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。
简介
本示例演示如何使用以下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，本示例应在 TensorFlow 2.5 或更高版本上运行。
数据集
本示例使用来自 UCI 机器学习仓库的 Covertype 数据集。任务是从制图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 个类别特征。每个实例被分为 7 个类别之一。
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，让我们从 UCI 机器学习仓库将数据集加载到 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 | ... | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2596 | 51 | 3 | 258 | 0 | 510 | 221 | 232 | 148 | 6279 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
| 1 | 2590 | 56 | 2 | 212 | -6 | 390 | 220 | 235 | 151 | 6225 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
| 2 | 2804 | 139 | 9 | 268 | 65 | 3180 | 234 | 238 | 135 | 6121 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
| 3 | 2785 | 155 | 18 | 242 | 118 | 3090 | 238 | 238 | 122 | 6211 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
| 4 | 2595 | 45 | 2 | 153 | -1 | 391 | 220 | 234 | 150 | 6172 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
5 行 × 55 列
数据集中的两个类别特征是二进制编码的。我们将把此数据集表示形式转换为典型的表示形式，其中每个类别特征都表示为单个整数值。
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 |
| 坡向 | 51 | 56 | 139 | 155 | 45 |
| 坡度 | 3 | 2 | 9 | 18 | 2 |
| 到水文设施的水平距离 | 258 | 212 | 268 | 242 | 153 |
| 到水文设施的垂直距离 | 0 | -6 | 65 | 118 | -1 |
| 到道路的水平距离 | 510 | 390 | 3180 | 3090 | 391 |
| 9am 山体阴影 | 221 | 220 | 234 | 238 | 220 |
| 中午山体阴影 | 232 | 235 | 238 | 238 | 234 |
| 3pm 山体阴影 | 148 | 151 | 135 | 122 | 150 |
| 到火源点的水平距离 | 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）。
让我们将数据拆分为训练集 (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，让我们定义一个输入函数，该函数读取和解析文件，然后将特征和标签转换为 tf.data.Dataset 以进行训练或评估。
# 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给定模型运行训练和评估实验的步骤。
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
我们创建输入特征的两种表示形式：稀疏和密集：1. 在稀疏表示形式中，类别特征使用 CategoryEncoding 层进行 one-hot 编码。这种表示形式对于模型记忆特定的特征值以进行某些预测可能很有用。 2. 在密集表示形式中，类别特征使用 Embedding 层进行低维嵌入编码。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，其中类别特征是 one-hot 编码的。
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")
让我们运行它
run_experiment(baseline_model)
Start training the model...
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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 - 损失：1.0614 - sparse_categorical_accuracy: 0.5901
1001/未知 268 秒 262 毫秒/步 - 损失：1.0612 - sparse_categorical_accuracy: 0.5902
1002/未知 268 秒 262 毫秒/步 - 损失：1.0610 - sparse_categorical_accuracy: 0.5903
1003/未知 269 秒 262 毫秒/步 - 损失：1.0608 - sparse_categorical_accuracy: 0.5903
1004/未知 269 秒 262 毫秒/步 - 损失：1.0605 - sparse_categorical_accuracy: 0.5904
1005/未知 269 秒 262 毫秒/步 - 损失：1.0603 - sparse_categorical_accuracy: 0.5905
1006/未知 270 秒 263 毫秒/步 - 损失：1.0601 - sparse_categorical_accuracy: 0.5905
1007/未知 270 秒 263 毫秒/步 - 损失：1.0599 - sparse_categorical_accuracy: 0.5906
1008/未知 270 秒 263 毫秒/步 - 损失：1.0597 - sparse_categorical_accuracy: 0.5907
1009/未知 271 秒 263 毫秒/步 - 损失：1.0595 - sparse_categorical_accuracy: 0.5907
1010/未知 271 秒 263 毫秒/步 - 损失：1.0592 - sparse_categorical_accuracy: 0.5908
1011/未知 271 秒 263 毫秒/步 - 损失：1.0590 - sparse_categorical_accuracy: 0.5909
1012/未知 271 秒 263 毫秒/步 - 损失：1.0588 - sparse_categorical_accuracy: 0.5909
1013/未知 272 秒 263 毫秒/步 - 损失：1.0586 - sparse_categorical_accuracy: 0.5910
1014/未知 272 秒 263 毫秒/步 - 损失：1.0584 - sparse_categorical_accuracy: 0.5911
1015/未知 272 秒 263 毫秒/步 - 损失：1.0582 - sparse_categorical_accuracy: 0.5912
1016/未知 272 秒 263 毫秒/步 - 损失：1.0580 - sparse_categorical_accuracy: 0.5912
1017/未知 273 秒 263 毫秒/步 - 损失：1.0578 - sparse_categorical_accuracy: 0.5913
1018/未知 273 秒 263 毫秒/步 - 损失：1.0575 - sparse_categorical_accuracy: 0.5914
1019/未知 273 秒 263 毫秒/步 - 损失：1.0573 - sparse_categorical_accuracy: 0.5914
1020/未知 273 秒 263 毫秒/步 - 损失：1.0571 - sparse_categorical_accuracy: 0.5915
1021/未知 274 秒 263 毫秒/步 - 损失：1.0569 - sparse_categorical_accuracy: 0.5916
1022/未知 274 秒 263 毫秒/步 - 损失：1.0567 - sparse_categorical_accuracy: 0.5916
1023/未知 274 秒 263 毫秒/步 - 损失：1.0565 - sparse_categorical_accuracy: 0.5917
1024/未知 275 秒 263 毫秒/步 - 损失：1.0563 - sparse_categorical_accuracy: 0.5918
1025/未知 275 秒 263 毫秒/步 - 损失：1.0561 - sparse_categorical_accuracy: 0.5918
1026/未知 275 秒 263 毫秒/步 - 损失：1.0559 - sparse_categorical_accuracy: 0.5919
1027/未知 275 秒 263 毫秒/步 - 损失：1.0556 - sparse_categorical_accuracy: 0.5920
1028/未知 276 秒 263 毫秒/步 - 损失：1.0554 - sparse_categorical_accuracy: 0.5920
1029/未知 276 秒 263 毫秒/步 - 损失：1.0552 - sparse_categorical_accuracy: 0.5921
1030/未知 276 秒 263 毫秒/步 - 损失：1.0550 - sparse_categorical_accuracy: 0.5922
1031/未知 276 秒 263 毫秒/步 - 损失：1.0548 - sparse_categorical_accuracy: 0.5922
1032/未知 277 秒 263 毫秒/步 - 损失：1.0546 - sparse_categorical_accuracy: 0.5923
1033/未知 277 秒 263 毫秒/步 - 损失：1.0544 - sparse_categorical_accuracy: 0.5924
1034/未知 277 秒 263 毫秒/步 - 损失：1.0542 - sparse_categorical_accuracy: 0.5924
1035/未知 278 秒 263 毫秒/步 - 损失：1.0540 - sparse_categorical_accuracy: 0.5925
1036/未知 278 秒 263 毫秒/步 - 损失：1.0538 - sparse_categorical_accuracy: 0.5926
1037/未知 278 秒 263 毫秒/步 - 损失：1.0536 - sparse_categorical_accuracy: 0.5926
1038/未知 278 秒 263 毫秒/步 - 损失：1.0533 - sparse_categorical_accuracy: 0.5927
1039/未知 279 秒 263 毫秒/步 - 损失：1.0531 - sparse_categorical_accuracy: 0.5928
1040/未知 279 秒 263 毫秒/步 - 损失：1.0529 - sparse_categorical_accuracy: 0.5928
1041/未知 279 秒 263 毫秒/步 - 损失：1.0527 - sparse_categorical_accuracy: 0.5929
1042/未知 280 秒 263 毫秒/步 - 损失：1.0525 - sparse_categorical_accuracy: 0.5930
1043/未知 280 秒 263 毫秒/步 - 损失：1.0523 - sparse_categorical_accuracy: 0.5930
1044/未知 280 秒 263 毫秒/步 - 损失：1.0521 - sparse_categorical_accuracy: 0.5931
1045/未知 280 秒 263 毫秒/步 - 损失：1.0519 - sparse_categorical_accuracy: 0.5932
1046/未知 281 秒 263 毫秒/步 - 损失：1.0517 - sparse_categorical_accuracy: 0.5932
1047/未知 281 秒 263 毫秒/步 - 损失：1.0515 - sparse_categorical_accuracy: 0.5933
1048/未知 281 秒 263 毫秒/步 - 损失：1.0513 - sparse_categorical_accuracy: 0.5934
1049/未知 282 秒 263 毫秒/步 - 损失：1.0511 - sparse_categorical_accuracy: 0.5934
1050/未知 282 秒 263 毫秒/步 - 损失：1.0509 - sparse_categorical_accuracy: 0.5935
1051/未知 282 秒 263 毫秒/步 - 损失：1.0507 - sparse_categorical_accuracy: 0.5935
1052/未知 283 秒 263 毫秒/步 - 损失：1.0505 - sparse_categorical_accuracy: 0.5936
1053/未知 283 秒 263 毫秒/步 - 损失：1.0503 - sparse_categorical_accuracy: 0.5937
1054/未知 283 秒 263 毫秒/步 - 损失：1.0501 - sparse_categorical_accuracy: 0.5937
1055/未知 283 秒 263 毫秒/步 - 损失：1.0499 - sparse_categorical_accuracy: 0.5938
1056/未知 284 秒 263 毫秒/步 - 损失：1.0497 - sparse_categorical_accuracy: 0.5939
1057/未知 284 秒 263 毫秒/步 - 损失：1.0495 - sparse_categorical_accuracy: 0.5939
1058/未知 284 秒 263 毫秒/步 - 损失：1.0493 - sparse_categorical_accuracy: 0.5940
1059/未知 285 秒 263 毫秒/步 - 损失：1.0491 - sparse_categorical_accuracy: 0.5941
1060/未知 285 秒 263 毫秒/步 - 损失：1.0489 - sparse_categorical_accuracy: 0.5941
1061/未知 285 秒 263 毫秒/步 - 损失：1.0487 - sparse_categorical_accuracy: 0.5942
1062/未知 285 秒 263 毫秒/步 - 损失：1.0485 - sparse_categorical_accuracy: 0.5943
1063/未知 285 秒 263 毫秒/步 - 损失：1.0483 - sparse_categorical_accuracy: 0.5943
1064/未知 286 秒 263 毫秒/步 - 损失：1.0481 - sparse_categorical_accuracy: 0.5944
1065/未知 286 秒 263 毫秒/步 - 损失：1.0479 - sparse_categorical_accuracy: 0.5944
1066/未知 286 秒 263 毫秒/步 - 损失：1.0477 - sparse_categorical_accuracy: 0.5945
1067/未知 286 秒 263 毫秒/步 - 损失：1.0475 - sparse_categorical_accuracy: 0.5946
1068/未知 287 秒 263 毫秒/步 - 损失：1.0473 - sparse_categorical_accuracy: 0.5946
1069/未知 287 秒 263 毫秒/步 - 损失：1.0471 - sparse_categorical_accuracy: 0.5947
1070/未知 287 秒 263 毫秒/步 - 损失：1.0469 - sparse_categorical_accuracy: 0.5948
1071/未知 287 秒 263 毫秒/步 - 损失：1.0467 - sparse_categorical_accuracy: 0.5948
1072/未知 288 秒 263 毫秒/步 - 损失：1.0465 - sparse_categorical_accuracy: 0.5949
1073/未知 288 秒 263 毫秒/步 - 损失：1.0463 - sparse_categorical_accuracy: 0.5949
1074/未知 288 秒 263 毫秒/步 - 损失：1.0461 - sparse_categorical_accuracy: 0.5950
1075/未知 289 秒 263 毫秒/步 - 损失：1.0459 - sparse_categorical_accuracy: 0.5951
1076/未知 289 秒 263 毫秒/步 - 损失：1.0457 - sparse_categorical_accuracy: 0.5951
1077/未知 289 秒 263 毫秒/步 - 损失：1.0455 - sparse_categorical_accuracy: 0.5952
1078/未知 290 秒 264 毫秒/步 - 损失：1.0453 - sparse_categorical_accuracy: 0.5953
1079/未知 290 秒 264 毫秒/步 - 损失：1.0451 - sparse_categorical_accuracy: 0.5953
1080/未知 290 秒 264 毫秒/步 - 损失：1.0449 - sparse_categorical_accuracy: 0.5954
1081/未知 291 秒 264 毫秒/步 - 损失：1.0447 - sparse_categorical_accuracy: 0.5954
1082/未知 291 秒 264 毫秒/步 - 损失：1.0445 - sparse_categorical_accuracy: 0.5955
1083/未知 291 秒 264 毫秒/步 - 损失：1.0443 - sparse_categorical_accuracy: 0.5956
1084/未知 291 秒 264 毫秒/步 - 损失：1.0441 - sparse_categorical_accuracy: 0.5956
1085/未知 292 秒 264 毫秒/步 - 损失：1.0439 - sparse_categorical_accuracy: 0.5957
1086/未知 292 秒 264 毫秒/步 - 损失：1.0437 - sparse_categorical_accuracy: 0.5957
1087/未知 292 秒 264 毫秒/步 - 损失：1.0436 - sparse_categorical_accuracy: 0.5958
1088/未知 293 秒 264 毫秒/步 - 损失：1.0434 - sparse_categorical_accuracy: 0.5959
1089/未知 293 秒 264 毫秒/步 - 损失：1.0432 - sparse_categorical_accuracy: 0.5959
1090/未知 293 秒 264 毫秒/步 - 损失：1.0430 - sparse_categorical_accuracy: 0.5960
1091/未知 293 秒 264 毫秒/步 - 损失：1.0428 - sparse_categorical_accuracy: 0.5961
1092/未知 294 秒 264 毫秒/步 - 损失：1.0426 - sparse_categorical_accuracy: 0.5961
1093/未知 294 秒 264 毫秒/步 - 损失：1.0424 - sparse_categorical_accuracy: 0.5962
1094/未知 294 秒 264 毫秒/步 - 损失：1.0422 - sparse_categorical_accuracy: 0.5962
1095/未知 294 秒 264 毫秒/步 - 损失：1.0420 - sparse_categorical_accuracy: 0.5963
1096/未知 295 秒 264 毫秒/步 - 损失：1.0418 - sparse_categorical_accuracy: 0.5964
1097/未知 295 秒 264 毫秒/步 - 损失：1.0416 - sparse_categorical_accuracy: 0.5964
1098/未知 295 秒 264 毫秒/步 - 损失：1.0414 - sparse_categorical_accuracy: 0.5965
1099/未知 295 秒 264 毫秒/步 - 损失：1.0413 - sparse_categorical_accuracy: 0.5965
1100/未知 296 秒 264 毫秒/步 - 损失：1.0411 - sparse_categorical_accuracy: 0.5966
1101/未知 296 秒 264 毫秒/步 - 损失：1.0409 - sparse_categorical_accuracy: 0.5967
1102/未知 296 秒 264 毫秒/步 - 损失：1.0407 - sparse_categorical_accuracy: 0.5967
1103/未知 296 秒 264 毫秒/步 - 损失：1.0405 - sparse_categorical_accuracy: 0.5968
1104/未知 297 秒 264 毫秒/步 - 损失：1.0403 - sparse_categorical_accuracy: 0.5968
1105/未知 297 秒 264 毫秒/步 - 损失：1.0401 - sparse_categorical_accuracy: 0.5969
1106/未知 297 秒 264 毫秒/步 - 损失：1.0399 - sparse_categorical_accuracy: 0.5970
1107/未知 298 秒 264 毫秒/步 - 损失：1.0397 - sparse_categorical_accuracy: 0.5970
1108/未知 298 秒 264 毫秒/步 - 损失：1.0396 - sparse_categorical_accuracy: 0.5971
1109/未知 298 秒 264 毫秒/步 - 损失：1.0394 - sparse_categorical_accuracy: 0.5971
1110/未知 299 秒 264 毫秒/步 - 损失：1.0392 - sparse_categorical_accuracy: 0.5972
1111/未知 299 秒 264 毫秒/步 - 损失：1.0390 - sparse_categorical_accuracy: 0.5973
1112/未知 299 秒 264 毫秒/步 - 损失：1.0388 - sparse_categorical_accuracy: 0.5973
1113/未知 299 秒 264 毫秒/步 - 损失：1.0386 - sparse_categorical_accuracy: 0.5974
1114/未知 300 秒 264 毫秒/步 - 损失：1.0384 - sparse_categorical_accuracy: 0.5974
1115/未知 300 秒 264 毫秒/步 - 损失：1.0382 - sparse_categorical_accuracy: 0.5975
1116/未知 300 秒 264 毫秒/步 - 损失：1.0381 - sparse_categorical_accuracy: 0.5976
1117/未知 300 秒 264 毫秒/步 - 损失：1.0379 - sparse_categorical_accuracy: 0.5976
1118/未知 301 秒 264 毫秒/步 - 损失：1.0377 - sparse_categorical_accuracy: 0.5977
1119/未知 301 秒 264 毫秒/步 - 损失：1.0375 - sparse_categorical_accuracy: 0.5977
1120/未知 301 秒 264 毫秒/步 - 损失：1.0373 - sparse_categorical_accuracy: 0.5978
1121/未知 301 秒 264 毫秒/步 - 损失：1.0371 - sparse_categorical_accuracy: 0.5978
1122/未知 302 秒 264 毫秒/步 - 损失：1.0369 - sparse_categorical_accuracy: 0.5979
1123/未知 302 秒 264 毫秒/步 - 损失：1.0368 - sparse_categorical_accuracy: 0.5980
1124/未知 302 秒 264 毫秒/步 - 损失：1.0366 - sparse_categorical_accuracy: 0.5980
1125/未知 302 秒 264 毫秒/步 - 损失：1.0364 - sparse_categorical_accuracy: 0.5981
1126/未知 303 秒 264 毫秒/步 - 损失：1.0362 - sparse_categorical_accuracy: 0.5981
1127/未知 303 秒 264 毫秒/步 - 损失：1.0360 - sparse_categorical_accuracy: 0.5982
1128/未知 303 秒 264 毫秒/步 - 损失：1.0358 - sparse_categorical_accuracy: 0.5983
1129/未知 303 秒 264 毫秒/步 - 损失：1.0357 - sparse_categorical_accuracy: 0.5983
1130/未知 304 秒 264 毫秒/步 - 损失：1.0355 - sparse_categorical_accuracy: 0.5984
1131/未知 304 秒 264 毫秒/步 - 损失：1.0353 - sparse_categorical_accuracy: 0.5984
1132/未知 304 秒 264 毫秒/步 - 损失：1.0351 - sparse_categorical_accuracy: 0.5985
1133/未知 305 秒 264 毫秒/步 - 损失：1.0349 - sparse_categorical_accuracy: 0.5985
1134/未知 305 秒 264 毫秒/步 - 损失：1.0347 - sparse_categorical_accuracy: 0.5986
1135/未知 305 秒 264 毫秒/步 - 损失：1.0346 - sparse_categorical_accuracy: 0.5987
1136/未知 306 秒 264 毫秒/步 - 损失：1.0344 - sparse_categorical_accuracy: 0.5987
1137/未知 306 秒 264 毫秒/步 - 损失：1.0342 - sparse_categorical_accuracy: 0.5988
1138/未知 306 秒 264 毫秒/步 - 损失：1.0340 - sparse_categorical_accuracy: 0.5988
1139/未知 306 秒 264 毫秒/步 - 损失：1.0338 - sparse_categorical_accuracy: 0.5989
1140/未知 307 秒 264 毫秒/步 - 损失：1.0337 - sparse_categorical_accuracy: 0.5990
1141/未知 307 秒 264 毫秒/步 - 损失：1.0335 - sparse_categorical_accuracy: 0.5990
1142/未知 307 秒 264 毫秒/步 - 损失：1.0333 - sparse_categorical_accuracy: 0.5991
1143/未知 308 秒 264 毫秒/步 - 损失：1.0331 - sparse_categorical_accuracy: 0.5991
1144/未知 308 秒 264 毫秒/步 - 损失：1.0329 - sparse_categorical_accuracy: 0.5992
1145/未知 308 秒 264 毫秒/步 - 损失：1.0328 - sparse_categorical_accuracy: 0.5992
1146/未知 308 秒 264 毫秒/步 - 损失：1.0326 - sparse_categorical_accuracy: 0.5993
1147/未知 309 秒 264 毫秒/步 - 损失：1.0324 - sparse_categorical_accuracy: 0.5993
1148/未知 309 秒 264 毫秒/步 - 损失：1.0322 - sparse_categorical_accuracy: 0.5994
1149/未知 309 秒 264 毫秒/步 - 损失：1.0320 - sparse_categorical_accuracy: 0.5995
1150/未知 310 秒 264 毫秒/步 - 损失：1.0319 - sparse_categorical_accuracy: 0.5995
1151/未知 310 秒 264 毫秒/步 - 损失：1.0317 - sparse_categorical_accuracy: 0.5996
1152/未知 310 秒 264 毫秒/步 - 损失：1.0315 - sparse_categorical_accuracy: 0.5996
1153/未知 310 秒 264 毫秒/步 - 损失：1.0313 - sparse_categorical_accuracy: 0.5997
1154/未知 311 秒 264 毫秒/步 - 损失：1.0311 - sparse_categorical_accuracy: 0.5997
1155/未知 311 秒 264 毫秒/步 - 损失：1.0310 - sparse_categorical_accuracy: 0.5998
1156/未知 311 秒 264 毫秒/步 - 损失：1.0308 - sparse_categorical_accuracy: 0.5999
1157/未知 312 秒 265 毫秒/步 - 损失：1.0306 - sparse_categorical_accuracy: 0.5999
1158/未知 312 秒 265 毫秒/步 - 损失：1.0304 - sparse_categorical_accuracy: 0.6000
1159/未知 312 秒 265 毫秒/步 - 损失：1.0303 - sparse_categorical_accuracy: 0.6000
1160/未知 312 秒 265 毫秒/步 - 损失：1.0301 - sparse_categorical_accuracy: 0.6001
1161/未知 313 秒 265 毫秒/步 - 损失：1.0299 - sparse_categorical_accuracy: 0.6001
1162/未知 313 秒 265 毫秒/步 - 损失：1.0297 - sparse_categorical_accuracy: 0.6002
1163/未知 313 秒 265 毫秒/步 - 损失：1.0296 - sparse_categorical_accuracy: 0.6003
1164/未知 314 秒 265 毫秒/步 - 损失：1.0294 - sparse_categorical_accuracy: 0.6003
1165/未知 314 秒 265 毫秒/步 - 损失：1.0292 - sparse_categorical_accuracy: 0.6004
1166/未知 314 秒 265 毫秒/步 - 损失：1.0290 - sparse_categorical_accuracy: 0.6004
1167/未知 314 秒 265 毫秒/步 - 损失：1.0289 - sparse_categorical_accuracy: 0.6005
1168/未知 315 秒 265 毫秒/步 - 损失：1.0287 - sparse_categorical_accuracy: 0.6005
1169/未知 315 秒 265 毫秒/步 - 损失：1.0285 - sparse_categorical_accuracy: 0.6006
1170/未知 315 秒 265 毫秒/步 - 损失：1.0283 - sparse_categorical_accuracy: 0.6006
1171/未知 315 秒 265 毫秒/步 - 损失：1.0282 - sparse_categorical_accuracy: 0.6007
1172/未知 316 秒 264 毫秒/步 - 损失：1.0280 - sparse_categorical_accuracy: 0.6008
1173/未知 316 秒 264 毫秒/步 - 损失：1.0278 - sparse_categorical_accuracy: 0.6008
1174/未知 316 秒 264 毫秒/步 - 损失：1.0276 - sparse_categorical_accuracy: 0.6009
1175/未知 316 秒 264 毫秒/步 - 损失：1.0275 - sparse_categorical_accuracy: 0.6009
1176/未知 316 秒 264 毫秒/步 - 损失：1.0273 - sparse_categorical_accuracy: 0.6010
1177/未知 317 秒 264 毫秒/步 - 损失：1.0271 - sparse_categorical_accuracy: 0.6010
1178/未知 317 秒 264 毫秒/步 - 损失：1.0269 - sparse_categorical_accuracy: 0.6011
1179/未知 317 秒 264 毫秒/步 - 损失：1.0268 - sparse_categorical_accuracy: 0.6011
1180/未知 317 秒 264 毫秒/步 - 损失：1.0266 - sparse_categorical_accuracy: 0.6012
1181/未知 318 秒 264 毫秒/步 - 损失：1.0264 - sparse_categorical_accuracy: 0.6012
1182/未知 318 秒 264 毫秒/步 - 损失：1.0263 - sparse_categorical_accuracy: 0.6013
1183/未知 318 秒 264 毫秒/步 - 损失：1.0261 - sparse_categorical_accuracy: 0.6014
1184/未知 318 秒 264 毫秒/步 - 损失：1.0259 - sparse_categorical_accuracy: 0.6014
1185/未知 319 秒 264 毫秒/步 - 损失：1.0257 - sparse_categorical_accuracy: 0.6015
1186/未知 319 秒 264 毫秒/步 - 损失：1.0256 - sparse_categorical_accuracy: 0.6015
1187/未知 319 秒 264 毫秒/步 - 损失：1.0254 - sparse_categorical_accuracy: 0.6016
1188/未知 319 秒 264 毫秒/步 - 损失：1.0252 - sparse_categorical_accuracy: 0.6016
1189/未知 320 秒 264 毫秒/步 - 损失：1.0251 - sparse_categorical_accuracy: 0.6017
1190/未知 320 秒 264 毫秒/步 - 损失：1.0249 - sparse_categorical_accuracy: 0.6017
1191/未知 320 秒 264 毫秒/步 - 损失：1.0247 - sparse_categorical_accuracy: 0.6018
1192/未知 320 秒 264 毫秒/步 - 损失：1.0245 - sparse_categorical_accuracy: 0.6018
1193/未知 321 秒 264 毫秒/步 - 损失：1.0244 - sparse_categorical_accuracy: 0.6019
1194/未知 321 秒 264 毫秒/步 - 损失：1.0242 - sparse_categorical_accuracy: 0.6019
1195/未知 321 秒 264 毫秒/步 - 损失：1.0240 - sparse_categorical_accuracy: 0.6020
1196/未知 321 秒 264 毫秒/步 - 损失：1.0239 - sparse_categorical_accuracy: 0.6021
1197/未知 322 秒 264 毫秒/步 - 损失：1.0237 - sparse_categorical_accuracy: 0.6021
1198/未知 322 秒 264 毫秒/步 - 损失：1.0235 - sparse_categorical_accuracy: 0.6022
1199/未知 322 秒 264 毫秒/步 - 损失：1.0234 - sparse_categorical_accuracy: 0.6022
1200/未知 322 秒 264 毫秒/步 - 损失：1.0232 - sparse_categorical_accuracy: 0.6023
1201/未知 323 秒 264 毫秒/步 - 损失：1.0230 - sparse_categorical_accuracy: 0.6023
1202/未知 323 秒 264 毫秒/步 - 损失：1.0229 - sparse_categorical_accuracy: 0.6024
1203/未知 323 秒 264 毫秒/步 - 损失：1.0227 - sparse_categorical_accuracy: 0.6024
1204/未知 323 秒 264 毫秒/步 - 损失：1.0225 - sparse_categorical_accuracy: 0.6025
1205/未知 324 秒 264 毫秒/步 - 损失：1.0224 - sparse_categorical_accuracy: 0.6025
1206/未知 324 秒 264 毫秒/步 - 损失：1.0222 - sparse_categorical_accuracy: 0.6026
1207/未知 324 秒 264 毫秒/步 - 损失：1.0220 - sparse_categorical_accuracy: 0.6026
1208/未知 324 秒 264 毫秒/步 - 损失：1.0219 - sparse_categorical_accuracy: 0.6027
1209/未知 325 秒 264 毫秒/步 - 损失：1.0217 - sparse_categorical_accuracy: 0.6027
1210/未知 325 秒 264 毫秒/步 - 损失：1.0215 - sparse_categorical_accuracy: 0.6028
1211/未知 325 秒 264 毫秒/步 - 损失：1.0214 - sparse_categorical_accuracy: 0.6029
1212/未知 326 秒 264 毫秒/步 - 损失：1.0212 - sparse_categorical_accuracy: 0.6029
1213/未知 326 秒 264 毫秒/步 - 损失：1.0210 - sparse_categorical_accuracy: 0.6030
1214/未知 326 秒 264 毫秒/步 - 损失：1.0209 - sparse_categorical_accuracy: 0.6030
1215/未知 327 秒 264 毫秒/步 - 损失：1.0207 - sparse_categorical_accuracy: 0.6031
1216/未知 327 秒 264 毫秒/步 - 损失：1.0205 - sparse_categorical_accuracy: 0.6031
1217/未知 327 秒 264 毫秒/步 - 损失：1.0204 - sparse_categorical_accuracy: 0.6032
1218/未知 327 秒 264 毫秒/步 - 损失：1.0202 - sparse_categorical_accuracy: 0.6032
1219/未知 328 秒 264 毫秒/步 - 损失：1.0200 - sparse_categorical_accuracy: 0.6033
1220/未知 328 秒 264 毫秒/步 - 损失：1.0199 - sparse_categorical_accuracy: 0.6033
1221/未知 328 秒 264 毫秒/步 - 损失：1.0197 - sparse_categorical_accuracy: 0.6034
1222/未知 328 秒 264 毫秒/步 - 损失：1.0196 - sparse_categorical_accuracy: 0.6034
1223/未知 329 秒 264 毫秒/步 - 损失：1.0194 - sparse_categorical_accuracy: 0.6035
1224/未知 329 秒 264 毫秒/步 - 损失：1.0192 - sparse_categorical_accuracy: 0.6035
1225/未知 329 秒 264 毫秒/步 - 损失：1.0191 - sparse_categorical_accuracy: 0.6036
1226/未知 329 秒 264 毫秒/步 - 损失：1.0189 - sparse_categorical_accuracy: 0.6036
1227/未知 330 秒 264 毫秒/步 - 损失：1.0187 - sparse_categorical_accuracy: 0.6037
1228/未知 330 秒 264 毫秒/步 - 损失：1.0186 - sparse_categorical_accuracy: 0.6037
1229/未知 330 秒 264 毫秒/步 - 损失：1.0184 - sparse_categorical_accuracy: 0.6038
1230/未知 330 秒 264 毫秒/步 - 损失：1.0183 - sparse_categorical_accuracy: 0.6038
1231/未知 331 秒 264 毫秒/步 - 损失：1.0181 - sparse_categorical_accuracy: 0.6039
1232/未知 331 秒 264 毫秒/步 - 损失：1.0179 - sparse_categorical_accuracy: 0.6039
1233/未知 331 秒 264 毫秒/步 - 损失：1.0178 - sparse_categorical_accuracy: 0.6040
1234/未知 331 秒 264 毫秒/步 - 损失：1.0176 - sparse_categorical_accuracy: 0.6040
1235/未知 332 秒 264 毫秒/步 - 损失：1.0174 - sparse_categorical_accuracy: 0.6041
1236/未知 332 秒 264 毫秒/步 - 损失：1.0173 - sparse_categorical_accuracy: 0.6041
1237/未知 332 秒 264 毫秒/步 - 损失：1.0171 - sparse_categorical_accuracy: 0.6042
1238/未知 332 秒 264 毫秒/步 - 损失：1.0170 - sparse_categorical_accuracy: 0.6042
1239/未知 333 秒 264 毫秒/步 - 损失：1.0168 - sparse_categorical_accuracy: 0.6043
1240/未知 333 秒 264 毫秒/步 - 损失：1.0166 - sparse_categorical_accuracy: 0.6043
1241/未知 334 秒 264 毫秒/步 - 损失：1.0165 - sparse_categorical_accuracy: 0.6044
1242/未知 334 秒 264 毫秒/步 - 损失：1.0163 - sparse_categorical_accuracy: 0.6044
1243/未知 334 秒 264 毫秒/步 - 损失：1.0162 - sparse_categorical_accuracy: 0.6045
1244/未知 335 秒 265 毫秒/步 - 损失：1.0160 - sparse_categorical_accuracy: 0.6045
1245/未知 335 秒 265 毫秒/步 - 损失：1.0158 - sparse_categorical_accuracy: 0.6046
1246/未知 335 秒 265 毫秒/步 - 损失：1.0157 - sparse_categorical_accuracy: 0.6046
1247/未知 335 秒 265 毫秒/步 - 损失：1.0155 - sparse_categorical_accuracy: 0.6047
1248/未知 336 秒 265 毫秒/步 - 损失：1.0154 - sparse_categorical_accuracy: 0.6048
1249/未知 336 秒 265 毫秒/步 - 损失：1.0152 - sparse_categorical_accuracy: 0.6048
1250/未知 336 秒 265 毫秒/步 - 损失：1.0150 - sparse_categorical_accuracy: 0.6049
1251/未知 337 秒 265 毫秒/步 - 损失：1.0149 - sparse_categorical_accuracy: 0.6049
1252/未知 337 秒 265 毫秒/步 - 损失：1.0147 - sparse_categorical_accuracy: 0.6050
1253/未知 337 秒 265 毫秒/步 - 损失：1.0146 - sparse_categorical_accuracy: 0.6050
1254/未知 337 秒 265 毫秒/步 - 损失：1.0144 - sparse_categorical_accuracy: 0.6051
1255/未知 338 秒 265 毫秒/步 - 损失：1.0143 - sparse_categorical_accuracy: 0.6051
1256/未知 338 秒 265 毫秒/步 - 损失：1.0141 - sparse_categorical_accuracy: 0.6052
1257/未知 338 秒 264 毫秒/步 - 损失：1.0139 - sparse_categorical_accuracy: 0.6052
1258/未知 338 秒 264 毫秒/步 - 损失：1.0138 - sparse_categorical_accuracy: 0.6053
1259/未知 338 秒 264 毫秒/步 - 损失：1.0136 - sparse_categorical_accuracy: 0.6053
1260/未知 339 秒 264 毫秒/步 - 损失：1.0135 - sparse_categorical_accuracy: 0.6054
1261/未知 339 秒 264 毫秒/步 - 损失：1.0133 - sparse_categorical_accuracy: 0.6054
1262/未知 339 秒 264 毫秒/步 - 损失：1.0132 - sparse_categorical_accuracy: 0.6055
1263/未知 339 秒 264 毫秒/步 - 损失：1.0130 - sparse_categorical_accuracy: 0.6055
1264/未知 340 秒 264 毫秒/步 - 损失：1.0128 - sparse_categorical_accuracy: 0.6055
1265/未知 340 秒 264 毫秒/步 - 损失：1.0127 - sparse_categorical_accuracy: 0.6056
1266/未知 340 秒 264 毫秒/步 - 损失：1.0125 - sparse_categorical_accuracy: 0.6056
1267/未知 340 秒 264 毫秒/步 - 损失：1.0124 - sparse_categorical_accuracy: 0.6057
1268/未知 341 秒 264 毫秒/步 - 损失：1.0122 - sparse_categorical_accuracy: 0.6057
1269/未知 341 秒 264 毫秒/步 - 损失：1.0121 - sparse_categorical_accuracy: 0.6058
1270/未知 341 秒 264 毫秒/步 - 损失：1.0119 - sparse_categorical_accuracy: 0.6058
1271/未知 341 秒 264 毫秒/步 - 损失：1.0118 - sparse_categorical_accuracy: 0.6059
1272/未知 342 秒 264 毫秒/步 - 损失：1.0116 - sparse_categorical_accuracy: 0.6059
1273/未知 342 秒 264 毫秒/步 - 损失：1.0114 - sparse_categorical_accuracy: 0.6060
1274/未知 342 秒 264 毫秒/步 - 损失：1.0113 - sparse_categorical_accuracy: 0.6060
1275/未知 342 秒 264 毫秒/步 - 损失：1.0111 - sparse_categorical_accuracy: 0.6061
1276/未知 343 秒 264 毫秒/步 - 损失：1.0110 - sparse_categorical_accuracy: 0.6061
1277/未知 343 秒 264 毫秒/步 - 损失：1.0108 - sparse_categorical_accuracy: 0.6062
1278/未知 343 秒 264 毫秒/步 - 损失：1.0107 - sparse_categorical_accuracy: 0.6062
1279/未知 344 秒 264 毫秒/步 - 损失：1.0105 - sparse_categorical_accuracy: 0.6063
1280/未知 344 秒 264 毫秒/步 - 损失：1.0104 - sparse_categorical_accuracy: 0.6063
1281/未知 344 秒 264 毫秒/步 - 损失：1.0102 - sparse_categorical_accuracy: 0.6064
1282/未知 345 秒 264 毫秒/步 - 损失：1.0101 - sparse_categorical_accuracy: 0.6064
1283/未知 345 秒 265 毫秒/步 - 损失：1.0099 - sparse_categorical_accuracy: 0.6065
1284/未知 345 秒 265 毫秒/步 - 损失：1.0098 - sparse_categorical_accuracy: 0.6065
1285/未知 345 秒 265 毫秒/步 - 损失：1.0096 - sparse_categorical_accuracy: 0.6066
1286/未知 346 秒 265 毫秒/步 - 损失：1.0095 - sparse_categorical_accuracy: 0.6066
1287/未知 346 秒 265 毫秒/步 - 损失：1.0093 - sparse_categorical_accuracy: 0.6067
1288/未知 346 秒 265 毫秒/步 - 损失：1.0092 - sparse_categorical_accuracy: 0.6067
1289/未知 347 秒 265 毫秒/步 - 损失：1.0090 - sparse_categorical_accuracy: 0.6068
1290/未知 347 秒 265 毫秒/步 - 损失：1.0088 - sparse_categorical_accuracy: 0.6068
1291/未知 347 秒 265 毫秒/步 - 损失：1.0087 - sparse_categorical_accuracy: 0.6069
1292/未知 347 秒 265 毫秒/步 - 损失：1.0085 - sparse_categorical_accuracy: 0.6069
1293/未知 348 秒 265 毫秒/步 - 损失：1.0084 - sparse_categorical_accuracy: 0.6070
1294/未知 348 秒 265 毫秒/步 - 损失：1.0082 - sparse_categorical_accuracy: 0.6070
1295/未知 348 秒 265 毫秒/步 - 损失：1.0081 - sparse_categorical_accuracy: 0.6071
1296/未知 349 秒 265 毫秒/步 - 损失：1.0079 - sparse_categorical_accuracy: 0.6071
1297/未知 349 秒 265 毫秒/步 - 损失：1.0078 - sparse_categorical_accuracy: 0.6071
1298/未知 349 秒 265 毫秒/步 - 损失：1.0076 - sparse_categorical_accuracy: 0.6072
1299/未知 350 秒 265 毫秒/步 - 损失：1.0075 - sparse_categorical_accuracy: 0.6072
1300/未知 350 秒 265 毫秒/步 - 损失：1.0073 - sparse_categorical_accuracy: 0.6073
1301/未知 350秒 265毫秒/步 - 损失: 1.0072 - 稀疏分类准确率: 0.6073
1302/未知 350秒 265毫秒/步 - 损失: 1.0070 - 稀疏分类准确率: 0.6074
1303/未知 351秒 265毫秒/步 - 损失: 1.0069 - 稀疏分类准确率: 0.6074
1304/未知 351秒 265毫秒/步 - 损失: 1.0067 - 稀疏分类准确率: 0.6075
1305/未知 351秒 265毫秒/步 - 损失: 1.0066 - 稀疏分类准确率: 0.6075
1306/未知 351秒 265毫秒/步 - 损失: 1.0064 - 稀疏分类准确率: 0.6076
1307/未知 352秒 265毫秒/步 - 损失: 1.0063 - 稀疏分类准确率: 0.6076
1308/未知 352秒 265毫秒/步 - 损失: 1.0061 - 稀疏分类准确率: 0.6077
1309/未知 352秒 265毫秒/步 - 损失: 1.0060 - 稀疏分类准确率: 0.6077
1310/未知 353秒 265毫秒/步 - 损失: 1.0059 - 稀疏分类准确率: 0.6078
1311/未知 353秒 265毫秒/步 - 损失: 1.0057 - 稀疏分类准确率: 0.6078
1312/未知 353秒 265毫秒/步 - 损失: 1.0056 - 稀疏分类准确率: 0.6079
1313/未知 354秒 265毫秒/步 - 损失: 1.0054 - 稀疏分类准确率: 0.6079
1314/未知 354秒 265毫秒/步 - 损失: 1.0053 - 稀疏分类准确率: 0.6079
1315/未知 354秒 265毫秒/步 - 损失: 1.0051 - 稀疏分类准确率: 0.6080
1316/未知 354秒 265毫秒/步 - 损失: 1.0050 - 稀疏分类准确率: 0.6080
1317/未知 355秒 265毫秒/步 - 损失: 1.0048 - 稀疏分类准确率: 0.6081
1318/未知 355秒 265毫秒/步 - 损失: 1.0047 - 稀疏分类准确率: 0.6081
1319/未知 355秒 265毫秒/步 - 损失: 1.0045 - 稀疏分类准确率: 0.6082
1320/未知 356秒 265毫秒/步 - 损失: 1.0044 - 稀疏分类准确率: 0.6082
1321/未知 356秒 265毫秒/步 - 损失: 1.0042 - 稀疏分类准确率: 0.6083
1322/未知 356秒 265毫秒/步 - 损失: 1.0041 - 稀疏分类准确率: 0.6083
1323/未知 356秒 265毫秒/步 - 损失: 1.0039 - 稀疏分类准确率: 0.6084
1324/未知 357秒 265毫秒/步 - 损失: 1.0038 - 稀疏分类准确率: 0.6084
1325/未知 357秒 265毫秒/步 - 损失: 1.0036 - 稀疏分类准确率: 0.6085
1326/未知 357秒 265毫秒/步 - 损失: 1.0035 - 稀疏分类准确率: 0.6085
1327/未知 358秒 265毫秒/步 - 损失: 1.0034 - 稀疏分类准确率: 0.6086
1328/未知 358秒 265毫秒/步 - 损失: 1.0032 - 稀疏分类准确率: 0.6086
1329/未知 358秒 265毫秒/步 - 损失: 1.0031 - 稀疏分类准确率: 0.6086
1330/未知 358秒 265毫秒/步 - 损失: 1.0029 - 稀疏分类准确率: 0.6087
1331/未知 359秒 265毫秒/步 - 损失: 1.0028 - 稀疏分类准确率: 0.6087
1332/未知 359秒 265毫秒/步 - 损失: 1.0026 - 稀疏分类准确率: 0.6088
1333/未知 359秒 265毫秒/步 - 损失: 1.0025 - 稀疏分类准确率: 0.6088
1334/未知 359秒 265毫秒/步 - 损失: 1.0023 - 稀疏分类准确率: 0.6089
1335/未知 360秒 265毫秒/步 - 损失: 1.0022 - 稀疏分类准确率: 0.6089
1336/未知 360秒 265毫秒/步 - 损失: 1.0021 - 稀疏分类准确率: 0.6090
1337/未知 360秒 265毫秒/步 - 损失: 1.0019 - 稀疏分类准确率: 0.6090
1338/未知 360秒 265毫秒/步 - 损失: 1.0018 - 稀疏分类准确率: 0.6091
1339/未知 361秒 265毫秒/步 - 损失: 1.0016 - 稀疏分类准确率: 0.6091
1340/未知 361秒 265毫秒/步 - 损失: 1.0015 - 稀疏分类准确率: 0.6091
1341/未知 361秒 265毫秒/步 - 损失: 1.0013 - 稀疏分类准确率: 0.6092
1342/未知 361秒 265毫秒/步 - 损失: 1.0012 - 稀疏分类准确率: 0.6092
1343/未知 362秒 265毫秒/步 - 损失: 1.0010 - 稀疏分类准确率: 0.6093
1344/未知 362秒 265毫秒/步 - 损失: 1.0009 - 稀疏分类准确率: 0.6093
1345/未知 362秒 265毫秒/步 - 损失: 1.0008 - 稀疏分类准确率: 0.6094
1346/未知 363秒 265毫秒/步 - 损失: 1.0006 - 稀疏分类准确率: 0.6094
1347/未知 363秒 265毫秒/步 - 损失: 1.0005 - 稀疏分类准确率: 0.6095
1348/未知 363秒 265毫秒/步 - 损失: 1.0003 - 稀疏分类准确率: 0.6095
1349/未知 364秒 265毫秒/步 - 损失: 1.0002 - 稀疏分类准确率: 0.6096
1350/未知 364秒 265毫秒/步 - 损失: 1.0000 - 稀疏分类准确率: 0.6096
1351/未知 364秒 265毫秒/步 - 损失: 0.9999 - 稀疏分类准确率: 0.6096
1352/未知 364秒 265毫秒/步 - 损失: 0.9998 - 稀疏分类准确率: 0.6097
1353/未知 365秒 265毫秒/步 - 损失: 0.9996 - 稀疏分类准确率: 0.6097
1354/未知 365秒 265毫秒/步 - 损失: 0.9995 - 稀疏分类准确率: 0.6098
1355/未知 365秒 265毫秒/步 - 损失: 0.9993 - 稀疏分类准确率: 0.6098
1356/未知 366秒 265毫秒/步 - 损失: 0.9992 - 稀疏分类准确率: 0.6099
1357/未知 366秒 266毫秒/步 - 损失: 0.9991 - 稀疏分类准确率: 0.6099
1358/未知 366秒 266毫秒/步 - 损失: 0.9989 - 稀疏分类准确率: 0.6100
1359/未知 366秒 266毫秒/步 - 损失: 0.9988 - 稀疏分类准确率: 0.6100
1360/未知 367秒 266毫秒/步 - 损失: 0.9986 - 稀疏分类准确率: 0.6100
1361/未知 367秒 266毫秒/步 - 损失: 0.9985 - 稀疏分类准确率: 0.6101
1362/未知 367秒 265毫秒/步 - 损失: 0.9984 - 稀疏分类准确率: 0.6101
1363/未知 367秒 265毫秒/步 - 损失: 0.9982 - 稀疏分类准确率: 0.6102
1364/未知 368秒 266毫秒/步 - 损失: 0.9981 - 稀疏分类准确率: 0.6102
1365/未知 368秒 266毫秒/步 - 损失: 0.9979 - 稀疏分类准确率: 0.6103
1366/未知 368秒 266毫秒/步 - 损失: 0.9978 - 稀疏分类准确率: 0.6103
1367/未知 369秒 266毫秒/步 - 损失: 0.9977 - 稀疏分类准确率: 0.6104
1368/未知 369秒 266毫秒/步 - 损失: 0.9975 - 稀疏分类准确率: 0.6104
1369/未知 369秒 266毫秒/步 - 损失: 0.9974 - 稀疏分类准确率: 0.6104
1370/未知 369秒 266毫秒/步 - 损失: 0.9972 - 稀疏分类准确率: 0.6105
1371/未知 370秒 266毫秒/步 - 损失: 0.9971 - 稀疏分类准确率: 0.6105
1372/未知 370秒 266毫秒/步 - 损失: 0.9970 - 稀疏分类准确率: 0.6106
1373/未知 370秒 266毫秒/步 - 损失: 0.9968 - 稀疏分类准确率: 0.6106
1374/未知 371秒 266毫秒/步 - 损失: 0.9967 - 稀疏分类准确率: 0.6107
1375/未知 371秒 266毫秒/步 - 损失: 0.9965 - 稀疏分类准确率: 0.6107
1376/未知 371秒 266毫秒/步 - 损失: 0.9964 - 稀疏分类准确率: 0.6107
1377/未知 372秒 266毫秒/步 - 损失: 0.9963 - 稀疏分类准确率: 0.6108
1378/未知 372秒 266毫秒/步 - 损失: 0.9961 - 稀疏分类准确率: 0.6108
1379/未知 372秒 266毫秒/步 - 损失: 0.9960 - 稀疏分类准确率: 0.6109
1380/未知 372秒 266毫秒/步 - 损失: 0.9959 - 稀疏分类准确率: 0.6109
1381/未知 373秒 266毫秒/步 - 损失: 0.9957 - 稀疏分类准确率: 0.6110
1382/未知 373秒 266毫秒/步 - 损失: 0.9956 - 稀疏分类准确率: 0.6110
1383/未知 373秒 266毫秒/步 - 损失: 0.9954 - 稀疏分类准确率: 0.6111
1384/未知 374秒 266毫秒/步 - 损失: 0.9953 - 稀疏分类准确率: 0.6111
1385/未知 374秒 266毫秒/步 - 损失: 0.9952 - 稀疏分类准确率: 0.6111
1386/未知 374秒 266毫秒/步 - 损失: 0.9950 - 稀疏分类准确率: 0.6112
1387/未知 374秒 266毫秒/步 - 损失: 0.9949 - 稀疏分类准确率: 0.6112
1388/未知 375秒 266毫秒/步 - 损失: 0.9948 - 稀疏分类准确率: 0.6113
1389/未知 375秒 266毫秒/步 - 损失: 0.9946 - 稀疏分类准确率: 0.6113
1390/未知 375秒 266毫秒/步 - 损失: 0.9945 - 稀疏分类准确率: 0.6114
1391/未知 376秒 266毫秒/步 - 损失: 0.9943 - 稀疏分类准确率: 0.6114
1392/未知 376秒 266毫秒/步 - 损失: 0.9942 - 稀疏分类准确率: 0.6114
1393/未知 376秒 266毫秒/步 - 损失: 0.9941 - 稀疏分类准确率: 0.6115
1394/未知 377秒 266毫秒/步 - 损失: 0.9939 - 稀疏分类准确率: 0.6115
1395/未知 377秒 266毫秒/步 - 损失: 0.9938 - 稀疏分类准确率: 0.6116
1396/未知 377秒 266毫秒/步 - 损失: 0.9937 - 稀疏分类准确率: 0.6116
1397/未知 378秒 266毫秒/步 - 损失: 0.9935 - 稀疏分类准确率: 0.6117
1398/未知 378秒 266毫秒/步 - 损失: 0.9934 - 稀疏分类准确率: 0.6117
1399/未知 378秒 266毫秒/步 - 损失: 0.9933 - 稀疏分类准确率: 0.6117
1400/未知 378秒 266毫秒/步 - 损失: 0.9931 - 稀疏分类准确率: 0.6118
1401/未知 379秒 266毫秒/步 - 损失: 0.9930 - 稀疏分类准确率: 0.6118
1402/未知 379秒 266毫秒/步 - 损失: 0.9929 - 稀疏分类准确率: 0.6119
1403/未知 379秒 266毫秒/步 - 损失: 0.9927 - 稀疏分类准确率: 0.6119
1404/未知 379秒 266毫秒/步 - 损失: 0.9926 - 稀疏分类准确率: 0.6120
1405/未知 380秒 266毫秒/步 - 损失: 0.9925 - 稀疏分类准确率: 0.6120
1406/未知 380秒 266毫秒/步 - 损失: 0.9923 - 稀疏分类准确率: 0.6120
1407/未知 380秒 266毫秒/步 - 损失: 0.9922 - 稀疏分类准确率: 0.6121
1408/未知 380秒 266毫秒/步 - 损失: 0.9921 - 稀疏分类准确率: 0.6121
1409/未知 381秒 266毫秒/步 - 损失: 0.9919 - 稀疏分类准确率: 0.6122
1410/未知 381秒 266毫秒/步 - 损失: 0.9918 - 稀疏分类准确率: 0.6122
1411/未知 381秒 266毫秒/步 - 损失: 0.9917 - 稀疏分类准确率: 0.6122
1412/未知 382秒 266毫秒/步 - 损失: 0.9915 - 稀疏分类准确率: 0.6123
1413/未知 382秒 266毫秒/步 - 损失: 0.9914 - 稀疏分类准确率: 0.6123
1414/未知 382秒 266毫秒/步 - 损失: 0.9913 - 稀疏分类准确率: 0.6124
1415/未知 382秒 266毫秒/步 - 损失: 0.9911 - 稀疏分类准确率: 0.6124
1416/未知 383秒 266毫秒/步 - 损失: 0.9910 - 稀疏分类准确率: 0.6125
1417/未知 383秒 266毫秒/步 - 损失: 0.9909 - 稀疏分类准确率: 0.6125
1418/未知 383秒 266毫秒/步 - 损失: 0.9907 - 稀疏分类准确率: 0.6125
1419/未知 384秒 266毫秒/步 - 损失: 0.9906 - 稀疏分类准确率: 0.6126
1420/未知 384秒 267毫秒/步 - 损失: 0.9905 - 稀疏分类准确率: 0.6126
1421/未知 384秒 267毫秒/步 - 损失: 0.9903 - 稀疏分类准确率: 0.6127
1422/未知 385秒 267毫秒/步 - 损失: 0.9902 - 稀疏分类准确率: 0.6127
1423/未知 385秒 267毫秒/步 - 损失: 0.9901 - 稀疏分类准确率: 0.6127
1424/未知 386秒 267毫秒/步 - 损失: 0.9899 - 稀疏分类准确率: 0.6128
1425/未知 386秒 267毫秒/步 - 损失: 0.9898 - 稀疏分类准确率: 0.6128
1426/未知 386秒 267毫秒/步 - 损失: 0.9897 - 稀疏分类准确率: 0.6129
1427/未知 386秒 267毫秒/步 - 损失: 0.9895 - 稀疏分类准确率: 0.6129
1428/未知 387秒 267毫秒/步 - 损失: 0.9894 - 稀疏分类准确率: 0.6130
1429/未知 387秒 267毫秒/步 - 损失: 0.9893 - 稀疏分类准确率: 0.6130
1430/未知 387秒 267毫秒/步 - 损失: 0.9891 - 稀疏分类准确率: 0.6130
1431/未知 388秒 267毫秒/步 - 损失: 0.9890 - 稀疏分类准确率: 0.6131
1432/未知 388秒 267毫秒/步 - 损失: 0.9889 - 稀疏分类准确率: 0.6131
1433/未知 388秒 267毫秒/步 - 损失: 0.9888 - 稀疏分类准确率: 0.6132
1434/未知 388秒 267毫秒/步 - 损失: 0.9886 - 稀疏分类准确率: 0.6132
1435/未知 389秒 267毫秒/步 - 损失: 0.9885 - 稀疏分类准确率: 0.6132
1436/未知 389秒 267毫秒/步 - 损失: 0.9884 - 稀疏分类准确率: 0.6133
1437/未知 389秒 267毫秒/步 - 损失: 0.9882 - 稀疏分类准确率: 0.6133
1438/未知 390秒 267毫秒/步 - 损失: 0.9881 - 稀疏分类准确率: 0.6134
1439/未知 390秒 267毫秒/步 - 损失: 0.9880 - 稀疏分类准确率: 0.6134
1440/未知 390秒 267毫秒/步 - 损失: 0.9878 - 稀疏分类准确率: 0.6134
1441/未知 391秒 267毫秒/步 - 损失: 0.9877 - 稀疏分类准确率: 0.6135
1442/未知 391秒 267毫秒/步 - 损失: 0.9876 - 稀疏分类准确率: 0.6135
1443/未知 391秒 267毫秒/步 - 损失: 0.9875 - 稀疏分类准确率: 0.6136
1444/未知 391秒 267毫秒/步 - 损失: 0.9873 - 稀疏分类准确率: 0.6136
1445/未知 392秒 267毫秒/步 - 损失: 0.9872 - 稀疏分类准确率: 0.6137
1446/未知 392秒 267毫秒/步 - 损失: 0.9871 - 稀疏分类准确率: 0.6137
1447/未知 392秒 267毫秒/步 - 损失: 0.9869 - 稀疏分类准确率: 0.6137
1448/未知 393秒 267毫秒/步 - 损失: 0.9868 - 稀疏分类准确率: 0.6138
1449/未知 393秒 268毫秒/步 - 损失: 0.9867 - 稀疏分类准确率: 0.6138
1450/未知 394秒 268毫秒/步 - 损失: 0.9866 - 稀疏分类准确率: 0.6139
1451/未知 394秒 268毫秒/步 - 损失: 0.9864 - 稀疏分类准确率: 0.6139
1452/未知 394秒 268毫秒/步 - 损失: 0.9863 - 稀疏分类准确率: 0.6139
1453/未知 395秒 268毫秒/步 - 损失: 0.9862 - 稀疏分类准确率: 0.6140
1454/未知 395秒 268毫秒/步 - 损失: 0.9861 - 稀疏分类准确率: 0.6140
1455/未知 395秒 268毫秒/步 - 损失: 0.9859 - 稀疏分类准确率: 0.6141
1456/未知 396秒 268毫秒/步 - 损失: 0.9858 - 稀疏分类准确率: 0.6141
1457/未知 396秒 268毫秒/步 - 损失: 0.9857 - 稀疏分类准确率: 0.6141
1458/未知 396秒 268毫秒/步 - 损失: 0.9855 - 稀疏分类准确率: 0.6142
1459/未知 396秒 268毫秒/步 - 损失: 0.9854 - 稀疏分类准确率: 0.6142
1460/未知 397秒 268毫秒/步 - 损失: 0.9853 - 稀疏分类准确率: 0.6143
1461/未知 397秒 268毫秒/步 - 损失: 0.9852 - 稀疏分类准确率: 0.6143
1462/未知 397秒 268毫秒/步 - 损失: 0.9850 - 稀疏分类准确率: 0.6143
1463/未知 397秒 268毫秒/步 - 损失: 0.9849 - 稀疏分类准确率: 0.6144
1464/未知 398秒 268毫秒/步 - 损失: 0.9848 - 稀疏分类准确率: 0.6144
1465/未知 398秒 268毫秒/步 - 损失: 0.9847 - 稀疏分类准确率: 0.6145
1466/未知 398秒 268毫秒/步 - 损失: 0.9845 - 稀疏分类准确率: 0.6145
1467/未知 399秒 268毫秒/步 - 损失: 0.9844 - 稀疏分类准确率: 0.6145
1468/未知 399秒 268毫秒/步 - 损失: 0.9843 - 稀疏分类准确率: 0.6146
1469/未知 399秒 268毫秒/步 - 损失: 0.9842 - 稀疏分类准确率: 0.6146
1470/未知 399秒 268毫秒/步 - 损失: 0.9840 - 稀疏分类准确率: 0.6147
1471/未知 400秒 268毫秒/步 - 损失: 0.9839 - 稀疏分类准确率: 0.6147
1472/未知 400秒 268毫秒/步 - 损失: 0.9838 - 稀疏分类准确率: 0.6147
1473/未知 400秒 268毫秒/步 - 损失: 0.9837 - 稀疏分类准确率: 0.6148
1474/未知 401秒 268毫秒/步 - 损失: 0.9835 - 稀疏分类准确率: 0.6148
1475/未知 401秒 268毫秒/步 - 损失: 0.9834 - 稀疏分类准确率: 0.6149
1476/未知 401秒 268毫秒/步 - 损失: 0.9833 - 稀疏分类准确率: 0.6149
1477/未知 401秒 268毫秒/步 - 损失: 0.9832 - 稀疏分类准确率: 0.6149
1478/未知 402秒 268毫秒/步 - 损失: 0.9830 - 稀疏分类准确率: 0.6150
1479/未知 402秒 268毫秒/步 - 损失: 0.9829 - 稀疏分类准确率: 0.6150
1480/未知 402秒 268毫秒/步 - 损失: 0.9828 - 稀疏分类准确率: 0.6150
1481/未知 403秒 268毫秒/步 - 损失: 0.9827 - 稀疏分类准确率: 0.6151
1482/未知 403秒 268毫秒/步 - 损失: 0.9825 - 稀疏分类准确率: 0.6151
1483/未知 403秒 268毫秒/步 - 损失: 0.9824 - 稀疏分类准确率: 0.6152
1484/未知 404秒 268毫秒/步 - 损失: 0.9823 - 稀疏分类准确率: 0.6152
1485/未知 404秒 268毫秒/步 - 损失: 0.9822 - 稀疏分类准确率: 0.6152
1486/未知 404秒 268毫秒/步 - 损失: 0.9820 - 稀疏分类准确率: 0.6153
1487/未知 404秒 268毫秒/步 - 损失: 0.9819 - 稀疏分类准确率: 0.6153
1488/未知 405秒 268毫秒/步 - 损失: 0.9818 - 稀疏分类准确率: 0.6154
1489/未知 405秒 268毫秒/步 - 损失: 0.9817 - 稀疏分类准确率: 0.6154
1490/未知 405秒 268毫秒/步 - 损失: 0.9815 - 稀疏分类准确率: 0.6154
1491/未知 406秒 268毫秒/步 - 损失: 0.9814 - 稀疏分类准确率: 0.6155
1492/未知 406秒 268毫秒/步 - 损失: 0.9813 - 稀疏分类准确率: 0.6155
1493/未知 406秒 268毫秒/步 - 损失: 0.9812 - 稀疏分类准确率: 0.6156
1494/未知 406秒 268毫秒/步 - 损失: 0.9810 - 稀疏分类准确率: 0.6156
1495/未知 407秒 268毫秒/步 - 损失: 0.9809 - 稀疏分类准确率: 0.6156
1496/未知 407秒 268毫秒/步 - 损失: 0.9808 - 稀疏分类准确率: 0.6157
1497/未知 407秒 268毫秒/步 - 损失: 0.9807 - 稀疏分类准确率: 0.6157
1498/未知 408秒 268毫秒/步 - 损失: 0.9806 - 稀疏分类准确率: 0.6157
1499/未知 408秒 268毫秒/步 - 损失: 0.9804 - 稀疏分类准确率: 0.6158
1500/未知 408秒 268毫秒/步 - 损失: 0.9803 - 稀疏分类准确率: 0.6158
1501/未知 408秒 268毫秒/步 - 损失: 0.9802 - 稀疏分类准确率: 0.6159
1502/未知 409秒 268毫秒/步 - 损失: 0.9801 - 稀疏分类准确率: 0.6159
1503/未知 409秒 268毫秒/步 - 损失: 0.9800 - 稀疏分类准确率: 0.6159
1504/未知 409秒 268毫秒/步 - 损失: 0.9798 - 稀疏分类准确率: 0.6160
1505/未知 410秒 268毫秒/步 - 损失: 0.9797 - 稀疏分类准确率: 0.6160
1506/未知 410秒 269毫秒/步 - 损失: 0.9796 - 稀疏分类准确率: 0.6161
1507/未知 410秒 269毫秒/步 - 损失: 0.9795 - 稀疏分类准确率: 0.6161
1508/未知 411秒 269毫秒/步 - 损失: 0.9793 - 稀疏分类准确率: 0.6161
1509/未知 411秒 269毫秒/步 - 损失: 0.9792 - 稀疏分类准确率: 0.6162
1510/未知 411秒 269毫秒/步 - 损失: 0.9791 - 稀疏分类准确率: 0.6162
1511/未知 411秒 269毫秒/步 - 损失: 0.9790 - 稀疏分类准确率: 0.6162
1512/未知 412秒 269毫秒/步 - 损失: 0.9789 - 稀疏分类准确率: 0.6163
1513/未知 412秒 269毫秒/步 - 损失: 0.9787 - 稀疏分类准确率: 0.6163
1514/未知 412秒 269毫秒/步 - 损失: 0.9786 - 稀疏分类准确率: 0.6164
1515/未知 413秒 269毫秒/步 - 损失: 0.9785 - 稀疏分类准确率: 0.6164
1516/未知 413秒 269毫秒/步 - 损失: 0.9784 - 稀疏分类准确率: 0.6164
1517/未知 413秒 269毫秒/步 - 损失: 0.9783 - 稀疏分类准确率: 0.6165
1518/未知 413秒 269毫秒/步 - 损失: 0.9781 - 稀疏分类准确率: 0.6165
1519/未知 414秒 269毫秒/步 - 损失: 0.9780 - 稀疏分类准确率: 0.6166
1520/未知 414秒 269毫秒/步 - 损失: 0.9779 - 稀疏分类准确率: 0.6166
1521/未知 414秒 269毫秒/步 - 损失: 0.9778 - 稀疏分类准确率: 0.6166
1522/未知 415秒 269毫秒/步 - 损失: 0.9777 - 稀疏分类准确率: 0.6167
1523/未知 415秒 269毫秒/步 - 损失: 0.9775 - 稀疏分类准确率: 0.6167
1524/未知 415秒 269毫秒/步 - 损失: 0.9774 - 稀疏分类准确率: 0.6167
1525/未知 415秒 269毫秒/步 - 损失: 0.9773 - 稀疏分类准确率: 0.6168
1526/未知 416秒 269毫秒/步 - 损失: 0.9772 - 稀疏分类准确率: 0.6168
1527/未知 416秒 269毫秒/步 - 损失: 0.9771 - 稀疏分类准确率: 0.6169
1528/未知 416秒 269毫秒/步 - 损失: 0.9769 - 稀疏分类准确率: 0.6169
1529/未知 417秒 269毫秒/步 - 损失: 0.9768 - 稀疏分类准确率: 0.6169
1530/未知 417秒 269毫秒/步 - 损失: 0.9767 - 稀疏分类准确率: 0.6170
1531/未知 417秒 269毫秒/步 - 损失: 0.9766 - 稀疏分类准确率: 0.6170
1532/未知 417秒 269毫秒/步 - 损失: 0.9765 - 稀疏分类准确率: 0.6170
1533/未知 418秒 269毫秒/步 - 损失: 0.9764 - 稀疏分类准确率: 0.6171
1534/未知 418秒 269毫秒/步 - 损失: 0.9762 - 稀疏分类准确率: 0.6171
1535/未知 418秒 269毫秒/步 - 损失: 0.9761 - 稀疏分类准确率: 0.6172
1536/未知 418秒 269毫秒/步 - 损失: 0.9760 - 稀疏分类准确率: 0.6172
1537/未知 419秒 269毫秒/步 - 损失: 0.9759 - 稀疏分类准确率: 0.6172
1538/未知 419秒 269毫秒/步 - 损失: 0.9758 - 稀疏分类准确率: 0.6173
1539/未知 419秒 269毫秒/步 - 损失: 0.9756 - 稀疏分类准确率: 0.6173
1540/未知 420秒 269毫秒/步 - 损失: 0.9755 - 稀疏分类准确率: 0.6173
1541/未知 420秒 269毫秒/步 - 损失: 0.9754 - 稀疏分类准确率: 0.6174
1542/未知 420秒 269毫秒/步 - 损失: 0.9753 - 稀疏分类准确率: 0.6174
1543/未知 420秒 269毫秒/步 - 损失: 0.9752 - 稀疏分类准确率: 0.6174
1544/未知 421秒 269毫秒/步 - 损失: 0.9751 - 稀疏分类准确率: 0.6175
1545/未知 421秒 269毫秒/步 - 损失: 0.9749 - 稀疏分类准确率: 0.6175
1546/未知 421秒 269毫秒/步 - 损失: 0.9748 - 稀疏分类准确率: 0.6176
1547/未知 422秒 269毫秒/步 - 损失: 0.9747 - 稀疏分类准确率: 0.6176
1548/未知 422秒 269毫秒/步 - 损失: 0.9746 - 稀疏分类准确率: 0.6176
1549/未知 422秒 269毫秒/步 - 损失: 0.9745 - 稀疏分类准确率: 0.6177
1550/未知 422秒 269毫秒/步 - 损失: 0.9744 - 稀疏分类准确率: 0.6177
1551/未知 423秒 269毫秒/步 - 损失: 0.9742 - 稀疏分类准确率: 0.6177
1552/未知 423秒 269毫秒/步 - 损失: 0.9741 - 稀疏分类准确率: 0.6178
1553/未知 423秒 269毫秒/步 - 损失: 0.9740 - 稀疏分类准确率: 0.6178
1554/未知 424秒 269毫秒/步 - 损失: 0.9739 - 稀疏分类准确率: 0.6179
1555/未知 424秒 269毫秒/步 - 损失: 0.9738 - 稀疏分类准确率: 0.6179
1556/未知 424秒 269毫秒/步 - 损失: 0.9737 - 稀疏分类准确率: 0.6179
1557/未知 424秒 269毫秒/步 - 损失: 0.9736 - 稀疏分类准确率: 0.6180
1558/未知 425秒 269毫秒/步 - 损失: 0.9734 - 稀疏分类准确率: 0.6180
1559/未知 425秒 269毫秒/步 - 损失: 0.9733 - 稀疏分类准确率: 0.6180
1560/未知 425秒 269毫秒/步 - 损失: 0.9732 - 稀疏分类准确率: 0.6181
1561/未知 426秒 269毫秒/步 - 损失: 0.9731 - 稀疏分类准确率: 0.6181
1562/未知 426秒 269毫秒/步 - 损失: 0.9730 - 稀疏分类准确率: 0.6181
1563/未知 426秒 269毫秒/步 - 损失: 0.9729 - 稀疏分类准确率: 0.6182
1564/未知 427秒 269毫秒/步 - 损失: 0.9727 - 稀疏分类准确率: 0.6182
1565/未知 427秒 269毫秒/步 - 损失: 0.9726 - 稀疏分类准确率: 0.6182
1566/未知 427秒 269毫秒/步 - 损失: 0.9725 - 稀疏分类准确率: 0.6183
1567/未知 427秒 269毫秒/步 - 损失: 0.9724 - 稀疏分类准确率: 0.6183
1568/未知 428秒 269毫秒/步 - 损失: 0.9723 - 稀疏分类准确率: 0.6184
1569/未知 428秒 269毫秒/步 - 损失: 0.9722 - 稀疏分类准确率: 0.6184
1570/未知 428秒 269毫秒/步 - 损失: 0.9721 - 稀疏分类准确率: 0.6184
1571/未知 428秒 269毫秒/步 - 损失: 0.9719 - 稀疏分类准确率: 0.6185
1572/未知 429秒 269毫秒/步 - 损失: 0.9718 - 稀疏分类准确率: 0.6185
1573/未知 429秒 269毫秒/步 - 损失: 0.9717 - 稀疏分类准确率: 0.6185
1574/未知 429秒 269毫秒/步 - 损失: 0.9716 - 稀疏分类准确率: 0.6186
1575/未知 430秒 269毫秒/步 - 损失: 0.9715 - 稀疏分类准确率: 0.6186
1576/未知 430秒 269毫秒/步 - 损失: 0.9714 - 稀疏分类准确率: 0.6186
1577/未知 430秒 269毫秒/步 - 损失: 0.9713 - 稀疏分类准确率: 0.6187
1578/未知 430秒 269毫秒/步 - 损失: 0.9712 - 稀疏分类准确率: 0.6187
1579/未知 431秒 269毫秒/步 - 损失: 0.9710 - 稀疏分类准确率: 0.6188
1580/未知 431秒 269毫秒/步 - 损失: 0.9709 - 稀疏分类准确率: 0.6188
1581/未知 431秒 269毫秒/步 - 损失: 0.9708 - 稀疏分类准确率: 0.6188
1582/未知 432秒 269毫秒/步 - 损失: 0.9707 - 稀疏分类准确率: 0.6189
1583/未知 432秒 269毫秒/步 - 损失: 0.9706 - 稀疏分类准确率: 0.6189
1584/未知 432秒 269毫秒/步 - 损失: 0.9705 - 稀疏分类准确率: 0.6189
1585/未知 433秒 269毫秒/步 - 损失: 0.9704 - 稀疏分类准确率: 0.6190
1586/未知 433秒 269毫秒/步 - 损失: 0.9702 - 稀疏分类准确率: 0.6190
1587/未知 433秒 269毫秒/步 - 损失: 0.9701 - 稀疏分类准确率: 0.6190
1588/未知 433秒 269毫秒/步 - 损失: 0.9700 - 稀疏分类准确率: 0.6191
1589/未知 434秒 269毫秒/步 - 损失: 0.9699 - 稀疏分类准确率: 0.6191
1590/未知 434秒 269毫秒/步 - 损失: 0.9698 - 稀疏分类准确率: 0.6191
1591/未知 434秒 269毫秒/步 - 损失: 0.9697 - 稀疏分类准确率: 0.6192
1592/未知 435秒 270毫秒/步 - 损失: 0.9696 - 稀疏分类准确率: 0.6192
1593/未知 435秒 270毫秒/步 - 损失: 0.9695 - 稀疏分类准确率: 0.6192
1594/未知 435秒 270毫秒/步 - 损失: 0.9694 - 稀疏分类准确率: 0.6193
1595/未知 435秒 270毫秒/步 - 损失: 0.9692 - 稀疏分类准确率: 0.6193
1596/未知 436秒 270毫秒/步 - 损失: 0.9691 - 稀疏分类准确率: 0.6194
1597/未知 436秒 270毫秒/步 - 损失: 0.9690 - 稀疏分类准确率: 0.6194
1598/未知 436秒 270毫秒/步 - 损失: 0.9689 - 稀疏分类准确率: 0.6194
1599/未知 437秒 270毫秒/步 - 损失: 0.9688 - 稀疏分类准确率: 0.6195
1600/未知 437秒 270毫秒/步 - 损失: 0.9687 - 稀疏分类准确率: 0.6195
1601/未知 437秒 270毫秒/步 - 损失: 0.9686 - 稀疏分类准确率: 0.6195
1602/未知 437秒 270毫秒/步 - 损失: 0.9685 - 稀疏分类准确率: 0.6196
1603/未知 438秒 270毫秒/步 - 损失: 0.9684 - 稀疏分类准确率: 0.6196
1604/未知 438秒 270毫秒/步 - 损失: 0.9682 - 稀疏分类准确率: 0.6196
1605/未知 438秒 270毫秒/步 - 损失: 0.9681 - 稀疏分类准确率: 0.6197
1606/未知 439秒 270毫秒/步 - 损失: 0.9680 - 稀疏分类准确率: 0.6197
1607/未知 439秒 270毫秒/步 - 损失: 0.9679 - 稀疏分类准确率: 0.6197
1608/未知 439秒 270毫秒/步 - 损失: 0.9678 - 稀疏分类准确率: 0.6198
1609/未知 439秒 270毫秒/步 - 损失: 0.9677 - 稀疏分类准确率: 0.6198
1610/未知 440秒 270毫秒/步 - 损失: 0.9676 - 稀疏分类准确率: 0.6198
1611/未知 440秒 270毫秒/步 - 损失: 0.9675 - 稀疏分类准确率: 0.6199
1612/未知 440秒 270毫秒/步 - 损失: 0.9674 - 稀疏分类准确率: 0.6199
1613/未知 441秒 270毫秒/步 - 损失: 0.9673 - 稀疏分类准确率: 0.6199
1614/未知 441秒 270毫秒/步 - 损失: 0.9671 - 稀疏分类准确率: 0.6200
1615/未知 441秒 270毫秒/步 - 损失: 0.9670 - 稀疏分类准确率: 0.6200
1616/未知 442秒 270毫秒/步 - 损失: 0.9669 - 稀疏分类准确率: 0.6200
1617/未知 442秒 270毫秒/步 - 损失: 0.9668 - 稀疏分类准确率: 0.6201
1618/未知 442秒 270毫秒/步 - 损失: 0.9667 - 稀疏分类准确率: 0.6201
1619/未知 442秒 270毫秒/步 - 损失: 0.9666 - 稀疏分类准确率: 0.6202
1620/未知 443秒 270毫秒/步 - 损失: 0.9665 - 稀疏分类准确率: 0.6202
1621/未知 443秒 270毫秒/步 - 损失: 0.9664 - 稀疏分类准确率: 0.6202
1622/未知 443秒 270毫秒/步 - 损失: 0.9663 - 稀疏分类准确率: 0.6203
1623/未知 444秒 270毫秒/步 - 损失: 0.9662 - 稀疏分类准确率: 0.6203
1624/未知 444秒 270毫秒/步 - 损失: 0.9661 - 稀疏分类准确率: 0.6203
1625/未知 444秒 270毫秒/步 - 损失: 0.9659 - 稀疏分类准确率: 0.6204
1626/未知 445秒 270毫秒/步 - 损失: 0.9658 - 稀疏分类准确率: 0.6204
1627/未知 445秒 270毫秒/步 - 损失: 0.9657 - 稀疏分类准确率: 0.6204
1628/未知 445秒 270毫秒/步 - 损失: 0.9656 - 稀疏分类准确率: 0.6205
1629/未知 446秒 270毫秒/步 - 损失: 0.9655 - 稀疏分类准确率: 0.6205
1630/未知 446秒 270毫秒/步 - 损失: 0.9654 - 稀疏分类准确率: 0.6205
1631/未知 446秒 270毫秒/步 - 损失: 0.9653 - 稀疏分类准确率: 0.6206
1632/未知 447秒 270毫秒/步 - 损失: 0.9652 - 稀疏分类准确率: 0.6206
1633/未知 447秒 270毫秒/步 - 损失: 0.9651 - 稀疏分类准确率: 0.6206
1634/未知 447秒 270毫秒/步 - 损失: 0.9650 - 稀疏分类准确率: 0.6207
1635/未知 448秒 271毫秒/步 - 损失: 0.9649 - 稀疏分类准确率: 0.6207
1636/未知 448秒 271毫秒/步 - 损失: 0.9648 - 稀疏分类准确率: 0.6207
1637/未知 448秒 271毫秒/步 - 损失: 0.9646 - 稀疏分类准确率: 0.6208
1638/未知 449秒 271毫秒/步 - 损失: 0.9645 - 稀疏分类准确率: 0.6208
1639/未知 449秒 271毫秒/步 - 损失: 0.9644 - 稀疏分类准确率: 0.6208
1640/未知 449秒 271毫秒/步 - 损失: 0.9643 - 稀疏分类准确率: 0.6209
1641/未知 450秒 271毫秒/步 - 损失: 0.9642 - 稀疏分类准确率: 0.6209
1642/未知 450秒 271毫秒/步 - 损失: 0.9641 - 稀疏分类准确率: 0.6209
1643/未知 450秒 271毫秒/步 - 损失: 0.9640 - 稀疏分类准确率: 0.6210
1644/未知 450秒 271毫秒/步 - 损失: 0.9639 - 稀疏分类准确率: 0.6210
1645/未知 451秒 271毫秒/步 - 损失: 0.9638 - 稀疏分类准确率: 0.6210
1646/未知 451秒 271毫秒/步 - 损失: 0.9637 - 稀疏分类准确率: 0.6211
1647/未知 451秒 271毫秒/步 - 损失: 0.9636 - 稀疏分类准确率: 0.6211
1648/未知 452秒 271毫秒/步 - 损失: 0.9635 - 稀疏分类准确率: 0.6211
1649/未知 452秒 271毫秒/步 - 损失: 0.9634 - 稀疏分类准确率: 0.6212
1650/未知 452秒 271毫秒/步 - 损失: 0.9633 - 稀疏分类准确率: 0.6212
1651/未知 452秒 271毫秒/步 - 损失: 0.9632 - 稀疏分类准确率: 0.6212
1652/未知 453秒 271毫秒/步 - 损失: 0.9631 - 稀疏分类准确率: 0.6213
1653/未知 453秒 271毫秒/步 - 损失: 0.9629 - 稀疏分类准确率: 0.6213
1654/未知 453秒 271毫秒/步 - 损失: 0.9628 - 稀疏分类准确率: 0.6213
1655/未知 454秒 271毫秒/步 - 损失: 0.9627 - 稀疏分类准确率: 0.6214
1656/未知 454秒 271毫秒/步 - 损失: 0.9626 - 稀疏分类准确率: 0.6214
1657/未知 454秒 271毫秒/步 - 损失: 0.9625 - 稀疏分类准确率: 0.6214
1658/未知 455秒 271毫秒/步 - 损失: 0.9624 - 稀疏分类准确率: 0.6215
1659/未知 455秒 271毫秒/步 - 损失: 0.9623 - 稀疏分类准确率: 0.6215
1660/未知 455秒 271毫秒/步 - 损失: 0.9622 - 稀疏分类准确率: 0.6215
1661/未知 455秒 271毫秒/步 - 损失: 0.9621 - 稀疏分类准确率: 0.6216
1662/未知 456秒 271毫秒/步 - 损失: 0.9620 - 稀疏分类准确率: 0.6216
1663/未知 456秒 271毫秒/步 - 损失: 0.9619 - 稀疏分类准确率: 0.6216
1664/未知 456秒 271毫秒/步 - 损失: 0.9618 - 稀疏分类准确率: 0.6217
1665/未知 457秒 271毫秒/步 - 损失: 0.9617 - 稀疏分类准确率: 0.6217
1666/未知 457秒 271毫秒/步 - 损失: 0.9616 - 稀疏分类准确率: 0.6217
1667/未知 457秒 271毫秒/步 - 损失: 0.9615 - 稀疏分类准确率: 0.6218
1668/未知 457秒 271毫秒/步 - 损失: 0.9614 - 稀疏分类准确率: 0.6218
1669/未知 458秒 271毫秒/步 - 损失: 0.9613 - 稀疏分类准确率: 0.6218
1670/未知 458秒 271毫秒/步 - 损失: 0.9612 - 稀疏分类准确率: 0.6219
1671/未知 458秒 271毫秒/步 - 损失: 0.9611 - 稀疏分类准确率: 0.6219
1672/未知 459秒 271毫秒/步 - 损失: 0.9610 - 稀疏分类准确率: 0.6219
1673/未知 459秒 271毫秒/步 - 损失: 0.9609 - 稀疏分类准确率: 0.6220
1674/未知 459秒 271毫秒/步 - 损失: 0.9607 - 稀疏分类准确率: 0.6220
1675/未知 460秒 271毫秒/步 - 损失: 0.9606 - 稀疏分类准确率: 0.6220
1676/未知 460秒 271毫秒/步 - 损失: 0.9605 - 稀疏分类准确率: 0.6221
1677/未知 460秒 271毫秒/步 - 损失: 0.9604 - 稀疏分类准确率: 0.6221
1678/未知 460秒 271毫秒/步 - 损失: 0.9603 - 稀疏分类准确率: 0.6221
1679/未知 461秒 271毫秒/步 - 损失: 0.9602 - 稀疏分类准确率: 0.6222
1680/未知 461秒 271毫秒/步 - 损失: 0.9601 - 稀疏分类准确率: 0.6222
1681/未知 461秒 271毫秒/步 - 损失: 0.9600 - 稀疏分类准确率: 0.6222
1682/未知 462秒 271毫秒/步 - 损失: 0.9599 - 稀疏分类准确率: 0.6223
1683/未知 462秒 271毫秒/步 - 损失: 0.9598 - 稀疏分类准确率: 0.6223
1684/未知 462秒 271毫秒/步 - 损失: 0.9597 - 稀疏分类准确率: 0.6223
1685/未知 462秒 271毫秒/步 - 损失: 0.9596 - 稀疏分类准确率: 0.6224
1686/未知 463秒 271毫秒/步 - 损失: 0.9595 - 稀疏分类准确率: 0.6224
1687/未知 463秒 271毫秒/步 - 损失: 0.9594 - 稀疏分类准确率: 0.6224
1688/未知 463秒 271毫秒/步 - 损失: 0.9593 - 稀疏分类准确率: 0.6224
1689/未知 463秒 271毫秒/步 - 损失: 0.9592 - 稀疏分类准确率: 0.6225
1690/未知 464秒 271毫秒/步 - 损失: 0.9591 - 稀疏分类准确率: 0.6225
1691/未知 464秒 271毫秒/步 - 损失: 0.9590 - 稀疏分类准确率: 0.6225
1692/未知 464秒 271毫秒/步 - 损失: 0.9589 - 稀疏分类准确率: 0.6226
1693/未知 464秒 271毫秒/步 - 损失: 0.9588 - 稀疏分类准确率: 0.6226
1694/未知 465秒 271毫秒/步 - 损失: 0.9587 - 稀疏分类准确率: 0.6226
1695/未知 465秒 271毫秒/步 - 损失: 0.9586 - 稀疏分类准确率: 0.6227
1696/未知 465秒 271毫秒/步 - 损失: 0.9585 - 稀疏分类准确率: 0.6227
1697/未知 465秒 271毫秒/步 - 损失: 0.9584 - 稀疏分类准确率: 0.6227
1698/未知 466秒 271毫秒/步 - 损失: 0.9583 - 稀疏分类准确率: 0.6228
1699/未知 466秒 271毫秒/步 - 损失: 0.9582 - 稀疏分类准确率: 0.6228
1700/未知 466秒 271毫秒/步 - 损失: 0.9581 - 稀疏分类准确率: 0.6228
1701/未知 466秒 271毫秒/步 - 损失: 0.9580 - 稀疏分类准确率: 0.6229
1702/未知 467秒 271毫秒/步 - 损失: 0.9579 - 稀疏分类准确率: 0.6229
1703/未知 467秒 271毫秒/步 - 损失: 0.9578 - 稀疏分类准确率: 0.6229
1704/未知 467秒 271毫秒/步 - 损失: 0.9577 - 稀疏分类准确率: 0.6230
1705/未知 468秒 271毫秒/步 - 损失: 0.9576 - 稀疏分类准确率: 0.6230
1706/未知 468秒 271毫秒/步 - 损失: 0.9575 - 稀疏分类准确率: 0.6230
1707/未知 468秒 271毫秒/步 - 损失: 0.9574 - 稀疏分类准确率: 0.6231
1708/未知 469秒 271毫秒/步 - 损失: 0.9573 - 稀疏分类准确率: 0.6231
1709/未知 469秒 271毫秒/步 - 损失: 0.9572 - 稀疏分类准确率: 0.6231
1710/未知 469秒 271毫秒/步 - 损失: 0.9571 - 稀疏分类准确率: 0.6232
1711/未知 470秒 271毫秒/步 - 损失: 0.9570 - 稀疏分类准确率: 0.6232
1712/未知 470秒 271毫秒/步 - 损失: 0.9569 - 稀疏分类准确率: 0.6232
1713/未知 470秒 271毫秒/步 - 损失: 0.9568 - 稀疏分类准确率: 0.6232
1714/未知 470秒 271毫秒/步 - 损失: 0.9567 - 稀疏分类准确率: 0.6233
1715/未知 471秒 271毫秒/步 - 损失: 0.9566 - 稀疏分类准确率: 0.6233
1716/未知 471秒 271毫秒/步 - 损失: 0.9565 - 稀疏分类准确率: 0.6233
1717/未知 471秒 271毫秒/步 - 损失: 0.9564 - 稀疏分类准确率: 0.6234
1718/未知 471秒 271毫秒/步 - 损失: 0.9563 - 稀疏分类准确率: 0.6234
1719/未知 472秒 271毫秒/步 - 损失: 0.9562 - 稀疏分类准确率: 0.6234
1720/未知 472秒 271毫秒/步 - 损失: 0.9561 - 稀疏分类准确率: 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
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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。模型的宽部是一个线性模型，而模型的深部是一个多层前馈网络。
在模型的宽部使用输入特征的稀疏表示，在模型的深部使用输入特征的密集表示。
请注意，每个输入特征都以不同的表示形式贡献于模型的两个部分。
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")
让我们运行它
run_experiment(wide_and_deep_model)
Start training the model...
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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 - 损失: 0.9455 - 稀疏分类准确率: 0.6229
1223/未知 508秒 413毫秒/步 - 损失: 0.9454 - 稀疏分类准确率: 0.6229
1224/未知 509秒 413毫秒/步 - 损失: 0.9452 - 稀疏分类准确率: 0.6230
1225/未知 509秒 413毫秒/步 - 损失: 0.9450 - 稀疏分类准确率: 0.6230
1226/未知 509秒 413毫秒/步 - 损失: 0.9449 - 稀疏分类准确率: 0.6231
1227/未知 510秒 413毫秒/步 - 损失: 0.9447 - 稀疏分类准确率: 0.6231
1228/未知 510秒 413毫秒/步 - 损失: 0.9446 - 稀疏分类准确率: 0.6232
1229/未知 511秒 413毫秒/步 - 损失: 0.9444 - 稀疏分类准确率: 0.6233
1230/未知 511秒 413毫秒/步 - 损失: 0.9442 - 稀疏分类准确率: 0.6233
1231/未知 512秒 413毫秒/步 - 损失: 0.9441 - 稀疏分类准确率: 0.6234
1232/未知 512秒 414毫秒/步 - 损失: 0.9439 - 稀疏分类准确率: 0.6234
1233/未知 513秒 414毫秒/步 - 损失: 0.9437 - 稀疏分类准确率: 0.6235
1234/未知 513秒 414毫秒/步 - 损失: 0.9436 - 稀疏分类准确率: 0.6235
1235/未知 513秒 414毫秒/步 - 损失: 0.9434 - 稀疏分类准确率: 0.6236
1236/未知 514秒 414毫秒/步 - 损失: 0.9432 - 稀疏分类准确率: 0.6236
1237/未知 514秒 414毫秒/步 - 损失: 0.9431 - 稀疏分类准确率: 0.6237
1238/未知 515秒 414毫秒/步 - 损失: 0.9429 - 稀疏分类准确率: 0.6237
1239/未知 515秒 414毫秒/步 - 损失: 0.9427 - 稀疏分类准确率: 0.6238
1240/未知 516秒 414毫秒/步 - 损失: 0.9426 - 稀疏分类准确率: 0.6239
1241/未知 516秒 414毫秒/步 - 损失: 0.9424 - 稀疏分类准确率: 0.6239
1242/未知 517秒 414毫秒/步 - 损失: 0.9423 - 稀疏分类准确率: 0.6240
1243/未知 517秒 414毫秒/步 - 损失: 0.9421 - 稀疏分类准确率: 0.6240
1244/未知 518秒 414毫秒/步 - 损失: 0.9419 - 稀疏分类准确率: 0.6241
1245/未知 518秒 414毫秒/步 - 损失: 0.9418 - 稀疏分类准确率: 0.6241
1246/未知 519秒 414毫秒/步 - 损失: 0.9416 - 稀疏分类准确率: 0.6242
1247/未知 519秒 414毫秒/步 - 损失: 0.9415 - 稀疏分类准确率: 0.6242
1248/未知 519秒 414毫秒/步 - 损失: 0.9413 - 稀疏分类准确率: 0.6243
1249/未知 520秒 414毫秒/步 - 损失: 0.9411 - 稀疏分类准确率: 0.6243
1250/未知 520秒 414毫秒/步 - 损失: 0.9410 - 稀疏分类准确率: 0.6244
1251/未知 521秒 414毫秒/步 - 损失: 0.9408 - 稀疏分类准确率: 0.6244
1252/未知 521秒 414毫秒/步 - 损失: 0.9406 - 稀疏分类准确率: 0.6245
1253/未知 521秒 414毫秒/步 - 损失: 0.9405 - 稀疏分类准确率: 0.6245
1254/未知 522秒 414毫秒/步 - 损失: 0.9403 - 稀疏分类准确率: 0.6246
1255/未知 522秒 414毫秒/步 - 损失: 0.9402 - 稀疏分类准确率: 0.6247
1256/未知 522秒 414毫秒/步 - 损失: 0.9400 - 稀疏分类准确率: 0.6247
1257/未知 523秒 414毫秒/步 - 损失: 0.9398 - 稀疏分类准确率: 0.6248
1258/未知 523秒 414毫秒/步 - 损失: 0.9397 - 稀疏分类准确率: 0.6248
1259/未知 524秒 414毫秒/步 - 损失: 0.9395 - 稀疏分类准确率: 0.6249
1260/未知 524秒 414毫秒/步 - 损失: 0.9394 - 稀疏分类准确率: 0.6249
1261/未知 524秒 414毫秒/步 - 损失: 0.9392 - 稀疏分类准确率: 0.6250
1262/未知 525秒 414毫秒/步 - 损失: 0.9391 - 稀疏分类准确率: 0.6250
1263/未知 525秒 414毫秒/步 - 损失: 0.9389 - 稀疏分类准确率: 0.6251
1264/未知 526秒 414毫秒/步 - 损失: 0.9387 - 稀疏分类准确率: 0.6251
1265/未知 526秒 414毫秒/步 - 损失: 0.9386 - 稀疏分类准确率: 0.6252
1266/未知 527秒 414毫秒/步 - 损失: 0.9384 - 稀疏分类准确率: 0.6252
1267/未知 527秒 414毫秒/步 - 损失: 0.9383 - 稀疏分类准确率: 0.6253
1268/未知 527秒 414毫秒/步 - 损失: 0.9381 - 稀疏分类准确率: 0.6253
1269/未知 528秒 414毫秒/步 - 损失: 0.9380 - 稀疏分类准确率: 0.6254
1270/未知 528秒 414毫秒/步 - 损失: 0.9378 - 稀疏分类准确率: 0.6254
1271/未知 529秒 414毫秒/步 - 损失: 0.9376 - 稀疏分类准确率: 0.6255
1272/未知 529秒 414毫秒/步 - 损失: 0.9375 - 稀疏分类准确率: 0.6255
1273/未知 530秒 414毫秒/步 - 损失: 0.9373 - 稀疏分类准确率: 0.6256
1274/未知 530秒 414毫秒/步 - 损失: 0.9372 - 稀疏分类准确率: 0.6256
1275/未知 531秒 414毫秒/步 - 损失: 0.9370 - 稀疏分类准确率: 0.6257
1276/未知 531秒 414毫秒/步 - 损失: 0.9369 - 稀疏分类准确率: 0.6257
1277/未知 532秒 414毫秒/步 - 损失: 0.9367 - 稀疏分类准确率: 0.6258
1278/未知 532秒 414毫秒/步 - 损失: 0.9365 - 稀疏分类准确率: 0.6259
1279/未知 532秒 414毫秒/步 - 损失: 0.9364 - 稀疏分类准确率: 0.6259
1280/未知 533秒 414毫秒/步 - 损失: 0.9362 - 稀疏分类准确率: 0.6260
1281/未知 533秒 414毫秒/步 - 损失: 0.9361 - 稀疏分类准确率: 0.6260
1282/未知 534秒 414毫秒/步 - 损失: 0.9359 - 稀疏分类准确率: 0.6261
1283/未知 534秒 414毫秒/步 - 损失: 0.9358 - 稀疏分类准确率: 0.6261
1284/未知 535秒 414毫秒/步 - 损失: 0.9356 - 稀疏分类准确率: 0.6262
1285/未知 535秒 414毫秒/步 - 损失: 0.9355 - 稀疏分类准确率: 0.6262
1286/未知 535秒 414毫秒/步 - 损失: 0.9353 - 稀疏分类准确率: 0.6263
1287/未知 536秒 414毫秒/步 - 损失: 0.9352 - 稀疏分类准确率: 0.6263
1288/未知 536秒 414毫秒/步 - 损失: 0.9350 - 稀疏分类准确率: 0.6264
1289/未知 537秒 414毫秒/步 - 损失: 0.9348 - 稀疏分类准确率: 0.6264
1290/未知 537秒 414毫秒/步 - 损失: 0.9347 - 稀疏分类准确率: 0.6265
1291/未知 537秒 414毫秒/步 - 损失: 0.9345 - 稀疏分类准确率: 0.6265
1292/未知 538秒 414毫秒/步 - 损失: 0.9344 - 稀疏分类准确率: 0.6266
1293/未知 538秒 414毫秒/步 - 损失: 0.9342 - 稀疏分类准确率: 0.6266
1294/未知 539秒 414毫秒/步 - 损失: 0.9341 - 稀疏分类准确率: 0.6267
1295/未知 539秒 414毫秒/步 - 损失: 0.9339 - 稀疏分类准确率: 0.6267
1296/未知 539秒 414毫秒/步 - 损失: 0.9338 - 稀疏分类准确率: 0.6268
1297/未知 540秒 414毫秒/步 - 损失: 0.9336 - 稀疏分类准确率: 0.6268
1298/未知 540秒 414毫秒/步 - 损失: 0.9335 - 稀疏分类准确率: 0.6269
1299/未知 540秒 414毫秒/步 - 损失: 0.9333 - 稀疏分类准确率: 0.6269
1300/未知 541秒 414毫秒/步 - 损失: 0.9332 - 稀疏分类准确率: 0.6270
1301/未知 541秒 414毫秒/步 - 损失: 0.9330 - 稀疏分类准确率: 0.6270
1302/未知 542秒 414毫秒/步 - 损失: 0.9329 - 稀疏分类准确率: 0.6271
1303/未知 542秒 414毫秒/步 - 损失: 0.9327 - 稀疏分类准确率: 0.6271
1304/未知 542秒 414毫秒/步 - 损失: 0.9326 - 稀疏分类准确率: 0.6272
1305/未知 543秒 414毫秒/步 - 损失: 0.9324 - 稀疏分类准确率: 0.6272
1306/未知 543秒 414毫秒/步 - 损失: 0.9323 - 稀疏分类准确率: 0.6273
1307/未知 544秒 414毫秒/步 - 损失: 0.9321 - 稀疏分类准确率: 0.6273
1308/未知 544秒 414毫秒/步 - 损失: 0.9320 - 稀疏分类准确率: 0.6274
1309/未知 544秒 414毫秒/步 - 损失: 0.9318 - 稀疏分类准确率: 0.6274
1310/未知 545秒 414毫秒/步 - 损失: 0.9317 - 稀疏分类准确率: 0.6275
1311/未知 545秒 414毫秒/步 - 损失: 0.9315 - 稀疏分类准确率: 0.6275
1312/未知 546秒 414毫秒/步 - 损失: 0.9314 - 稀疏分类准确率: 0.6276
1313/未知 546秒 414毫秒/步 - 损失: 0.9312 - 稀疏分类准确率: 0.6276
1314/未知 547秒 414毫秒/步 - 损失: 0.9311 - 稀疏分类准确率: 0.6277
1315/未知 547秒 414毫秒/步 - 损失: 0.9309 - 稀疏分类准确率: 0.6277
1316/未知 548秒 414毫秒/步 - 损失: 0.9308 - 稀疏分类准确率: 0.6278
1317/未知 548秒 414毫秒/步 - 损失: 0.9306 - 稀疏分类准确率: 0.6278
1318/未知 549秒 414毫秒/步 - 损失: 0.9305 - 稀疏分类准确率: 0.6279
1319/未知 549秒 414毫秒/步 - 损失: 0.9303 - 稀疏分类准确率: 0.6279
1320/未知 550秒 414毫秒/步 - 损失: 0.9302 - 稀疏分类准确率: 0.6280
1321/未知 550秒 414毫秒/步 - 损失: 0.9300 - 稀疏分类准确率: 0.6280
1322/未知 551秒 414毫秒/步 - 损失: 0.9299 - 稀疏分类准确率: 0.6281
1323/未知 551秒 415毫秒/步 - 损失: 0.9297 - 稀疏分类准确率: 0.6281
1324/未知 552秒 415毫秒/步 - 损失: 0.9296 - 稀疏分类准确率: 0.6282
1325/未知 552秒 415毫秒/步 - 损失: 0.9294 - 稀疏分类准确率: 0.6282
1326/未知 553秒 415毫秒/步 - 损失: 0.9293 - 稀疏分类准确率: 0.6283
1327/未知 553秒 415毫秒/步 - 损失: 0.9291 - 稀疏分类准确率: 0.6283
1328/未知 553秒 415毫秒/步 - 损失: 0.9290 - 稀疏分类准确率: 0.6284
1329/未知 554秒 415毫秒/步 - 损失: 0.9288 - 稀疏分类准确率: 0.6284
1330/未知 554秒 415毫秒/步 - 损失: 0.9287 - 稀疏分类准确率: 0.6285
1331/未知 555秒 415毫秒/步 - 损失: 0.9285 - 稀疏分类准确率: 0.6285
1332/未知 555秒 415毫秒/步 - 损失: 0.9284 - 稀疏分类准确率: 0.6285
1333/未知 556秒 415毫秒/步 - 损失: 0.9283 - 稀疏分类准确率: 0.6286
1334/未知 556秒 415毫秒/步 - 损失: 0.9281 - 稀疏分类准确率: 0.6286
1335/未知 556秒 415毫秒/步 - 损失: 0.9280 - 稀疏分类准确率: 0.6287
1336/未知 557秒 415毫秒/步 - 损失: 0.9278 - 稀疏分类准确率: 0.6287
1337/未知 557秒 415毫秒/步 - 损失: 0.9277 - 稀疏分类准确率: 0.6288
1338/未知 558秒 415毫秒/步 - 损失: 0.9275 - 稀疏分类准确率: 0.6288
1339/未知 558秒 415毫秒/步 - 损失: 0.9274 - 稀疏分类准确率: 0.6289
1340/未知 559秒 415毫秒/步 - 损失: 0.9272 - 稀疏分类准确率: 0.6289
1341/未知 559秒 415毫秒/步 - 损失: 0.9271 - 稀疏分类准确率: 0.6290
1342/未知 560秒 415毫秒/步 - 损失: 0.9269 - 稀疏分类准确率: 0.6290
1343/未知 560秒 415毫秒/步 - 损失: 0.9268 - 稀疏分类准确率: 0.6291
1344/未知 561秒 415毫秒/步 - 损失: 0.9267 - 稀疏分类准确率: 0.6291
1345/未知 561秒 415毫秒/步 - 损失: 0.9265 - 稀疏分类准确率: 0.6292
1346/未知 561秒 415毫秒/步 - 损失: 0.9264 - 稀疏分类准确率: 0.6292
1347/未知 562秒 415毫秒/步 - 损失: 0.9262 - 稀疏分类准确率: 0.6293
1348/未知 562秒 415毫秒/步 - 损失: 0.9261 - 稀疏分类准确率: 0.6293
1349/未知 563秒 415毫秒/步 - 损失: 0.9259 - 稀疏分类准确率: 0.6294
1350/未知 563秒 415毫秒/步 - 损失: 0.9258 - 稀疏分类准确率: 0.6294
1351/未知 564秒 415毫秒/步 - 损失: 0.9256 - 稀疏分类准确率: 0.6295
1352/未知 564秒 415毫秒/步 - 损失: 0.9255 - 稀疏分类准确率: 0.6295
1353/未知 564秒 415毫秒/步 - 损失: 0.9254 - 稀疏分类准确率: 0.6296
1354/未知 565秒 415毫秒/步 - 损失: 0.9252 - 稀疏分类准确率: 0.6296
1355/未知 565秒 415毫秒/步 - 损失: 0.9251 - 稀疏分类准确率: 0.6296
1356/未知 565秒 415毫秒/步 - 损失: 0.9249 - 稀疏分类准确率: 0.6297
1357/未知 566秒 415毫秒/步 - 损失: 0.9248 - 稀疏分类准确率: 0.6297
1358/未知 566秒 415毫秒/步 - 损失: 0.9246 - 稀疏分类准确率: 0.6298
1359/未知 566秒 415毫秒/步 - 损失: 0.9245 - 稀疏分类准确率: 0.6298
1360/未知 567秒 415毫秒/步 - 损失: 0.9244 - 稀疏分类准确率: 0.6299
1361/未知 567秒 415毫秒/步 - 损失: 0.9242 - 稀疏分类准确率: 0.6299
1362/未知 568秒 415毫秒/步 - 损失: 0.9241 - 稀疏分类准确率: 0.6300
1363/未知 568秒 415毫秒/步 - 损失: 0.9239 - 稀疏分类准确率: 0.6300
1364/未知 568秒 415毫秒/步 - 损失: 0.9238 - 稀疏分类准确率: 0.6301
1365/未知 569秒 415毫秒/步 - 损失: 0.9237 - 稀疏分类准确率: 0.6301
1366/未知 569秒 415毫秒/步 - 损失: 0.9235 - 稀疏分类准确率: 0.6302
1367/未知 570秒 415毫秒/步 - 损失: 0.9234 - 稀疏分类准确率: 0.6302
1368/未知 570秒 415毫秒/步 - 损失: 0.9232 - 稀疏分类准确率: 0.6303
1369/未知 571秒 415毫秒/步 - 损失: 0.9231 - 稀疏分类准确率: 0.6303
1370/未知 571秒 415毫秒/步 - 损失: 0.9229 - 稀疏分类准确率: 0.6304
1371/未知 572秒 415毫秒/步 - 损失: 0.9228 - 稀疏分类准确率: 0.6304
1372/未知 572秒 415毫秒/步 - 损失: 0.9227 - 稀疏分类准确率: 0.6304
1373/未知 573秒 415毫秒/步 - 损失: 0.9225 - 稀疏分类准确率: 0.6305
1374/未知 573秒 415毫秒/步 - 损失: 0.9224 - 稀疏分类准确率: 0.6305
1375/未知 574秒 415毫秒/步 - 损失: 0.9222 - 稀疏分类准确率: 0.6306
1376/未知 574秒 415毫秒/步 - 损失: 0.9221 - 稀疏分类准确率: 0.6306
1377/未知 574秒 415毫秒/步 - 损失: 0.9220 - 稀疏分类准确率: 0.6307
1378/未知 575秒 415毫秒/步 - 损失: 0.9218 - 稀疏分类准确率: 0.6307
1379/未知 575秒 415毫秒/步 - 损失: 0.9217 - 稀疏分类准确率: 0.6308
1380/未知 575秒 415毫秒/步 - 损失: 0.9215 - 稀疏分类准确率: 0.6308
1381/未知 576秒 415毫秒/步 - 损失: 0.9214 - 稀疏分类准确率: 0.6309
1382/未知 576秒 415毫秒/步 - 损失: 0.9213 - 稀疏分类准确率: 0.6309
1383/未知 576秒 415毫秒/步 - 损失: 0.9211 - 稀疏分类准确率: 0.6309
1384/未知 577秒 415毫秒/步 - 损失: 0.9210 - 稀疏分类准确率: 0.6310
1385/未知 577秒 415毫秒/步 - 损失: 0.9209 - 稀疏分类准确率: 0.6310
1386/未知 578秒 415毫秒/步 - 损失: 0.9207 - 稀疏分类准确率: 0.6311
1387/未知 578秒 415毫秒/步 - 损失: 0.9206 - 稀疏分类准确率: 0.6311
1388/未知 578秒 415毫秒/步 - 损失: 0.9204 - 稀疏分类准确率: 0.6312
1389/未知 579秒 415毫秒/步 - 损失: 0.9203 - 稀疏分类准确率: 0.6312
1390/未知 579秒 415毫秒/步 - 损失: 0.9202 - 稀疏分类准确率: 0.6313
1391/未知 580秒 415毫秒/步 - 损失: 0.9200 - 稀疏分类准确率: 0.6313
1392/未知 580秒 415毫秒/步 - 损失: 0.9199 - 稀疏分类准确率: 0.6314
1393/未知 580秒 415毫秒/步 - 损失: 0.9198 - 稀疏分类准确率: 0.6314
1394/未知 581秒 415毫秒/步 - 损失: 0.9196 - 稀疏分类准确率: 0.6315
1395/未知 581秒 415毫秒/步 - 损失: 0.9195 - 稀疏分类准确率: 0.6315
1396/未知 582秒 415毫秒/步 - 损失: 0.9193 - 稀疏分类准确率: 0.6315
1397/未知 582秒 415毫秒/步 - 损失: 0.9192 - 稀疏分类准确率: 0.6316
1398/未知 583秒 415毫秒/步 - 损失: 0.9191 - 稀疏分类准确率: 0.6316
1399/未知 583秒 415毫秒/步 - 损失: 0.9189 - 稀疏分类准确率: 0.6317
1400/未知 583秒 415毫秒/步 - 损失: 0.9188 - 稀疏分类准确率: 0.6317
1401/未知 584秒 415毫秒/步 - 损失: 0.9187 - 稀疏分类准确率: 0.6318
1402/未知 584秒 415毫秒/步 - 损失: 0.9185 - 稀疏分类准确率: 0.6318
1403/未知 585秒 415毫秒/步 - 损失: 0.9184 - 稀疏分类准确率: 0.6319
1404/未知 585秒 415毫秒/步 - 损失: 0.9183 - 稀疏分类准确率: 0.6319
1405/未知 586秒 415毫秒/步 - 损失: 0.9181 - 稀疏分类准确率: 0.6319
1406/未知 586秒 415毫秒/步 - 损失: 0.9180 - 稀疏分类准确率: 0.6320
1407/未知 587秒 415毫秒/步 - 损失: 0.9178 - 稀疏分类准确率: 0.6320
1408/未知 587秒 415毫秒/步 - 损失: 0.9177 - 稀疏分类准确率: 0.6321
1409/未知 588秒 415毫秒/步 - 损失: 0.9176 - 稀疏分类准确率: 0.6321
1410/未知 588秒 415毫秒/步 - 损失: 0.9174 - 稀疏分类准确率: 0.6322
1411/未知 589秒 415毫秒/步 - 损失: 0.9173 - 稀疏分类准确率: 0.6322
1412/未知 589秒 415毫秒/步 - 损失: 0.9172 - 稀疏分类准确率: 0.6323
1413/未知 590秒 415毫秒/步 - 损失: 0.9170 - 稀疏分类准确率: 0.6323
1414/未知 590秒 415毫秒/步 - 损失: 0.9169 - 稀疏分类准确率: 0.6323
1415/未知 591秒 415毫秒/步 - 损失: 0.9168 - 稀疏分类准确率: 0.6324
1416/未知 591秒 415毫秒/步 - 损失: 0.9166 - 稀疏分类准确率: 0.6324
1417/未知 591秒 415毫秒/步 - 损失: 0.9165 - 稀疏分类准确率: 0.6325
1418/未知 592秒 415毫秒/步 - 损失: 0.9164 - 稀疏分类准确率: 0.6325
1419/未知 592秒 415毫秒/步 - 损失: 0.9162 - 稀疏分类准确率: 0.6326
1420/未知 592秒 415毫秒/步 - 损失: 0.9161 - 稀疏分类准确率: 0.6326
1421/未知 593秒 415毫秒/步 - 损失: 0.9160 - 稀疏分类准确率: 0.6327
1422/未知 593秒 415毫秒/步 - 损失: 0.9158 - 稀疏分类准确率: 0.6327
1423/未知 594秒 415毫秒/步 - 损失: 0.9157 - 稀疏分类准确率: 0.6327
1424/未知 594秒 415毫秒/步 - 损失: 0.9156 - 稀疏分类准确率: 0.6328
1425/未知 594秒 415毫秒/步 - 损失: 0.9154 - 稀疏分类准确率: 0.6328
1426/未知 595秒 415毫秒/步 - 损失: 0.9153 - 稀疏分类准确率: 0.6329
1427/未知 595秒 415毫秒/步 - 损失: 0.9152 - 稀疏分类准确率: 0.6329
1428/未知 596秒 415毫秒/步 - 损失: 0.9150 - 稀疏分类准确率: 0.6330
1429/未知 596秒 415毫秒/步 - 损失: 0.9149 - 稀疏分类准确率: 0.6330
1430/未知 596秒 415毫秒/步 - 损失: 0.9148 - 稀疏分类准确率: 0.6331
1431/未知 597秒 415毫秒/步 - 损失: 0.9146 - 稀疏分类准确率: 0.6331
1432/未知 597秒 415毫秒/步 - 损失: 0.9145 - 稀疏分类准确率: 0.6331
1433/未知 598秒 415毫秒/步 - 损失: 0.9144 - 稀疏分类准确率: 0.6332
1434/未知 598秒 415毫秒/步 - 损失: 0.9142 - 稀疏分类准确率: 0.6332
1435/未知 599秒 415毫秒/步 - 损失: 0.9141 - 稀疏分类准确率: 0.6333
1436/未知 599秒 415毫秒/步 - 损失: 0.9140 - 稀疏分类准确率: 0.6333
1437/未知 599秒 415毫秒/步 - 损失: 0.9139 - 稀疏分类准确率: 0.6334
1438/未知 600秒 415毫秒/步 - 损失: 0.9137 - 稀疏分类准确率: 0.6334
1439/未知 600秒 415毫秒/步 - 损失: 0.9136 - 稀疏分类准确率: 0.6334
1440/未知 601秒 415毫秒/步 - 损失: 0.9135 - 稀疏分类准确率: 0.6335
1441/未知 601秒 415毫秒/步 - 损失: 0.9133 - 稀疏分类准确率: 0.6335
1442/未知 602秒 416毫秒/步 - 损失: 0.9132 - 稀疏分类准确率: 0.6336
1443/未知 602秒 416毫秒/步 - 损失: 0.9131 - 稀疏分类准确率: 0.6336
1444/未知 603秒 416毫秒/步 - 损失: 0.9129 - 稀疏分类准确率: 0.6337
1445/未知 603秒 416毫秒/步 - 损失: 0.9128 - 稀疏分类准确率: 0.6337
1446/未知 604秒 416毫秒/步 - 损失: 0.9127 - 稀疏分类准确率: 0.6337
1447/未知 604秒 416毫秒/步 - 损失: 0.9126 - 稀疏分类准确率: 0.6338
1448/未知 605秒 416毫秒/步 - 损失: 0.9124 - 稀疏分类准确率: 0.6338
1449/未知 605秒 416毫秒/步 - 损失: 0.9123 - 稀疏分类准确率: 0.6339
1450/未知 606秒 416毫秒/步 - 损失: 0.9122 - 稀疏分类准确率: 0.6339
1451/未知 606秒 416毫秒/步 - 损失: 0.9120 - 稀疏分类准确率: 0.6340
1452/未知 606秒 416毫秒/步 - 损失: 0.9119 - 稀疏分类准确率: 0.6340
1453/未知 607秒 416毫秒/步 - 损失: 0.9118 - 稀疏分类准确率: 0.6340
1454/未知 607秒 416毫秒/步 - 损失: 0.9116 - 稀疏分类准确率: 0.6341
1455/未知 608秒 416毫秒/步 - 损失: 0.9115 - 稀疏分类准确率: 0.6341
1456/未知 608秒 416毫秒/步 - 损失: 0.9114 - 稀疏分类准确率: 0.6342
1457/未知 609秒 416毫秒/步 - 损失: 0.9113 - 稀疏分类准确率: 0.6342
1458/未知 609秒 416毫秒/步 - 损失: 0.9111 - 稀疏分类准确率: 0.6343
1459/未知 610秒 416毫秒/步 - 损失: 0.9110 - 稀疏分类准确率: 0.6343
1460/未知 610秒 416毫秒/步 - 损失: 0.9109 - 稀疏分类准确率: 0.6343
1461/未知 610秒 416毫秒/步 - 损失: 0.9108 - 稀疏分类准确率: 0.6344
1462/未知 611秒 416毫秒/步 - 损失: 0.9106 - 稀疏分类准确率: 0.6344
1463/未知 611秒 416毫秒/步 - 损失: 0.9105 - 稀疏分类准确率: 0.6345
1464/未知 612秒 416毫秒/步 - 损失: 0.9104 - 稀疏分类准确率: 0.6345
1465/未知 612秒 416毫秒/步 - 损失: 0.9102 - 稀疏分类准确率: 0.6345
1466/未知 613秒 416毫秒/步 - 损失: 0.9101 - 稀疏分类准确率: 0.6346
1467/未知 613秒 416毫秒/步 - 损失: 0.9100 - 稀疏分类准确率: 0.6346
1468/未知 613秒 416毫秒/步 - 损失: 0.9099 - 稀疏分类准确率: 0.6347
1469/未知 614秒 416毫秒/步 - 损失: 0.9097 - 稀疏分类准确率: 0.6347
1470/未知 614秒 416毫秒/步 - 损失: 0.9096 - 稀疏分类准确率: 0.6348
1471/未知 614秒 416毫秒/步 - 损失: 0.9095 - 稀疏分类准确率: 0.6348
1472/未知 615秒 416毫秒/步 - 损失: 0.9094 - 稀疏分类准确率: 0.6348
1473/未知 615秒 416毫秒/步 - 损失: 0.9092 - 稀疏分类准确率: 0.6349
1474/未知 615秒 416毫秒/步 - 损失: 0.9091 - 稀疏分类准确率: 0.6349
1475/未知 616秒 416毫秒/步 - 损失: 0.9090 - 稀疏分类准确率: 0.6350
1476/未知 616秒 416毫秒/步 - 损失: 0.9089 - 稀疏分类准确率: 0.6350
1477/未知 616秒 416毫秒/步 - 损失: 0.9087 - 稀疏分类准确率: 0.6350
1478/未知 617秒 415毫秒/步 - 损失: 0.9086 - 稀疏分类准确率: 0.6351
1479/未知 617秒 415毫秒/步 - 损失: 0.9085 - 稀疏分类准确率: 0.6351
1480/未知 617秒 415毫秒/步 - 损失: 0.9083 - 稀疏分类准确率: 0.6352
1481/未知 618秒 415毫秒/步 - 损失: 0.9082 - 稀疏分类准确率: 0.6352
1482/未知 618秒 415毫秒/步 - 损失: 0.9081 - 稀疏分类准确率: 0.6353
1483/未知 619秒 415毫秒/步 - 损失: 0.9080 - 稀疏分类准确率: 0.6353
1484/未知 619秒 415毫秒/步 - 损失: 0.9078 - 稀疏分类准确率: 0.6353
1485/未知 620秒 415毫秒/步 - 损失: 0.9077 - 稀疏分类准确率: 0.6354
1486/未知 620秒 415毫秒/步 - 损失: 0.9076 - 稀疏分类准确率: 0.6354
1487/未知 620秒 415毫秒/步 - 损失: 0.9075 - 稀疏分类准确率: 0.6355
1488/未知 621秒 416毫秒/步 - 损失: 0.9073 - 稀疏分类准确率: 0.6355
1489/未知 621秒 416毫秒/步 - 损失: 0.9072 - 稀疏分类准确率: 0.6355
1490/未知 622秒 416毫秒/步 - 损失: 0.9071 - 稀疏分类准确率: 0.6356
1491/未知 622秒 416毫秒/步 - 损失: 0.9070 - 稀疏分类准确率: 0.6356
1492/未知 623秒 416毫秒/步 - 损失: 0.9069 - 稀疏分类准确率: 0.6357
1493/未知 623秒 416毫秒/步 - 损失: 0.9067 - 稀疏分类准确率: 0.6357
1494/未知 624秒 416毫秒/步 - 损失: 0.9066 - 稀疏分类准确率: 0.6358
1495/未知 624秒 416毫秒/步 - 损失: 0.9065 - 稀疏分类准确率: 0.6358
1496/未知 624秒 416毫秒/步 - 损失: 0.9064 - 稀疏分类准确率: 0.6358
1497/未知 625秒 416毫秒/步 - 损失: 0.9062 - 稀疏分类准确率: 0.6359
1498/未知 625秒 416毫秒/步 - 损失: 0.9061 - 稀疏分类准确率: 0.6359
1499/未知 626秒 416毫秒/步 - 损失: 0.9060 - 稀疏分类准确率: 0.6360
1500/未知 626秒 416毫秒/步 - 损失: 0.9059 - 稀疏分类准确率: 0.6360
1501/未知 627秒 416毫秒/步 - 损失: 0.9057 - 稀疏分类准确率: 0.6360
1502/未知 627秒 416毫秒/步 - 损失: 0.9056 - 稀疏分类准确率: 0.6361
1503/未知 628秒 416毫秒/步 - 损失: 0.9055 - 稀疏分类准确率: 0.6361
1504/未知 628秒 416毫秒/步 - 损失: 0.9054 - 稀疏分类准确率: 0.6362
1505/未知 628秒 416毫秒/步 - 损失: 0.9053 - 稀疏分类准确率: 0.6362
1506/未知 629秒 416毫秒/步 - 损失: 0.9051 - 稀疏分类准确率: 0.6362
1507/未知 629秒 416毫秒/步 - 损失: 0.9050 - 稀疏分类准确率: 0.6363
1508/未知 630秒 416毫秒/步 - 损失: 0.9049 - 稀疏分类准确率: 0.6363
1509/未知 630秒 416毫秒/步 - 损失: 0.9048 - 稀疏分类准确率: 0.6364
1510/未知 631秒 416毫秒/步 - 损失: 0.9046 - 稀疏分类准确率: 0.6364
1511/未知 631秒 416毫秒/步 - 损失: 0.9045 - 稀疏分类准确率: 0.6364
1512/未知 631秒 416毫秒/步 - 损失: 0.9044 - 稀疏分类准确率: 0.6365
1513/未知 632秒 416毫秒/步 - 损失: 0.9043 - 稀疏分类准确率: 0.6365
1514/未知 632秒 416毫秒/步 - 损失: 0.9042 - 稀疏分类准确率: 0.6366
1515/未知 632秒 416毫秒/步 - 损失: 0.9040 - 稀疏分类准确率: 0.6366
1516/未知 633秒 416毫秒/步 - 损失: 0.9039 - 稀疏分类准确率: 0.6366
1517/未知 633秒 416毫秒/步 - 损失: 0.9038 - 稀疏分类准确率: 0.6367
1518/未知 634秒 416毫秒/步 - 损失: 0.9037 - 稀疏分类准确率: 0.6367
1519/未知 634秒 416毫秒/步 - 损失: 0.9036 - 稀疏分类准确率: 0.6368
1520/未知 634秒 415毫秒/步 - 损失: 0.9034 - 稀疏分类准确率: 0.6368
1521/未知 635秒 415毫秒/步 - 损失: 0.9033 - 稀疏分类准确率: 0.6368
1522/未知 635秒 415毫秒/步 - 损失: 0.9032 - 稀疏分类准确率: 0.6369
1523/未知 635秒 415毫秒/步 - 损失: 0.9031 - 稀疏分类准确率: 0.6369
1524/未知 636秒 415毫秒/步 - 损失: 0.9029 - 稀疏分类准确率: 0.6370
1525/未知 636秒 415毫秒/步 - 损失: 0.9028 - 稀疏分类准确率: 0.6370
1526/未知 637秒 415毫秒/步 - 损失: 0.9027 - 稀疏分类准确率: 0.6370
1527/未知 637秒 415毫秒/步 - 损失: 0.9026 - 稀疏分类准确率: 0.6371
1528/未知 638秒 416毫秒/步 - 损失: 0.9025 - 稀疏分类准确率: 0.6371
1529/未知 638秒 416毫秒/步 - 损失: 0.9023 - 稀疏分类准确率: 0.6372
1530/未知 639秒 416毫秒/步 - 损失: 0.9022 - 稀疏分类准确率: 0.6372
1531/未知 639秒 416毫秒/步 - 损失: 0.9021 - 稀疏分类准确率: 0.6372
1532/未知 640秒 416毫秒/步 - 损失: 0.9020 - 稀疏分类准确率: 0.6373
1533/未知 640秒 416毫秒/步 - 损失: 0.9019 - 稀疏分类准确率: 0.6373
1534/未知 641秒 416毫秒/步 - 损失: 0.9018 - 稀疏分类准确率: 0.6374
1535/未知 641秒 416毫秒/步 - 损失: 0.9016 - 稀疏分类准确率: 0.6374
1536/未知 641秒 416毫秒/步 - 损失: 0.9015 - 稀疏分类准确率: 0.6374
1537/未知 642秒 416毫秒/步 - 损失: 0.9014 - 稀疏分类准确率: 0.6375
1538/未知 642秒 416毫秒/步 - 损失: 0.9013 - 稀疏分类准确率: 0.6375
1539/未知 643秒 416毫秒/步 - 损失: 0.9012 - 稀疏分类准确率: 0.6376
1540/未知 643秒 416毫秒/步 - 损失: 0.9010 - 稀疏分类准确率: 0.6376
1541/未知 644秒 416毫秒/步 - 损失: 0.9009 - 稀疏分类准确率: 0.6376
1542/未知 644秒 416毫秒/步 - 损失: 0.9008 - 稀疏分类准确率: 0.6377
1543/未知 645秒 416毫秒/步 - 损失: 0.9007 - 稀疏分类准确率: 0.6377
1544/未知 645秒 416毫秒/步 - 损失: 0.9006 - 稀疏分类准确率: 0.6378
1545/未知 645秒 416毫秒/步 - 损失: 0.9004 - 稀疏分类准确率: 0.6378
1546/未知 646秒 416毫秒/步 - 损失: 0.9003 - 稀疏分类准确率: 0.6378
1547/未知 646秒 416毫秒/步 - 损失: 0.9002 - 稀疏分类准确率: 0.6379
1548/未知 646秒 416毫秒/步 - 损失: 0.9001 - 稀疏分类准确率: 0.6379
1549/未知 647秒 416毫秒/步 - 损失: 0.9000 - 稀疏分类准确率: 0.6379
1550/未知 647秒 416毫秒/步 - 损失: 0.8999 - 稀疏分类准确率: 0.6380
1551/未知 648秒 416毫秒/步 - 损失: 0.8997 - 稀疏分类准确率: 0.6380
1552/未知 648秒 416毫秒/步 - 损失: 0.8996 - 稀疏分类准确率: 0.6381
1553/未知 648秒 416毫秒/步 - 损失: 0.8995 - 稀疏分类准确率: 0.6381
1554/未知 649秒 416毫秒/步 - 损失: 0.8994 - 稀疏分类准确率: 0.6381
1555/未知 649秒 416毫秒/步 - 损失: 0.8993 - 稀疏分类准确率: 0.6382
1556/未知 650秒 416毫秒/步 - 损失: 0.8992 - 稀疏分类准确率: 0.6382
1557/未知 650秒 416毫秒/步 - 损失: 0.8990 - 稀疏分类准确率: 0.6383
1558/未知 650秒 416毫秒/步 - 损失: 0.8989 - 稀疏分类准确率: 0.6383
1559/未知 651秒 416毫秒/步 - 损失: 0.8988 - 稀疏分类准确率: 0.6383
1560/未知 651秒 416毫秒/步 - 损失: 0.8987 - 稀疏分类准确率: 0.6384
1561/未知 652秒 416毫秒/步 - 损失: 0.8986 - 稀疏分类准确率: 0.6384
1562/未知 652秒 416毫秒/步 - 损失: 0.8985 - 稀疏分类准确率: 0.6385
1563/未知 653秒 416毫秒/步 - 损失: 0.8983 - 稀疏分类准确率: 0.6385
1564/未知 653秒 416毫秒/步 - 损失: 0.8982 - 稀疏分类准确率: 0.6385
1565/未知 654秒 416毫秒/步 - 损失: 0.8981 - 稀疏分类准确率: 0.6386
1566/未知 654秒 416毫秒/步 - 损失: 0.8980 - 稀疏分类准确率: 0.6386
1567/未知 655秒 416毫秒/步 - 损失: 0.8979 - 稀疏分类准确率: 0.6386
1568/未知 655秒 416毫秒/步 - 损失: 0.8978 - 稀疏分类准确率: 0.6387
1569/未知 656秒 416毫秒/步 - 损失: 0.8977 - 稀疏分类准确率: 0.6387
1570/未知 656秒 416毫秒/步 - 损失: 0.8975 - 稀疏分类准确率: 0.6388
1571/未知 656秒 416毫秒/步 - 损失: 0.8974 - 稀疏分类准确率: 0.6388
1572/未知 657秒 416毫秒/步 - 损失: 0.8973 - 稀疏分类准确率: 0.6388
1573/未知 657秒 416毫秒/步 - 损失: 0.8972 - 稀疏分类准确率: 0.6389
1574/未知 658秒 416毫秒/步 - 损失: 0.8971 - 稀疏分类准确率: 0.6389
1575/未知 658秒 416毫秒/步 - 损失: 0.8970 - 稀疏分类准确率: 0.6389
1576/未知 659秒 416毫秒/步 - 损失: 0.8969 - 稀疏分类准确率: 0.6390
1577/未知 659秒 416毫秒/步 - 损失: 0.8967 - 稀疏分类准确率: 0.6390
1578/未知 660秒 416毫秒/步 - 损失: 0.8966 - 稀疏分类准确率: 0.6391
1579/未知 660秒 416毫秒/步 - 损失: 0.8965 - 稀疏分类准确率: 0.6391
1580/未知 661秒 416毫秒/步 - 损失: 0.8964 - 稀疏分类准确率: 0.6391
1581/未知 661秒 416毫秒/步 - 损失: 0.8963 - 稀疏分类准确率: 0.6392
1582/未知 662秒 416毫秒/步 - 损失: 0.8962 - 稀疏分类准确率: 0.6392
1583/未知 662秒 417毫秒/步 - 损失: 0.8961 - 稀疏分类准确率: 0.6392
1584/未知 662秒 417毫秒/步 - 损失: 0.8959 - 稀疏分类准确率: 0.6393
1585/未知 663秒 417毫秒/步 - 损失: 0.8958 - 稀疏分类准确率: 0.6393
1586/未知 663秒 417毫秒/步 - 损失: 0.8957 - 稀疏分类准确率: 0.6394
1587/未知 664秒 417毫秒/步 - 损失: 0.8956 - 稀疏分类准确率: 0.6394
1588/未知 664秒 417毫秒/步 - 损失: 0.8955 - 稀疏分类准确率: 0.6394
1589/未知 665秒 417毫秒/步 - 损失: 0.8954 - 稀疏分类准确率: 0.6395
1590/未知 665秒 417毫秒/步 - 损失: 0.8953 - 稀疏分类准确率: 0.6395
1591/未知 666秒 417毫秒/步 - 损失: 0.8952 - 稀疏分类准确率: 0.6395
1592/未知 666秒 417毫秒/步 - 损失: 0.8950 - 稀疏分类准确率: 0.6396
1593/未知 666秒 417毫秒/步 - 损失: 0.8949 - 稀疏分类准确率: 0.6396
1594/未知 667秒 417毫秒/步 - 损失: 0.8948 - 稀疏分类准确率: 0.6397
1595/未知 667秒 417毫秒/步 - 损失: 0.8947 - 稀疏分类准确率: 0.6397
1596/未知 668秒 417毫秒/步 - 损失: 0.8946 - 稀疏分类准确率: 0.6397
1597/未知 668秒 417毫秒/步 - 损失: 0.8945 - 稀疏分类准确率: 0.6398
1598/未知 669秒 417毫秒/步 - 损失: 0.8944 - 稀疏分类准确率: 0.6398
1599/未知 669秒 417毫秒/步 - 损失: 0.8943 - 稀疏分类准确率: 0.6398
1600/未知 669秒 417毫秒/步 - 损失: 0.8941 - 稀疏分类准确率: 0.6399
1601/未知 670秒 417毫秒/步 - 损失: 0.8940 - 稀疏分类准确率: 0.6399
1602/未知 670秒 417毫秒/步 - 损失: 0.8939 - 稀疏分类准确率: 0.6400
1603/未知 671秒 417毫秒/步 - 损失: 0.8938 - 稀疏分类准确率: 0.6400
1604/未知 671秒 417毫秒/步 - 损失: 0.8937 - 稀疏分类准确率: 0.6400
1605/未知 672秒 417毫秒/步 - 损失: 0.8936 - 稀疏分类准确率: 0.6401
1606/未知 672秒 417毫秒/步 - 损失: 0.8935 - 稀疏分类准确率: 0.6401
1607/未知 673秒 417毫秒/步 - 损失: 0.8934 - 稀疏分类准确率: 0.6401
1608/未知 673秒 417毫秒/步 - 损失: 0.8933 - 稀疏分类准确率: 0.6402
1609/未知 673秒 417毫秒/步 - 损失: 0.8931 - 稀疏分类准确率: 0.6402
1610/未知 674秒 417毫秒/步 - 损失: 0.8930 - 稀疏分类准确率: 0.6403
1611/未知 674秒 417毫秒/步 - 损失: 0.8929 - 稀疏分类准确率: 0.6403
1612/未知 675秒 417毫秒/步 - 损失: 0.8928 - 稀疏分类准确率: 0.6403
1613/未知 675秒 417毫秒/步 - 损失: 0.8927 - 稀疏分类准确率: 0.6404
1614/未知 675秒 417毫秒/步 - 损失: 0.8926 - 稀疏分类准确率: 0.6404
1615/未知 676秒 417毫秒/步 - 损失: 0.8925 - 稀疏分类准确率: 0.6404
1616/未知 676秒 417毫秒/步 - 损失: 0.8924 - 稀疏分类准确率: 0.6405
1617/未知 677秒 417毫秒/步 - 损失: 0.8923 - 稀疏分类准确率: 0.6405
1618/未知 677秒 417毫秒/步 - 损失: 0.8922 - 稀疏分类准确率: 0.6405
1619/未知 677秒 417毫秒/步 - 损失: 0.8920 - 稀疏分类准确率: 0.6406
1620/未知 678秒 417毫秒/步 - 损失: 0.8919 - 稀疏分类准确率: 0.6406
1621/未知 678秒 417毫秒/步 - 损失: 0.8918 - 稀疏分类准确率: 0.6407
1622/未知 678秒 417毫秒/步 - 损失: 0.8917 - 稀疏分类准确率: 0.6407
1623/未知 679秒 417毫秒/步 - 损失: 0.8916 - 稀疏分类准确率: 0.6407
1624/未知 679秒 417毫秒/步 - 损失: 0.8915 - 稀疏分类准确率: 0.6408
1625/未知 679秒 416毫秒/步 - 损失: 0.8914 - 稀疏分类准确率: 0.6408
1626/未知 680秒 416毫秒/步 - 损失: 0.8913 - 稀疏分类准确率: 0.6408
1627/未知 680秒 417毫秒/步 - 损失: 0.8912 - 稀疏分类准确率: 0.6409
1628/未知 681秒 417毫秒/步 - 损失: 0.8911 - 稀疏分类准确率: 0.6409
1629/未知 681秒 417毫秒/步 - 损失: 0.8909 - 稀疏分类准确率: 0.6409
1630/未知 682秒 417毫秒/步 - 损失: 0.8908 - 稀疏分类准确率: 0.6410
1631/未知 682秒 417毫秒/步 - 损失: 0.8907 - 稀疏分类准确率: 0.6410
1632/未知 683秒 417毫秒/步 - 损失: 0.8906 - 稀疏分类准确率: 0.6411
1633/未知 683秒 417毫秒/步 - 损失: 0.8905 - 稀疏分类准确率: 0.6411
1634/未知 684秒 417毫秒/步 - 损失: 0.8904 - 稀疏分类准确率: 0.6411
1635/未知 684秒 417毫秒/步 - 损失: 0.8903 - 稀疏分类准确率: 0.6412
1636/未知 685秒 417毫秒/步 - 损失: 0.8902 - 稀疏分类准确率: 0.6412
1637/未知 685秒 417毫秒/步 - 损失: 0.8901 - 稀疏分类准确率: 0.6412
1638/未知 686秒 417毫秒/步 - 损失: 0.8900 - 稀疏分类准确率: 0.6413
1639/未知 686秒 417毫秒/步 - 损失: 0.8899 - 稀疏分类准确率: 0.6413
1640/未知 686秒 417毫秒/步 - 损失: 0.8898 - 稀疏分类准确率: 0.6413
1641/未知 687秒 417毫秒/步 - 损失: 0.8897 - 稀疏分类准确率: 0.6414
1642/未知 687秒 417毫秒/步 - 损失: 0.8895 - 稀疏分类准确率: 0.6414
1643/未知 688秒 417毫秒/步 - 损失: 0.8894 - 稀疏分类准确率: 0.6414
1644/未知 688秒 417毫秒/步 - 损失: 0.8893 - 稀疏分类准确率: 0.6415
1645/未知 689秒 417毫秒/步 - 损失: 0.8892 - 稀疏分类准确率: 0.6415
1646/未知 689秒 417毫秒/步 - 损失: 0.8891 - 稀疏分类准确率: 0.6416
1647/未知 690秒 417毫秒/步 - 损失: 0.8890 - 稀疏分类准确率: 0.6416
1648/未知 690秒 417毫秒/步 - 损失: 0.8889 - 稀疏分类准确率: 0.6416
1649/未知 690秒 417毫秒/步 - 损失: 0.8888 - 稀疏分类准确率: 0.6417
1650/未知 691秒 417毫秒/步 - 损失: 0.8887 - 稀疏分类准确率: 0.6417
1651/未知 691秒 417毫秒/步 - 损失: 0.8886 - 稀疏分类准确率: 0.6417
1652/未知 692秒 417毫秒/步 - 损失: 0.8885 - 稀疏分类准确率: 0.6418
1653/未知 692秒 417毫秒/步 - 损失: 0.8884 - 稀疏分类准确率: 0.6418
1654/未知 693秒 417毫秒/步 - 损失: 0.8883 - 稀疏分类准确率: 0.6418
1655/未知 693秒 417毫秒/步 - 损失: 0.8882 - 稀疏分类准确率: 0.6419
1656/未知 693秒 417毫秒/步 - 损失: 0.8880 - 稀疏分类准确率: 0.6419
1657/未知 694秒 417毫秒/步 - 损失: 0.8879 - 稀疏分类准确率: 0.6419
1658/未知 694秒 417毫秒/步 - 损失: 0.8878 - 稀疏分类准确率: 0.6420
1659/未知 695秒 417毫秒/步 - 损失: 0.8877 - 稀疏分类准确率: 0.6420
1660/未知 695秒 417毫秒/步 - 损失: 0.8876 - 稀疏分类准确率: 0.6420
1661/未知 695秒 417毫秒/步 - 损失: 0.8875 - 稀疏分类准确率: 0.6421
1662/未知 696秒 417毫秒/步 - 损失: 0.8874 - 稀疏分类准确率: 0.6421
1663/未知 696秒 417毫秒/步 - 损失: 0.8873 - 稀疏分类准确率: 0.6422
1664/未知 696秒 417毫秒/步 - 损失: 0.8872 - 稀疏分类准确率: 0.6422
1665/未知 697秒 417毫秒/步 - 损失: 0.8871 - 稀疏分类准确率: 0.6422
1666/未知 697秒 417毫秒/步 - 损失: 0.8870 - 稀疏分类准确率: 0.6423
1667/未知 698秒 417毫秒/步 - 损失: 0.8869 - 稀疏分类准确率: 0.6423
1668/未知 698秒 417毫秒/步 - 损失: 0.8868 - 稀疏分类准确率: 0.6423
1669/未知 698秒 417毫秒/步 - 损失: 0.8867 - 稀疏分类准确率: 0.6424
1670/未知 699秒 417毫秒/步 - 损失: 0.8866 - 稀疏分类准确率: 0.6424
1671/未知 699秒 417毫秒/步 - 损失: 0.8865 - 稀疏分类准确率: 0.6424
1672/未知 700秒 417毫秒/步 - 损失: 0.8864 - 稀疏分类准确率: 0.6425
1673/未知 700秒 417毫秒/步 - 损失: 0.8863 - 稀疏分类准确率: 0.6425
1674/未知 700秒 417毫秒/步 - 损失: 0.8862 - 稀疏分类准确率: 0.6425
1675/未知 701秒 417毫秒/步 - 损失: 0.8861 - 稀疏分类准确率: 0.6426
1676/未知 701秒 417毫秒/步 - 损失: 0.8859 - 稀疏分类准确率: 0.6426
1677/未知 702秒 417毫秒/步 - 损失: 0.8858 - 稀疏分类准确率: 0.6426
1678/未知 702秒 417毫秒/步 - 损失: 0.8857 - 稀疏分类准确率: 0.6427
1679/未知 703秒 417毫秒/步 - 损失: 0.8856 - 稀疏分类准确率: 0.6427
1680/未知 703秒 417毫秒/步 - 损失: 0.8855 - 稀疏分类准确率: 0.6427
1681/未知 704秒 417毫秒/步 - 损失: 0.8854 - 稀疏分类准确率: 0.6428
1682/未知 704秒 417毫秒/步 - 损失: 0.8853 - 稀疏分类准确率: 0.6428
1683/未知 705秒 417毫秒/步 - 损失: 0.8852 - 稀疏分类准确率: 0.6428
1684/未知 705秒 417毫秒/步 - 损失: 0.8851 - 稀疏分类准确率: 0.6429
1685/未知 706秒 417毫秒/步 - 损失: 0.8850 - 稀疏分类准确率: 0.6429
1686/未知 706秒 417毫秒/步 - 损失: 0.8849 - 稀疏分类准确率: 0.6429
1687/未知 706秒 417毫秒/步 - 损失: 0.8848 - 稀疏分类准确率: 0.6430
1688/未知 707秒 417毫秒/步 - 损失: 0.8847 - 稀疏分类准确率: 0.6430
1689/未知 707秒 417毫秒/步 - 损失: 0.8846 - 稀疏分类准确率: 0.6431
1690/未知 708秒 417毫秒/步 - 损失: 0.8845 - 稀疏分类准确率: 0.6431
1691/未知 708秒 417毫秒/步 - 损失: 0.8844 - 稀疏分类准确率: 0.6431
1692/未知 709秒 417毫秒/步 - 损失: 0.8843 - 稀疏分类准确率: 0.6432
1693/未知 709秒 417毫秒/步 - 损失: 0.8842 - 稀疏分类准确率: 0.6432
1694/未知 709秒 417毫秒/步 - 损失: 0.8841 - 稀疏分类准确率: 0.6432
1695/未知 710秒 417毫秒/步 - 损失: 0.8840 - 稀疏分类准确率: 0.6433
1696/未知 710秒 417毫秒/步 - 损失: 0.8839 - 稀疏分类准确率: 0.6433
1697/未知 711秒 417毫秒/步 - 损失: 0.8838 - 稀疏分类准确率: 0.6433
1698/未知 711秒 417毫秒/步 - 损失: 0.8837 - 稀疏分类准确率: 0.6434
1699/未知 711秒 417毫秒/步 - 损失: 0.8836 - 稀疏分类准确率: 0.6434
1700/未知 712秒 417毫秒/步 - 损失: 0.8835 - 稀疏分类准确率: 0.6434
1701/未知 712秒 417毫秒/步 - 损失: 0.8834 - 稀疏分类准确率: 0.6435
1702/未知 713秒 417毫秒/步 - 损失: 0.8833 - 稀疏分类准确率: 0.6435
1703/未知 713秒 417毫秒/步 - 损失: 0.8832 - 稀疏分类准确率: 0.6435
1704/未知 713秒 417毫秒/步 - 损失: 0.8831 - 稀疏分类准确率: 0.6436
1705/未知 714秒 417毫秒/步 - 损失: 0.8830 - 稀疏分类准确率: 0.6436
1706/未知 714秒 417毫秒/步 - 损失: 0.8829 - 稀疏分类准确率: 0.6436
1707/未知 714秒 417毫秒/步 - 损失: 0.8828 - 稀疏分类准确率: 0.6437
1708/未知 715秒 417毫秒/步 - 损失: 0.8827 - 稀疏分类准确率: 0.6437
1709/未知 715秒 417毫秒/步 - 损失: 0.8826 - 稀疏分类准确率: 0.6437
1710/未知 716秒 417毫秒/步 - 损失: 0.8825 - 稀疏分类准确率: 0.6438
1711/未知 716秒 417毫秒/步 - 损失: 0.8824 - 稀疏分类准确率: 0.6438
1712/未知 717秒 417毫秒/步 - 损失: 0.8823 - 稀疏分类准确率: 0.6438
1713/未知 717秒 417毫秒/步 - 损失: 0.8822 - 稀疏分类准确率: 0.6439
1714/未知 718秒 417毫秒/步 - 损失: 0.8821 - 稀疏分类准确率: 0.6439
1715/未知 718秒 417毫秒/步 - 损失: 0.8820 - 稀疏分类准确率: 0.6439
1716/未知 719秒 417毫秒/步 - 损失: 0.8818 - 稀疏分类准确率: 0.6440
1717/未知 719秒 417毫秒/步 - 损失: 0.8817 - 稀疏分类准确率: 0.6440
1718/未知 719秒 417毫秒/步 - 损失: 0.8816 - 稀疏分类准确率: 0.6440
1719/未知 720秒 417毫秒/步 - 损失: 0.8815 - 稀疏分类准确率: 0.6441
1720/未知 720秒 417毫秒/步 - 损失: 0.8814 - 稀疏分类准确率: 0.6441
1721/未知 720秒 417毫秒/步 - 损失: 0.8813 - 稀疏分类准确率: 0.6441
1722/未知 721秒 417毫秒/步 - 损失: 0.8812 - 稀疏分类准确率: 0.6442
1723/未知 721秒 417毫秒/步 - 损失: 0.8811 - 稀疏分类准确率: 0.6442
1724/未知 722秒 417毫秒/步 - 损失: 0.8810 - 稀疏分类准确率: 0.6442
1725/未知 722秒 417毫秒/步 - 损失: 0.8809 - 稀疏分类准确率: 0.6443
1726/未知 722秒 417毫秒/步 - 损失: 0.8808 - 稀疏分类准确率: 0.6443
1727/未知 723秒 417毫秒/步 - 损失: 0.8807 - 稀疏分类准确率: 0.6443
1728/未知 723秒 417毫秒/步 - 损失: 0.8806 - 稀疏分类准确率: 0.6444
1729/未知 723秒 417毫秒/步 - 损失: 0.8805 - 稀疏分类准确率: 0.6444
1730/未知 724秒 417毫秒/步 - 损失: 0.8804 - 稀疏分类准确率: 0.6444
1731/未知 724秒 417毫秒/步 - 损失: 0.8804 - 稀疏分类准确率: 0.6445
1732/未知 725秒 417毫秒/步 - 损失: 0.8803 - 稀疏分类准确率: 0.6445
1733/未知 725秒 417毫秒/步 - 损失: 0.8802 - 稀疏分类准确率: 0.6445
1734/未知 726秒 417毫秒/步 - 损失: 0.8801 - 稀疏分类准确率: 0.6446
1735/未知 726秒 417毫秒/步 - 损失: 0.8800 - 稀疏分类准确率: 0.6446
1736/未知 727秒 417毫秒/步 - 损失: 0.8799 - 稀疏分类准确率: 0.6446
1737/未知 727秒 417毫秒/步 - 损失: 0.8798 - 稀疏分类准确率: 0.6447
1738/未知 727秒 417毫秒/步 - 损失: 0.8797 - 稀疏分类准确率: 0.6447
1739/未知 728秒 417毫秒/步 - 损失: 0.8796 - 稀疏分类准确率: 0.6447
1740/未知 728秒 417毫秒/步 - 损失: 0.8795 - 稀疏分类准确率: 0.6448
1741/未知 729秒 417毫秒/步 - 损失: 0.8794 - 稀疏分类准确率: 0.6448
1742/未知 729秒 417毫秒/步 - 损失: 0.8793 - 稀疏分类准确率: 0.6448
1743/未知 730秒 417毫秒/步 - 损失: 0.8792 - 稀疏分类准确率: 0.6449
1744/未知 730秒 417毫秒/步 - 损失: 0.8791 - 稀疏分类准确率: 0.6449
1745/未知 730秒 417毫秒/步 - 损失: 0.8790 - 稀疏分类准确率: 0.6449
1746/未知 731秒 417毫秒/步 - 损失: 0.8789 - 稀疏分类准确率: 0.6450
1747/未知 731秒 417毫秒/步 - 损失: 0.8788 - 稀疏分类准确率: 0.6450
1748/未知 731秒 417毫秒/步 - 损失: 0.8787 - 稀疏分类准确率: 0.6450
1749/未知 732秒 417毫秒/步 - 损失: 0.8786 - 稀疏分类准确率: 0.6451
1750/未知 732秒 417毫秒/步 - 损失: 0.8785 - 稀疏分类准确率: 0.6451
1751/未知 733秒 417毫秒/步 - 损失: 0.8784 - 稀疏分类准确率: 0.6451
1752/未知 733秒 417毫秒/步 - 损失: 0.8783 - 稀疏分类准确率: 0.6452
1753/未知 733秒 417毫秒/步 - 损失: 0.8782 - 稀疏分类准确率: 0.6452
1754/未知 734秒 417毫秒/步 - 损失: 0.8781 - 稀疏分类准确率: 0.6452
1755/未知 734秒 417毫秒/步 - 损失: 0.8780 - 稀疏分类准确率: 0.6453
1756/未知 735秒 417毫秒/步 - 损失: 0.8779 - 稀疏分类准确率: 0.6453
1757/未知 735秒 417毫秒/步 - 损失: 0.8778 - 稀疏分类准确率: 0.6453
1758/未知 736秒 417毫秒/步 - 损失: 0.8777 - 稀疏分类准确率: 0.6453
1759/未知 736秒 417毫秒/步 - 损失: 0.8776 - 稀疏分类准确率: 0.6454
1760/未知 737秒 417毫秒/步 - 损失: 0.8775 - 稀疏分类准确率: 0.6454
1761/未知 737秒 417毫秒/步 - 损失: 0.8774 - 稀疏分类准确率: 0.6454
1762/未知 738秒 417毫秒/步 - 损失: 0.8773 - 稀疏分类准确率: 0.6455
1763/未知 738秒 417毫秒/步 - 损失: 0.8772 - 稀疏分类准确率: 0.6455
1764/未知 738秒 417毫秒/步 - 损失: 0.8771 - 稀疏分类准确率: 0.6455
1765/未知 739秒 417毫秒/步 - 损失: 0.8770 - 稀疏分类准确率: 0.6456
1766/未知 739秒 417毫秒/步 - 损失: 0.8769 - 稀疏分类准确率: 0.6456
1767/未知 739秒 417毫秒/步 - 损失: 0.8768 - 稀疏分类准确率: 0.6456
1768/未知 740秒 417毫秒/步 - 损失: 0.8767 - 稀疏分类准确率: 0.6457
1769/未知 740秒 417毫秒/步 - 损失: 0.8766 - 稀疏分类准确率: 0.6457
1770/未知 741秒 417毫秒/步 - 损失: 0.8765 - 稀疏分类准确率: 0.6457
1771/未知 741秒 417毫秒/步 - 损失: 0.8764 - 稀疏分类准确率: 0.6458
1772/未知 741秒 417毫秒/步 - 损失: 0.8763 - 稀疏分类准确率: 0.6458
1773/未知 742秒 417毫秒/步 - 损失: 0.8763 - 稀疏分类准确率: 0.6458
1774/未知 742秒 417毫秒/步 - 损失: 0.8762 - 稀疏分类准确率: 0.6459
1775/未知 743秒 417毫秒/步 - 损失: 0.8761 - 稀疏分类准确率: 0.6459
1776/未知 743秒 417毫秒/步 - 损失: 0.8760 - 稀疏分类准确率: 0.6459
1777/未知 743秒 417毫秒/步 - 损失: 0.8759 - 稀疏分类准确率: 0.6460
1778/未知 744秒 417毫秒/步 - 损失: 0.8758 - 稀疏分类准确率: 0.6460
1779/未知 744秒 417毫秒/步 - 损失: 0.8757 - 稀疏分类准确率: 0.6460
1780/未知 745秒 417毫秒/步 - 损失: 0.8756 - 稀疏分类准确率: 0.6461
1781/未知 745秒 417毫秒/步 - 损失: 0.8755 - 稀疏分类准确率: 0.6461
1782/未知 746秒 417毫秒/步 - 损失: 0.8754 - 稀疏分类准确率: 0.6461
1783/未知 746秒 417毫秒/步 - 损失: 0.8753 - 稀疏分类准确率: 0.6461
1784/未知 747秒 417毫秒/步 - 损失: 0.8752 - 稀疏分类准确率: 0.6462
1785/未知 747秒 417毫秒/步 - 损失: 0.8751 - 稀疏分类准确率: 0.6462
1786/未知 747秒 417毫秒/步 - 损失: 0.8750 - 稀疏分类准确率: 0.6462
1787/未知 748秒 417毫秒/步 - 损失: 0.8749 - 稀疏分类准确率: 0.6463
1788/未知 748秒 417毫秒/步 - 损失: 0.8748 - 稀疏分类准确率: 0.6463
1789/未知 749秒 417毫秒/步 - 损失: 0.8747 - 稀疏分类准确率: 0.6463
1790/未知 749秒 417毫秒/步 - 损失: 0.8746 - 稀疏分类准确率: 0.6464
1791/未知 750秒 417毫秒/步 - 损失: 0.8745 - 稀疏分类准确率: 0.6464
1792/未知 750秒 417毫秒/步 - 损失: 0.8744 - 稀疏分类准确率: 0.6464
1793/未知 751秒 417毫秒/步 - 损失: 0.8743 - 稀疏分类准确率: 0.6465
1794/未知 751秒 417毫秒/步 - 损失: 0.8743 - 稀疏分类准确率: 0.6465
1795/未知 752秒 417毫秒/步 - 损失: 0.8742 - 稀疏分类准确率: 0.6465
1796/未知 752秒 417毫秒/步 - 损失: 0.8741 - 稀疏分类准确率: 0.6466
1797/未知 753秒 417毫秒/步 - 损失: 0.8740 - 稀疏分类准确率: 0.6466
1798/未知 753秒 417毫秒/步 - 损失: 0.8739 - 稀疏分类准确率: 0.6466
1799/未知 753秒 417毫秒/步 - 损失: 0.8738 - 稀疏分类准确率: 0.6466
1800/未知 754秒 417毫秒/步 - 损失: 0.8737 - 稀疏分类准确率: 0.6467
1801/未知 754秒 417毫秒/步 - 损失: 0.8736 - 稀疏分类准确率: 0.6467
1802/未知 755秒 417毫秒/步 - 损失: 0.8735 - 稀疏分类准确率: 0.6467
1803/未知 755秒 417毫秒/步 - 损失: 0.8734 - 稀疏分类准确率: 0.6468
1804/未知 756秒 417毫秒/步 - 损失: 0.8733 - 稀疏分类准确率: 0.6468
1805/未知 756秒 417毫秒/步 - 损失: 0.8732 - 稀疏分类准确率: 0.6468
1806/未知 757秒 417毫秒/步 - 损失: 0.8731 - 稀疏分类准确率: 0.6469
1807/未知 757秒 417毫秒/步 - 损失: 0.8730 - 稀疏分类准确率: 0.6469
1808/未知 757秒 417毫秒/步 - 损失: 0.8729 - 稀疏分类准确率: 0.6469
1809/未知 758秒 417毫秒/步 - 损失: 0.8729 - 稀疏分类准确率: 0.6470
1810/未知 758秒 417毫秒/步 - 损失: 0.8728 - 稀疏分类准确率: 0.6470
1811/未知 758秒 417毫秒/步 - 损失: 0.8727 - 稀疏分类准确率: 0.6470
1812/未知 759秒 417毫秒/步 - 损失: 0.8726 - 稀疏分类准确率: 0.6471
1813/未知 759秒 417毫秒/步 - 损失: 0.8725 - 稀疏分类准确率: 0.6471
1814/未知 760秒 417毫秒/步 - 损失: 0.8724 - 稀疏分类准确率: 0.6471
1815/未知 760秒 417毫秒/步 - 损失: 0.8723 - 稀疏分类准确率: 0.6471
1816/未知 760秒 417毫秒/步 - 损失: 0.8722 - 稀疏分类准确率: 0.6472
1817/未知 761秒 417毫秒/步 - 损失: 0.8721 - 稀疏分类准确率: 0.6472
1818/未知 761秒 417毫秒/步 - 损失: 0.8720 - 稀疏分类准确率: 0.6472
1819/未知 761秒 417毫秒/步 - 损失: 0.8719 - 稀疏分类准确率: 0.6473
1820/未知 762秒 417毫秒/步 - 损失: 0.8718 - 稀疏分类准确率: 0.6473
1821/未知 762秒 417毫秒/步 - 损失: 0.8717 - 稀疏分类准确率: 0.6473
1822/未知 763秒 417毫秒/步 - 损失: 0.8717 - 稀疏分类准确率: 0.6474
1823/未知 763秒 417毫秒/步 - 损失: 0.8716 - 稀疏分类准确率: 0.6474
1824/未知 764秒 417毫秒/步 - 损失: 0.8715 - 稀疏分类准确率: 0.6474
1825/未知 764秒 417毫秒/步 - 损失: 0.8714 - 稀疏分类准确率: 0.6475
1826/未知 765秒 417毫秒/步 - 损失: 0.8713 - 稀疏分类准确率: 0.6475
1827/未知 765秒 417毫秒/步 - 损失: 0.8712 - 稀疏分类准确率: 0.6475
1828/未知 766秒 417毫秒/步 - 损失: 0.8711 - 稀疏分类准确率: 0.6475
1829/未知 766秒 417毫秒/步 - 损失: 0.8710 - 稀疏分类准确率: 0.6476
1830/未知 767秒 417毫秒/步 - 损失: 0.8709 - 稀疏分类准确率: 0.6476
1831/未知 767秒 417毫秒/步 - 损失: 0.8708 - 稀疏分类准确率: 0.6476
1832/未知 767秒 417毫秒/步 - 损失: 0.8707 - 稀疏分类准确率: 0.6477
1833/未知 768秒 417毫秒/步 - 损失: 0.8706 - 稀疏分类准确率: 0.6477
1834/未知 768秒 417毫秒/步 - 损失: 0.8706 - 稀疏分类准确率: 0.6477
1835/未知 769秒 418毫秒/步 - 损失: 0.8705 - 稀疏分类准确率: 0.6478
1836/未知 769秒 418毫秒/步 - 损失: 0.8704 - 稀疏分类准确率: 0.6478
1837/未知 770秒 418毫秒/步 - 损失: 0.8703 - 稀疏分类准确率: 0.6478
1838/未知 770秒 418毫秒/步 - 损失: 0.8702 - 稀疏分类准确率: 0.6478
1839/未知 771秒 418毫秒/步 - 损失: 0.8701 - 稀疏分类准确率: 0.6479
1840/未知 771秒 418毫秒/步 - 损失: 0.8700 - 稀疏分类准确率: 0.6479
1841/未知 771秒 417毫秒/步 - 损失: 0.8699 - 稀疏分类准确率: 0.6479
1842/未知 772秒 417毫秒/步 - 损失: 0.8698 - 稀疏分类准确率: 0.6480
1843/未知 772秒 417毫秒/步 - 损失: 0.8697 - 稀疏分类准确率: 0.6480
1844/未知 772秒 417毫秒/步 - 损失: 0.8696 - 稀疏分类准确率: 0.6480
1845/未知 773秒 417毫秒/步 - 损失: 0.8696 - 稀疏分类准确率: 0.6481
1846/未知 773秒 417毫秒/步 - 损失: 0.8695 - 稀疏分类准确率: 0.6481
1847/未知 774秒 417毫秒/步 - 损失: 0.8694 - 稀疏分类准确率: 0.6481
1848/未知 774秒 417毫秒/步 - 损失: 0.8693 - 稀疏分类准确率: 0.6481
1849/未知 774秒 417毫秒/步 - 损失: 0.8692 - 稀疏分类准确率: 0.6482
1850/未知 775秒 417毫秒/步 - 损失: 0.8691 - 稀疏分类准确率: 0.6482
1851/未知 775秒 417毫秒/步 - 损失: 0.8690 - 稀疏分类准确率: 0.6482
1852/未知 776秒 417毫秒/步 - 损失: 0.8689 - 稀疏分类准确率: 0.6483
1853/未知 776秒 417毫秒/步 - 损失: 0.8688 - 稀疏分类准确率: 0.6483
1854/未知 777秒 417毫秒/步 - 损失: 0.8688 - 稀疏分类准确率: 0.6483
1855/未知 777秒 417毫秒/步 - 损失: 0.8687 - 稀疏分类准确率: 0.6484
1856/未知 778秒 417毫秒/步 - 损失: 0.8686 - 稀疏分类准确率: 0.6484
1857/未知 778秒 417毫秒/步 - 损失: 0.8685 - 稀疏分类准确率: 0.6484
1858/未知 778秒 417毫秒/步 - 损失: 0.8684 - 稀疏分类准确率: 0.6484
1859/未知 779秒 417毫秒/步 - 损失: 0.8683 - 稀疏分类准确率: 0.6485
1860/未知 779秒 417毫秒/步 - 损失: 0.8682 - 稀疏分类准确率: 0.6485
1861/未知 779秒 417毫秒/步 - 损失: 0.8681 - 稀疏分类准确率: 0.6485
1862/未知 780秒 417毫秒/步 - 损失: 0.8680 - 稀疏分类准确率: 0.6486
1863/未知 780秒 417毫秒/步 - 损失: 0.8679 - 稀疏分类准确率: 0.6486
1864/未知 781秒 417毫秒/步 - 损失: 0.8679 - 稀疏分类准确率: 0.6486
1865/未知 781秒 417毫秒/步 - 损失: 0.8678 - 稀疏分类准确率: 0.6486
1865/1865 ━━━━━━━━━━━━━━━━━━━━ 781秒 417毫秒/步 - 损失: 0.8677 - 稀疏分类准确率: 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%
Wide & Deep 模型达到了约 79% 的测试准确率。
实验 3: Deep & Cross 模型
在第三个实验中，我们创建了一个 Deep & Cross 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有效的方式应用显式特征交叉，其中交叉特征的程度随层深度而增长。
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")
让我们运行它
run_experiment(deep_and_cross_model)
Start training the model...
1/Unknown 1s 993ms/step - loss: 2.4838 - sparse_categorical_accuracy: 0.1057
2/Unknown 1s 465ms/step - loss: 2.4552 - sparse_categorical_accuracy: 0.1113
3/Unknown 2s 483ms/step - loss: 2.4419 - sparse_categorical_accuracy: 0.1124
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34/Unknown 18s 502ms/step - loss: 2.0888 - sparse_categorical_accuracy: 0.2156
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1000/未知 459秒 458毫秒/步 - 损失: 0.9624 - 稀疏分类准确率: 0.6280
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1008/未知 462秒 458毫秒/步 - 损失: 0.9607 - 稀疏分类准确率: 0.6286
1009/未知 463秒 458毫秒/步 - 损失: 0.9605 - 稀疏分类准确率: 0.6286
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1013/未知 465秒 458毫秒/步 - 损失: 0.9597 - 稀疏分类准确率: 0.6289
1014/未知 465秒 459毫秒/步 - 损失: 0.9595 - 稀疏分类准确率: 0.6289
1015/未知 466秒 459毫秒/步 - 损失: 0.9593 - 稀疏分类准确率: 0.6290
1016/未知 466秒 459毫秒/步 - 损失: 0.9591 - 稀疏分类准确率: 0.6291
1017/未知 467秒 459毫秒/步 - 损失: 0.9589 - 稀疏分类准确率: 0.6291
1018/未知 467秒 459毫秒/步 - 损失: 0.9587 - 稀疏分类准确率: 0.6292
1019/未知 468秒 459毫秒/步 - 损失: 0.9584 - 稀疏分类准确率: 0.6293
1020/未知 468秒 459毫秒/步 - 损失: 0.9582 - 稀疏分类准确率: 0.6293
1021/未知 469秒 459毫秒/步 - 损失: 0.9580 - 稀疏分类准确率: 0.6294
1022/未知 469秒 459毫秒/步 - 损失: 0.9578 - 稀疏分类准确率: 0.6295
1023/未知 470秒 459毫秒/步 - 损失: 0.9576 - 稀疏分类准确率: 0.6295
1024/未知 470秒 459毫秒/步 - 损失: 0.9574 - 稀疏分类准确率: 0.6296
1025/未知 471秒 459毫秒/步 - 损失: 0.9572 - 稀疏分类准确率: 0.6297
1026/未知 471秒 459毫秒/步 - 损失: 0.9570 - 稀疏分类准确率: 0.6297
1027/未知 472秒 459毫秒/步 - 损失: 0.9568 - 稀疏分类准确率: 0.6298
1028/未知 472秒 459毫秒/步 - 损失: 0.9566 - 稀疏分类准确率: 0.6298
1029/未知 473秒 459毫秒/步 - 损失: 0.9564 - 稀疏分类准确率: 0.6299
1030/未知 473秒 459毫秒/步 - 损失: 0.9562 - 稀疏分类准确率: 0.6300
1031/未知 474秒 459毫秒/步 - 损失: 0.9560 - 稀疏分类准确率: 0.6300
1032/未知 474秒 459毫秒/步 - 损失: 0.9558 - 稀疏分类准确率: 0.6301
1033/未知 475秒 459毫秒/步 - 损失: 0.9556 - 稀疏分类准确率: 0.6302
1034/未知 475秒 459毫秒/步 - 损失: 0.9554 - 稀疏分类准确率: 0.6302
1035/未知 476秒 459毫秒/步 - 损失: 0.9552 - 稀疏分类准确率: 0.6303
1036/未知 476秒 459毫秒/步 - 损失: 0.9550 - 稀疏分类准确率: 0.6304
1037/未知 477秒 459毫秒/步 - 损失: 0.9548 - 稀疏分类准确率: 0.6304
1038/未知 477秒 459毫秒/步 - 损失: 0.9546 - 稀疏分类准确率: 0.6305
1039/未知 478秒 459毫秒/步 - 损失: 0.9544 - 稀疏分类准确率: 0.6305
1040/未知 478秒 459毫秒/步 - 损失: 0.9542 - 稀疏分类准确率: 0.6306
1041/未知 479秒 459毫秒/步 - 损失: 0.9540 - 稀疏分类准确率: 0.6307
1042/未知 479秒 459毫秒/步 - 损失: 0.9538 - 稀疏分类准确率: 0.6307
1043/未知 480秒 459毫秒/步 - 损失: 0.9536 - 稀疏分类准确率: 0.6308
1044/未知 480秒 459毫秒/步 - 损失: 0.9534 - 稀疏分类准确率: 0.6309
1045/未知 481秒 459毫秒/步 - 损失: 0.9532 - 稀疏分类准确率: 0.6309
1046/未知 481秒 459毫秒/步 - 损失: 0.9530 - 稀疏分类准确率: 0.6310
1047/未知 482秒 459毫秒/步 - 损失: 0.9528 - 稀疏分类准确率: 0.6310
1048/未知 482秒 459毫秒/步 - 损失: 0.9526 - 稀疏分类准确率: 0.6311
1049/未知 483秒 459毫秒/步 - 损失: 0.9524 - 稀疏分类准确率: 0.6312
1050/未知 483秒 460毫秒/步 - 损失: 0.9522 - 稀疏分类准确率: 0.6312
1051/未知 484秒 460毫秒/步 - 损失: 0.9520 - 稀疏分类准确率: 0.6313
1052/未知 484秒 460毫秒/步 - 损失: 0.9518 - 稀疏分类准确率: 0.6314
1053/未知 484秒 460毫秒/步 - 损失: 0.9516 - 稀疏分类准确率: 0.6314
1054/未知 485秒 460毫秒/步 - 损失: 0.9514 - 稀疏分类准确率: 0.6315
1055/未知 485秒 460毫秒/步 - 损失: 0.9512 - 稀疏分类准确率: 0.6315
1056/未知 486秒 460毫秒/步 - 损失: 0.9510 - 稀疏分类准确率: 0.6316
1057/未知 486秒 460毫秒/步 - 损失: 0.9508 - 稀疏分类准确率: 0.6317
1058/未知 487秒 460毫秒/步 - 损失: 0.9506 - 稀疏分类准确率: 0.6317
1059/未知 487秒 460毫秒/步 - 损失: 0.9504 - 稀疏分类准确率: 0.6318
1060/未知 488秒 460毫秒/步 - 损失: 0.9502 - 稀疏分类准确率: 0.6318
1061/未知 488秒 460毫秒/步 - 损失: 0.9500 - 稀疏分类准确率: 0.6319
1062/未知 489秒 460毫秒/步 - 损失: 0.9498 - 稀疏分类准确率: 0.6320
1063/未知 489秒 460毫秒/步 - 损失: 0.9496 - 稀疏分类准确率: 0.6320
1064/未知 490秒 460毫秒/步 - 损失: 0.9495 - 稀疏分类准确率: 0.6321
1065/未知 490秒 460毫秒/步 - 损失: 0.9493 - 稀疏分类准确率: 0.6321
1066/未知 491秒 460毫秒/步 - 损失: 0.9491 - 稀疏分类准确率: 0.6322
1067/未知 491秒 460毫秒/步 - 损失: 0.9489 - 稀疏分类准确率: 0.6323
1068/未知 492秒 460毫秒/步 - 损失: 0.9487 - 稀疏分类准确率: 0.6323
1069/未知 492秒 460毫秒/步 - 损失: 0.9485 - 稀疏分类准确率: 0.6324
1070/未知 493秒 460毫秒/步 - 损失: 0.9483 - 稀疏分类准确率: 0.6324
1071/未知 493秒 460毫秒/步 - 损失: 0.9481 - 稀疏分类准确率: 0.6325
1072/未知 494秒 460毫秒/步 - 损失: 0.9479 - 稀疏分类准确率: 0.6326
1073/未知 494秒 460毫秒/步 - 损失: 0.9477 - 稀疏分类准确率: 0.6326
1074/未知 495秒 460毫秒/步 - 损失: 0.9475 - 稀疏分类准确率: 0.6327
1075/未知 495秒 460毫秒/步 - 损失: 0.9473 - 稀疏分类准确率: 0.6327
1076/未知 496秒 460毫秒/步 - 损失: 0.9471 - 稀疏分类准确率: 0.6328
1077/未知 496秒 460毫秒/步 - 损失: 0.9470 - 稀疏分类准确率: 0.6329
1078/未知 496秒 460毫秒/步 - 损失: 0.9468 - 稀疏分类准确率: 0.6329
1079/未知 497秒 460毫秒/步 - 损失: 0.9466 - 稀疏分类准确率: 0.6330
1080/未知 497秒 460毫秒/步 - 损失: 0.9464 - 稀疏分类准确率: 0.6330
1081/未知 498秒 460毫秒/步 - 损失: 0.9462 - 稀疏分类准确率: 0.6331
1082/未知 498秒 460毫秒/步 - 损失: 0.9460 - 稀疏分类准确率: 0.6332
1083/未知 499秒 460毫秒/步 - 损失: 0.9458 - 稀疏分类准确率: 0.6332
1084/未知 499秒 460毫秒/步 - 损失: 0.9456 - 稀疏分类准确率: 0.6333
1085/未知 500秒 460毫秒/步 - 损失: 0.9454 - 稀疏分类准确率: 0.6333
1086/未知 500秒 460毫秒/步 - 损失: 0.9453 - 稀疏分类准确率: 0.6334
1087/未知 501秒 460毫秒/步 - 损失: 0.9451 - 稀疏分类准确率: 0.6335
1088/未知 501秒 460毫秒/步 - 损失: 0.9449 - 稀疏分类准确率: 0.6335
1089/未知 502秒 460毫秒/步 - 损失: 0.9447 - 稀疏分类准确率: 0.6336
1090/未知 502秒 460毫秒/步 - 损失: 0.9445 - 稀疏分类准确率: 0.6336
1091/未知 503秒 460毫秒/步 - 损失: 0.9443 - 稀疏分类准确率: 0.6337
1092/未知 503秒 460毫秒/步 - 损失: 0.9441 - 稀疏分类准确率: 0.6337
1093/未知 503秒 460毫秒/步 - 损失: 0.9439 - 稀疏分类准确率: 0.6338
1094/未知 504秒 460毫秒/步 - 损失: 0.9438 - 稀疏分类准确率: 0.6339
1095/未知 504秒 460毫秒/步 - 损失: 0.9436 - 稀疏分类准确率: 0.6339
1096/未知 505秒 460毫秒/步 - 损失: 0.9434 - 稀疏分类准确率: 0.6340
1097/未知 505秒 460毫秒/步 - 损失: 0.9432 - 稀疏分类准确率: 0.6340
1098/未知 506秒 460毫秒/步 - 损失: 0.9430 - 稀疏分类准确率: 0.6341
1099/未知 506秒 460毫秒/步 - 损失: 0.9428 - 稀疏分类准确率: 0.6342
1100/未知 507秒 460毫秒/步 - 损失: 0.9427 - 稀疏分类准确率: 0.6342
1101/未知 507秒 460毫秒/步 - 损失: 0.9425 - 稀疏分类准确率: 0.6343
1102/未知 508秒 460毫秒/步 - 损失: 0.9423 - 稀疏分类准确率: 0.6343
1103/未知 508秒 460毫秒/步 - 损失: 0.9421 - 稀疏分类准确率: 0.6344
1104/未知 508秒 460毫秒/步 - 损失: 0.9419 - 稀疏分类准确率: 0.6344
1105/未知 509秒 460毫秒/步 - 损失: 0.9417 - 稀疏分类准确率: 0.6345
1106/未知 509秒 460毫秒/步 - 损失: 0.9416 - 稀疏分类准确率: 0.6346
1107/未知 510秒 460毫秒/步 - 损失: 0.9414 - 稀疏分类准确率: 0.6346
1108/未知 510秒 460毫秒/步 - 损失: 0.9412 - 稀疏分类准确率: 0.6347
1109/未知 510秒 460毫秒/步 - 损失: 0.9410 - 稀疏分类准确率: 0.6347
1110/未知 511秒 459毫秒/步 - 损失: 0.9408 - 稀疏分类准确率: 0.6348
1111/未知 511秒 459毫秒/步 - 损失: 0.9406 - 稀疏分类准确率: 0.6348
1112/未知 511秒 459毫秒/步 - 损失: 0.9405 - 稀疏分类准确率: 0.6349
1113/未知 512秒 459毫秒/步 - 损失: 0.9403 - 稀疏分类准确率: 0.6349
1114/未知 512秒 459毫秒/步 - 损失: 0.9401 - 稀疏分类准确率: 0.6350
1115/未知 512秒 459毫秒/步 - 损失: 0.9399 - 稀疏分类准确率: 0.6351
1116/未知 513秒 459毫秒/步 - 损失: 0.9397 - 稀疏分类准确率: 0.6351
1117/未知 513秒 459毫秒/步 - 损失: 0.9396 - 稀疏分类准确率: 0.6352
1118/未知 513秒 459毫秒/步 - 损失: 0.9394 - 稀疏分类准确率: 0.6352
1119/未知 514秒 459毫秒/步 - 损失: 0.9392 - 稀疏分类准确率: 0.6353
1120/未知 514秒 458毫秒/步 - 损失: 0.9390 - 稀疏分类准确率: 0.6353
1121/未知 515秒 458毫秒/步 - 损失: 0.9388 - 稀疏分类准确率: 0.6354
1122/未知 515秒 459毫秒/步 - 损失: 0.9387 - 稀疏分类准确率: 0.6355
1123/未知 515秒 459毫秒/步 - 损失: 0.9385 - 稀疏分类准确率: 0.6355
1124/未知 516秒 459毫秒/步 - 损失: 0.9383 - 稀疏分类准确率: 0.6356
1125/未知 516秒 459毫秒/步 - 损失: 0.9381 - 稀疏分类准确率: 0.6356
1126/未知 517秒 458毫秒/步 - 损失: 0.9379 - 稀疏分类准确率: 0.6357
1127/未知 517秒 458毫秒/步 - 损失: 0.9378 - 稀疏分类准确率: 0.6357
1128/未知 518秒 458毫秒/步 - 损失: 0.9376 - 稀疏分类准确率: 0.6358
1129/未知 518秒 458毫秒/步 - 损失: 0.9374 - 稀疏分类准确率: 0.6358
1130/未知 519秒 458毫秒/步 - 损失: 0.9372 - 稀疏分类准确率: 0.6359
1131/未知 519秒 458毫秒/步 - 损失: 0.9371 - 稀疏分类准确率: 0.6360
1132/未知 519秒 458毫秒/步 - 损失: 0.9369 - 稀疏分类准确率: 0.6360
1133/未知 520秒 458毫秒/步 - 损失: 0.9367 - 稀疏分类准确率: 0.6361
1134/未知 520秒 458毫秒/步 - 损失: 0.9365 - 稀疏分类准确率: 0.6361
1135/未知 521秒 458毫秒/步 - loss: 0.9364 - sparse_categorical_accuracy: 0.6362
1136/未知 521秒 458毫秒/步 - loss: 0.9362 - sparse_categorical_accuracy: 0.6362
1137/未知 522秒 458毫秒/步 - loss: 0.9360 - sparse_categorical_accuracy: 0.6363
1138/未知 522秒 458毫秒/步 - loss: 0.9358 - sparse_categorical_accuracy: 0.6363
1139/未知 523秒 458毫秒/步 - loss: 0.9356 - sparse_categorical_accuracy: 0.6364
1140/未知 523秒 458毫秒/步 - loss: 0.9355 - sparse_categorical_accuracy: 0.6364
1141/未知 524秒 458毫秒/步 - loss: 0.9353 - sparse_categorical_accuracy: 0.6365
1142/未知 524秒 458毫秒/步 - loss: 0.9351 - sparse_categorical_accuracy: 0.6366
1143/未知 525秒 458毫秒/步 - loss: 0.9350 - sparse_categorical_accuracy: 0.6366
1144/未知 525秒 458毫秒/步 - loss: 0.9348 - sparse_categorical_accuracy: 0.6367
1145/未知 525秒 458毫秒/步 - loss: 0.9346 - sparse_categorical_accuracy: 0.6367
1146/未知 526秒 458毫秒/步 - loss: 0.9344 - sparse_categorical_accuracy: 0.6368
1147/未知 526秒 458毫秒/步 - loss: 0.9343 - sparse_categorical_accuracy: 0.6368
1148/未知 527秒 458毫秒/步 - loss: 0.9341 - sparse_categorical_accuracy: 0.6369
1149/未知 527秒 458毫秒/步 - loss: 0.9339 - sparse_categorical_accuracy: 0.6369
1150/未知 528秒 458毫秒/步 - loss: 0.9337 - sparse_categorical_accuracy: 0.6370
1151/未知 528秒 458毫秒/步 - loss: 0.9336 - sparse_categorical_accuracy: 0.6370
1152/未知 528秒 458毫秒/步 - loss: 0.9334 - sparse_categorical_accuracy: 0.6371
1153/未知 529秒 458毫秒/步 - loss: 0.9332 - sparse_categorical_accuracy: 0.6372
1154/未知 529秒 458毫秒/步 - loss: 0.9330 - sparse_categorical_accuracy: 0.6372
1155/未知 530秒 458毫秒/步 - loss: 0.9329 - sparse_categorical_accuracy: 0.6373
1156/未知 530秒 458毫秒/步 - loss: 0.9327 - sparse_categorical_accuracy: 0.6373
1157/未知 530秒 458毫秒/步 - loss: 0.9325 - sparse_categorical_accuracy: 0.6374
1158/未知 531秒 458毫秒/步 - loss: 0.9324 - sparse_categorical_accuracy: 0.6374
1159/未知 531秒 458毫秒/步 - loss: 0.9322 - sparse_categorical_accuracy: 0.6375
1160/未知 532秒 458毫秒/步 - loss: 0.9320 - sparse_categorical_accuracy: 0.6375
1161/未知 532秒 458毫秒/步 - loss: 0.9318 - sparse_categorical_accuracy: 0.6376
1162/未知 532秒 458毫秒/步 - loss: 0.9317 - sparse_categorical_accuracy: 0.6376
1163/未知 533秒 458毫秒/步 - loss: 0.9315 - sparse_categorical_accuracy: 0.6377
1164/未知 533秒 458毫秒/步 - loss: 0.9313 - sparse_categorical_accuracy: 0.6377
1165/未知 534秒 458毫秒/步 - loss: 0.9312 - sparse_categorical_accuracy: 0.6378
1166/未知 534秒 458毫秒/步 - loss: 0.9310 - sparse_categorical_accuracy: 0.6378
1167/未知 535秒 458毫秒/步 - loss: 0.9308 - sparse_categorical_accuracy: 0.6379
1168/未知 535秒 458毫秒/步 - loss: 0.9307 - sparse_categorical_accuracy: 0.6380
1169/未知 536秒 458毫秒/步 - loss: 0.9305 - sparse_categorical_accuracy: 0.6380
1170/未知 536秒 458毫秒/步 - loss: 0.9303 - sparse_categorical_accuracy: 0.6381
1171/未知 537秒 458毫秒/步 - loss: 0.9302 - sparse_categorical_accuracy: 0.6381
1172/未知 537秒 458毫秒/步 - loss: 0.9300 - sparse_categorical_accuracy: 0.6382
1173/未知 538秒 458毫秒/步 - loss: 0.9298 - sparse_categorical_accuracy: 0.6382
1174/未知 538秒 458毫秒/步 - loss: 0.9297 - sparse_categorical_accuracy: 0.6383
1175/未知 538秒 458毫秒/步 - loss: 0.9295 - sparse_categorical_accuracy: 0.6383
1176/未知 539秒 458毫秒/步 - loss: 0.9293 - sparse_categorical_accuracy: 0.6384
1177/未知 539秒 458毫秒/步 - loss: 0.9292 - sparse_categorical_accuracy: 0.6384
1178/未知 540秒 458毫秒/步 - loss: 0.9290 - sparse_categorical_accuracy: 0.6385
1179/未知 540秒 458毫秒/步 - loss: 0.9288 - sparse_categorical_accuracy: 0.6385
1180/未知 541秒 458毫秒/步 - loss: 0.9287 - sparse_categorical_accuracy: 0.6386
1181/未知 541秒 458毫秒/步 - loss: 0.9285 - sparse_categorical_accuracy: 0.6386
1182/未知 542秒 458毫秒/步 - loss: 0.9283 - sparse_categorical_accuracy: 0.6387
1183/未知 542秒 458毫秒/步 - loss: 0.9282 - sparse_categorical_accuracy: 0.6387
1184/未知 543秒 458毫秒/步 - loss: 0.9280 - sparse_categorical_accuracy: 0.6388
1185/未知 543秒 458毫秒/步 - loss: 0.9278 - sparse_categorical_accuracy: 0.6388
1186/未知 543秒 458毫秒/步 - loss: 0.9277 - sparse_categorical_accuracy: 0.6389
1187/未知 544秒 458毫秒/步 - loss: 0.9275 - sparse_categorical_accuracy: 0.6389
1188/未知 544秒 458毫秒/步 - loss: 0.9273 - sparse_categorical_accuracy: 0.6390
1189/未知 545秒 458毫秒/步 - loss: 0.9272 - sparse_categorical_accuracy: 0.6390
1190/未知 545秒 458毫秒/步 - loss: 0.9270 - sparse_categorical_accuracy: 0.6391
1191/未知 546秒 458毫秒/步 - loss: 0.9268 - sparse_categorical_accuracy: 0.6391
1192/未知 546秒 458毫秒/步 - loss: 0.9267 - sparse_categorical_accuracy: 0.6392
1193/未知 547秒 458毫秒/步 - loss: 0.9265 - sparse_categorical_accuracy: 0.6392
1194/未知 547秒 458毫秒/步 - loss: 0.9263 - sparse_categorical_accuracy: 0.6393
1195/未知 548秒 458毫秒/步 - loss: 0.9262 - sparse_categorical_accuracy: 0.6394
1196/未知 548秒 458毫秒/步 - loss: 0.9260 - sparse_categorical_accuracy: 0.6394
1197/未知 548秒 458毫秒/步 - loss: 0.9259 - sparse_categorical_accuracy: 0.6395
1198/未知 549秒 458毫秒/步 - loss: 0.9257 - sparse_categorical_accuracy: 0.6395
1199/未知 549秒 458毫秒/步 - loss: 0.9255 - sparse_categorical_accuracy: 0.6396
1200/未知 550秒 458毫秒/步 - loss: 0.9254 - sparse_categorical_accuracy: 0.6396
1201/未知 550秒 458毫秒/步 - loss: 0.9252 - sparse_categorical_accuracy: 0.6397
1202/未知 551秒 458毫秒/步 - loss: 0.9250 - sparse_categorical_accuracy: 0.6397
1203/未知 551秒 458毫秒/步 - loss: 0.9249 - sparse_categorical_accuracy: 0.6398
1204/未知 552秒 458毫秒/步 - loss: 0.9247 - sparse_categorical_accuracy: 0.6398
1205/未知 552秒 458毫秒/步 - loss: 0.9246 - sparse_categorical_accuracy: 0.6399
1206/未知 553秒 458毫秒/步 - loss: 0.9244 - sparse_categorical_accuracy: 0.6399
1207/未知 553秒 458毫秒/步 - loss: 0.9242 - sparse_categorical_accuracy: 0.6400
1208/未知 554秒 458毫秒/步 - loss: 0.9241 - sparse_categorical_accuracy: 0.6400
1209/未知 554秒 458毫秒/步 - loss: 0.9239 - sparse_categorical_accuracy: 0.6401
1210/未知 555秒 458毫秒/步 - loss: 0.9238 - sparse_categorical_accuracy: 0.6401
1211/未知 555秒 458毫秒/步 - loss: 0.9236 - sparse_categorical_accuracy: 0.6402
1212/未知 556秒 458毫秒/步 - loss: 0.9234 - sparse_categorical_accuracy: 0.6402
1213/未知 556秒 458毫秒/步 - loss: 0.9233 - sparse_categorical_accuracy: 0.6403
1214/未知 557秒 458毫秒/步 - loss: 0.9231 - sparse_categorical_accuracy: 0.6403
1215/未知 557秒 458毫秒/步 - loss: 0.9230 - sparse_categorical_accuracy: 0.6404
1216/未知 558秒 458毫秒/步 - loss: 0.9228 - sparse_categorical_accuracy: 0.6404
1217/未知 558秒 458毫秒/步 - loss: 0.9226 - sparse_categorical_accuracy: 0.6405
1218/未知 559秒 458毫秒/步 - loss: 0.9225 - sparse_categorical_accuracy: 0.6405
1219/未知 559秒 458毫秒/步 - loss: 0.9223 - sparse_categorical_accuracy: 0.6406
1220/未知 560秒 458毫秒/步 - loss: 0.9222 - sparse_categorical_accuracy: 0.6406
1221/未知 560秒 458毫秒/步 - loss: 0.9220 - sparse_categorical_accuracy: 0.6407
1222/未知 560秒 458毫秒/步 - loss: 0.9218 - sparse_categorical_accuracy: 0.6407
1223/未知 561秒 458毫秒/步 - loss: 0.9217 - sparse_categorical_accuracy: 0.6408
1224/未知 561秒 458毫秒/步 - loss: 0.9215 - sparse_categorical_accuracy: 0.6408
1225/未知 562秒 458毫秒/步 - loss: 0.9214 - sparse_categorical_accuracy: 0.6409
1226/未知 562秒 458毫秒/步 - loss: 0.9212 - sparse_categorical_accuracy: 0.6409
1227/未知 563秒 458毫秒/步 - loss: 0.9211 - sparse_categorical_accuracy: 0.6410
1228/未知 563秒 458毫秒/步 - loss: 0.9209 - sparse_categorical_accuracy: 0.6410
1229/未知 564秒 458毫秒/步 - loss: 0.9207 - sparse_categorical_accuracy: 0.6410
1230/未知 564秒 458毫秒/步 - loss: 0.9206 - sparse_categorical_accuracy: 0.6411
1231/未知 565秒 458毫秒/步 - loss: 0.9204 - sparse_categorical_accuracy: 0.6411
1232/未知 565秒 458毫秒/步 - loss: 0.9203 - sparse_categorical_accuracy: 0.6412
1233/未知 566秒 458毫秒/步 - loss: 0.9201 - sparse_categorical_accuracy: 0.6412
1234/未知 566秒 458毫秒/步 - loss: 0.9200 - sparse_categorical_accuracy: 0.6413
1235/未知 567秒 458毫秒/步 - loss: 0.9198 - sparse_categorical_accuracy: 0.6413
1236/未知 567秒 458毫秒/步 - loss: 0.9197 - sparse_categorical_accuracy: 0.6414
1237/未知 568秒 458毫秒/步 - loss: 0.9195 - sparse_categorical_accuracy: 0.6414
1238/未知 568秒 458毫秒/步 - loss: 0.9193 - sparse_categorical_accuracy: 0.6415
1239/未知 569秒 458毫秒/步 - loss: 0.9192 - sparse_categorical_accuracy: 0.6415
1240/未知 569秒 458毫秒/步 - loss: 0.9190 - sparse_categorical_accuracy: 0.6416
1241/未知 569秒 458毫秒/步 - loss: 0.9189 - sparse_categorical_accuracy: 0.6416
1242/未知 570秒 458毫秒/步 - loss: 0.9187 - sparse_categorical_accuracy: 0.6417
1243/未知 570秒 458毫秒/步 - loss: 0.9186 - sparse_categorical_accuracy: 0.6417
1244/未知 571秒 458毫秒/步 - loss: 0.9184 - sparse_categorical_accuracy: 0.6418
1245/未知 571秒 458毫秒/步 - loss: 0.9183 - sparse_categorical_accuracy: 0.6418
1246/未知 572秒 458毫秒/步 - loss: 0.9181 - sparse_categorical_accuracy: 0.6419
1247/未知 572秒 458毫秒/步 - loss: 0.9180 - sparse_categorical_accuracy: 0.6419
1248/未知 573秒 458毫秒/步 - loss: 0.9178 - sparse_categorical_accuracy: 0.6420
1249/未知 573秒 458毫秒/步 - loss: 0.9177 - sparse_categorical_accuracy: 0.6420
1250/未知 574秒 458毫秒/步 - loss: 0.9175 - sparse_categorical_accuracy: 0.6421
1251/未知 574秒 458毫秒/步 - loss: 0.9173 - sparse_categorical_accuracy: 0.6421
1252/未知 574秒 458毫秒/步 - loss: 0.9172 - sparse_categorical_accuracy: 0.6422
1253/未知 575秒 458毫秒/步 - loss: 0.9170 - sparse_categorical_accuracy: 0.6422
1254/未知 575秒 458毫秒/步 - loss: 0.9169 - sparse_categorical_accuracy: 0.6423
1255/未知 576秒 458毫秒/步 - loss: 0.9167 - sparse_categorical_accuracy: 0.6423
1256/未知 576秒 458毫秒/步 - loss: 0.9166 - sparse_categorical_accuracy: 0.6424
1257/未知 577秒 458毫秒/步 - loss: 0.9164 - sparse_categorical_accuracy: 0.6424
1258/未知 577秒 458毫秒/步 - loss: 0.9163 - sparse_categorical_accuracy: 0.6424
1259/未知 578秒 458毫秒/步 - loss: 0.9161 - sparse_categorical_accuracy: 0.6425
1260/未知 578秒 459毫秒/步 - loss: 0.9160 - sparse_categorical_accuracy: 0.6425
1261/未知 579秒 459毫秒/步 - loss: 0.9158 - sparse_categorical_accuracy: 0.6426
1262/未知 579秒 458毫秒/步 - loss: 0.9157 - sparse_categorical_accuracy: 0.6426
1263/未知 580秒 459毫秒/步 - loss: 0.9155 - sparse_categorical_accuracy: 0.6427
1264/未知 580秒 459毫秒/步 - loss: 0.9154 - sparse_categorical_accuracy: 0.6427
1265/未知 581秒 459毫秒/步 - loss: 0.9152 - sparse_categorical_accuracy: 0.6428
1266/未知 581秒 459毫秒/步 - loss: 0.9151 - sparse_categorical_accuracy: 0.6428
1267/未知 582秒 459毫秒/步 - loss: 0.9149 - sparse_categorical_accuracy: 0.6429
1268/未知 582秒 459毫秒/步 - loss: 0.9148 - sparse_categorical_accuracy: 0.6429
1269/未知 583秒 459毫秒/步 - loss: 0.9146 - sparse_categorical_accuracy: 0.6430
1270/未知 583秒 459毫秒/步 - loss: 0.9145 - sparse_categorical_accuracy: 0.6430
1271/未知 584秒 459毫秒/步 - loss: 0.9143 - sparse_categorical_accuracy: 0.6431
1272/未知 584秒 459毫秒/步 - loss: 0.9142 - sparse_categorical_accuracy: 0.6431
1273/未知 584秒 459毫秒/步 - loss: 0.9140 - sparse_categorical_accuracy: 0.6432
1274/未知 585秒 459毫秒/步 - loss: 0.9139 - sparse_categorical_accuracy: 0.6432
1275/未知 585秒 459毫秒/步 - loss: 0.9137 - sparse_categorical_accuracy: 0.6432
1276/未知 586秒 459毫秒/步 - loss: 0.9136 - sparse_categorical_accuracy: 0.6433
1277/未知 586秒 459毫秒/步 - loss: 0.9134 - sparse_categorical_accuracy: 0.6433
1278/未知 587秒 459毫秒/步 - loss: 0.9133 - sparse_categorical_accuracy: 0.6434
1279/未知 587秒 459毫秒/步 - loss: 0.9131 - sparse_categorical_accuracy: 0.6434
1280/未知 588秒 459毫秒/步 - loss: 0.9130 - sparse_categorical_accuracy: 0.6435
1281/未知 588秒 459毫秒/步 - loss: 0.9128 - sparse_categorical_accuracy: 0.6435
1282/未知 589秒 459毫秒/步 - loss: 0.9127 - sparse_categorical_accuracy: 0.6436
1283/未知 589秒 459毫秒/步 - loss: 0.9126 - sparse_categorical_accuracy: 0.6436
1284/未知 589秒 459毫秒/步 - loss: 0.9124 - sparse_categorical_accuracy: 0.6437
1285/未知 590秒 458毫秒/步 - loss: 0.9123 - sparse_categorical_accuracy: 0.6437
1286/未知 590秒 458毫秒/步 - loss: 0.9121 - sparse_categorical_accuracy: 0.6438
1287/未知 591秒 458毫秒/步 - loss: 0.9120 - sparse_categorical_accuracy: 0.6438
1288/未知 591秒 458毫秒/步 - loss: 0.9118 - sparse_categorical_accuracy: 0.6438
1289/未知 591秒 458毫秒/步 - loss: 0.9117 - sparse_categorical_accuracy: 0.6439
1290/未知 592秒 458毫秒/步 - loss: 0.9115 - sparse_categorical_accuracy: 0.6439
1291/未知 592秒 458毫秒/步 - loss: 0.9114 - sparse_categorical_accuracy: 0.6440
1292/未知 593秒 458毫秒/步 - loss: 0.9112 - sparse_categorical_accuracy: 0.6440
1293/未知 593秒 458毫秒/步 - loss: 0.9111 - sparse_categorical_accuracy: 0.6441
1294/未知 594秒 458毫秒/步 - loss: 0.9109 - sparse_categorical_accuracy: 0.6441
1295/未知 594秒 458毫秒/步 - loss: 0.9108 - sparse_categorical_accuracy: 0.6442
1296/未知 595秒 458毫秒/步 - loss: 0.9107 - sparse_categorical_accuracy: 0.6442
1297/未知 595秒 458毫秒/步 - loss: 0.9105 - sparse_categorical_accuracy: 0.6443
1298/未知 596秒 458毫秒/步 - loss: 0.9104 - sparse_categorical_accuracy: 0.6443
1299/未知 596秒 458毫秒/步 - loss: 0.9102 - sparse_categorical_accuracy: 0.6443
1300/未知 596秒 458毫秒/步 - loss: 0.9101 - sparse_categorical_accuracy: 0.6444
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1302/未知 597秒 458毫秒/步 - loss: 0.9098 - sparse_categorical_accuracy: 0.6445
1303/未知 598秒 458毫秒/步 - loss: 0.9096 - sparse_categorical_accuracy: 0.6445
1304/未知 598秒 458毫秒/步 - loss: 0.9095 - sparse_categorical_accuracy: 0.6446
1305/未知 599秒 458毫秒/步 - loss: 0.9094 - sparse_categorical_accuracy: 0.6446
1306/未知 599秒 458毫秒/步 - loss: 0.9092 - sparse_categorical_accuracy: 0.6447
1307/未知 600秒 458毫秒/步 - loss: 0.9091 - sparse_categorical_accuracy: 0.6447
1308/未知 600秒 458毫秒/步 - loss: 0.9089 - sparse_categorical_accuracy: 0.6448
1309/未知 601秒 458毫秒/步 - loss: 0.9088 - sparse_categorical_accuracy: 0.6448
1310/未知 601秒 458毫秒/步 - loss: 0.9086 - sparse_categorical_accuracy: 0.6448
1311/未知 602秒 458毫秒/步 - loss: 0.9085 - sparse_categorical_accuracy: 0.6449
1312/未知 602秒 458毫秒/步 - loss: 0.9084 - sparse_categorical_accuracy: 0.6449
1313/未知 602秒 458毫秒/步 - loss: 0.9082 - sparse_categorical_accuracy: 0.6450
1314/未知 603秒 458毫秒/步 - loss: 0.9081 - sparse_categorical_accuracy: 0.6450
1315/未知 603秒 458毫秒/步 - loss: 0.9079 - sparse_categorical_accuracy: 0.6451
1316/未知 604秒 458毫秒/步 - loss: 0.9078 - sparse_categorical_accuracy: 0.6451
1317/未知 604秒 458毫秒/步 - loss: 0.9076 - sparse_categorical_accuracy: 0.6452
1318/未知 604秒 458毫秒/步 - loss: 0.9075 - sparse_categorical_accuracy: 0.6452
1319/未知 605秒 458毫秒/步 - loss: 0.9074 - sparse_categorical_accuracy: 0.6452
1320/未知 605秒 458毫秒/步 - loss: 0.9072 - sparse_categorical_accuracy: 0.6453
1321/未知 605秒 458毫秒/步 - loss: 0.9071 - sparse_categorical_accuracy: 0.6453
1322/未知 606秒 458毫秒/步 - loss: 0.9069 - sparse_categorical_accuracy: 0.6454
1323/未知 606秒 458毫秒/步 - loss: 0.9068 - sparse_categorical_accuracy: 0.6454
1324/未知 607秒 458毫秒/步 - loss: 0.9067 - sparse_categorical_accuracy: 0.6455
1325/未知 607秒 458毫秒/步 - loss: 0.9065 - sparse_categorical_accuracy: 0.6455
1326/未知 608秒 458毫秒/步 - loss: 0.9064 - sparse_categorical_accuracy: 0.6455
1327/未知 608秒 458毫秒/步 - loss: 0.9062 - sparse_categorical_accuracy: 0.6456
1328/未知 609秒 458毫秒/步 - loss: 0.9061 - sparse_categorical_accuracy: 0.6456
1329/未知 609秒 458毫秒/步 - loss: 0.9060 - sparse_categorical_accuracy: 0.6457
1330/未知 609秒 458毫秒/步 - loss: 0.9058 - sparse_categorical_accuracy: 0.6457
1331/未知 610秒 458毫秒/步 - loss: 0.9057 - sparse_categorical_accuracy: 0.6458
1332/未知 610秒 458毫秒/步 - loss: 0.9055 - sparse_categorical_accuracy: 0.6458
1333/未知 611秒 458毫秒/步 - loss: 0.9054 - sparse_categorical_accuracy: 0.6459
1334/未知 611秒 458毫秒/步 - loss: 0.9053 - sparse_categorical_accuracy: 0.6459
1335/未知 612秒 458毫秒/步 - loss: 0.9051 - sparse_categorical_accuracy: 0.6459
1336/未知 612秒 458毫秒/步 - loss: 0.9050 - sparse_categorical_accuracy: 0.6460
1337/未知 613秒 458毫秒/步 - loss: 0.9048 - sparse_categorical_accuracy: 0.6460
1338/未知 613秒 458毫秒/步 - loss: 0.9047 - sparse_categorical_accuracy: 0.6461
1339/未知 614秒 458毫秒/步 - loss: 0.9046 - sparse_categorical_accuracy: 0.6461
1340/未知 614秒 458毫秒/步 - loss: 0.9044 - sparse_categorical_accuracy: 0.6462
1341/未知 614秒 458毫秒/步 - loss: 0.9043 - sparse_categorical_accuracy: 0.6462
1342/未知 615秒 458毫秒/步 - loss: 0.9042 - sparse_categorical_accuracy: 0.6462
1343/未知 615秒 458毫秒/步 - loss: 0.9040 - sparse_categorical_accuracy: 0.6463
1344/未知 615秒 458毫秒/步 - loss: 0.9039 - sparse_categorical_accuracy: 0.6463
1345/未知 616秒 458毫秒/步 - loss: 0.9037 - sparse_categorical_accuracy: 0.6464
1346/未知 616秒 458毫秒/步 - loss: 0.9036 - sparse_categorical_accuracy: 0.6464
1347/未知 617秒 457毫秒/步 - loss: 0.9035 - sparse_categorical_accuracy: 0.6465
1348/未知 617秒 457毫秒/步 - loss: 0.9033 - sparse_categorical_accuracy: 0.6465
1349/未知 618秒 457毫秒/步 - loss: 0.9032 - sparse_categorical_accuracy: 0.6465
1350/未知 618秒 457毫秒/步 - loss: 0.9031 - sparse_categorical_accuracy: 0.6466
1351/未知 618秒 457毫秒/步 - loss: 0.9029 - sparse_categorical_accuracy: 0.6466
1352/未知 619秒 457毫秒/步 - loss: 0.9028 - sparse_categorical_accuracy: 0.6467
1353/未知 619秒 457毫秒/步 - loss: 0.9026 - sparse_categorical_accuracy: 0.6467
1354/未知 620秒 457毫秒/步 - loss: 0.9025 - sparse_categorical_accuracy: 0.6468
1355/未知 620秒 457毫秒/步 - loss: 0.9024 - sparse_categorical_accuracy: 0.6468
1356/未知 621秒 457毫秒/步 - loss: 0.9022 - sparse_categorical_accuracy: 0.6468
1357/未知 621秒 457毫秒/步 - loss: 0.9021 - sparse_categorical_accuracy: 0.6469
1358/未知 622秒 457毫秒/步 - loss: 0.9020 - sparse_categorical_accuracy: 0.6469
1359/未知 622秒 457毫秒/步 - loss: 0.9018 - sparse_categorical_accuracy: 0.6470
1360/未知 623秒 457毫秒/步 - loss: 0.9017 - sparse_categorical_accuracy: 0.6470
1361/未知 623秒 457毫秒/步 - loss: 0.9016 - sparse_categorical_accuracy: 0.6471
1362/未知 624秒 457毫秒/步 - loss: 0.9014 - sparse_categorical_accuracy: 0.6471
1363/未知 624秒 457毫秒/步 - loss: 0.9013 - sparse_categorical_accuracy: 0.6471
1364/未知 624秒 457毫秒/步 - loss: 0.9012 - sparse_categorical_accuracy: 0.6472
1365/未知 625秒 457毫秒/步 - loss: 0.9010 - sparse_categorical_accuracy: 0.6472
1366/未知 625秒 457毫秒/步 - loss: 0.9009 - sparse_categorical_accuracy: 0.6473
1367/未知 625秒 457毫秒/步 - loss: 0.9008 - sparse_categorical_accuracy: 0.6473
1368/未知 626秒 457毫秒/步 - loss: 0.9006 - sparse_categorical_accuracy: 0.6474
1369/未知 626秒 457毫秒/步 - loss: 0.9005 - sparse_categorical_accuracy: 0.6474
1370/未知 627秒 457毫秒/步 - loss: 0.9004 - sparse_categorical_accuracy: 0.6474
1371/未知 627秒 457毫秒/步 - loss: 0.9002 - sparse_categorical_accuracy: 0.6475
1372/未知 627秒 457毫秒/步 - loss: 0.9001 - sparse_categorical_accuracy: 0.6475
1373/未知 628秒 457毫秒/步 - loss: 0.9000 - sparse_categorical_accuracy: 0.6476
1374/未知 628秒 457毫秒/步 - loss: 0.8998 - sparse_categorical_accuracy: 0.6476
1375/未知 629秒 457毫秒/步 - loss: 0.8997 - sparse_categorical_accuracy: 0.6476
1376/未知 629秒 457毫秒/步 - loss: 0.8996 - sparse_categorical_accuracy: 0.6477
1377/未知 630秒 457毫秒/步 - loss: 0.8994 - sparse_categorical_accuracy: 0.6477
1378/未知 630秒 457毫秒/步 - loss: 0.8993 - sparse_categorical_accuracy: 0.6478
1379/未知 631秒 457毫秒/步 - loss: 0.8992 - sparse_categorical_accuracy: 0.6478
1380/未知 631秒 457毫秒/步 - loss: 0.8990 - sparse_categorical_accuracy: 0.6479
1381/未知 632秒 457毫秒/步 - loss: 0.8989 - sparse_categorical_accuracy: 0.6479
1382/未知 632秒 457毫秒/步 - loss: 0.8988 - sparse_categorical_accuracy: 0.6479
1383/未知 633秒 457毫秒/步 - loss: 0.8986 - sparse_categorical_accuracy: 0.6480
1384/未知 633秒 457毫秒/步 - loss: 0.8985 - sparse_categorical_accuracy: 0.6480
1385/未知 633秒 457毫秒/步 - loss: 0.8984 - sparse_categorical_accuracy: 0.6481
1386/未知 634秒 457毫秒/步 - loss: 0.8982 - sparse_categorical_accuracy: 0.6481
1387/未知 634秒 457毫秒/步 - loss: 0.8981 - sparse_categorical_accuracy: 0.6481
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1389/未知 635秒 457毫秒/步 - loss: 0.8978 - sparse_categorical_accuracy: 0.6482
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1391/未知 636秒 457毫秒/步 - loss: 0.8976 - sparse_categorical_accuracy: 0.6483
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1398/未知 639秒 456毫秒/步 - loss: 0.8967 - sparse_categorical_accuracy: 0.6486
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1410/未知 644秒 457毫秒/步 - loss: 0.8951 - sparse_categorical_accuracy: 0.6491
1411/未知 645秒 457毫秒/步 - loss: 0.8950 - sparse_categorical_accuracy: 0.6491
1412/未知 645秒 457毫秒/步 - loss: 0.8949 - sparse_categorical_accuracy: 0.6492
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1416/未知 647秒 457毫秒/步 - loss: 0.8944 - sparse_categorical_accuracy: 0.6493
1417/未知 647秒 457毫秒/步 - loss: 0.8942 - sparse_categorical_accuracy: 0.6494
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1419/未知 648秒 457毫秒/步 - loss: 0.8940 - sparse_categorical_accuracy: 0.6494
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1423/未知 650秒 456毫秒/步 - loss: 0.8935 - sparse_categorical_accuracy: 0.6496
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1425/未知 651秒 456毫秒/步 - loss: 0.8932 - sparse_categorical_accuracy: 0.6497
1426/未知 651秒 456毫秒/步 - loss: 0.8931 - sparse_categorical_accuracy: 0.6497
1427/未知 652秒 456毫秒/步 - loss: 0.8930 - sparse_categorical_accuracy: 0.6497
1428/未知 652秒 456毫秒/步 - loss: 0.8928 - sparse_categorical_accuracy: 0.6498
1429/未知 653秒 456毫秒/步 - loss: 0.8927 - sparse_categorical_accuracy: 0.6498
1430/未知 653秒 456毫秒/步 - loss: 0.8926 - sparse_categorical_accuracy: 0.6499
1431/未知 653秒 456毫秒/步 - loss: 0.8925 - sparse_categorical_accuracy: 0.6499
1432/未知 654秒 456毫秒/步 - loss: 0.8923 - sparse_categorical_accuracy: 0.6499
1433/未知 654秒 456毫秒/步 - loss: 0.8922 - sparse_categorical_accuracy: 0.6500
1434/未知 655秒 456毫秒/步 - loss: 0.8921 - sparse_categorical_accuracy: 0.6500
1435/未知 655秒 456毫秒/步 - loss: 0.8920 - sparse_categorical_accuracy: 0.6501
1436/未知 655秒 456毫秒/步 - loss: 0.8918 - sparse_categorical_accuracy: 0.6501
1437/未知 656秒 456毫秒/步 - loss: 0.8917 - sparse_categorical_accuracy: 0.6501
1438/未知 656秒 456毫秒/步 - loss: 0.8916 - sparse_categorical_accuracy: 0.6502
1439/未知 657秒 456毫秒/步 - loss: 0.8915 - sparse_categorical_accuracy: 0.6502
1440/未知 657秒 456毫秒/步 - loss: 0.8913 - sparse_categorical_accuracy: 0.6503
1441/未知 657秒 456毫秒/步 - loss: 0.8912 - sparse_categorical_accuracy: 0.6503
1442/未知 658秒 456毫秒/步 - loss: 0.8911 - sparse_categorical_accuracy: 0.6503
1443/未知 658秒 456毫秒/步 - loss: 0.8910 - sparse_categorical_accuracy: 0.6504
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1449/未知 661秒 456毫秒/步 - loss: 0.8902 - sparse_categorical_accuracy: 0.6506
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1452/未知 662秒 456毫秒/步 - loss: 0.8899 - sparse_categorical_accuracy: 0.6507
1453/未知 663秒 456毫秒/步 - loss: 0.8897 - sparse_categorical_accuracy: 0.6508
1454/未知 663秒 456毫秒/步 - loss: 0.8896 - sparse_categorical_accuracy: 0.6508
1455/未知 664秒 456毫秒/步 - loss: 0.8895 - sparse_categorical_accuracy: 0.6508
1456/未知 664秒 456毫秒/步 - loss: 0.8894 - sparse_categorical_accuracy: 0.6509
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1458/未知 665秒 456毫秒/步 - loss: 0.8891 - sparse_categorical_accuracy: 0.6509
1459/未知 665秒 456毫秒/步 - loss: 0.8890 - sparse_categorical_accuracy: 0.6510
1460/未知 666秒 456毫秒/步 - loss: 0.8889 - sparse_categorical_accuracy: 0.6510
1461/未知 666秒 456毫秒/步 - loss: 0.8888 - sparse_categorical_accuracy: 0.6511
1462/未知 667秒 456毫秒/步 - loss: 0.8887 - sparse_categorical_accuracy: 0.6511
1463/未知 667秒 455毫秒/步 - loss: 0.8885 - sparse_categorical_accuracy: 0.6511
1464/未知 667秒 455毫秒/步 - loss: 0.8884 - sparse_categorical_accuracy: 0.6512
1465/未知 668秒 455毫秒/步 - loss: 0.8883 - sparse_categorical_accuracy: 0.6512
1466/未知 668秒 455毫秒/步 - loss: 0.8882 - sparse_categorical_accuracy: 0.6512
1467/未知 669秒 455毫秒/步 - loss: 0.8880 - sparse_categorical_accuracy: 0.6513
1468/未知 669秒 455毫秒/步 - loss: 0.8879 - sparse_categorical_accuracy: 0.6513
1469/未知 669秒 455毫秒/步 - loss: 0.8878 - sparse_categorical_accuracy: 0.6514
1470/未知 670秒 455毫秒/步 - loss: 0.8877 - sparse_categorical_accuracy: 0.6514
1471/未知 670秒 455毫秒/步 - loss: 0.8876 - sparse_categorical_accuracy: 0.6514
1472/未知 671秒 455毫秒/步 - loss: 0.8874 - sparse_categorical_accuracy: 0.6515
1473/未知 671秒 455毫秒/步 - loss: 0.8873 - sparse_categorical_accuracy: 0.6515
1474/未知 672秒 455毫秒/步 - loss: 0.8872 - sparse_categorical_accuracy: 0.6515
1475/未知 672秒 455毫秒/步 - loss: 0.8871 - sparse_categorical_accuracy: 0.6516
1476/未知 673秒 455毫秒/步 - loss: 0.8870 - sparse_categorical_accuracy: 0.6516
1477/未知 673秒 455毫秒/步 - loss: 0.8868 - sparse_categorical_accuracy: 0.6517
1478/未知 673秒 455毫秒/步 - loss: 0.8867 - sparse_categorical_accuracy: 0.6517
1479/未知 674秒 455毫秒/步 - loss: 0.8866 - sparse_categorical_accuracy: 0.6517
1480/未知 674秒 455毫秒/步 - loss: 0.8865 - sparse_categorical_accuracy: 0.6518
1481/未知 674秒 455毫秒/步 - loss: 0.8864 - sparse_categorical_accuracy: 0.6518
1482/未知 675秒 455毫秒/步 - loss: 0.8863 - sparse_categorical_accuracy: 0.6518
1483/未知 675秒 455毫秒/步 - loss: 0.8861 - sparse_categorical_accuracy: 0.6519
1484/未知 676秒 455毫秒/步 - loss: 0.8860 - sparse_categorical_accuracy: 0.6519
1485/未知 676秒 455毫秒/步 - loss: 0.8859 - sparse_categorical_accuracy: 0.6520
1486/未知 677秒 455毫秒/步 - loss: 0.8858 - sparse_categorical_accuracy: 0.6520
1487/未知 677秒 455毫秒/步 - loss: 0.8857 - sparse_categorical_accuracy: 0.6520
1488/未知 677秒 455毫秒/步 - loss: 0.8855 - sparse_categorical_accuracy: 0.6521
1489/未知 678秒 455毫秒/步 - loss: 0.8854 - sparse_categorical_accuracy: 0.6521
1490/未知 678秒 455毫秒/步 - loss: 0.8853 - sparse_categorical_accuracy: 0.6521
1491/未知 679秒 455毫秒/步 - loss: 0.8852 - sparse_categorical_accuracy: 0.6522
1492/未知 679秒 455毫秒/步 - loss: 0.8851 - sparse_categorical_accuracy: 0.6522
1493/未知 679秒 455毫秒/步 - loss: 0.8850 - sparse_categorical_accuracy: 0.6523
1494/未知 680秒 455毫秒/步 - loss: 0.8848 - sparse_categorical_accuracy: 0.6523
1495/未知 680秒 455毫秒/步 - loss: 0.8847 - sparse_categorical_accuracy: 0.6523
1496/未知 681秒 455毫秒/步 - loss: 0.8846 - sparse_categorical_accuracy: 0.6524
1497/未知 681秒 455毫秒/步 - loss: 0.8845 - sparse_categorical_accuracy: 0.6524
1498/未知 682秒 455毫秒/步 - loss: 0.8844 - sparse_categorical_accuracy: 0.6524
1499/未知 682秒 455毫秒/步 - loss: 0.8843 - sparse_categorical_accuracy: 0.6525
1500/未知 683秒 455毫秒/步 - loss: 0.8841 - sparse_categorical_accuracy: 0.6525
1501/未知 683秒 455毫秒/步 - loss: 0.8840 - sparse_categorical_accuracy: 0.6525
1502/未知 684秒 455毫秒/步 - loss: 0.8839 - sparse_categorical_accuracy: 0.6526
1503/未知 684秒 455毫秒/步 - loss: 0.8838 - sparse_categorical_accuracy: 0.6526
1504/未知 685秒 455毫秒/步 - loss: 0.8837 - sparse_categorical_accuracy: 0.6527
1505/未知 685秒 455毫秒/步 - loss: 0.8836 - sparse_categorical_accuracy: 0.6527
1506/未知 685秒 455毫秒/步 - loss: 0.8834 - sparse_categorical_accuracy: 0.6527
1507/未知 686秒 455毫秒/步 - loss: 0.8833 - sparse_categorical_accuracy: 0.6528
1508/未知 686秒 455毫秒/步 - loss: 0.8832 - sparse_categorical_accuracy: 0.6528
1509/未知 687秒 455毫秒/步 - loss: 0.8831 - sparse_categorical_accuracy: 0.6528
1510/未知 687秒 455毫秒/步 - loss: 0.8830 - sparse_categorical_accuracy: 0.6529
1511/未知 687秒 455毫秒/步 - loss: 0.8829 - sparse_categorical_accuracy: 0.6529
1512/未知 688秒 454毫秒/步 - loss: 0.8827 - sparse_categorical_accuracy: 0.6529
1513/未知 688秒 454毫秒/步 - loss: 0.8826 - sparse_categorical_accuracy: 0.6530
1514/未知 688秒 454毫秒/步 - loss: 0.8825 - sparse_categorical_accuracy: 0.6530
1515/未知 689秒 454毫秒/步 - loss: 0.8824 - sparse_categorical_accuracy: 0.6531
1516/未知 689秒 454毫秒/步 - loss: 0.8823 - sparse_categorical_accuracy: 0.6531
1517/未知 690秒 454毫秒/步 - loss: 0.8822 - sparse_categorical_accuracy: 0.6531
1518/未知 690秒 454毫秒/步 - loss: 0.8821 - sparse_categorical_accuracy: 0.6532
1519/未知 690秒 454毫秒/步 - loss: 0.8819 - sparse_categorical_accuracy: 0.6532
1520/未知 691秒 454毫秒/步 - loss: 0.8818 - sparse_categorical_accuracy: 0.6532
1521/未知 691秒 454毫秒/步 - loss: 0.8817 - sparse_categorical_accuracy: 0.6533
1522/未知 692秒 454毫秒/步 - loss: 0.8816 - sparse_categorical_accuracy: 0.6533
1523/未知 692秒 454毫秒/步 - loss: 0.8815 - sparse_categorical_accuracy: 0.6533
1524/未知 693秒 454毫秒/步 - loss: 0.8814 - sparse_categorical_accuracy: 0.6534
1525/未知 693秒 454毫秒/步 - loss: 0.8813 - sparse_categorical_accuracy: 0.6534
1526/未知 694秒 454毫秒/步 - loss: 0.8811 - sparse_categorical_accuracy: 0.6534
1527/未知 694秒 454毫秒/步 - loss: 0.8810 - sparse_categorical_accuracy: 0.6535
1528/未知 695秒 454毫秒/步 - loss: 0.8809 - sparse_categorical_accuracy: 0.6535
1529/未知 695秒 454毫秒/步 - loss: 0.8808 - sparse_categorical_accuracy: 0.6536
1530/未知 695秒 454毫秒/步 - loss: 0.8807 - sparse_categorical_accuracy: 0.6536
1531/未知 696秒 454毫秒/步 - loss: 0.8806 - sparse_categorical_accuracy: 0.6536
1532/未知 696秒 454毫秒/步 - loss: 0.8805 - sparse_categorical_accuracy: 0.6537
1533/未知 697秒 454毫秒/步 - loss: 0.8803 - sparse_categorical_accuracy: 0.6537
1534/未知 697秒 454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 - 损失: 0.8787 - 稀疏分类准确率: 0.6542
1549/未知 703秒 454毫秒/步 - 损失: 0.8786 - 稀疏分类准确率: 0.6543
1550/未知 704秒 454毫秒/步 - 损失: 0.8784 - 稀疏分类准确率: 0.6543
1551/未知 704秒 454毫秒/步 - 损失: 0.8783 - 稀疏分类准确率: 0.6543
1552/未知 705秒 454毫秒/步 - 损失: 0.8782 - 稀疏分类准确率: 0.6544
1553/未知 705秒 454毫秒/步 - 损失: 0.8781 - 稀疏分类准确率: 0.6544
1554/未知 705秒 454毫秒/步 - 损失: 0.8780 - 稀疏分类准确率: 0.6544
1555/未知 706秒 453毫秒/步 - 损失: 0.8779 - 稀疏分类准确率: 0.6545
1556/未知 706秒 453毫秒/步 - 损失: 0.8778 - 稀疏分类准确率: 0.6545
1557/未知 706秒 453毫秒/步 - 损失: 0.8777 - 稀疏分类准确率: 0.6545
1558/未知 707秒 453毫秒/步 - 损失: 0.8776 - 稀疏分类准确率: 0.6546
1559/未知 707秒 453毫秒/步 - 损失: 0.8774 - 稀疏分类准确率: 0.6546
1560/未知 708秒 453毫秒/步 - 损失: 0.8773 - 稀疏分类准确率: 0.6546
1561/未知 708秒 453毫秒/步 - 损失: 0.8772 - 稀疏分类准确率: 0.6547
1562/未知 708秒 453毫秒/步 - 损失: 0.8771 - 稀疏分类准确率: 0.6547
1563/未知 709秒 453毫秒/步 - 损失: 0.8770 - 稀疏分类准确率: 0.6547
1564/未知 709秒 453毫秒/步 - 损失: 0.8769 - 稀疏分类准确率: 0.6548
1565/未知 710秒 453毫秒/步 - 损失: 0.8768 - 稀疏分类准确率: 0.6548
1566/未知 710秒 453毫秒/步 - 损失: 0.8767 - 稀疏分类准确率: 0.6548
1567/未知 711秒 453毫秒/步 - 损失: 0.8766 - 稀疏分类准确率: 0.6549
1568/未知 711秒 453毫秒/步 - 损失: 0.8765 - 稀疏分类准确率: 0.6549
1569/未知 711秒 453毫秒/步 - 损失: 0.8763 - 稀疏分类准确率: 0.6549
1570/未知 712秒 453毫秒/步 - 损失: 0.8762 - 稀疏分类准确率: 0.6550
1571/未知 712秒 453毫秒/步 - 损失: 0.8761 - 稀疏分类准确率: 0.6550
1572/未知 713秒 453毫秒/步 - 损失: 0.8760 - 稀疏分类准确率: 0.6550
1573/未知 713秒 453毫秒/步 - 损失: 0.8759 - 稀疏分类准确率: 0.6551
1574/未知 714秒 453毫秒/步 - 损失: 0.8758 - 稀疏分类准确率: 0.6551
1575/未知 714秒 453毫秒/步 - 损失: 0.8757 - 稀疏分类准确率: 0.6552
1576/未知 715秒 453毫秒/步 - 损失: 0.8756 - 稀疏分类准确率: 0.6552
1577/未知 715秒 453毫秒/步 - 损失: 0.8755 - 稀疏分类准确率: 0.6552
1578/未知 715秒 453毫秒/步 - 损失: 0.8754 - 稀疏分类准确率: 0.6553
1579/未知 716秒 453毫秒/步 - 损失: 0.8753 - 稀疏分类准确率: 0.6553
1580/未知 716秒 453毫秒/步 - 损失: 0.8752 - 稀疏分类准确率: 0.6553
1581/未知 716秒 453毫秒/步 - 损失: 0.8750 - 稀疏分类准确率: 0.6554
1582/未知 717秒 453毫秒/步 - 损失: 0.8749 - 稀疏分类准确率: 0.6554
1583/未知 717秒 453毫秒/步 - 损失: 0.8748 - 稀疏分类准确率: 0.6554
1584/未知 718秒 453毫秒/步 - 损失: 0.8747 - 稀疏分类准确率: 0.6555
1585/未知 718秒 453毫秒/步 - 损失: 0.8746 - 稀疏分类准确率: 0.6555
1586/未知 718秒 453毫秒/步 - 损失: 0.8745 - 稀疏分类准确率: 0.6555
1587/未知 719秒 453毫秒/步 - 损失: 0.8744 - 稀疏分类准确率: 0.6556
1588/未知 719秒 453毫秒/步 - 损失: 0.8743 - 稀疏分类准确率: 0.6556
1589/未知 720秒 453毫秒/步 - 损失: 0.8742 - 稀疏分类准确率: 0.6556
1590/未知 720秒 453毫秒/步 - 损失: 0.8741 - 稀疏分类准确率: 0.6557
1591/未知 721秒 453毫秒/步 - 损失: 0.8740 - 稀疏分类准确率: 0.6557
1592/未知 721秒 452毫秒/步 - 损失: 0.8739 - 稀疏分类准确率: 0.6557
1593/未知 721秒 452毫秒/步 - 损失: 0.8738 - 稀疏分类准确率: 0.6558
1594/未知 722秒 452毫秒/步 - 损失: 0.8737 - 稀疏分类准确率: 0.6558
1595/未知 722秒 452毫秒/步 - 损失: 0.8735 - 稀疏分类准确率: 0.6558
1596/未知 723秒 452毫秒/步 - 损失: 0.8734 - 稀疏分类准确率: 0.6559
1597/未知 723秒 453毫秒/步 - 损失: 0.8733 - 稀疏分类准确率: 0.6559
1598/未知 724秒 453毫秒/步 - 损失: 0.8732 - 稀疏分类准确率: 0.6559
1599/未知 724秒 453毫秒/步 - 损失: 0.8731 - 稀疏分类准确率: 0.6560
1600/未知 725秒 453毫秒/步 - 损失: 0.8730 - 稀疏分类准确率: 0.6560
1601/未知 725秒 452毫秒/步 - 损失: 0.8729 - 稀疏分类准确率: 0.6560
1602/未知 725秒 452毫秒/步 - 损失: 0.8728 - 稀疏分类准确率: 0.6561
1603/未知 726秒 452毫秒/步 - 损失: 0.8727 - 稀疏分类准确率: 0.6561
1604/未知 726秒 452毫秒/步 - 损失: 0.8726 - 稀疏分类准确率: 0.6561
1605/未知 726秒 452毫秒/步 - 损失: 0.8725 - 稀疏分类准确率: 0.6562
1606/未知 727秒 452毫秒/步 - 损失: 0.8724 - 稀疏分类准确率: 0.6562
1607/未知 727秒 452毫秒/步 - 损失: 0.8723 - 稀疏分类准确率: 0.6562
1608/未知 728秒 452毫秒/步 - 损失: 0.8722 - 稀疏分类准确率: 0.6563
1609/未知 728秒 452毫秒/步 - 损失: 0.8721 - 稀疏分类准确率: 0.6563
1610/未知 728秒 452毫秒/步 - 损失: 0.8720 - 稀疏分类准确率: 0.6563
1611/未知 729秒 452毫秒/步 - 损失: 0.8719 - 稀疏分类准确率: 0.6564
1612/未知 729秒 452毫秒/步 - 损失: 0.8717 - 稀疏分类准确率: 0.6564
1613/未知 730秒 452毫秒/步 - 损失: 0.8716 - 稀疏分类准确率: 0.6564
1614/未知 730秒 452毫秒/步 - 损失: 0.8715 - 稀疏分类准确率: 0.6565
1615/未知 730秒 452毫秒/步 - 损失: 0.8714 - 稀疏分类准确率: 0.6565
1616/未知 731秒 452毫秒/步 - 损失: 0.8713 - 稀疏分类准确率: 0.6565
1617/未知 731秒 452毫秒/步 - 损失: 0.8712 - 稀疏分类准确率: 0.6566
1618/未知 732秒 452毫秒/步 - 损失: 0.8711 - 稀疏分类准确率: 0.6566
1619/未知 732秒 452毫秒/步 - 损失: 0.8710 - 稀疏分类准确率: 0.6566
1620/未知 733秒 452毫秒/步 - 损失: 0.8709 - 稀疏分类准确率: 0.6567
1621/未知 733秒 452毫秒/步 - 损失: 0.8708 - 稀疏分类准确率: 0.6567
1622/未知 734秒 452毫秒/步 - 损失: 0.8707 - 稀疏分类准确率: 0.6567
1623/未知 734秒 452毫秒/步 - 损失: 0.8706 - 稀疏分类准确率: 0.6567
1624/未知 734秒 452毫秒/步 - 损失: 0.8705 - 稀疏分类准确率: 0.6568
1625/未知 735秒 452毫秒/步 - 损失: 0.8704 - 稀疏分类准确率: 0.6568
1626/未知 735秒 452毫秒/步 - 损失: 0.8703 - 稀疏分类准确率: 0.6568
1627/未知 736秒 452毫秒/步 - 损失: 0.8702 - 稀疏分类准确率: 0.6569
1628/未知 736秒 452毫秒/步 - 损失: 0.8701 - 稀疏分类准确率: 0.6569
1629/未知 736秒 452毫秒/步 - 损失: 0.8700 - 稀疏分类准确率: 0.6569
1630/未知 737秒 452毫秒/步 - 损失: 0.8699 - 稀疏分类准确率: 0.6570
1631/未知 737秒 452毫秒/步 - 损失: 0.8698 - 稀疏分类准确率: 0.6570
1632/未知 738秒 452毫秒/步 - 损失: 0.8697 - 稀疏分类准确率: 0.6570
1633/未知 738秒 452毫秒/步 - 损失: 0.8696 - 稀疏分类准确率: 0.6571
1634/未知 738秒 451毫秒/步 - 损失: 0.8695 - 稀疏分类准确率: 0.6571
1635/未知 739秒 451毫秒/步 - 损失: 0.8694 - 稀疏分类准确率: 0.6571
1636/未知 739秒 451毫秒/步 - 损失: 0.8693 - 稀疏分类准确率: 0.6572
1637/未知 739秒 451毫秒/步 - 损失: 0.8692 - 稀疏分类准确率: 0.6572
1638/未知 740秒 451毫秒/步 - 损失: 0.8690 - 稀疏分类准确率: 0.6572
1639/未知 740秒 451毫秒/步 - 损失: 0.8689 - 稀疏分类准确率: 0.6573
1640/未知 741秒 451毫秒/步 - 损失: 0.8688 - 稀疏分类准确率: 0.6573
1641/未知 741秒 451毫秒/步 - 损失: 0.8687 - 稀疏分类准确率: 0.6573
1642/未知 742秒 451毫秒/步 - 损失: 0.8686 - 稀疏分类准确率: 0.6574
1643/未知 742秒 451毫秒/步 - 损失: 0.8685 - 稀疏分类准确率: 0.6574
1644/未知 743秒 451毫秒/步 - 损失: 0.8684 - 稀疏分类准确率: 0.6574
1645/未知 743秒 451毫秒/步 - 损失: 0.8683 - 稀疏分类准确率: 0.6575
1646/未知 743秒 451毫秒/步 - 损失: 0.8682 - 稀疏分类准确率: 0.6575
1647/未知 744秒 451毫秒/步 - 损失: 0.8681 - 稀疏分类准确率: 0.6575
1648/未知 744秒 451毫秒/步 - 损失: 0.8680 - 稀疏分类准确率: 0.6576
1649/未知 744秒 451毫秒/步 - 损失: 0.8679 - 稀疏分类准确率: 0.6576
1650/未知 745秒 451毫秒/步 - 损失: 0.8678 - 稀疏分类准确率: 0.6576
1651/未知 745秒 451毫秒/步 - 损失: 0.8677 - 稀疏分类准确率: 0.6577
1652/未知 746秒 451毫秒/步 - 损失: 0.8676 - 稀疏分类准确率: 0.6577
1653/未知 746秒 451毫秒/步 - 损失: 0.8675 - 稀疏分类准确率: 0.6577
1654/未知 746秒 451毫秒/步 - 损失: 0.8674 - 稀疏分类准确率: 0.6577
1655/未知 747秒 451毫秒/步 - 损失: 0.8673 - 稀疏分类准确率: 0.6578
1656/未知 747秒 451毫秒/步 - 损失: 0.8672 - 稀疏分类准确率: 0.6578
1657/未知 748秒 451毫秒/步 - 损失: 0.8671 - 稀疏分类准确率: 0.6578
1658/未知 748秒 451毫秒/步 - 损失: 0.8670 - 稀疏分类准确率: 0.6579
1659/未知 749秒 451毫秒/步 - 损失: 0.8669 - 稀疏分类准确率: 0.6579
1660/未知 749秒 451毫秒/步 - 损失: 0.8668 - 稀疏分类准确率: 0.6579
1661/未知 749秒 451毫秒/步 - 损失: 0.8667 - 稀疏分类准确率: 0.6580
1662/未知 750秒 451毫秒/步 - 损失: 0.8666 - 稀疏分类准确率: 0.6580
1663/未知 750秒 451毫秒/步 - 损失: 0.8665 - 稀疏分类准确率: 0.6580
1664/未知 750秒 451毫秒/步 - 损失: 0.8664 - 稀疏分类准确率: 0.6581
1665/未知 751秒 451毫秒/步 - 损失: 0.8663 - 稀疏分类准确率: 0.6581
1666/未知 751秒 451毫秒/步 - 损失: 0.8662 - 稀疏分类准确率: 0.6581
1667/未知 752秒 451毫秒/步 - 损失: 0.8661 - 稀疏分类准确率: 0.6582
1668/未知 752秒 451毫秒/步 - 损失: 0.8660 - 稀疏分类准确率: 0.6582
1669/未知 753秒 451毫秒/步 - 损失: 0.8659 - 稀疏分类准确率: 0.6582
1670/未知 753秒 451毫秒/步 - 损失: 0.8658 - 稀疏分类准确率: 0.6583
1671/未知 754秒 451毫秒/步 - 损失: 0.8657 - 稀疏分类准确率: 0.6583
1672/未知 754秒 451毫秒/步 - 损失: 0.8656 - 稀疏分类准确率: 0.6583
1673/未知 755秒 451毫秒/步 - 损失: 0.8655 - 稀疏分类准确率: 0.6583
1674/未知 755秒 451毫秒/步 - 损失: 0.8654 - 稀疏分类准确率: 0.6584
1675/未知 755秒 451毫秒/步 - 损失: 0.8653 - 稀疏分类准确率: 0.6584
1676/未知 756秒 451毫秒/步 - 损失: 0.8652 - 稀疏分类准确率: 0.6584
1677/未知 756秒 451毫秒/步 - 损失: 0.8651 - 稀疏分类准确率: 0.6585
1678/未知 757秒 451毫秒/步 - 损失: 0.8650 - 稀疏分类准确率: 0.6585
1679/未知 757秒 450毫秒/步 - 损失: 0.8649 - 稀疏分类准确率: 0.6585
1680/未知 757秒 450毫秒/步 - 损失: 0.8648 - 稀疏分类准确率: 0.6586
1681/未知 758秒 450毫秒/步 - 损失: 0.8647 - 稀疏分类准确率: 0.6586
1682/未知 758秒 450毫秒/步 - 损失: 0.8646 - 稀疏分类准确率: 0.6586
1683/未知 758秒 450毫秒/步 - 损失: 0.8645 - 稀疏分类准确率: 0.6587
1684/未知 759秒 450毫秒/步 - 损失: 0.8644 - 稀疏分类准确率: 0.6587
1685/未知 759秒 450毫秒/步 - 损失: 0.8643 - 稀疏分类准确率: 0.6587
1686/未知 760秒 450毫秒/步 - 损失: 0.8642 - 稀疏分类准确率: 0.6587
1687/未知 760秒 450毫秒/步 - 损失: 0.8641 - 稀疏分类准确率: 0.6588
1688/未知 760秒 450毫秒/步 - 损失: 0.8640 - 稀疏分类准确率: 0.6588
1689/未知 761秒 450毫秒/步 - 损失: 0.8639 - 稀疏分类准确率: 0.6588
1690/未知 761秒 450毫秒/步 - 损失: 0.8638 - 稀疏分类准确率: 0.6589
1691/未知 762秒 450毫秒/步 - 损失: 0.8637 - 稀疏分类准确率: 0.6589
1692/未知 762秒 450毫秒/步 - 损失: 0.8636 - 稀疏分类准确率: 0.6589
1693/未知 762秒 450毫秒/步 - 损失: 0.8635 - 稀疏分类准确率: 0.6590
1694/未知 763秒 450毫秒/步 - 损失: 0.8634 - 稀疏分类准确率: 0.6590
1695/未知 763秒 450毫秒/步 - 损失: 0.8633 - 稀疏分类准确率: 0.6590
1696/未知 764秒 450毫秒/步 - 损失: 0.8632 - 稀疏分类准确率: 0.6591
1697/未知 764秒 450毫秒/步 - 损失: 0.8632 - 稀疏分类准确率: 0.6591
1698/未知 765秒 450毫秒/步 - 损失: 0.8631 - 稀疏分类准确率: 0.6591
1699/未知 765秒 450毫秒/步 - 损失: 0.8630 - 稀疏分类准确率: 0.6591
1700/未知 766秒 450毫秒/步 - 损失: 0.8629 - 稀疏分类准确率: 0.6592
1701/未知 766秒 450毫秒/步 - 损失: 0.8628 - 稀疏分类准确率: 0.6592
1702/未知 766秒 450毫秒/步 - 损失: 0.8627 - 稀疏分类准确率: 0.6592
1703/未知 767秒 450毫秒/步 - 损失: 0.8626 - 稀疏分类准确率: 0.6593
1704/未知 767秒 450毫秒/步 - 损失: 0.8625 - 稀疏分类准确率: 0.6593
1705/未知 768秒 450毫秒/步 - 损失: 0.8624 - 稀疏分类准确率: 0.6593
1706/未知 768秒 450毫秒/步 - 损失: 0.8623 - 稀疏分类准确率: 0.6594
1707/未知 768秒 450毫秒/步 - 损失: 0.8622 - 稀疏分类准确率: 0.6594
1708/未知 769秒 450毫秒/步 - 损失: 0.8621 - 稀疏分类准确率: 0.6594
1709/未知 769秒 450毫秒/步 - 损失: 0.8620 - 稀疏分类准确率: 0.6595
1710/未知 770秒 450毫秒/步 - 损失: 0.8619 - 稀疏分类准确率: 0.6595
1711/未知 770秒 450毫秒/步 - 损失: 0.8618 - 稀疏分类准确率: 0.6595
1712/未知 770秒 450毫秒/步 - 损失: 0.8617 - 稀疏分类准确率: 0.6595
1713/未知 771秒 450毫秒/步 - 损失: 0.8616 - 稀疏分类准确率: 0.6596
1714/未知 771秒 450毫秒/步 - 损失: 0.8615 - 稀疏分类准确率: 0.6596
1715/未知 772秒 450毫秒/步 - 损失: 0.8614 - 稀疏分类准确率: 0.6596
1716/未知 772秒 450毫秒/步 - 损失: 0.8613 - 稀疏分类准确率: 0.6597
1717/未知 773秒 450毫秒/步 - 损失: 0.8612 - 稀疏分类准确率: 0.6597
1718/未知 773秒 450毫秒/步 - 损失: 0.8611 - 稀疏分类准确率: 0.6597
1719/未知 774秒 450毫秒/步 - 损失: 0.8610 - 稀疏分类准确率: 0.6598
1720/未知 774秒 450毫秒/步 - 损失: 0.8609 - 稀疏分类准确率: 0.6598
1721/未知 774秒 450毫秒/步 - 损失: 0.8608 - 稀疏分类准确率: 0.6598
1722/未知 775秒 450毫秒/步 - 损失: 0.8607 - 稀疏分类准确率: 0.6598
1723/未知 775秒 450毫秒/步 - 损失: 0.8606 - 稀疏分类准确率: 0.6599
1724/未知 776秒 450毫秒/步 - 损失: 0.8606 - 稀疏分类准确率: 0.6599
1725/未知 776秒 450毫秒/步 - 损失: 0.8605 - 稀疏分类准确率: 0.6599
1726/未知 777秒 450毫秒/步 - 损失: 0.8604 - 稀疏分类准确率: 0.6600
1727/未知 777秒 450毫秒/步 - 损失: 0.8603 - 稀疏分类准确率: 0.6600
1728/未知 777秒 450毫秒/步 - 损失: 0.8602 - 稀疏分类准确率: 0.6600
1729/未知 778秒 450毫秒/步 - 损失: 0.8601 - 稀疏分类准确率: 0.6601
1730/未知 778秒 449毫秒/步 - 损失: 0.8600 - 稀疏分类准确率: 0.6601
1731/未知 779秒 449毫秒/步 - 损失: 0.8599 - 稀疏分类准确率: 0.6601
1732/未知 779秒 449毫秒/步 - 损失: 0.8598 - 稀疏分类准确率: 0.6601
1733/未知 779秒 449毫秒/步 - 损失: 0.8597 - 稀疏分类准确率: 0.6602
1734/未知 780秒 449毫秒/步 - 损失: 0.8596 - 稀疏分类准确率: 0.6602
1735/未知 780秒 449毫秒/步 - 损失: 0.8595 - 稀疏分类准确率: 0.6602
1736/未知 780秒 449毫秒/步 - 损失: 0.8594 - 稀疏分类准确率: 0.6603
1737/未知 781秒 449毫秒/步 - 损失: 0.8593 - 稀疏分类准确率: 0.6603
1738/未知 781秒 449毫秒/步 - 损失: 0.8592 - 稀疏分类准确率: 0.6603
1739/未知 782秒 449毫秒/步 - 损失: 0.8591 - 稀疏分类准确率: 0.6603
1740/未知 782秒 449毫秒/步 - 损失: 0.8590 - 稀疏分类准确率: 0.6604
1741/未知 782秒 449毫秒/步 - 损失: 0.8589 - 稀疏分类准确率: 0.6604
1742/未知 783秒 449毫秒/步 - 损失: 0.8589 - 稀疏分类准确率: 0.6604
1743/未知 783秒 449毫秒/步 - 损失: 0.8588 - 稀疏分类准确率: 0.6605
1744/未知 784秒 449毫秒/步 - 损失: 0.8587 - 稀疏分类准确率: 0.6605
1745/未知 784秒 449毫秒/步 - 损失: 0.8586 - 稀疏分类准确率: 0.6605
1746/未知 785秒 449毫秒/步 - 损失: 0.8585 - 稀疏分类准确率: 0.6606
1747/未知 785秒 449毫秒/步 - 损失: 0.8584 - 稀疏分类准确率: 0.6606
1748/未知 786秒 449毫秒/步 - 损失: 0.8583 - 稀疏分类准确率: 0.6606
1749/未知 786秒 449毫秒/步 - 损失: 0.8582 - 稀疏分类准确率: 0.6606
1750/未知 787秒 449毫秒/步 - 损失: 0.8581 - 稀疏分类准确率: 0.6607
1751/未知 787秒 449毫秒/步 - 损失: 0.8580 - 稀疏分类准确率: 0.6607
1752/未知 787秒 449毫秒/步 - 损失: 0.8579 - 稀疏分类准确率: 0.6607
1753/未知 788秒 449毫秒/步 - 损失: 0.8578 - 稀疏分类准确率: 0.6608
1754/未知 788秒 449毫秒/步 - 损失: 0.8577 - 稀疏分类准确率: 0.6608
1755/未知 789秒 449毫秒/步 - 损失: 0.8576 - 稀疏分类准确率: 0.6608
1756/未知 789秒 449毫秒/步 - 损失: 0.8576 - 稀疏分类准确率: 0.6608
1757/未知 789秒 449毫秒/步 - 损失: 0.8575 - 稀疏分类准确率: 0.6609
1758/未知 790秒 449毫秒/步 - 损失: 0.8574 - 稀疏分类准确率: 0.6609
1759/未知 790秒 449毫秒/步 - 损失: 0.8573 - 稀疏分类准确率: 0.6609
1760/未知 791秒 449毫秒/步 - 损失: 0.8572 - 稀疏分类准确率: 0.6610
1761/未知 791秒 449毫秒/步 - 损失: 0.8571 - 稀疏分类准确率: 0.6610
1762/未知 792秒 449毫秒/步 - 损失: 0.8570 - 稀疏分类准确率: 0.6610
1763/未知 792秒 449毫秒/步 - 损失: 0.8569 - 稀疏分类准确率: 0.6610
1764/未知 792秒 449毫秒/步 - 损失: 0.8568 - 稀疏分类准确率: 0.6611
1765/未知 793秒 449毫秒/步 - 损失: 0.8567 - 稀疏分类准确率: 0.6611
1766/未知 793秒 449毫秒/步 - 损失: 0.8566 - 稀疏分类准确率: 0.6611
1767/未知 794秒 449毫秒/步 - 损失: 0.8565 - 稀疏分类准确率: 0.6612
1768/未知 794秒 449毫秒/步 - 损失: 0.8564 - 稀疏分类准确率: 0.6612
1769/未知 795秒 449毫秒/步 - 损失: 0.8564 - 稀疏分类准确率: 0.6612
1770/未知 795秒 449毫秒/步 - 损失: 0.8563 - 稀疏分类准确率: 0.6612
1771/未知 796秒 449毫秒/步 - 损失: 0.8562 - 稀疏分类准确率: 0.6613
1772/未知 796秒 449毫秒/步 - 损失: 0.8561 - 稀疏分类准确率: 0.6613
1773/未知 796秒 449毫秒/步 - 损失: 0.8560 - 稀疏分类准确率: 0.6613
1774/未知 797秒 449毫秒/步 - 损失: 0.8559 - 稀疏分类准确率: 0.6614
1775/未知 797秒 449毫秒/步 - 损失: 0.8558 - 稀疏分类准确率: 0.6614
1776/未知 797秒 449毫秒/步 - 损失: 0.8557 - 稀疏分类准确率: 0.6614
1777/未知 798秒 449毫秒/步 - 损失: 0.8556 - 稀疏分类准确率: 0.6614
1778/未知 798秒 449毫秒/步 - 损失: 0.8555 - 稀疏分类准确率: 0.6615
1779/未知 799秒 449毫秒/步 - 损失: 0.8554 - 稀疏分类准确率: 0.6615
1780/未知 799秒 449毫秒/步 - 损失: 0.8554 - 稀疏分类准确率: 0.6615
1781/未知 799秒 449毫秒/步 - 损失: 0.8553 - 稀疏分类准确率: 0.6616
1782/未知 800秒 449毫秒/步 - 损失: 0.8552 - 稀疏分类准确率: 0.6616
1783/未知 800秒 448毫秒/步 - 损失: 0.8551 - 稀疏分类准确率: 0.6616
1784/未知 801秒 448毫秒/步 - 损失: 0.8550 - 稀疏分类准确率: 0.6616
1785/未知 801秒 448毫秒/步 - 损失: 0.8549 - 稀疏分类准确率: 0.6617
1786/未知 801秒 448毫秒/步 - 损失: 0.8548 - 稀疏分类准确率: 0.6617
1787/未知 802秒 448毫秒/步 - 损失: 0.8547 - 稀疏分类准确率: 0.6617
1788/未知 802秒 448毫秒/步 - 损失: 0.8546 - 稀疏分类准确率: 0.6618
1789/未知 803秒 448毫秒/步 - 损失: 0.8545 - 稀疏分类准确率: 0.6618
1790/未知 803秒 448毫秒/步 - 损失: 0.8545 - 稀疏分类准确率: 0.6618
1791/未知 803秒 448毫秒/步 - 损失: 0.8544 - 稀疏分类准确率: 0.6618
1792/未知 804秒 448毫秒/步 - 损失: 0.8543 - 稀疏分类准确率: 0.6619
1793/未知 804秒 448毫秒/步 - 损失: 0.8542 - 稀疏分类准确率: 0.6619
1794/未知 805秒 448毫秒/步 - 损失: 0.8541 - 稀疏分类准确率: 0.6619
1795/未知 805秒 448毫秒/步 - 损失: 0.8540 - 稀疏分类准确率: 0.6620
1796/未知 805秒 448毫秒/步 - 损失: 0.8539 - 稀疏分类准确率: 0.6620
1797/未知 806秒 448毫秒/步 - 损失: 0.8538 - 稀疏分类准确率: 0.6620
1798/未知 806秒 448毫秒/步 - 损失: 0.8537 - 稀疏分类准确率: 0.6620
1799/未知 807秒 448毫秒/步 - 损失: 0.8536 - 稀疏分类准确率: 0.6621
1800/未知 807秒 448毫秒/步 - 损失: 0.8536 - 稀疏分类准确率: 0.6621
1801/未知 808秒 448毫秒/步 - 损失: 0.8535 - 稀疏分类准确率: 0.6621
1802/未知 808秒 448毫秒/步 - 损失: 0.8534 - 稀疏分类准确率: 0.6622
1803/未知 808秒 448毫秒/步 - 损失: 0.8533 - 稀疏分类准确率: 0.6622
1804/未知 809秒 448毫秒/步 - 损失: 0.8532 - 稀疏分类准确率: 0.6622
1805/未知 809秒 448毫秒/步 - 损失: 0.8531 - 稀疏分类准确率: 0.6622
1806/未知 810秒 448毫秒/步 - 损失: 0.8530 - 稀疏分类准确率: 0.6623
1807/未知 810秒 448毫秒/步 - 损失: 0.8529 - 稀疏分类准确率: 0.6623
1808/未知 811秒 448毫秒/步 - 损失: 0.8528 - 稀疏分类准确率: 0.6623
1809/未知 811秒 448毫秒/步 - 损失: 0.8528 - 稀疏分类准确率: 0.6623
1810/未知 811秒 448毫秒/步 - 损失: 0.8527 - 稀疏分类准确率: 0.6624
1811/未知 812秒 448毫秒/步 - 损失: 0.8526 - 稀疏分类准确率: 0.6624
1812/未知 812秒 448毫秒/步 - 损失: 0.8525 - 稀疏分类准确率: 0.6624
1813/未知 812秒 448毫秒/步 - 损失: 0.8524 - 稀疏分类准确率: 0.6625
1814/未知 813秒 448毫秒/步 - 损失: 0.8523 - 稀疏分类准确率: 0.6625
1815/未知 813秒 448毫秒/步 - 损失: 0.8522 - 稀疏分类准确率: 0.6625
1816/未知 814秒 448毫秒/步 - 损失: 0.8521 - 稀疏分类准确率: 0.6625
1817/未知 814秒 448毫秒/步 - 损失: 0.8520 - 稀疏分类准确率: 0.6626
1818/未知 814秒 448毫秒/步 - 损失: 0.8520 - 稀疏分类准确率: 0.6626
1819/未知 815秒 448毫秒/步 - 损失: 0.8519 - 稀疏分类准确率: 0.6626
1820/未知 815秒 448毫秒/步 - 损失: 0.8518 - 稀疏分类准确率: 0.6627
1821/未知 816秒 448毫秒/步 - 损失: 0.8517 - 稀疏分类准确率: 0.6627
1822/未知 816秒 448毫秒/步 - 损失: 0.8516 - 稀疏分类准确率: 0.6627
1823/未知 817秒 448毫秒/步 - 损失: 0.8515 - 稀疏分类准确率: 0.6627
1824/未知 817秒 448毫秒/步 - 损失: 0.8514 - 稀疏分类准确率: 0.6628
1825/未知 818秒 448毫秒/步 - 损失: 0.8513 - 稀疏分类准确率: 0.6628
1826/未知 818秒 448毫秒/步 - 损失: 0.8513 - 稀疏分类准确率: 0.6628
1827/未知 819秒 448毫秒/步 - 损失: 0.8512 - 稀疏分类准确率: 0.6628
1828/未知 819秒 448毫秒/步 - 损失: 0.8511 - 稀疏分类准确率: 0.6629
1829/未知 819秒 448毫秒/步 - 损失: 0.8510 - 稀疏分类准确率: 0.6629
1830/未知 820秒 448毫秒/步 - 损失: 0.8509 - 稀疏分类准确率: 0.6629
1831/未知 820秒 448毫秒/步 - 损失: 0.8508 - 稀疏分类准确率: 0.6630
1832/未知 821秒 448毫秒/步 - 损失: 0.8507 - 稀疏分类准确率: 0.6630
1833/未知 821秒 448毫秒/步 - 损失: 0.8507 - 稀疏分类准确率: 0.6630
1834/未知 821秒 448毫秒/步 - 损失: 0.8506 - 稀疏分类准确率: 0.6630
1835/未知 822秒 447毫秒/步 - 损失: 0.8505 - 稀疏分类准确率: 0.6631
1836/未知 822秒 447毫秒/步 - 损失: 0.8504 - 稀疏分类准确率: 0.6631
1837/未知 822秒 447毫秒/步 - 损失: 0.8503 - 稀疏分类准确率: 0.6631
1838/未知 823秒 447毫秒/步 - 损失: 0.8502 - 稀疏分类准确率: 0.6631
1839/未知 823秒 447毫秒/步 - 损失: 0.8501 - 稀疏分类准确率: 0.6632
1840/未知 823秒 447毫秒/步 - 损失: 0.8500 - 稀疏分类准确率: 0.6632
1841/未知 824秒 447毫秒/步 - 损失: 0.8500 - 稀疏分类准确率: 0.6632
1842/未知 824秒 447毫秒/步 - 损失: 0.8499 - 稀疏分类准确率: 0.6633
1843/未知 825秒 447毫秒/步 - 损失: 0.8498 - 稀疏分类准确率: 0.6633
1844/未知 825秒 447毫秒/步 - 损失: 0.8497 - 稀疏分类准确率: 0.6633
1845/未知 825秒 447毫秒/步 - 损失: 0.8496 - 稀疏分类准确率: 0.6633
1846/未知 826秒 447毫秒/步 - 损失: 0.8495 - 稀疏分类准确率: 0.6634
1847/未知 826秒 447毫秒/步 - 损失: 0.8494 - 稀疏分类准确率: 0.6634
1848/未知 827秒 447毫秒/步 - 损失: 0.8494 - 稀疏分类准确率: 0.6634
1849/未知 827秒 447毫秒/步 - 损失: 0.8493 - 稀疏分类准确率: 0.6634
1850/未知 828秒 447毫秒/步 - 损失: 0.8492 - 稀疏分类准确率: 0.6635
1851/未知 828秒 447毫秒/步 - 损失: 0.8491 - 稀疏分类准确率: 0.6635
1852/未知 828秒 447毫秒/步 - 损失: 0.8490 - 稀疏分类准确率: 0.6635
1853/未知 829秒 447毫秒/步 - 损失: 0.8489 - 稀疏分类准确率: 0.6636
1854/未知 829秒 447毫秒/步 - 损失: 0.8488 - 稀疏分类准确率: 0.6636
1855/未知 830秒 447毫秒/步 - 损失: 0.8488 - 稀疏分类准确率: 0.6636
1856/未知 830秒 447毫秒/步 - 损失: 0.8487 - 稀疏分类准确率: 0.6636
1857/未知 830秒 447毫秒/步 - 损失: 0.8486 - 稀疏分类准确率: 0.6637
1858/未知 831秒 447毫秒/步 - 损失: 0.8485 - 稀疏分类准确率: 0.6637
1859/未知 831秒 447毫秒/步 - 损失: 0.8484 - 稀疏分类准确率: 0.6637
1860/未知 832秒 447毫秒/步 - 损失: 0.8483 - 稀疏分类准确率: 0.6637
1861/未知 832秒 447毫秒/步 - 损失: 0.8482 - 稀疏分类准确率: 0.6638
1862/未知 832秒 447毫秒/步 - 损失: 0.8482 - 稀疏分类准确率: 0.6638
1863/未知 833秒 447毫秒/步 - 损失: 0.8481 - 稀疏分类准确率: 0.6638
1864/未知 833秒 447毫秒/步 - 损失: 0.8480 - 稀疏分类准确率: 0.6638
1865/未知 834秒 447毫秒/步 - 损失: 0.8479 - 稀疏分类准确率: 0.6639
1865/1865 ━━━━━━━━━━━━━━━━━━━━ 834秒 447毫秒/步 - 损失: 0.8478 - 稀疏分类准确率: 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%
Deep & Cross 模型达到了约 81% 的测试准确率。
结论
你可以使用 Keras 预处理层轻松处理具有不同编码机制的类别特征，包括 one-hot 编码和特征嵌入。此外，不同的模型架构（如 Wide、Deep 和 Cross 网络）在不同的数据集属性方面具有不同的优势。你可以探索独立使用或组合使用它们，以便为你的数据集获得最佳结果。