss: 0.8806 - sparse_categorical_accuracy: 0.6444

1729/未知 723毫秒/步 - loss: 0.8805 - sparse_categorical_accuracy: 0.6444

1730/未知 724毫秒/步 - loss: 0.8804 - sparse_categorical_accuracy: 0.6444

1731/未知 724毫秒/步 - loss: 0.8804 - sparse_categorical_accuracy: 0.6445

1732/未知 725毫秒/步 - loss: 0.8803 - sparse_categorical_accuracy: 0.6445

1733/未知 725毫秒/步 - loss: 0.8802 - sparse_categorical_accuracy: 0.6445

1734/未知 726毫秒/步 - loss: 0.8801 - sparse_categorical_accuracy: 0.6446

1735/未知 726毫秒/步 - loss: 0.8800 - sparse_categorical_accuracy: 0.6446

1736/未知 727毫秒/步 - loss: 0.8799 - sparse_categorical_accuracy: 0.6446

1737/未知 727毫秒/步 - loss: 0.8798 - sparse_categorical_accuracy: 0.6447

1738/未知 727毫秒/步 - loss: 0.8797 - sparse_categorical_accuracy: 0.6447

1739/未知 728毫秒/步 - loss: 0.8796 - sparse_categorical_accuracy: 0.6447

1740/未知 728毫秒/步 - loss: 0.8795 - sparse_categorical_accuracy: 0.6448

1741/未知 729毫秒/步 - loss: 0.8794 - sparse_categorical_accuracy: 0.6448

1742/未知 729毫秒/步 - loss: 0.8793 - sparse_categorical_accuracy: 0.6448

1743/未知 730毫秒/步 - loss: 0.8792 - sparse_categorical_accuracy: 0.6449

1744/未知 730毫秒/步 - loss: 0.8791 - sparse_categorical_accuracy: 0.6449

1745/未知 730毫秒/步 - loss: 0.8790 - sparse_categorical_accuracy: 0.6449

1746/未知 731毫秒/步 - loss: 0.8789 - sparse_categorical_accuracy: 0.6450

1747/未知 731毫秒/步 - loss: 0.8788 - sparse_categorical_accuracy: 0.6450

1748/未知 731毫秒/步 - loss: 0.8787 - sparse_categorical_accuracy: 0.6450

1749/未知 732毫秒/步 - loss: 0.8786 - sparse_categorical_accuracy: 0.6451

1750/未知 732毫秒/步 - loss: 0.8785 - sparse_categorical_accuracy: 0.6451

1751/未知 733毫秒/步 - loss: 0.8784 - sparse_categorical_accuracy: 0.6451

1752/未知 733毫秒/步 - loss: 0.8783 - sparse_categorical_accuracy: 0.6452

1753/未知 733毫秒/步 - loss: 0.8782 - sparse_categorical_accuracy: 0.6452

1754/未知 734毫秒/步 - loss: 0.8781 - sparse_categorical_accuracy: 0.6452

1755/未知 734毫秒/步 - loss: 0.8780 - sparse_categorical_accuracy: 0.6453

1756/未知 735毫秒/步 - loss: 0.8779 - sparse_categorical_accuracy: 0.6453

1757/未知 735毫秒/步 - loss: 0.8778 - sparse_categorical_accuracy: 0.6453

1758/未知 736毫秒/步 - loss: 0.8777 - sparse_categorical_accuracy: 0.6453

1759/未知 736毫秒/步 - loss: 0.8776 - sparse_categorical_accuracy: 0.6454

1760/未知 737毫秒/步 - loss: 0.8775 - sparse_categorical_accuracy: 0.6454

1761/未知 737毫秒/步 - loss: 0.8774 - sparse_categorical_accuracy: 0.6454

1762/未知 738毫秒/步 - loss: 0.8773 - sparse_categorical_accuracy: 0.6455

1763/未知 738毫秒/步 - loss: 0.8772 - sparse_categorical_accuracy: 0.6455

1764/未知 738毫秒/步 - loss: 0.8771 - sparse_categorical_accuracy: 0.6455

1765/未知 739毫秒/步 - loss: 0.8770 - sparse_categorical_accuracy: 0.6456

1766/未知 739毫秒/步 - loss: 0.8769 - sparse_categorical_accuracy: 0.6456

1767/未知 739毫秒/步 - loss: 0.8768 - sparse_categorical_accuracy: 0.6456

1768/未知 740毫秒/步 - loss: 0.8767 - sparse_categorical_accuracy: 0.6457

1769/未知 740毫秒/步 - loss: 0.8766 - sparse_categorical_accuracy: 0.6457

1770/未知 741毫秒/步 - loss: 0.8765 - sparse_categorical_accuracy: 0.6457

1771/未知 741毫秒/步 - loss: 0.8764 - sparse_categorical_accuracy: 0.6458

1772/未知 741毫秒/步 - loss: 0.8763 - sparse_categorical_accuracy: 0.6458

1773/未知 742毫秒/步 - loss: 0.8763 - sparse_categorical_accuracy: 0.6458

1774/未知 742毫秒/步 - loss: 0.8762 - sparse_categorical_accuracy: 0.6459

1775/未知 743毫秒/步 - loss: 0.8761 - sparse_categorical_accuracy: 0.6459

1776/未知 743毫秒/步 - loss: 0.8760 - sparse_categorical_accuracy: 0.6459

1777/未知 743毫秒/步 - loss: 0.8759 - sparse_categorical_accuracy: 0.6460

1778/未知 744毫秒/步 - loss: 0.8758 - sparse_categorical_accuracy: 0.6460

1779/未知 744毫秒/步 - loss: 0.8757 - sparse_categorical_accuracy: 0.6460

1780/未知 745毫秒/步 - loss: 0.8756 - sparse_categorical_accuracy: 0.6461

1781/未知 745毫秒/步 - loss: 0.8755 - sparse_categorical_accuracy: 0.6461

1782/未知 746毫秒/步 - loss: 0.8754 - sparse_categorical_accuracy: 0.6461

1783/未知 746毫秒/步 - loss: 0.8753 - sparse_categorical_accuracy: 0.6461

1784/未知 747毫秒/步 - loss: 0.8752 - sparse_categorical_accuracy: 0.6462

1785/未知 747毫秒/步 - loss: 0.8751 - sparse_categorical_accuracy: 0.6462

1786/未知 747毫秒/步 - loss: 0.8750 - sparse_categorical_accuracy: 0.6462

1787/未知 748毫秒/步 - loss: 0.8749 - sparse_categorical_accuracy: 0.6463

1788/未知 748毫秒/步 - loss: 0.8748 - sparse_categorical_accuracy: 0.6463

1789/未知 749毫秒/步 - loss: 0.8747 - sparse_categorical_accuracy: 0.6463

1790/未知 749毫秒/步 - loss: 0.8746 - sparse_categorical_accuracy: 0.6464

1791/未知 750毫秒/步 - loss: 0.8745 - sparse_categorical_accuracy: 0.6464

1792/未知 750毫秒/步 - loss: 0.8744 - sparse_categorical_accuracy: 0.6464

1793/未知 751毫秒/步 - loss: 0.8743 - sparse_categorical_accuracy: 0.6465

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1795/未知 752毫秒/步 - loss: 0.8742 - sparse_categorical_accuracy: 0.6465

1796/未知 752毫秒/步 - loss: 0.8741 - sparse_categorical_accuracy: 0.6466

1797/未知 753毫秒/步 - loss: 0.8740 - sparse_categorical_accuracy: 0.6466

1798/未知 753毫秒/步 - loss: 0.8739 - sparse_categorical_accuracy: 0.6466

1799/未知 753毫秒/步 - loss: 0.8738 - sparse_categorical_accuracy: 0.6466

1800/未知 754毫秒/步 - loss: 0.8737 - sparse_categorical_accuracy: 0.6467

1801/未知 754毫秒/步 - loss: 0.8736 - sparse_categorical_accuracy: 0.6467

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1803/未知 755毫秒/步 - loss: 0.8734 - sparse_categorical_accuracy: 0.6468

1804/未知 756毫秒/步 - loss: 0.8733 - sparse_categorical_accuracy: 0.6468

1805/未知 756毫秒/步 - loss: 0.8732 - sparse_categorical_accuracy: 0.6468

1806/未知 757毫秒/步 - loss: 0.8731 - sparse_categorical_accuracy: 0.6469

1807/未知 757毫秒/步 - loss: 0.8730 - sparse_categorical_accuracy: 0.6469

1808/未知 757毫秒/步 - loss: 0.8729 - sparse_categorical_accuracy: 0.6469

1809/未知 758毫秒/步 - loss: 0.8729 - sparse_categorical_accuracy: 0.6470

1810/未知 758毫秒/步 - loss: 0.8728 - sparse_categorical_accuracy: 0.6470

1811/未知 758毫秒/步 - loss: 0.8727 - sparse_categorical_accuracy: 0.6470

1812/未知 759毫秒/步 - loss: 0.8726 - sparse_categorical_accuracy: 0.6471

1813/未知 759毫秒/步 - loss: 0.8725 - sparse_categorical_accuracy: 0.6471

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1816/未知 760毫秒/步 - loss: 0.8722 - sparse_categorical_accuracy: 0.6472

1817/未知 761毫秒/步 - loss: 0.8721 - sparse_categorical_accuracy: 0.6472

1818/未知 761毫秒/步 - loss: 0.8720 - sparse_categorical_accuracy: 0.6472

1819/未知 761毫秒/步 - loss: 0.8719 - sparse_categorical_accuracy: 0.6473

1820/未知 762毫秒/步 - loss: 0.8718 - sparse_categorical_accuracy: 0.6473

1821/未知 762毫秒/步 - loss: 0.8717 - sparse_categorical_accuracy: 0.6473

1822/未知 763毫秒/步 - loss: 0.8717 - sparse_categorical_accuracy: 0.6474

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1825/未知 764毫秒/步 - loss: 0.8714 - sparse_categorical_accuracy: 0.6475

1826/未知 765毫秒/步 - loss: 0.8713 - sparse_categorical_accuracy: 0.6475

1827/未知 765毫秒/步 - loss: 0.8712 - sparse_categorical_accuracy: 0.6475

1828/未知 766毫秒/步 - loss: 0.8711 - sparse_categorical_accuracy: 0.6475

1829/未知 766毫秒/步 - loss: 0.8710 - sparse_categorical_accuracy: 0.6476

1830/未知 767毫秒/步 - loss: 0.8709 - sparse_categorical_accuracy: 0.6476

1831/未知 767毫秒/步 - loss: 0.8708 - sparse_categorical_accuracy: 0.6476

1832/未知 767毫秒/步 - loss: 0.8707 - sparse_categorical_accuracy: 0.6477

1833/未知 768毫秒/步 - loss: 0.8706 - sparse_categorical_accuracy: 0.6477

1834/未知 768毫秒/步 - loss: 0.8706 - sparse_categorical_accuracy: 0.6477

1835/未知 769毫秒/步 - loss: 0.8705 - sparse_categorical_accuracy: 0.6478

1836/未知 769毫秒/步 - loss: 0.8704 - sparse_categorical_accuracy: 0.6478

1837/未知 770毫秒/步 - loss: 0.8703 - sparse_categorical_accuracy: 0.6478

1838/未知 770毫秒/步 - loss: 0.8702 - sparse_categorical_accuracy: 0.6478

1839/未知 771毫秒/步 - loss: 0.8701 - sparse_categorical_accuracy: 0.6479

1840/未知 771毫秒/步 - loss: 0.8700 - sparse_categorical_accuracy: 0.6479

1841/未知 771毫秒/步 - loss: 0.8699 - sparse_categorical_accuracy: 0.6479

1842/未知 772毫秒/步 - loss: 0.8698 - sparse_categorical_accuracy: 0.6480

1843/未知 772毫秒/步 - loss: 0.8697 - sparse_categorical_accuracy: 0.6480

1844/未知 772毫秒/步 - loss: 0.8696 - sparse_categorical_accuracy: 0.6480

1845/未知 773毫秒/步 - loss: 0.8696 - sparse_categorical_accuracy: 0.6481

1846/未知 773毫秒/步 - loss: 0.8695 - sparse_categorical_accuracy: 0.6481

1847/未知 774毫秒/步 - loss: 0.8694 - sparse_categorical_accuracy: 0.6481

1848/未知 774毫秒/步 - loss: 0.8693 - sparse_categorical_accuracy: 0.6481

1849/未知 774毫秒/步 - loss: 0.8692 - sparse_categorical_accuracy: 0.6482

1850/未知 775毫秒/步 - loss: 0.8691 - sparse_categorical_accuracy: 0.6482

1851/未知 775毫秒/步 - loss: 0.8690 - sparse_categorical_accuracy: 0.6482

1852/未知 776毫秒/步 - loss: 0.8689 - sparse_categorical_accuracy: 0.6483

1853/未知 776毫秒/步 - loss: 0.8688 - sparse_categorical_accuracy: 0.6483

1854/未知 777毫秒/步 - loss: 0.8688 - sparse_categorical_accuracy: 0.6483

1855/未知 777毫秒/步 - loss: 0.8687 - sparse_categorical_accuracy: 0.6484

1856/未知 778毫秒/步 - loss: 0.8686 - sparse_categorical_accuracy: 0.6484

1857/未知 778毫秒/步 - loss: 0.8685 - sparse_categorical_accuracy: 0.6484

1858/未知 778毫秒/步 - loss: 0.8684 - sparse_categorical_accuracy: 0.6484

1859/未知 779毫秒/步 - loss: 0.8683 - sparse_categorical_accuracy: 0.6485

1860/未知 779毫秒/步 - loss: 0.8682 - sparse_categorical_accuracy: 0.6485

1861/未知 779毫秒/步 - loss: 0.8681 - sparse_categorical_accuracy: 0.6485

1862/未知 780毫秒/步 - loss: 0.8680 - sparse_categorical_accuracy: 0.6486

1863/未知 780毫秒/步 - loss: 0.8679 - sparse_categorical_accuracy: 0.6486

1864/未知 781毫秒/步 - loss: 0.8679 - sparse_categorical_accuracy: 0.6486

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1865/1865 ━━━━━━━━━━━━━━━━━━━━ 781毫秒/步 - loss: 0.8677 - sparse_categorical_accuracy: 0.6487

Model training finished

/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/trainers/epoch_iterator.py:151: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.
  self._interrupted_warning()

Test accuracy: 74.5%

宽深模型达到约79%的测试准确率。

实验3：深度与交叉模型

在第三个实验中，我们创建了一个深度与交叉模型。该模型的深度部分与前一个实验中创建的深度部分相同。交叉部分的关键思想是以高效的方式应用显式的特征交叉，其中交叉特征的阶数随层深度的增加而增长。

def create_deep_and_cross_model():
    inputs = create_model_inputs()
    x0 = encode_inputs(inputs, use_embedding=True)

    cross = x0
    for _ in hidden_units:
        units = cross.shape[-1]
        x = layers.Dense(units)(cross)
        cross = x0 * x + cross
    cross = layers.BatchNormalization()(cross)

    deep = x0
    for units in hidden_units:
        deep = layers.Dense(units)(deep)
        deep = layers.BatchNormalization()(deep)
        deep = layers.ReLU()(deep)
        deep = layers.Dropout(dropout_rate)(deep)

    merged = layers.concatenate([cross, deep])
    outputs = layers.Dense(units=NUM_CLASSES, activation="softmax")(merged)
    model = keras.Model(inputs=inputs, outputs=outputs)
    return model


deep_and_cross_model = create_deep_and_cross_model()
keras.utils.plot_model(deep_and_cross_model, show_shapes=True, rankdir="LR")

png

让我们开始运行

run_experiment(deep_and_cross_model)

Start training the model...

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1000/未知 459毫秒/步 - loss: 0.9624 - sparse_categorical_accuracy: 0.6280

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1069/未知 492毫秒/步 - loss: 0.9485 - sparse_categorical_accuracy: 0.6324

1070/未知 493毫秒/步 - loss: 0.9483 - sparse_categorical_accuracy: 0.6324

1071/未知 493毫秒/步 - loss: 0.9481 - sparse_categorical_accuracy: 0.6325

1072/未知 494毫秒/步 - loss: 0.9479 - sparse_categorical_accuracy: 0.6326

1073/未知 494毫秒/步 - loss: 0.9477 - sparse_categorical_accuracy: 0.6326

1074/未知 495毫秒/步 - loss: 0.9475 - sparse_categorical_accuracy: 0.6327

1075/未知 495毫秒/步 - loss: 0.9473 - sparse_categorical_accuracy: 0.6327

1076/未知 496毫秒/步 - loss: 0.9471 - sparse_categorical_accuracy: 0.6328

1077/未知 496毫秒/步 - loss: 0.9470 - sparse_categorical_accuracy: 0.6329

1078/未知 496毫秒/步 - loss: 0.9468 - sparse_categorical_accuracy: 0.6329

1079/未知 497毫秒/步 - loss: 0.9466 - sparse_categorical_accuracy: 0.6330

1080/未知 497毫秒/步 - loss: 0.9464 - sparse_categorical_accuracy: 0.6330

1081/未知 498毫秒/步 - loss: 0.9462 - sparse_categorical_accuracy: 0.6331

1082/未知 498毫秒/步 - loss: 0.9460 - sparse_categorical_accuracy: 0.6332

1083/未知 499毫秒/步 - loss: 0.9458 - sparse_categorical_accuracy: 0.6332

1084/未知 499毫秒/步 - loss: 0.9456 - sparse_categorical_accuracy: 0.6333

1085/未知 500毫秒/步 - loss: 0.9454 - sparse_categorical_accuracy: 0.6333

1086/未知 500毫秒/步 - loss: 0.9453 - sparse_categorical_accuracy: 0.6334

1087/未知 501毫秒/步 - loss: 0.9451 - sparse_categorical_accuracy: 0.6335

1088/未知 501毫秒/步 - loss: 0.9449 - sparse_categorical_accuracy: 0.6335

1089/未知 502毫秒/步 - loss: 0.9447 - sparse_categorical_accuracy: 0.6336

1090/未知 502毫秒/步 - loss: 0.9445 - sparse_categorical_accuracy: 0.6336

1091/未知 503毫秒/步 - loss: 0.9443 - sparse_categorical_accuracy: 0.6337

1092/未知 503毫秒/步 - loss: 0.9441 - sparse_categorical_accuracy: 0.6337

1093/未知 503毫秒/步 - loss: 0.9439 - sparse_categorical_accuracy: 0.6338

1094/未知 504毫秒/步 - loss: 0.9438 - sparse_categorical_accuracy: 0.6339

1095/未知 504毫秒/步 - loss: 0.9436 - sparse_categorical_accuracy: 0.6339

1096/未知 505毫秒/步 - loss: 0.9434 - sparse_categorical_accuracy: 0.6340

1097/未知 505毫秒/步 - loss: 0.9432 - sparse_categorical_accuracy: 0.6340

1098/未知 506毫秒/步 - loss: 0.9430 - sparse_categorical_accuracy: 0.6341

1099/未知 506毫秒/步 - loss: 0.9428 - sparse_categorical_accuracy: 0.6342

1100/未知 507毫秒/步 - loss: 0.9427 - sparse_categorical_accuracy: 0.6342

1101/未知 507毫秒/步 - loss: 0.9425 - sparse_categorical_accuracy: 0.6343

1102/未知 508毫秒/步 - loss: 0.9423 - sparse_categorical_accuracy: 0.6343

1103/未知 508毫秒/步 - loss: 0.9421 - sparse_categorical_accuracy: 0.6344

1104/未知 508毫秒/步 - loss: 0.9419 - sparse_categorical_accuracy: 0.6344

1105/未知 509毫秒/步 - loss: 0.9417 - sparse_categorical_accuracy: 0.6345

1106/未知 509毫秒/步 - loss: 0.9416 - sparse_categorical_accuracy: 0.6346

1107/未知 510毫秒/步 - loss: 0.9414 - sparse_categorical_accuracy: 0.6346

1108/未知 510毫秒/步 - loss: 0.9412 - sparse_categorical_accuracy: 0.6347

1109/未知 510毫秒/步 - loss: 0.9410 - sparse_categorical_accuracy: 0.6347

1110/未知 511毫秒/步 - loss: 0.9408 - sparse_categorical_accuracy: 0.6348

1111/未知 511毫秒/步 - loss: 0.9406 - sparse_categorical_accuracy: 0.6348

1112/未知 511毫秒/步 - loss: 0.9405 - sparse_categorical_accuracy: 0.6349

1113/未知 512毫秒/步 - loss: 0.9403 - sparse_categorical_accuracy: 0.6349

1114/未知 512毫秒/步 - loss: 0.9401 - sparse_categorical_accuracy: 0.6350

1115/未知 512毫秒/步 - loss: 0.9399 - sparse_categorical_accuracy: 0.6351

1116/未知 513毫秒/步 - loss: 0.9397 - sparse_categorical_accuracy: 0.6351

1117/未知 513毫秒/步 - loss: 0.9396 - sparse_categorical_accuracy: 0.6352

1118/未知 513毫秒/步 - loss: 0.9394 - sparse_categorical_accuracy: 0.6352

1119/未知 514毫秒/步 - loss: 0.9392 - sparse_categorical_accuracy: 0.6353

1120/未知 514毫秒/步 - loss: 0.9390 - sparse_categorical_accuracy: 0.6353

1121/未知 515毫秒/步 - loss: 0.9388 - sparse_categorical_accuracy: 0.6354

1122/未知 515毫秒/步 - loss: 0.9387 - sparse_categorical_accuracy: 0.6355

1123/未知 515毫秒/步 - loss: 0.9385 - sparse_categorical_accuracy: 0.6355

1124/未知 516毫秒/步 - loss: 0.9383 - sparse_categorical_accuracy: 0.6356

1125/未知 516毫秒/步 - loss: 0.9381 - sparse_categorical_accuracy: 0.6356

1126/未知 517毫秒/步 - loss: 0.9379 - sparse_categorical_accuracy: 0.6357

1127/未知 517毫秒/步 - loss: 0.9378 - sparse_categorical_accuracy: 0.6357

1128/未知 518毫秒/步 - loss: 0.9376 - sparse_categorical_accuracy: 0.6358

1129/未知 518毫秒/步 - loss: 0.9374 - sparse_categorical_accuracy: 0.6358

1130/未知 519毫秒/步 - loss: 0.9372 - sparse_categorical_accuracy: 0.6359

1131/未知 519毫秒/步 - loss: 0.9371 - sparse_categorical_accuracy: 0.6360

1132/未知 519毫秒/步 - loss: 0.9369 - sparse_categorical_accuracy: 0.6360

1133/未知 520毫秒/步 - loss: 0.9367 - sparse_categorical_accuracy: 0.6361

1134/未知 520毫秒/步 - loss: 0.9365 - sparse_categorical_accuracy: 0.6361

1135/未知 521毫秒/步 - loss: 0.9364 - sparse_categorical_accuracy: 0.6362

1136/未知 521毫秒/步 - loss: 0.9362 - sparse_categorical_accuracy: 0.6362

1137/未知 522毫秒/步 - loss: 0.9360 - sparse_categorical_accuracy: 0.6363

1138/未知 522毫秒/步 - loss: 0.9358 - sparse_categorical_accuracy: 0.6363

1139/未知 523毫秒/步 - loss: 0.9356 - sparse_categorical_accuracy: 0.6364

1140/未知 523毫秒/步 - loss: 0.9355 - sparse_categorical_accuracy: 0.6364

1141/未知 524毫秒/步 - loss: 0.9353 - sparse_categorical_accuracy: 0.6365

1142/未知 524毫秒/步 - loss: 0.9351 - sparse_categorical_accuracy: 0.6366

1143/未知 525毫秒/步 - loss: 0.9350 - sparse_categorical_accuracy: 0.6366

1144/未知 525毫秒/步 - loss: 0.9348 - sparse_categorical_accuracy: 0.6367

1145/未知 525毫秒/步 - loss: 0.9346 - sparse_categorical_accuracy: 0.6367

1146/未知 526毫秒/步 - loss: 0.9344 - sparse_categorical_accuracy: 0.6368

1147/未知 526毫秒/步 - loss: 0.9343 - sparse_categorical_accuracy: 0.6368

1148/未知 527毫秒/步 - loss: 0.9341 - sparse_categorical_accuracy: 0.6369

1149/未知 527毫秒/步 - loss: 0.9339 - sparse_categorical_accuracy: 0.6369

1150/未知 528毫秒/步 - loss: 0.9337 - sparse_categorical_accuracy: 0.6370

1151/未知 528毫秒/步 - loss: 0.9336 - sparse_categorical_accuracy: 0.6370

1152/未知 528毫秒/步 - loss: 0.9334 - sparse_categorical_accuracy: 0.6371

1153/未知 529毫秒/步 - loss: 0.9332 - sparse_categorical_accuracy: 0.6372

1154/未知 529毫秒/步 - loss: 0.9330 - sparse_categorical_accuracy: 0.6372

1155/未知 530毫秒/步 - loss: 0.9329 - sparse_categorical_accuracy: 0.6373

1156/未知 530毫秒/步 - loss: 0.9327 - sparse_categorical_accuracy: 0.6373

1157/未知 530毫秒/步 - loss: 0.9325 - sparse_categorical_accuracy: 0.6374

1158/未知 531毫秒/步 - loss: 0.9324 - sparse_categorical_accuracy: 0.6374

1159/未知 531毫秒/步 - loss: 0.9322 - sparse_categorical_accuracy: 0.6375

1160/未知 532毫秒/步 - loss: 0.9320 - sparse_categorical_accuracy: 0.6375

1161/未知 532毫秒/步 - loss: 0.9318 - sparse_categorical_accuracy: 0.6376

1162/未知 532毫秒/步 - loss: 0.9317 - sparse_categorical_accuracy: 0.6376

1163/未知 533毫秒/步 - loss: 0.9315 - sparse_categorical_accuracy: 0.6377

1164/未知 533毫秒/步 - loss: 0.9313 - sparse_categorical_accuracy: 0.6377

1165/未知 534毫秒/步 - loss: 0.9312 - sparse_categorical_accuracy: 0.6378

1166/未知 534毫秒/步 - loss: 0.9310 - sparse_categorical_accuracy: 0.6378

1167/未知 535毫秒/步 - loss: 0.9308 - sparse_categorical_accuracy: 0.6379

1168/未知 535毫秒/步 - loss: 0.9307 - sparse_categorical_accuracy: 0.6380

1169/未知 536毫秒/步 - loss: 0.9305 - sparse_categorical_accuracy: 0.6380

1170/未知 536毫秒/步 - loss: 0.9303 - sparse_categorical_accuracy: 0.6381

1171/未知 537毫秒/步 - loss: 0.9302 - sparse_categorical_accuracy: 0.6381

1172/未知 537毫秒/步 - loss: 0.9300 - sparse_categorical_accuracy: 0.6382

1173/未知 538毫秒/步 - loss: 0.9298 - sparse_categorical_accuracy: 0.6382

1174/未知 538毫秒/步 - loss: 0.9297 - sparse_categorical_accuracy: 0.6383

1175/未知 538毫秒/步 - loss: 0.9295 - sparse_categorical_accuracy: 0.6383

1176/未知 539毫秒/步 - loss: 0.9293 - sparse_categorical_accuracy: 0.6384

1177/未知 539毫秒/步 - loss: 0.9292 - sparse_categorical_accuracy: 0.6384

1178/未知 540毫秒/步 - loss: 0.9290 - sparse_categorical_accuracy: 0.6385

1179/未知 540毫秒/步 - loss: 0.9288 - sparse_categorical_accuracy: 0.6385

1180/未知 541毫秒/步 - loss: 0.9287 - sparse_categorical_accuracy: 0.6386

1181/未知 541毫秒/步 - loss: 0.9285 - sparse_categorical_accuracy: 0.6386

1182/未知 542毫秒/步 - loss: 0.9283 - sparse_categorical_accuracy: 0.6387

1183/未知 542毫秒/步 - loss: 0.9282 - sparse_categorical_accuracy: 0.6387

1184/未知 543毫秒/步 - loss: 0.9280 - sparse_categorical_accuracy: 0.6388

1185/未知 543毫秒/步 - loss: 0.9278 - sparse_categorical_accuracy: 0.6388

1186/未知 543毫秒/步 - loss: 0.9277 - sparse_categorical_accuracy: 0.6389

1187/未知 544毫秒/步 - loss: 0.9275 - sparse_categorical_accuracy: 0.6389

1188/未知 544毫秒/步 - loss: 0.9273 - sparse_categorical_accuracy: 0.6390

1189/未知 545毫秒/步 - loss: 0.9272 - sparse_categorical_accuracy: 0.6390

1190/未知 545毫秒/步 - loss: 0.9270 - sparse_categorical_accuracy: 0.6391

1191/未知 546毫秒/步 - loss: 0.9268 - sparse_categorical_accuracy: 0.6391

1192/未知 546毫秒/步 - loss: 0.9267 - sparse_categorical_accuracy: 0.6392

1193/未知 547毫秒/步 - loss: 0.9265 - sparse_categorical_accuracy: 0.6392

1194/未知 547毫秒/步 - loss: 0.9263 - sparse_categorical_accuracy: 0.6393

1195/未知 548毫秒/步 - loss: 0.9262 - sparse_categorical_accuracy: 0.6394

1196/未知 548毫秒/步 - loss: 0.9260 - sparse_categorical_accuracy: 0.6394

1197/未知 548毫秒/步 - loss: 0.9259 - sparse_categorical_accuracy: 0.6395

1198/未知 549毫秒/步 - loss: 0.9257 - sparse_categorical_accuracy: 0.6395

1199/未知 549毫秒/步 - loss: 0.9255 - sparse_categorical_accuracy: 0.6396

1200/未知 550毫秒/步 - loss: 0.9254 - sparse_categorical_accuracy: 0.6396

1201/未知 550毫秒/步 - loss: 0.9252 - sparse_categorical_accuracy: 0.6397

1202/未知 551毫秒/步 - loss: 0.9250 - sparse_categorical_accuracy: 0.6397

1203/未知 551毫秒/步 - loss: 0.9249 - sparse_categorical_accuracy: 0.6398

1204/未知 552毫秒/步 - loss: 0.9247 - sparse_categorical_accuracy: 0.6398

1205/未知 552毫秒/步 - loss: 0.9246 - sparse_categorical_accuracy: 0.6399

1206/未知 553毫秒/步 - loss: 0.9244 - sparse_categorical_accuracy: 0.6399

1207/未知 553毫秒/步 - loss: 0.9242 - sparse_categorical_accuracy: 0.6400

1208/未知 554毫秒/步 - loss: 0.9241 - sparse_categorical_accuracy: 0.6400

1209/未知 554毫秒/步 - loss: 0.9239 - sparse_categorical_accuracy: 0.6401

1210/未知 555毫秒/步 - loss: 0.9238 - sparse_categorical_accuracy: 0.6401

1211/未知 555毫秒/步 - loss: 0.9236 - sparse_categorical_accuracy: 0.6402

1212/未知 556毫秒/步 - loss: 0.9234 - sparse_categorical_accuracy: 0.6402

1213/未知 556毫秒/步 - loss: 0.9233 - sparse_categorical_accuracy: 0.6403

1214/未知 557毫秒/步 - loss: 0.9231 - sparse_categorical_accuracy: 0.6403

1215/未知 557毫秒/步 - loss: 0.9230 - sparse_categorical_accuracy: 0.6404

1216/未知 558毫秒/步 - loss: 0.9228 - sparse_categorical_accuracy: 0.6404

1217/未知 558毫秒/步 - loss: 0.9226 - sparse_categorical_accuracy: 0.6405

1218/未知 559毫秒/步 - loss: 0.9225 - sparse_categorical_accuracy: 0.6405

1219/未知 559毫秒/步 - loss: 0.9223 - sparse_categorical_accuracy: 0.6406

1220/未知 560毫秒/步 - loss: 0.9222 - sparse_categorical_accuracy: 0.6406

1221/未知 560毫秒/步 - loss: 0.9220 - sparse_categorical_accuracy: 0.6407

1222/未知 560毫秒/步 - loss: 0.9218 - sparse_categorical_accuracy: 0.6407

1223/未知 561毫秒/步 - loss: 0.9217 - sparse_categorical_accuracy: 0.6408

1224/未知 561毫秒/步 - loss: 0.9215 - sparse_categorical_accuracy: 0.6408

1225/未知 562毫秒/步 - loss: 0.9214 - sparse_categorical_accuracy: 0.6409

1226/未知 562毫秒/步 - loss: 0.9212 - sparse_categorical_accuracy: 0.6409

1227/未知 563毫秒/步 - loss: 0.9211 - sparse_categorical_accuracy: 0.6410

1228/未知 563毫秒/步 - loss: 0.9209 - sparse_categorical_accuracy: 0.6410

1229/未知 564毫秒/步 - loss: 0.9207 - sparse_categorical_accuracy: 0.6410

1230/未知 564毫秒/步 - loss: 0.9206 - sparse_categorical_accuracy: 0.6411

1231/未知 565毫秒/步 - loss: 0.9204 - sparse_categorical_accuracy: 0.6411

1232/未知 565毫秒/步 - loss: 0.9203 - sparse_categorical_accuracy: 0.6412

1233/未知 566毫秒/步 - loss: 0.9201 - sparse_categorical_accuracy: 0.6412

1234/未知 566毫秒/步 - loss: 0.9200 - sparse_categorical_accuracy: 0.6413

1235/未知 567毫秒/步 - loss: 0.9198 - sparse_categorical_accuracy: 0.6413

1236/未知 567毫秒/步 - loss: 0.9197 - sparse_categorical_accuracy: 0.6414

1237/未知 568毫秒/步 - loss: 0.9195 - sparse_categorical_accuracy: 0.6414

1238/未知 568毫秒/步 - loss: 0.9193 - sparse_categorical_accuracy: 0.6415

1239/未知 569毫秒/步 - loss: 0.9192 - sparse_categorical_accuracy: 0.6415

1240/未知 569毫秒/步 - loss: 0.9190 - sparse_categorical_accuracy: 0.6416

1241/未知 569毫秒/步 - loss: 0.9189 - sparse_categorical_accuracy: 0.6416

1242/未知 570毫秒/步 - loss: 0.9187 - sparse_categorical_accuracy: 0.6417

1243/未知 570毫秒/步 - loss: 0.9186 - sparse_categorical_accuracy: 0.6417

1244/未知 571毫秒/步 - loss: 0.9184 - sparse_categorical_accuracy: 0.6418

1245/未知 571毫秒/步 - loss: 0.9183 - sparse_categorical_accuracy: 0.6418

1246/未知 572毫秒/步 - loss: 0.9181 - sparse_categorical_accuracy: 0.6419

1247/未知 572毫秒/步 - loss: 0.9180 - sparse_categorical_accuracy: 0.6419

1248/未知 573毫秒/步 - loss: 0.9178 - sparse_categorical_accuracy: 0.6420

1249/未知 573毫秒/步 - loss: 0.9177 - sparse_categorical_accuracy: 0.6420

1250/未知 574毫秒/步 - loss: 0.9175 - sparse_categorical_accuracy: 0.6421

1251/未知 574毫秒/步 - loss: 0.9173 - sparse_categorical_accuracy: 0.6421

1252/未知 574毫秒/步 - loss: 0.9172 - sparse_categorical_accuracy: 0.6422

1253/未知 575毫秒/步 - loss: 0.9170 - sparse_categorical_accuracy: 0.6422

1254/未知 575毫秒/步 - loss: 0.9169 - sparse_categorical_accuracy: 0.6423

1255/未知 576毫秒/步 - loss: 0.9167 - sparse_categorical_accuracy: 0.6423

1256/未知 576毫秒/步 - loss: 0.9166 - sparse_categorical_accuracy: 0.6424

1257/未知 577毫秒/步 - loss: 0.9164 - sparse_categorical_accuracy: 0.6424

1258/未知 577毫秒/步 - loss: 0.9163 - sparse_categorical_accuracy: 0.6424

1259/未知 578毫秒/步 - loss: 0.9161 - sparse_categorical_accuracy: 0.6425

1260/未知 578毫秒/步 - loss: 0.9160 - sparse_categorical_accuracy: 0.6425

1261/未知 579毫秒/步 - loss: 0.9158 - sparse_categorical_accuracy: 0.6426

1262/未知 579毫秒/步 - loss: 0.9157 - sparse_categorical_accuracy: 0.6426

1263/未知 580毫秒/步 - loss: 0.9155 - sparse_categorical_accuracy: 0.6427

1264/未知 580毫秒/步 - loss: 0.9154 - sparse_categorical_accuracy: 0.6427

1265/未知 581毫秒/步 - loss: 0.9152 - sparse_categorical_accuracy: 0.6428

1266/未知 581毫秒/步 - loss: 0.9151 - sparse_categorical_accuracy: 0.6428

1267/未知 582毫秒/步 - loss: 0.9149 - sparse_categorical_accuracy: 0.6429

1268/未知 582毫秒/步 - loss: 0.9148 - sparse_categorical_accuracy: 0.6429

1269/未知 583毫秒/步 - loss: 0.9146 - sparse_categorical_accuracy: 0.6430

1270/未知 583毫秒/步 - loss: 0.9145 - sparse_categorical_accuracy: 0.6430

1271/未知 584毫秒/步 - loss: 0.9143 - sparse_categorical_accuracy: 0.6431

1272/未知 584毫秒/步 - loss: 0.9142 - sparse_categorical_accuracy: 0.6431

1273/未知 584毫秒/步 - loss: 0.9140 - sparse_categorical_accuracy: 0.6432

1274/未知 585毫秒/步 - loss: 0.9139 - sparse_categorical_accuracy: 0.6432

1275/未知 585毫秒/步 - loss: 0.9137 - sparse_categorical_accuracy: 0.6432

1276/未知 586毫秒/步 - loss: 0.9136 - sparse_categorical_accuracy: 0.6433

1277/未知 586毫秒/步 - loss: 0.9134 - sparse_categorical_accuracy: 0.6433

1278/未知 587毫秒/步 - loss: 0.9133 - sparse_categorical_accuracy: 0.6434

1279/未知 587毫秒/步 - loss: 0.9131 - sparse_categorical_accuracy: 0.6434

1280/未知 588毫秒/步 - loss: 0.9130 - sparse_categorical_accuracy: 0.6435

1281/未知 588毫秒/步 - loss: 0.9128 - sparse_categorical_accuracy: 0.6435

1282/未知 589毫秒/步 - loss: 0.9127 - sparse_categorical_accuracy: 0.6436

1283/未知 589毫秒/步 - loss: 0.9126 - sparse_categorical_accuracy: 0.6436

1284/未知 589毫秒/步 - loss: 0.9124 - sparse_categorical_accuracy: 0.6437

1285/未知 590毫秒/步 - loss: 0.9123 - sparse_categorical_accuracy: 0.6437

1286/未知 590毫秒/步 - loss: 0.9121 - sparse_categorical_accuracy: 0.6438

1287/未知 591毫秒/步 - loss: 0.9120 - sparse_categorical_accuracy: 0.6438

1288/未知 591毫秒/步 - loss: 0.9118 - sparse_categorical_accuracy: 0.6438

1289/未知 591毫秒/步 - loss: 0.9117 - sparse_categorical_accuracy: 0.6439

1290/未知 592毫秒/步 - loss: 0.9115 - sparse_categorical_accuracy: 0.6439

1291/未知 592毫秒/步 - loss: 0.9114 - sparse_categorical_accuracy: 0.6440

1292/未知 593毫秒/步 - loss: 0.9112 - sparse_categorical_accuracy: 0.6440

1293/未知 593毫秒/步 - loss: 0.9111 - sparse_categorical_accuracy: 0.6441

1294/未知 594毫秒/步 - loss: 0.9109 - sparse_categorical_accuracy: 0.6441

1295/未知 594毫秒/步 - loss: 0.9108 - sparse_categorical_accuracy: 0.6442

1296/未知 595毫秒/步 - loss: 0.9107 - sparse_categorical_accuracy: 0.6442

1297/未知 595毫秒/步 - loss: 0.9105 - sparse_categorical_accuracy: 0.6443

1298/未知 596毫秒/步 - loss: 0.9104 - sparse_categorical_accuracy: 0.6443

1299/未知 596毫秒/步 - loss: 0.9102 - sparse_categorical_accuracy: 0.6443

1300/未知 596毫秒/步 - loss: 0.9101 - sparse_categorical_accuracy: 0.6444

1301/未知 597毫秒/步 - loss: 0.9099 - sparse_categorical_accuracy: 0.6444

1302/未知 597毫秒/步 - loss: 0.9098 - sparse_categorical_accuracy: 0.6445

1303/未知 598毫秒/步 - loss: 0.9096 - sparse_categorical_accuracy: 0.6445

1304/未知 598毫秒/步 - loss: 0.9095 - sparse_categorical_accuracy: 0.6446

1305/未知 599毫秒/步 - loss: 0.9094 - sparse_categorical_accuracy: 0.6446

1306/未知 599毫秒/步 - loss: 0.9092 - sparse_categorical_accuracy: 0.6447

1307/未知 600毫秒/步 - loss: 0.9091 - sparse_categorical_accuracy: 0.6447

1308/未知 600毫秒/步 - loss: 0.9089 - sparse_categorical_accuracy: 0.6448

1309/未知 601毫秒/步 - loss: 0.9088 - sparse_categorical_accuracy: 0.6448

1310/未知 601毫秒/步 - loss: 0.9086 - sparse_categorical_accuracy: 0.6448

1311/未知 602毫秒/步 - loss: 0.9085 - sparse_categorical_accuracy: 0.6449

1312/未知 602毫秒/步 - loss: 0.9084 - sparse_categorical_accuracy: 0.6449

1313/未知 602毫秒/步 - loss: 0.9082 - sparse_categorical_accuracy: 0.6450

1314/未知 603毫秒/步 - loss: 0.9081 - sparse_categorical_accuracy: 0.6450

1315/未知 603毫秒/步 - loss: 0.9079 - sparse_categorical_accuracy: 0.6451

1316/未知 604毫秒/步 - loss: 0.9078 - sparse_categorical_accuracy: 0.6451

1317/未知 604毫秒/步 - loss: 0.9076 - sparse_categorical_accuracy: 0.6452

1318/未知 604毫秒/步 - loss: 0.9075 - sparse_categorical_accuracy: 0.6452

1319/未知 605毫秒/步 - loss: 0.9074 - sparse_categorical_accuracy: 0.6452

1320/未知 605毫秒/步 - loss: 0.9072 - sparse_categorical_accuracy: 0.6453

1321/未知 605毫秒/步 - loss: 0.9071 - sparse_categorical_accuracy: 0.6453

1322/未知 606毫秒/步 - loss: 0.9069 - sparse_categorical_accuracy: 0.6454

1323/未知 606毫秒/步 - loss: 0.9068 - sparse_categorical_accuracy: 0.6454

1324/未知 607毫秒/步 - loss: 0.9067 - sparse_categorical_accuracy: 0.6455

1325/未知 607毫秒/步 - loss: 0.9065 - sparse_categorical_accuracy: 0.6455

1326/未知 608毫秒/步 - loss: 0.9064 - sparse_categorical_accuracy: 0.6455

1327/未知 608毫秒/步 - loss: 0.9062 - sparse_categorical_accuracy: 0.6456

1328/未知 609毫秒/步 - loss: 0.9061 - sparse_categorical_accuracy: 0.6456

1329/未知 609毫秒/步 - loss: 0.9060 - sparse_categorical_accuracy: 0.6457

1330/未知 609毫秒/步 - loss: 0.9058 - sparse_categorical_accuracy: 0.6457

1331/未知 610毫秒/步 - loss: 0.9057 - sparse_categorical_accuracy: 0.6458

1332/未知 610毫秒/步 - loss: 0.9055 - sparse_categorical_accuracy: 0.6458

1333/未知 611毫秒/步 - loss: 0.9054 - sparse_categorical_accuracy: 0.6459

1334/未知 611毫秒/步 - loss: 0.9053 - sparse_categorical_accuracy: 0.6459

1335/未知 612毫秒/步 - loss: 0.9051 - sparse_categorical_accuracy: 0.6459

1336/未知 612毫秒/步 - loss: 0.9050 - sparse_categorical_accuracy: 0.6460

1337/未知 613毫秒/步 - loss: 0.9048 - sparse_categorical_accuracy: 0.6460

1338/未知 613毫秒/步 - loss: 0.9047 - sparse_categorical_accuracy: 0.6461

1339/未知 614毫秒/步 - loss: 0.9046 - sparse_categorical_accuracy: 0.6461

1340/未知 614毫秒/步 - loss: 0.9044 - sparse_categorical_accuracy: 0.6462

1341/未知 614毫秒/步 - loss: 0.9043 - sparse_categorical_accuracy: 0.6462

1342/未知 615毫秒/步 - loss: 0.9042 - sparse_categorical_accuracy: 0.6462

1343/未知 615毫秒/步 - loss: 0.9040 - sparse_categorical_accuracy: 0.6463

1344/未知 615毫秒/步 - loss: 0.9039 - sparse_categorical_accuracy: 0.6463

1345/未知 616毫秒/步 - loss: 0.9037 - sparse_categorical_accuracy: 0.6464

1346/未知 616毫秒/步 - loss: 0.9036 - sparse_categorical_accuracy: 0.6464

1347/未知 617毫秒/步 - loss: 0.9035 - sparse_categorical_accuracy: 0.6465

1348/未知 617毫秒/步 - loss: 0.9033 - sparse_categorical_accuracy: 0.6465

1349/未知 618毫秒/步 - loss: 0.9032 - sparse_categorical_accuracy: 0.6465

1350/未知 618毫秒/步 - loss: 0.9031 - sparse_categorical_accuracy: 0.6466

1351/未知 618毫秒/步 - loss: 0.9029 - sparse_categorical_accuracy: 0.6466

1352/未知 619毫秒/步 - loss: 0.9028 - sparse_categorical_accuracy: 0.6467

1353/未知 619毫秒/步 - loss: 0.9026 - sparse_categorical_accuracy: 0.6467

1354/未知 620毫秒/步 - loss: 0.9025 - sparse_categorical_accuracy: 0.6468

1355/未知 620毫秒/步 - loss: 0.9024 - sparse_categorical_accuracy: 0.6468

1356/未知 621毫秒/步 - loss: 0.9022 - sparse_categorical_accuracy: 0.6468

1357/未知 621毫秒/步 - loss: 0.9021 - sparse_categorical_accuracy: 0.6469

1358/未知 622毫秒/步 - loss: 0.9020 - sparse_categorical_accuracy: 0.6469

1359/未知 622毫秒/步 - loss: 0.9018 - sparse_categorical_accuracy: 0.6470

1360/未知 623毫秒/步 - loss: 0.9017 - sparse_categorical_accuracy: 0.6470

1361/未知 623毫秒/步 - loss: 0.9016 - sparse_categorical_accuracy: 0.6471

1362/未知 624毫秒/步 - loss: 0.9014 - sparse_categorical_accuracy: 0.6471

1363/未知 624毫秒/步 - loss: 0.9013 - sparse_categorical_accuracy: 0.6471

1364/未知 624毫秒/步 - loss: 0.9012 - sparse_categorical_accuracy: 0.6472

1365/未知 625毫秒/步 - loss: 0.9010 - sparse_categorical_accuracy: 0.6472

1366/未知 625毫秒/步 - loss: 0.9009 - sparse_categorical_accuracy: 0.6473

1367/未知 625毫秒/步 - loss: 0.9008 - sparse_categorical_accuracy: 0.6473

1368/未知 626毫秒/步 - loss: 0.9006 - sparse_categorical_accuracy: 0.6474

1369/未知 626毫秒/步 - loss: 0.9005 - sparse_categorical_accuracy: 0.6474

1370/未知 627毫秒/步 - loss: 0.9004 - sparse_categorical_accuracy: 0.6474

1371/未知 627毫秒/步 - loss: 0.9002 - sparse_categorical_accuracy: 0.6475

1372/未知 627毫秒/步 - loss: 0.9001 - sparse_categorical_accuracy: 0.6475

1373/未知 628毫秒/步 - loss: 0.9000 - sparse_categorical_accuracy: 0.6476

1374/未知 628毫秒/步 - loss: 0.8998 - sparse_categorical_accuracy: 0.6476

1375/未知 629毫秒/步 - loss: 0.8997 - sparse_categorical_accuracy: 0.6476

1376/未知 629毫秒/步 - loss: 0.8996 - sparse_categorical_accuracy: 0.6477

1377/未知 630毫秒/步 - loss: 0.8994 - sparse_categorical_accuracy: 0.6477

1378/未知 630毫秒/步 - loss: 0.8993 - sparse_categorical_accuracy: 0.6478

1379/未知 631毫秒/步 - loss: 0.8992 - sparse_categorical_accuracy: 0.6478

1380/未知 631毫秒/步 - loss: 0.8990 - sparse_categorical_accuracy: 0.6479

1381/未知 632毫秒/步 - loss: 0.8989 - sparse_categorical_accuracy: 0.6479

1382/未知 632毫秒/步 - loss: 0.8988 - sparse_categorical_accuracy: 0.6479

1383/未知 633毫秒/步 - loss: 0.8986 - sparse_categorical_accuracy: 0.6480

1384/未知 633毫秒/步 - loss: 0.8985 - sparse_categorical_accuracy: 0.6480

1385/未知 633毫秒/步 - loss: 0.8984 - sparse_categorical_accuracy: 0.6481

1386/未知 634毫秒/步 - loss: 0.8982 - sparse_categorical_accuracy: 0.6481

1387/未知 634毫秒/步 - loss: 0.8981 - sparse_categorical_accuracy: 0.6481

1388/未知 634毫秒/步 - loss: 0.8980 - sparse_categorical_accuracy: 0.6482

1389/未知 635毫秒/步 - loss: 0.8978 - sparse_categorical_accuracy: 0.6482

1390/未知 635毫秒/步 - loss: 0.8977 - sparse_categorical_accuracy: 0.6483

1391/未知 636毫秒/步 - loss: 0.8976 - sparse_categorical_accuracy: 0.6483

1392/未知 636毫秒/步 - loss: 0.8974 - sparse_categorical_accuracy: 0.6483

1393/未知 636毫秒/步 - loss: 0.8973 - sparse_categorical_accuracy: 0.6484

1394/未知 637毫秒/步 - loss: 0.8972 - sparse_categorical_accuracy: 0.6484

1395/未知 637毫秒/步 - loss: 0.8971 - sparse_categorical_accuracy: 0.6485

1396/未知 638毫秒/步 - loss: 0.8969 - sparse_categorical_accuracy: 0.6485

1397/未知 638毫秒/步 - loss: 0.8968 - sparse_categorical_accuracy: 0.6485

1398/未知 639毫秒/步 - loss: 0.8967 - sparse_categorical_accuracy: 0.6486

1399/未知 639毫秒/步 - loss: 0.8965 - sparse_categorical_accuracy: 0.6486

1400/未知 640毫秒/步 - loss: 0.8964 - sparse_categorical_accuracy: 0.6487

1401/未知 640毫秒/步 - loss: 0.8963 - sparse_categorical_accuracy: 0.6487

1402/未知 640毫秒/步 - loss: 0.8962 - sparse_categorical_accuracy: 0.6488

1403/未知 641毫秒/步 - loss: 0.8960 - sparse_categorical_accuracy: 0.6488

1404/未知 641毫秒/步 - loss: 0.8959 - sparse_categorical_accuracy: 0.6488

1405/未知 642毫秒/步 - loss: 0.8958 - sparse_categorical_accuracy: 0.6489

1406/未知 642毫秒/步 - loss: 0.8956 - sparse_categorical_accuracy: 0.6489

1407/未知 643毫秒/步 - loss: 0.8955 - sparse_categorical_accuracy: 0.6490

1408/未知 643毫秒/步 - loss: 0.8954 - sparse_categorical_accuracy: 0.6490

1409/未知 644毫秒/步 - loss: 0.8953 - sparse_categorical_accuracy: 0.6490

1410/未知 644毫秒/步 - loss: 0.8951 - sparse_categorical_accuracy: 0.6491

1411/未知 645毫秒/步 - loss: 0.8950 - sparse_categorical_accuracy: 0.6491

1412/未知 645毫秒/步 - loss: 0.8949 - sparse_categorical_accuracy: 0.6492

1413/未知 646毫秒/步 - loss: 0.8947 - sparse_categorical_accuracy: 0.6492

1414/未知 646毫秒/步 - loss: 0.8946 - sparse_categorical_accuracy: 0.6492

1415/未知 647毫秒/步 - loss: 0.8945 - sparse_categorical_accuracy: 0.6493

1416/未知 647毫秒/步 - loss: 0.8944 - sparse_categorical_accuracy: 0.6493

1417/未知 647毫秒/步 - loss: 0.8942 - sparse_categorical_accuracy: 0.6494

1418/未知 648毫秒/步 - loss: 0.8941 - sparse_categorical_accuracy: 0.6494

1419/未知 648毫秒/步 - loss: 0.8940 - sparse_categorical_accuracy: 0.6494

1420/未知 649毫秒/步 - loss: 0.8939 - sparse_categorical_accuracy: 0.6495

1421/未知 649毫秒/步 - loss: 0.8937 - sparse_categorical_accuracy: 0.6495

1422/未知 650毫秒/步 - loss: 0.8936 - sparse_categorical_accuracy: 0.6495

1423/未知 650毫秒/步 - loss: 0.8935 - sparse_categorical_accuracy: 0.6496

1424/未知 651毫秒/步 - loss: 0.8933 - sparse_categorical_accuracy: 0.6496

1425/未知 651毫秒/步 - loss: 0.8932 - sparse_categorical_accuracy: 0.6497

1426/未知 651毫秒/步 - loss: 0.8931 - sparse_categorical_accuracy: 0.6497

1427/未知 652毫秒/步 - loss: 0.8930 - sparse_categorical_accuracy: 0.6497

1428/未知 652毫秒/步 - loss: 0.8928 - sparse_categorical_accuracy: 0.6498

1429/未知 653毫秒/步 - loss: 0.8927 - sparse_categorical_accuracy: 0.6498

1430/未知 653毫秒/步 - loss: 0.8926 - sparse_categorical_accuracy: 0.6499

1431/未知 653毫秒/步 - loss: 0.8925 - sparse_categorical_accuracy: 0.6499

1432/未知 654毫秒/步 - loss: 0.8923 - sparse_categorical_accuracy: 0.6499

1433/未知 654毫秒/步 - loss: 0.8922 - sparse_categorical_accuracy: 0.6500

1434/未知 655毫秒/步 - loss: 0.8921 - sparse_categorical_accuracy: 0.6500

1435/未知 655毫秒/步 - loss: 0.8920 - sparse_categorical_accuracy: 0.6501

1436/未知 655毫秒/步 - loss: 0.8918 - sparse_categorical_accuracy: 0.6501

1437/未知 656毫秒/步 - loss: 0.8917 - sparse_categorical_accuracy: 0.6501

1438/未知 656毫秒/步 - loss: 0.8916 - sparse_categorical_accuracy: 0.6502

1439/未知 657毫秒/步 - loss: 0.8915 - sparse_categorical_accuracy: 0.6502

1440/未知 657毫秒/步 - loss: 0.8913 - sparse_categorical_accuracy: 0.6503

1441/未知 657毫秒/步 - loss: 0.8912 - sparse_categorical_accuracy: 0.6503

1442/未知 658毫秒/步 - loss: 0.8911 - sparse_categorical_accuracy: 0.6503

1443/未知 658毫秒/步 - loss: 0.8910 - sparse_categorical_accuracy: 0.6504

1444/未知 659毫秒/步 - loss: 0.8909 - sparse_categorical_accuracy: 0.6504

1445/未知 659毫秒/步 - loss: 0.8907 - sparse_categorical_accuracy: 0.6504

1446/未知 660毫秒/步 - loss: 0.8906 - sparse_categorical_accuracy: 0.6505

1447/未知 660毫秒/步 - loss: 0.8905 - sparse_categorical_accuracy: 0.6505

1448/未知 661毫秒/步 - loss: 0.8904 - sparse_categorical_accuracy: 0.6506

1449/未知 661毫秒/步 - loss: 0.8902 - sparse_categorical_accuracy: 0.6506

1450/未知 662毫秒/步 - loss: 0.8901 - sparse_categorical_accuracy: 0.6506

1451/未知 662毫秒/步 - loss: 0.8900 - sparse_categorical_accuracy: 0.6507

1452/未知 662毫秒/步 - loss: 0.8899 - sparse_categorical_accuracy: 0.6507

1453/未知 663毫秒/步 - loss: 0.8897 - sparse_categorical_accuracy: 0.6508

1454/未知 663毫秒/步 - loss: 0.8896 - sparse_categorical_accuracy: 0.6508

1455/未知 664毫秒/步 - loss: 0.8895 - sparse_categorical_accuracy: 0.6508

1456/未知 664毫秒/步 - loss: 0.8894 - sparse_categorical_accuracy: 0.6509

1457/未知 665毫秒/步 - loss: 0.8893 - sparse_categorical_accuracy: 0.6509

1458/未知 665毫秒/步 - loss: 0.8891 - sparse_categorical_accuracy: 0.6509

1459/未知 665毫秒/步 - loss: 0.8890 - sparse_categorical_accuracy: 0.6510

1460/未知 666毫秒/步 - loss: 0.8889 - sparse_categorical_accuracy: 0.6510

1461/未知 666毫秒/步 - loss: 0.8888 - sparse_categorical_accuracy: 0.6511

1462/未知 667毫秒/步 - loss: 0.8887 - sparse_categorical_accuracy: 0.6511

1463/未知 667毫秒/步 - loss: 0.8885 - sparse_categorical_accuracy: 0.6511

1464/未知 667毫秒/步 - loss: 0.8884 - sparse_categorical_accuracy: 0.6512

1465/未知 668毫秒/步 - loss: 0.8883 - sparse_categorical_accuracy: 0.6512

1466/未知 668毫秒/步 - loss: 0.8882 - sparse_categorical_accuracy: 0.6512

1467/未知 669毫秒/步 - loss: 0.8880 - sparse_categorical_accuracy: 0.6513

1468/未知 669毫秒/步 - loss: 0.8879 - sparse_categorical_accuracy: 0.6513

1469/未知 669毫秒/步 - loss: 0.8878 - sparse_categorical_accuracy: 0.6514

1470/未知 670毫秒/步 - loss: 0.8877 - sparse_categorical_accuracy: 0.6514

1471/未知 670毫秒/步 - loss: 0.8876 - sparse_categorical_accuracy: 0.6514

1472/未知 671毫秒/步 - loss: 0.8874 - sparse_categorical_accuracy: 0.6515

1473/未知 671毫秒/步 - loss: 0.8873 - sparse_categorical_accuracy: 0.6515

1474/未知 672毫秒/步 - loss: 0.8872 - sparse_categorical_accuracy: 0.6515

1475/未知 672毫秒/步 - loss: 0.8871 - sparse_categorical_accuracy: 0.6516

1476/未知 673毫秒/步 - loss: 0.8870 - sparse_categorical_accuracy: 0.6516

1477/未知 673毫秒/步 - loss: 0.8868 - sparse_categorical_accuracy: 0.6517

1478/未知 673毫秒/步 - loss: 0.8867 - sparse_categorical_accuracy: 0.6517

1479/未知 674毫秒/步 - loss: 0.8866 - sparse_categorical_accuracy: 0.6517

1480/未知 674毫秒/步 - loss: 0.8865 - sparse_categorical_accuracy: 0.6518

1481/未知 674毫秒/步 - loss: 0.8864 - sparse_categorical_accuracy: 0.6518

1482/未知 675毫秒/步 - loss: 0.8863 - sparse_categorical_accuracy: 0.6518

1483/未知 675毫秒/步 - loss: 0.8861 - sparse_categorical_accuracy: 0.6519

1484/未知 676毫秒/步 - loss: 0.8860 - sparse_categorical_accuracy: 0.6519

1485/未知 676毫秒/步 - loss: 0.8859 - sparse_categorical_accuracy: 0.6520

1486/未知 677毫秒/步 - loss: 0.8858 - sparse_categorical_accuracy: 0.6520

1487/未知 677毫秒/步 - loss: 0.8857 - sparse_categorical_accuracy: 0.6520

1488/未知 677毫秒/步 - loss: 0.8855 - sparse_categorical_accuracy: 0.6521

1489/未知 678毫秒/步 - loss: 0.8854 - sparse_categorical_accuracy: 0.6521

1490/未知 678毫秒/步 - loss: 0.8853 - sparse_categorical_accuracy: 0.6521

1491/未知 679毫秒/步 - loss: 0.8852 - sparse_categorical_accuracy: 0.6522

1492/未知 679毫秒/步 - loss: 0.8851 - sparse_categorical_accuracy: 0.6522

1493/未知 679毫秒/步 - loss: 0.8850 - sparse_categorical_accuracy: 0.6523

1494/未知 680毫秒/步 - loss: 0.8848 - sparse_categorical_accuracy: 0.6523

1495/未知 680毫秒/步 - loss: 0.8847 - sparse_categorical_accuracy: 0.6523

1496/未知 681毫秒/步 - loss: 0.8846 - sparse_categorical_accuracy: 0.6524

1497/未知 681毫秒/步 - loss: 0.8845 - sparse_categorical_accuracy: 0.6524

1498/未知 682毫秒/步 - loss: 0.8844 - sparse_categorical_accuracy: 0.6524

1499/未知 682毫秒/步 - loss: 0.8843 - sparse_categorical_accuracy: 0.6525

1500/未知 683毫秒/步 - loss: 0.8841 - sparse_categorical_accuracy: 0.6525

1501/未知 683毫秒/步 - loss: 0.8840 - sparse_categorical_accuracy: 0.6525

1502/未知 684毫秒/步 - loss: 0.8839 - sparse_categorical_accuracy: 0.6526

1503/未知 684毫秒/步 - loss: 0.8838 - sparse_categorical_accuracy: 0.6526

1504/未知 685毫秒/步 - loss: 0.8837 - sparse_categorical_accuracy: 0.6527

1505/未知 685毫秒/步 - loss: 0.8836 - sparse_categorical_accuracy: 0.6527

1506/未知 685毫秒/步 - loss: 0.8834 - sparse_categorical_accuracy: 0.6527

1507/未知 686毫秒/步 - loss: 0.8833 - sparse_categorical_accuracy: 0.6528

1508/未知 686毫秒/步 - loss: 0.8832 - sparse_categorical_accuracy: 0.6528

1509/未知 687毫秒/步 - loss: 0.8831 - sparse_categorical_accuracy: 0.6528

1510/未知 687毫秒/步 - loss: 0.8830 - sparse_categorical_accuracy: 0.6529

1511/未知 687毫秒/步 - loss: 0.8829 - sparse_categorical_accuracy: 0.6529

1512/未知 688毫秒/步 - loss: 0.8827 - sparse_categorical_accuracy: 0.6529

1513/未知 688毫秒/步 - loss: 0.8826 - sparse_categorical_accuracy: 0.6530

1514/未知 688毫秒/步 - loss: 0.8825 - sparse_categorical_accuracy: 0.6530

1515/未知 689毫秒/步 - loss: 0.8824 - sparse_categorical_accuracy: 0.6531

1516/未知 689毫秒/步 - loss: 0.8823 - sparse_categorical_accuracy: 0.6531

1517/未知 690毫秒/步 - loss: 0.8822 - sparse_categorical_accuracy: 0.6531

1518/未知 690毫秒/步 - loss: 0.8821 - sparse_categorical_accuracy: 0.6532

1519/未知 690毫秒/步 - loss: 0.8819 - sparse_categorical_accuracy: 0.6532

1520/未知 691毫秒/步 - loss: 0.8818 - sparse_categorical_accuracy: 0.6532

1521/未知 691毫秒/步 - loss: 0.8817 - sparse_categorical_accuracy: 0.6533

1522/未知 692毫秒/步 - loss: 0.8816 - sparse_categorical_accuracy: 0.6533

1523/未知 692毫秒/步 - loss: 0.8815 - sparse_categorical_accuracy: 0.6533

1524/未知 693毫秒/步 - loss: 0.8814 - sparse_categorical_accuracy: 0.6534

1525/未知 693毫秒/步 - loss: 0.8813 - sparse_categorical_accuracy: 0.6534

1526/未知 694毫秒/步 - loss: 0.8811 - sparse_categorical_accuracy: 0.6534

1527/未知 694毫秒/步 - loss: 0.8810 - sparse_categorical_accuracy: 0.6535

1528/未知 695毫秒/步 - loss: 0.8809 - sparse_categorical_accuracy: 0.6535

1529/未知 695毫秒/步 - loss: 0.8808 - sparse_categorical_accuracy: 0.6536

1530/未知 695毫秒/步 - loss: 0.8807 - sparse_categorical_accuracy: 0.6536

1531/未知 696毫秒/步 - loss: 0.8806 - sparse_categorical_accuracy: 0.6536

1532/未知 696毫秒/步 - loss: 0.8805 - sparse_categorical_accuracy: 0.6537

1533/未知 697毫秒/步 - loss: 0.8803 - sparse_categorical_accuracy: 0.6537

1534/未知 697毫秒/步 - loss: 0.8802 - sparse_categorical_accuracy: 0.6537

1535/未知 697 秒 454 毫秒/步 - loss: 0.8801 - sparse_categorical_accuracy: 0.6538

1536/未知 698 秒 454 毫秒/步 - loss: 0.8800 - sparse_categorical_accuracy: 0.6538

1537/未知 698 秒 454 毫秒/步 - loss: 0.8799 - sparse_categorical_accuracy: 0.6538

1538/未知 699 秒 454 毫秒/步 - loss: 0.8798 - sparse_categorical_accuracy: 0.6539

1539/未知 699 秒 454 毫秒/步 - loss: 0.8797 - sparse_categorical_accuracy: 0.6539

1540/未知 699 秒 454 毫秒/步 - loss: 0.8796 - sparse_categorical_accuracy: 0.6539

1541/未知 700 秒 454 毫秒/步 - loss: 0.8794 - sparse_categorical_accuracy: 0.6540

1542/未知 700 秒 454 毫秒/步 - loss: 0.8793 - sparse_categorical_accuracy: 0.6540

1543/未知 701 秒 454 毫秒/步 - loss: 0.8792 - sparse_categorical_accuracy: 0.6540

1544/未知 701 秒 454 毫秒/步 - loss: 0.8791 - sparse_categorical_accuracy: 0.6541

1545/未知 702 秒 454 毫秒/步 - loss: 0.8790 - sparse_categorical_accuracy: 0.6541

1546/未知 702 秒 454 毫秒/步 - loss: 0.8789 - sparse_categorical_accuracy: 0.6542

1547/未知 702 秒 454 毫秒/步 - loss: 0.8788 - sparse_categorical_accuracy: 0.6542

1548/未知 703 秒 454 毫秒/步 - loss: 0.8787 - sparse_categorical_accuracy: 0.6542

1549/未知 703 秒 454 毫秒/步 - loss: 0.8786 - sparse_categorical_accuracy: 0.6543

1550/未知 704 秒 454 毫秒/步 - loss: 0.8784 - sparse_categorical_accuracy: 0.6543

1551/未知 704 秒 454 毫秒/步 - loss: 0.8783 - sparse_categorical_accuracy: 0.6543

1552/未知 705 秒 454 毫秒/步 - loss: 0.8782 - sparse_categorical_accuracy: 0.6544

1553/未知 705 秒 454 毫秒/步 - loss: 0.8781 - sparse_categorical_accuracy: 0.6544

1554/未知 705 秒 454 毫秒/步 - loss: 0.8780 - sparse_categorical_accuracy: 0.6544

1555/未知 706 秒 453 毫秒/步 - loss: 0.8779 - sparse_categorical_accuracy: 0.6545

1556/未知 706 秒 453 毫秒/步 - loss: 0.8778 - sparse_categorical_accuracy: 0.6545

1557/未知 706 秒 453 毫秒/步 - loss: 0.8777 - sparse_categorical_accuracy: 0.6545

1558/未知 707 秒 453 毫秒/步 - loss: 0.8776 - sparse_categorical_accuracy: 0.6546

1559/未知 707 秒 453 毫秒/步 - loss: 0.8774 - sparse_categorical_accuracy: 0.6546

1560/未知 708 秒 453 毫秒/步 - loss: 0.8773 - sparse_categorical_accuracy: 0.6546

1561/未知 708 秒 453 毫秒/步 - loss: 0.8772 - sparse_categorical_accuracy: 0.6547

1562/未知 708 秒 453 毫秒/步 - loss: 0.8771 - sparse_categorical_accuracy: 0.6547

1563/未知 709 秒 453 毫秒/步 - loss: 0.8770 - sparse_categorical_accuracy: 0.6547

1564/未知 709 秒 453 毫秒/步 - loss: 0.8769 - sparse_categorical_accuracy: 0.6548

1565/未知 710 秒 453 毫秒/步 - loss: 0.8768 - sparse_categorical_accuracy: 0.6548

1566/未知 710 秒 453 毫秒/步 - loss: 0.8767 - sparse_categorical_accuracy: 0.6548

1567/未知 711 秒 453 毫秒/步 - loss: 0.8766 - sparse_categorical_accuracy: 0.6549

1568/未知 711 秒 453 毫秒/步 - loss: 0.8765 - sparse_categorical_accuracy: 0.6549

1569/未知 711 秒 453 毫秒/步 - loss: 0.8763 - sparse_categorical_accuracy: 0.6549

1570/未知 712 秒 453 毫秒/步 - loss: 0.8762 - sparse_categorical_accuracy: 0.6550

1571/未知 712 秒 453 毫秒/步 - loss: 0.8761 - sparse_categorical_accuracy: 0.6550

1572/未知 713 秒 453 毫秒/步 - loss: 0.8760 - sparse_categorical_accuracy: 0.6550

1573/未知 713 秒 453 毫秒/步 - loss: 0.8759 - sparse_categorical_accuracy: 0.6551

1574/未知 714 秒 453 毫秒/步 - loss: 0.8758 - sparse_categorical_accuracy: 0.6551

1575/未知 714 秒 453 毫秒/步 - loss: 0.8757 - sparse_categorical_accuracy: 0.6552

1576/未知 715 秒 453 毫秒/步 - loss: 0.8756 - sparse_categorical_accuracy: 0.6552

1577/未知 715 秒 453 毫秒/步 - loss: 0.8755 - sparse_categorical_accuracy: 0.6552

1578/未知 715 秒 453 毫秒/步 - loss: 0.8754 - sparse_categorical_accuracy: 0.6553

1579/未知 716 秒 453 毫秒/步 - loss: 0.8753 - sparse_categorical_accuracy: 0.6553

1580/未知 716 秒 453 毫秒/步 - loss: 0.8752 - sparse_categorical_accuracy: 0.6553

1581/未知 716 秒 453 毫秒/步 - loss: 0.8750 - sparse_categorical_accuracy: 0.6554

1582/未知 717 秒 453 毫秒/步 - loss: 0.8749 - sparse_categorical_accuracy: 0.6554

1583/未知 717 秒 453 毫秒/步 - loss: 0.8748 - sparse_categorical_accuracy: 0.6554

1584/未知 718 秒 453 毫秒/步 - loss: 0.8747 - sparse_categorical_accuracy: 0.6555

1585/未知 718 秒 453 毫秒/步 - loss: 0.8746 - sparse_categorical_accuracy: 0.6555

1586/未知 718 秒 453 毫秒/步 - loss: 0.8745 - sparse_categorical_accuracy: 0.6555

1587/未知 719 秒 453 毫秒/步 - loss: 0.8744 - sparse_categorical_accuracy: 0.6556

1588/未知 719 秒 453 毫秒/步 - loss: 0.8743 - sparse_categorical_accuracy: 0.6556

1589/未知 720 秒 453 毫秒/步 - loss: 0.8742 - sparse_categorical_accuracy: 0.6556

1590/未知 720 秒 453 毫秒/步 - loss: 0.8741 - sparse_categorical_accuracy: 0.6557

1591/未知 721 秒 453 毫秒/步 - loss: 0.8740 - sparse_categorical_accuracy: 0.6557

1592/未知 721 秒 452 毫秒/步 - loss: 0.8739 - sparse_categorical_accuracy: 0.6557

1593/未知 721 秒 452 毫秒/步 - loss: 0.8738 - sparse_categorical_accuracy: 0.6558

1594/未知 722 秒 452 毫秒/步 - loss: 0.8737 - sparse_categorical_accuracy: 0.6558

1595/未知 722 秒 452 毫秒/步 - loss: 0.8735 - sparse_categorical_accuracy: 0.6558

1596/未知 723 秒 452 毫秒/步 - loss: 0.8734 - sparse_categorical_accuracy: 0.6559

1597/未知 723 秒 453 毫秒/步 - loss: 0.8733 - sparse_categorical_accuracy: 0.6559

1598/未知 724 秒 453 毫秒/步 - loss: 0.8732 - sparse_categorical_accuracy: 0.6559

1599/未知 724 秒 453 毫秒/步 - loss: 0.8731 - sparse_categorical_accuracy: 0.6560

1600/未知 725 秒 453 毫秒/步 - loss: 0.8730 - sparse_categorical_accuracy: 0.6560

1601/未知 725 秒 452 毫秒/步 - loss: 0.8729 - sparse_categorical_accuracy: 0.6560

1602/未知 725 秒 452 毫秒/步 - loss: 0.8728 - sparse_categorical_accuracy: 0.6561

1603/未知 726 秒 452 毫秒/步 - loss: 0.8727 - sparse_categorical_accuracy: 0.6561

1604/未知 726 秒 452 毫秒/步 - loss: 0.8726 - sparse_categorical_accuracy: 0.6561

1605/未知 726 秒 452 毫秒/步 - loss: 0.8725 - sparse_categorical_accuracy: 0.6562

1606/未知 727 秒 452 毫秒/步 - loss: 0.8724 - sparse_categorical_accuracy: 0.6562

1607/未知 727 秒 452 毫秒/步 - loss: 0.8723 - sparse_categorical_accuracy: 0.6562

1608/未知 728 秒 452 毫秒/步 - loss: 0.8722 - sparse_categorical_accuracy: 0.6563

1609/未知 728 秒 452 毫秒/步 - loss: 0.8721 - sparse_categorical_accuracy: 0.6563

1610/未知 728 秒 452 毫秒/步 - loss: 0.8720 - sparse_categorical_accuracy: 0.6563

1611/未知 729 秒 452 毫秒/步 - loss: 0.8719 - sparse_categorical_accuracy: 0.6564

1612/未知 729 秒 452 毫秒/步 - loss: 0.8717 - sparse_categorical_accuracy: 0.6564

1613/未知 730 秒 452 毫秒/步 - loss: 0.8716 - sparse_categorical_accuracy: 0.6564

1614/未知 730 秒 452 毫秒/步 - loss: 0.8715 - sparse_categorical_accuracy: 0.6565

1615/未知 730 秒 452 毫秒/步 - loss: 0.8714 - sparse_categorical_accuracy: 0.6565

1616/未知 731 秒 452 毫秒/步 - loss: 0.8713 - sparse_categorical_accuracy: 0.6565

1617/未知 731 秒 452 毫秒/步 - loss: 0.8712 - sparse_categorical_accuracy: 0.6566

1618/未知 732 秒 452 毫秒/步 - loss: 0.8711 - sparse_categorical_accuracy: 0.6566

1619/未知 732 秒 452 毫秒/步 - loss: 0.8710 - sparse_categorical_accuracy: 0.6566

1620/未知 733 秒 452 毫秒/步 - loss: 0.8709 - sparse_categorical_accuracy: 0.6567

1621/未知 733 秒 452 毫秒/步 - loss: 0.8708 - sparse_categorical_accuracy: 0.6567

1622/未知 734 秒 452 毫秒/步 - loss: 0.8707 - sparse_categorical_accuracy: 0.6567

1623/未知 734 秒 452 毫秒/步 - loss: 0.8706 - sparse_categorical_accuracy: 0.6567

1624/未知 734 秒 452 毫秒/步 - loss: 0.8705 - sparse_categorical_accuracy: 0.6568

1625/未知 735 秒 452 毫秒/步 - loss: 0.8704 - sparse_categorical_accuracy: 0.6568

1626/未知 735 秒 452 毫秒/步 - loss: 0.8703 - sparse_categorical_accuracy: 0.6568

1627/未知 736 秒 452 毫秒/步 - loss: 0.8702 - sparse_categorical_accuracy: 0.6569

1628/未知 736 秒 452 毫秒/步 - loss: 0.8701 - sparse_categorical_accuracy: 0.6569

1629/未知 736 秒 452 毫秒/步 - loss: 0.8700 - sparse_categorical_accuracy: 0.6569

1630/未知 737 秒 452 毫秒/步 - loss: 0.8699 - sparse_categorical_accuracy: 0.6570

1631/未知 737 秒 452 毫秒/步 - loss: 0.8698 - sparse_categorical_accuracy: 0.6570

1632/未知 738 秒 452 毫秒/步 - loss: 0.8697 - sparse_categorical_accuracy: 0.6570

1633/未知 738 秒 452 毫秒/步 - loss: 0.8696 - sparse_categorical_accuracy: 0.6571

1634/未知 738 秒 451 毫秒/步 - loss: 0.8695 - sparse_categorical_accuracy: 0.6571

1635/未知 739 秒 451 毫秒/步 - loss: 0.8694 - sparse_categorical_accuracy: 0.6571

1636/未知 739 秒 451 毫秒/步 - loss: 0.8693 - sparse_categorical_accuracy: 0.6572

1637/未知 739 秒 451 毫秒/步 - loss: 0.8692 - sparse_categorical_accuracy: 0.6572

1638/未知 740 秒 451 毫秒/步 - loss: 0.8690 - sparse_categorical_accuracy: 0.6572

1639/未知 740 秒 451 毫秒/步 - loss: 0.8689 - sparse_categorical_accuracy: 0.6573

1640/未知 741 秒 451 毫秒/步 - loss: 0.8688 - sparse_categorical_accuracy: 0.6573

1641/未知 741 秒 451 毫秒/步 - loss: 0.8687 - sparse_categorical_accuracy: 0.6573

1642/未知 742 秒 451 毫秒/步 - loss: 0.8686 - sparse_categorical_accuracy: 0.6574

1643/未知 742 秒 451 毫秒/步 - loss: 0.8685 - sparse_categorical_accuracy: 0.6574

1644/未知 743 秒 451 毫秒/步 - loss: 0.8684 - sparse_categorical_accuracy: 0.6574

1645/未知 743 秒 451 毫秒/步 - loss: 0.8683 - sparse_categorical_accuracy: 0.6575

1646/未知 743 秒 451 毫秒/步 - loss: 0.8682 - sparse_categorical_accuracy: 0.6575

1647/未知 744 秒 451 毫秒/步 - loss: 0.8681 - sparse_categorical_accuracy: 0.6575

1648/未知 744 秒 451 毫秒/步 - loss: 0.8680 - sparse_categorical_accuracy: 0.6576

1649/未知 744 秒 451 毫秒/步 - loss: 0.8679 - sparse_categorical_accuracy: 0.6576

1650/未知 745 秒 451 毫秒/步 - loss: 0.8678 - sparse_categorical_accuracy: 0.6576

1651/未知 745 秒 451 毫秒/步 - loss: 0.8677 - sparse_categorical_accuracy: 0.6577

1652/未知 746 秒 451 毫秒/步 - loss: 0.8676 - sparse_categorical_accuracy: 0.6577

1653/未知 746 秒 451 毫秒/步 - loss: 0.8675 - sparse_categorical_accuracy: 0.6577

1654/未知 746 秒 451 毫秒/步 - loss: 0.8674 - sparse_categorical_accuracy: 0.6577

1655/未知 747 秒 451 毫秒/步 - loss: 0.8673 - sparse_categorical_accuracy: 0.6578

1656/未知 747 秒 451 毫秒/步 - loss: 0.8672 - sparse_categorical_accuracy: 0.6578

1657/未知 748 秒 451 毫秒/步 - loss: 0.8671 - sparse_categorical_accuracy: 0.6578

1658/未知 748 秒 451 毫秒/步 - loss: 0.8670 - sparse_categorical_accuracy: 0.6579

1659/未知 749 秒 451 毫秒/步 - loss: 0.8669 - sparse_categorical_accuracy: 0.6579

1660/未知 749 秒 451 毫秒/步 - loss: 0.8668 - sparse_categorical_accuracy: 0.6579

1661/未知 749 秒 451 毫秒/步 - loss: 0.8667 - sparse_categorical_accuracy: 0.6580

1662/未知 750 秒 451 毫秒/步 - loss: 0.8666 - sparse_categorical_accuracy: 0.6580

1663/未知 750 秒 451 毫秒/步 - loss: 0.8665 - sparse_categorical_accuracy: 0.6580

1664/未知 750 秒 451 毫秒/步 - loss: 0.8664 - sparse_categorical_accuracy: 0.6581

1665/未知 751 秒 451 毫秒/步 - loss: 0.8663 - sparse_categorical_accuracy: 0.6581

1666/未知 751 秒 451 毫秒/步 - loss: 0.8662 - sparse_categorical_accuracy: 0.6581

1667/未知 752 秒 451 毫秒/步 - loss: 0.8661 - sparse_categorical_accuracy: 0.6582

1668/未知 752 秒 451 毫秒/步 - loss: 0.8660 - sparse_categorical_accuracy: 0.6582

1669/未知 753 秒 451 毫秒/步 - loss: 0.8659 - sparse_categorical_accuracy: 0.6582

1670/未知 753 秒 451 毫秒/步 - loss: 0.8658 - sparse_categorical_accuracy: 0.6583

1671/未知 754 秒 451 毫秒/步 - loss: 0.8657 - sparse_categorical_accuracy: 0.6583

1672/未知 754 秒 451 毫秒/步 - loss: 0.8656 - sparse_categorical_accuracy: 0.6583

1673/未知 755 秒 451 毫秒/步 - loss: 0.8655 - sparse_categorical_accuracy: 0.6583

1674/未知 755 秒 451 毫秒/步 - loss: 0.8654 - sparse_categorical_accuracy: 0.6584

1675/未知 755 秒 451 毫秒/步 - loss: 0.8653 - sparse_categorical_accuracy: 0.6584

1676/未知 756 秒 451 毫秒/步 - loss: 0.8652 - sparse_categorical_accuracy: 0.6584

1677/未知 756 秒 451 毫秒/步 - loss: 0.8651 - sparse_categorical_accuracy: 0.6585

1678/未知 757 秒 451 毫秒/步 - loss: 0.8650 - sparse_categorical_accuracy: 0.6585

1679/未知 757 秒 450 毫秒/步 - loss: 0.8649 - sparse_categorical_accuracy: 0.6585

1680/未知 757 秒 450 毫秒/步 - loss: 0.8648 - sparse_categorical_accuracy: 0.6586

1681/未知 758 秒 450 毫秒/步 - loss: 0.8647 - sparse_categorical_accuracy: 0.6586

1682/未知 758 秒 450 毫秒/步 - loss: 0.8646 - sparse_categorical_accuracy: 0.6586

1683/未知 758 秒 450 毫秒/步 - loss: 0.8645 - sparse_categorical_accuracy: 0.6587

1684/未知 759 秒 450 毫秒/步 - loss: 0.8644 - sparse_categorical_accuracy: 0.6587

1685/未知 759 秒 450 毫秒/步 - loss: 0.8643 - sparse_categorical_accuracy: 0.6587

1686/未知 760 秒 450 毫秒/步 - loss: 0.8642 - sparse_categorical_accuracy: 0.6587

1687/未知 760 秒 450 毫秒/步 - loss: 0.8641 - sparse_categorical_accuracy: 0.6588

1688/未知 760 秒 450 毫秒/步 - loss: 0.8640 - sparse_categorical_accuracy: 0.6588

1689/未知 761 秒 450 毫秒/步 - loss: 0.8639 - sparse_categorical_accuracy: 0.6588

1690/未知 761 秒 450 毫秒/步 - loss: 0.8638 - sparse_categorical_accuracy: 0.6589

1691/未知 762 秒 450 毫秒/步 - loss: 0.8637 - sparse_categorical_accuracy: 0.6589

1692/未知 762 秒 450 毫秒/步 - loss: 0.8636 - sparse_categorical_accuracy: 0.6589

1693/未知 762 秒 450 毫秒/步 - loss: 0.8635 - sparse_categorical_accuracy: 0.6590

1694/未知 763 秒 450 毫秒/步 - loss: 0.8634 - sparse_categorical_accuracy: 0.6590

1695/未知 763 秒 450 毫秒/步 - loss: 0.8633 - sparse_categorical_accuracy: 0.6590

1696/未知 764 秒 450 毫秒/步 - loss: 0.8632 - sparse_categorical_accuracy: 0.6591

1697/未知 764 秒 450 毫秒/步 - loss: 0.8632 - sparse_categorical_accuracy: 0.6591

1698/未知 765 秒 450 毫秒/步 - loss: 0.8631 - sparse_categorical_accuracy: 0.6591

1699/未知 765 秒 450 毫秒/步 - loss: 0.8630 - sparse_categorical_accuracy: 0.6591

1700/未知 766 秒 450 毫秒/步 - loss: 0.8629 - sparse_categorical_accuracy: 0.6592

1701/未知 766 秒 450 毫秒/步 - loss: 0.8628 - sparse_categorical_accuracy: 0.6592

1702/未知 766 秒 450 毫秒/步 - loss: 0.8627 - sparse_categorical_accuracy: 0.6592

1703/未知 767 秒 450 毫秒/步 - loss: 0.8626 - sparse_categorical_accuracy: 0.6593

1704/未知 767 秒 450 毫秒/步 - loss: 0.8625 - sparse_categorical_accuracy: 0.6593

1705/未知 768 秒 450 毫秒/步 - loss: 0.8624 - sparse_categorical_accuracy: 0.6593

1706/未知 768 秒 450 毫秒/步 - loss: 0.8623 - sparse_categorical_accuracy: 0.6594

1707/未知 768 秒 450 毫秒/步 - loss: 0.8622 - sparse_categorical_accuracy: 0.6594

1708/未知 769 秒 450 毫秒/步 - loss: 0.8621 - sparse_categorical_accuracy: 0.6594

1709/未知 769 秒 450 毫秒/步 - loss: 0.8620 - sparse_categorical_accuracy: 0.6595

1710/未知 770 秒 450 毫秒/步 - loss: 0.8619 - sparse_categorical_accuracy: 0.6595

1711/未知 770 秒 450 毫秒/步 - loss: 0.8618 - sparse_categorical_accuracy: 0.6595

1712/未知 770 秒 450 毫秒/步 - loss: 0.8617 - sparse_categorical_accuracy: 0.6595

1713/未知 771 秒 450 毫秒/步 - loss: 0.8616 - sparse_categorical_accuracy: 0.6596

1714/未知 771 秒 450 毫秒/步 - loss: 0.8615 - sparse_categorical_accuracy: 0.6596

1715/未知 772 秒 450 毫秒/步 - loss: 0.8614 - sparse_categorical_accuracy: 0.6596

1716/未知 772 秒 450 毫秒/步 - loss: 0.8613 - sparse_categorical_accuracy: 0.6597

1717/未知 773 秒 450 毫秒/步 - loss: 0.8612 - sparse_categorical_accuracy: 0.6597

1718/未知 773 秒 450 毫秒/步 - loss: 0.8611 - sparse_categorical_accuracy: 0.6597

1719/未知 774 秒 450 毫秒/步 - loss: 0.8610 - sparse_categorical_accuracy: 0.6598

1720/未知 774 秒 450 毫秒/步 - loss: 0.8609 - sparse_categorical_accuracy: 0.6598

1721/未知 774 秒 450 毫秒/步 - loss: 0.8608 - sparse_categorical_accuracy: 0.6598

1722/未知 775 秒 450 毫秒/步 - loss: 0.8607 - sparse_categorical_accuracy: 0.6598

1723/未知 775 秒 450 毫秒/步 - loss: 0.8606 - sparse_categorical_accuracy: 0.6599

1724/未知 776 秒 450 毫秒/步 - loss: 0.8606 - sparse_categorical_accuracy: 0.6599

1725/未知 776 秒 450 毫秒/步 - loss: 0.8605 - sparse_categorical_accuracy: 0.6599

1726/未知 777 秒 450 毫秒/步 - loss: 0.8604 - sparse_categorical_accuracy: 0.6600

1727/未知 777 秒 450 毫秒/步 - loss: 0.8603 - sparse_categorical_accuracy: 0.6600

1728/未知 777 秒 450 毫秒/步 - loss: 0.8602 - sparse_categorical_accuracy: 0.6600

1729/未知 778 秒 450 毫秒/步 - loss: 0.8601 - sparse_categorical_accuracy: 0.6601

1730/未知 778 秒 449 毫秒/步 - loss: 0.8600 - sparse_categorical_accuracy: 0.6601

1731/未知 779 秒 449 毫秒/步 - loss: 0.8599 - sparse_categorical_accuracy: 0.6601

1732/未知 779 秒 449 毫秒/步 - loss: 0.8598 - sparse_categorical_accuracy: 0.6601

1733/未知 779 秒 449 毫秒/步 - loss: 0.8597 - sparse_categorical_accuracy: 0.6602

1734/未知 780 秒 449 毫秒/步 - loss: 0.8596 - sparse_categorical_accuracy: 0.6602

1735/未知 780 秒 449 毫秒/步 - loss: 0.8595 - sparse_categorical_accuracy: 0.6602

1736/未知 780 秒 449 毫秒/步 - loss: 0.8594 - sparse_categorical_accuracy: 0.6603

1737/未知 781 秒 449 毫秒/步 - loss: 0.8593 - sparse_categorical_accuracy: 0.6603

1738/未知 781 秒 449 毫秒/步 - loss: 0.8592 - sparse_categorical_accuracy: 0.6603

1739/未知 782 秒 449 毫秒/步 - loss: 0.8591 - sparse_categorical_accuracy: 0.6603

1740/未知 782 秒 449 毫秒/步 - loss: 0.8590 - sparse_categorical_accuracy: 0.6604

1741/未知 782 秒 449 毫秒/步 - loss: 0.8589 - sparse_categorical_accuracy: 0.6604

1742/未知 783 秒 449 毫秒/步 - loss: 0.8589 - sparse_categorical_accuracy: 0.6604

1743/未知 783 秒 449 毫秒/步 - loss: 0.8588 - sparse_categorical_accuracy: 0.6605

1744/未知 784 秒 449 毫秒/步 - loss: 0.8587 - sparse_categorical_accuracy: 0.6605

1745/未知 784 秒 449 毫秒/步 - loss: 0.8586 - sparse_categorical_accuracy: 0.6605

1746/未知 785 秒 449 毫秒/步 - loss: 0.8585 - sparse_categorical_accuracy: 0.6606

1747/未知 785 秒 449 毫秒/步 - loss: 0.8584 - sparse_categorical_accuracy: 0.6606

1748/未知 786 秒 449 毫秒/步 - loss: 0.8583 - sparse_categorical_accuracy: 0.6606

1749/未知 786 秒 449 毫秒/步 - loss: 0.8582 - sparse_categorical_accuracy: 0.6606

1750/未知 787 秒 449 毫秒/步 - loss: 0.8581 - sparse_categorical_accuracy: 0.6607

1751/未知 787 秒 449 毫秒/步 - loss: 0.8580 - sparse_categorical_accuracy: 0.6607

1752/未知 787 秒 449 毫秒/步 - loss: 0.8579 - sparse_categorical_accuracy: 0.6607

1753/未知 788 秒 449 毫秒/步 - loss: 0.8578 - sparse_categorical_accuracy: 0.6608

1754/未知 788 秒 449 毫秒/步 - loss: 0.8577 - sparse_categorical_accuracy: 0.6608

1755/未知 789 秒 449 毫秒/步 - loss: 0.8576 - sparse_categorical_accuracy: 0.6608

1756/未知 789 秒 449 毫秒/步 - loss: 0.8576 - sparse_categorical_accuracy: 0.6608

1757/未知 789 秒 449 毫秒/步 - loss: 0.8575 - sparse_categorical_accuracy: 0.6609

1758/未知 790 秒 449 毫秒/步 - loss: 0.8574 - sparse_categorical_accuracy: 0.6609

1759/未知 790 秒 449 毫秒/步 - loss: 0.8573 - sparse_categorical_accuracy: 0.6609

1760/未知 791 秒 449 毫秒/步 - loss: 0.8572 - sparse_categorical_accuracy: 0.6610

1761/未知 791 秒 449 毫秒/步 - loss: 0.8571 - sparse_categorical_accuracy: 0.6610

1762/未知 792 秒 449 毫秒/步 - loss: 0.8570 - sparse_categorical_accuracy: 0.6610

1763/未知 792 秒 449 毫秒/步 - loss: 0.8569 - sparse_categorical_accuracy: 0.6610

1764/未知 792 秒 449 毫秒/步 - loss: 0.8568 - sparse_categorical_accuracy: 0.6611

1765/未知 793 秒 449 毫秒/步 - loss: 0.8567 - sparse_categorical_accuracy: 0.6611

1766/未知 793 秒 449 毫秒/步 - loss: 0.8566 - sparse_categorical_accuracy: 0.6611

1767/未知 794 秒 449 毫秒/步 - loss: 0.8565 - sparse_categorical_accuracy: 0.6612

1768/未知 794 秒 449 毫秒/步 - loss: 0.8564 - sparse_categorical_accuracy: 0.6612

1769/未知 795 秒 449 毫秒/步 - loss: 0.8564 - sparse_categorical_accuracy: 0.6612

1770/未知 795 秒 449 毫秒/步 - loss: 0.8563 - sparse_categorical_accuracy: 0.6612

1771/未知 796 秒 449 毫秒/步 - loss: 0.8562 - sparse_categorical_accuracy: 0.6613

1772/未知 796 秒 449 毫秒/步 - loss: 0.8561 - sparse_categorical_accuracy: 0.6613

1773/未知 796 秒 449 毫秒/步 - loss: 0.8560 - sparse_categorical_accuracy: 0.6613

1774/未知 797 秒 449 毫秒/步 - loss: 0.8559 - sparse_categorical_accuracy: 0.6614

1775/未知 797 秒 449 毫秒/步 - loss: 0.8558 - sparse_categorical_accuracy: 0.6614

1776/未知 797 秒 449 毫秒/步 - loss: 0.8557 - sparse_categorical_accuracy: 0.6614

1777/未知 798 秒 449 毫秒/步 - loss: 0.8556 - sparse_categorical_accuracy: 0.6614

1778/未知 798 秒 449 毫秒/步 - loss: 0.8555 - sparse_categorical_accuracy: 0.6615

1779/未知 799 秒 449 毫秒/步 - loss: 0.8554 - sparse_categorical_accuracy: 0.6615

1780/未知 799 秒 449 毫秒/步 - loss: 0.8554 - sparse_categorical_accuracy: 0.6615

1781/未知 799 秒 449 毫秒/步 - loss: 0.8553 - sparse_categorical_accuracy: 0.6616

1782/未知 800 秒 449 毫秒/步 - loss: 0.8552 - sparse_categorical_accuracy: 0.6616

1783/未知 800 秒 448 毫秒/步 - loss: 0.8551 - sparse_categorical_accuracy: 0.6616

1784/未知 801 秒 448 毫秒/步 - loss: 0.8550 - sparse_categorical_accuracy: 0.6616

1785/未知 801 秒 448 毫秒/步 - loss: 0.8549 - sparse_categorical_accuracy: 0.6617

1786/未知 801 秒 448 毫秒/步 - loss: 0.8548 - sparse_categorical_accuracy: 0.6617

1787/未知 802 秒 448 毫秒/步 - loss: 0.8547 - sparse_categorical_accuracy: 0.6617

1788/未知 802 秒 448 毫秒/步 - loss: 0.8546 - sparse_categorical_accuracy: 0.6618

1789/未知 803 秒 448 毫秒/步 - loss: 0.8545 - sparse_categorical_accuracy: 0.6618

1790/未知 803 秒 448 毫秒/步 - loss: 0.8545 - sparse_categorical_accuracy: 0.6618

1791/未知 803 秒 448 毫秒/步 - loss: 0.8544 - sparse_categorical_accuracy: 0.6618

1792/未知 804 秒 448 毫秒/步 - loss: 0.8543 - sparse_categorical_accuracy: 0.6619

1793/未知 804 秒 448 毫秒/步 - loss: 0.8542 - sparse_categorical_accuracy: 0.6619

1794/未知 805 秒 448 毫秒/步 - loss: 0.8541 - sparse_categorical_accuracy: 0.6619

1795/未知 805 秒 448 毫秒/步 - loss: 0.8540 - sparse_categorical_accuracy: 0.6620

1796/未知 805 秒 448 毫秒/步 - loss: 0.8539 - sparse_categorical_accuracy: 0.6620

1797/未知 806 秒 448 毫秒/步 - loss: 0.8538 - sparse_categorical_accuracy: 0.6620

1798/未知 806 秒 448 毫秒/步 - loss: 0.8537 - sparse_categorical_accuracy: 0.6620

1799/未知 807 秒 448 毫秒/步 - loss: 0.8536 - sparse_categorical_accuracy: 0.6621

1800/未知 807 秒 448 毫秒/步 - loss: 0.8536 - sparse_categorical_accuracy: 0.6621

1801/未知 808 秒 448 毫秒/步 - loss: 0.8535 - sparse_categorical_accuracy: 0.6621

1802/未知 808 秒 448 毫秒/步 - loss: 0.8534 - sparse_categorical_accuracy: 0.6622

1803/未知 808 秒 448 毫秒/步 - loss: 0.8533 - sparse_categorical_accuracy: 0.6622

1804/未知 809 秒 448 毫秒/步 - loss: 0.8532 - sparse_categorical_accuracy: 0.6622

1805/未知 809 秒 448 毫秒/步 - loss: 0.8531 - sparse_categorical_accuracy: 0.6622

1806/未知 810 秒 448 毫秒/步 - loss: 0.8530 - sparse_categorical_accuracy: 0.6623

1807/未知 810 秒 448 毫秒/步 - loss: 0.8529 - sparse_categorical_accuracy: 0.6623

1808/未知 811 秒 448 毫秒/步 - loss: 0.8528 - sparse_categorical_accuracy: 0.6623

1809/未知 811 秒 448 毫秒/步 - loss: 0.8528 - sparse_categorical_accuracy: 0.6623

1810/未知 811 秒 448 毫秒/步 - loss: 0.8527 - sparse_categorical_accuracy: 0.6624

1811/未知 812 秒 448 毫秒/步 - loss: 0.8526 - sparse_categorical_accuracy: 0.6624

1812/未知 812 秒 448 毫秒/步 - loss: 0.8525 - sparse_categorical_accuracy: 0.6624

1813/未知 812 秒 448 毫秒/步 - loss: 0.8524 - sparse_categorical_accuracy: 0.6625

1814/未知 813 秒 448 毫秒/步 - loss: 0.8523 - sparse_categorical_accuracy: 0.6625

1815/未知 813 秒 448 毫秒/步 - loss: 0.8522 - sparse_categorical_accuracy: 0.6625

1816/未知 814 秒 448 毫秒/步 - loss: 0.8521 - sparse_categorical_accuracy: 0.6625

1817/未知 814 秒 448 毫秒/步 - loss: 0.8520 - sparse_categorical_accuracy: 0.6626

1818/未知 814 秒 448 毫秒/步 - loss: 0.8520 - sparse_categorical_accuracy: 0.6626

1819/未知 815 秒 448 毫秒/步 - loss: 0.8519 - sparse_categorical_accuracy: 0.6626

1820/未知 815 秒 448 毫秒/步 - loss: 0.8518 - sparse_categorical_accuracy: 0.6627

1821/未知 816 秒 448 毫秒/步 - loss: 0.8517 - sparse_categorical_accuracy: 0.6627

1822/未知 816 秒 448 毫秒/步 - loss: 0.8516 - sparse_categorical_accuracy: 0.6627

1823/未知 817 秒 448 毫秒/步 - loss: 0.8515 - sparse_categorical_accuracy: 0.6627

1824/未知 817 秒 448 毫秒/步 - loss: 0.8514 - sparse_categorical_accuracy: 0.6628

1825/未知 818 秒 448 毫秒/步 - loss: 0.8513 - sparse_categorical_accuracy: 0.6628

1826/未知 818 秒 448 毫秒/步 - loss: 0.8513 - sparse_categorical_accuracy: 0.6628

1827/未知 819 秒 448 毫秒/步 - loss: 0.8512 - sparse_categorical_accuracy: 0.6628

1828/未知 819 秒 448 毫秒/步 - loss: 0.8511 - sparse_categorical_accuracy: 0.6629

1829/未知 819 秒 448 毫秒/步 - loss: 0.8510 - sparse_categorical_accuracy: 0.6629

1830/未知 820 秒 448 毫秒/步 - loss: 0.8509 - sparse_categorical_accuracy: 0.6629

1831/未知 820 秒 448 毫秒/步 - loss: 0.8508 - sparse_categorical_accuracy: 0.6630

1832/未知 821 秒 448 毫秒/步 - loss: 0.8507 - sparse_categorical_accuracy: 0.6630

1833/未知 821 秒 448 毫秒/步 - loss: 0.8507 - sparse_categorical_accuracy: 0.6630

1834/未知 821 秒 448 毫秒/步 - loss: 0.8506 - sparse_categorical_accuracy: 0.6630

1835/未知 822 秒 447 毫秒/步 - loss: 0.8505 - sparse_categorical_accuracy: 0.6631

1836/未知 822 秒 447 毫秒/步 - loss: 0.8504 - sparse_categorical_accuracy: 0.6631

1837/未知 822 秒 447 毫秒/步 - loss: 0.8503 - sparse_categorical_accuracy: 0.6631

1838/未知 823 秒 447 毫秒/步 - loss: 0.8502 - sparse_categorical_accuracy: 0.6631

1839/未知 823 秒 447 毫秒/步 - loss: 0.8501 - sparse_categorical_accuracy: 0.6632

1840/未知 823 秒 447 毫秒/步 - loss: 0.8500 - sparse_categorical_accuracy: 0.6632

1841/未知 824 秒 447 毫秒/步 - loss: 0.8500 - sparse_categorical_accuracy: 0.6632

1842/未知 824 秒 447 毫秒/步 - loss: 0.8499 - sparse_categorical_accuracy: 0.6633

1843/未知 825 秒 447 毫秒/步 - loss: 0.8498 - sparse_categorical_accuracy: 0.6633

1844/未知 825 秒 447 毫秒/步 - loss: 0.8497 - sparse_categorical_accuracy: 0.6633

1845/未知 825 秒 447 毫秒/步 - loss: 0.8496 - sparse_categorical_accuracy: 0.6633

1846/未知 826 秒 447 毫秒/步 - loss: 0.8495 - sparse_categorical_accuracy: 0.6634

1847/未知 826 秒 447 毫秒/步 - loss: 0.8494 - sparse_categorical_accuracy: 0.6634

1848/未知 827 秒 447 毫秒/步 - loss: 0.8494 - sparse_categorical_accuracy: 0.6634

1849/未知 827 秒 447 毫秒/步 - loss: 0.8493 - sparse_categorical_accuracy: 0.6634

1850/未知 828 秒 447 毫秒/步 - loss: 0.8492 - sparse_categorical_accuracy: 0.6635

1851/未知 828 秒 447 毫秒/步 - loss: 0.8491 - sparse_categorical_accuracy: 0.6635

1852/未知 828 秒 447 毫秒/步 - loss: 0.8490 - sparse_categorical_accuracy: 0.6635

1853/未知 829 秒 447 毫秒/步 - loss: 0.8489 - sparse_categorical_accuracy: 0.6636

1854/未知 829 秒 447 毫秒/步 - loss: 0.8488 - sparse_categorical_accuracy: 0.6636

1855/未知 830 秒 447 毫秒/步 - loss: 0.8488 - sparse_categorical_accuracy: 0.6636

1856/未知 830 秒 447 毫秒/步 - loss: 0.8487 - sparse_categorical_accuracy: 0.6636

1857/未知 830 秒 447 毫秒/步 - loss: 0.8486 - sparse_categorical_accuracy: 0.6637

1858/未知 831 秒 447 毫秒/步 - loss: 0.8485 - sparse_categorical_accuracy: 0.6637

1859/未知 831 秒 447 毫秒/步 - loss: 0.8484 - sparse_categorical_accuracy: 0.6637

1860/未知 832 秒 447 毫秒/步 - loss: 0.8483 - sparse_categorical_accuracy: 0.6637

1861/未知 832 秒 447 毫秒/步 - loss: 0.8482 - sparse_categorical_accuracy: 0.6638

1862/未知 832 秒 447 毫秒/步 - loss: 0.8482 - sparse_categorical_accuracy: 0.6638

1863/未知 833 秒 447 毫秒/步 - loss: 0.8481 - sparse_categorical_accuracy: 0.6638

1864/未知 833 秒 447 毫秒/步 - loss: 0.8480 - sparse_categorical_accuracy: 0.6638

1865/未知 834 秒 447 毫秒/步 - loss: 0.8479 - sparse_categorical_accuracy: 0.6639

1865/1865 ━━━━━━━━━━━━━━━━━━━━ 834 秒 447 毫秒/步 - loss: 0.8478 - sparse_categorical_accuracy: 0.6639

Model training finished

/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/trainers/epoch_iterator.py:151: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.
  self._interrupted_warning()

Test accuracy: 75.0%

深度交叉模型实现了约 81% 的测试准确率。

结论

您可以使用 Keras 预处理层来轻松处理具有不同编码机制（包括独热编码和特征嵌入）的类别特征。此外，不同的模型架构（如宽模型、深度模型和交叉模型）在不同数据集属性方面具有不同的优势。您可以探索独立使用它们，或将它们组合起来，以获得最佳的数据集结果。

使用 Wide、Deep 和 Cross 网络进行结构化数据学习

简介

数据集

设置

准备数据

定义数据集元数据

实验设置

创建模型输入

编码特征

实验 1：基线模型

实验 2：Wide & Deep 模型

实验3：深度与交叉模型

结论