使用 Wide, Deep, 和 Cross 網路進行結構化資料學習
作者： Khalid Salama
建立日期 2020/12/31
上次修改日期 2025/01/03
描述： 使用 Wide & Deep 和 Deep & Cross 網路進行結構化資料分類。
簡介
本範例示範如何使用兩種建模技術進行結構化資料分類
- Wide & Deep 模型
- Deep & Cross 模型
請注意，本範例應使用 TensorFlow 2.5 或更高版本執行。
資料集
本範例使用來自 UCI Machine Learning Repository 的 Covertype 資料集。任務是從製圖變數預測森林覆蓋類型。資料集包含 506,011 個實例，具有 12 個輸入特徵：10 個數值特徵和 2 個類別特徵。每個實例分為 7 個類別之一。
設定
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
準備資料
首先，讓我們從 UCI Machine Learning Repository 將資料集載入到 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 列 × 55 行
資料集中的兩個類別特徵以二元編碼。我們將把此資料集表示法轉換為典型表示法，其中每個類別特徵都表示為單一整數值。
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 |
| 到水文的水平距離 | 258 | 212 | 268 | 242 | 153 |
| 到水文的垂直距離 | 0 | -6 | 65 | 118 | -1 |
| 到道路的水平距離 | 510 | 390 | 3180 | 3090 | 391 |
| 上午 9 點山陰 | 221 | 220 | 234 | 238 | 220 |
| 中午山陰 | 232 | 235 | 238 | 238 | 234 |
| 下午 3 點山陰 | 148 | 151 | 135 | 122 | 150 |
| 到火點的水平距離 | 6279 | 6225 | 6121 | 6211 | 6172 |
| 荒野區域 | 區域類型 1 | 區域類型 1 | 區域類型 1 | 區域類型 1 | 區域類型 1 |
| 土壤類型 | 土壤類型 29 | 土壤類型 29 | 土壤類型 12 | 土壤類型 30 | 土壤類型 29 |
| 覆蓋類型 | 4 | 4 | 1 | 1 | 4 |
DataFrame 的形狀顯示每個樣本有 13 行（12 行用於特徵，1 行用於目標標籤）。
讓我們將資料分割成訓練集 (85%) 和測試集 (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
接下來，將訓練和測試資料儲存在個別的 CSV 檔案中。
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)
定義資料集元數據
在這裡，我們定義資料集的元數據，這些元數據對於將資料讀取和解析為輸入特徵，以及根據其類型編碼輸入特徵非常有用。
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)
實驗設定
接下來，讓我們定義一個輸入函數，該函數讀取並解析檔案，然後將特徵和標籤轉換為 tf.data.Dataset 以進行訓練或評估。
# 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()
在這裡，我們配置參數並實作程序，以針對給定模型執行訓練和評估實驗。
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)}%")
建立模型輸入
現在，將模型的輸入定義為字典，其中鍵是特徵名稱，值是具有相應特徵形狀和資料類型的 keras.layers.Input 張量。
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
編碼特徵
我們為輸入特徵建立兩種表示形式：稀疏和密集：1. 在稀疏表示形式中，類別特徵使用 CategoryEncoding 層以 one-hot 編碼進行編碼。這種表示形式對於模型記憶特定特徵值以做出某些預測可能很有用。2. 在密集表示形式中，類別特徵使用 Embedding 層以低維度嵌入進行編碼。這種表示形式有助於模型很好地泛化到看不見的特徵組合。
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
實驗 1：基準模型
在第一個實驗中，讓我們建立一個多層前饋網路，其中類別特徵是 one-hot 編碼的。
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")
讓我們執行它
run_experiment(baseline_model)
Start training the model...
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882/Unknown 235s 260ms/step - loss: 1.0895 - sparse_categorical_accuracy: 0.5811
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884/Unknown 235s 260ms/step - loss: 1.0890 - sparse_categorical_accuracy: 0.5813
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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
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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
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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
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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
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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
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958/Unknown 256s 262ms/step - loss: 1.0708 - sparse_categorical_accuracy: 0.5871
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1000/Unknown 268 秒 262 毫秒/步 - 損失: 1.0614 - 稀疏類別準確度: 0.5901
1001/Unknown 268 秒 262 毫秒/步 - 損失: 1.0612 - 稀疏類別準確度: 0.5902
1002/Unknown 268 秒 262 毫秒/步 - 損失: 1.0610 - 稀疏類別準確度: 0.5903
1003/Unknown 269 秒 262 毫秒/步 - 損失: 1.0608 - 稀疏類別準確度: 0.5903
1004/Unknown 269 秒 262 毫秒/步 - 損失: 1.0605 - 稀疏類別準確度: 0.5904
1005/Unknown 269 秒 262 毫秒/步 - 損失: 1.0603 - 稀疏類別準確度: 0.5905
1006/Unknown 270 秒 263 毫秒/步 - 損失: 1.0601 - 稀疏類別準確度: 0.5905
1007/Unknown 270 秒 263 毫秒/步 - 損失: 1.0599 - 稀疏類別準確度: 0.5906
1008/Unknown 270 秒 263 毫秒/步 - 損失: 1.0597 - 稀疏類別準確度: 0.5907
1009/Unknown 271 秒 263 毫秒/步 - 損失: 1.0595 - 稀疏類別準確度: 0.5907
1010/Unknown 271 秒 263 毫秒/步 - 損失: 1.0592 - 稀疏類別準確度: 0.5908
1011/Unknown 271 秒 263 毫秒/步 - 損失: 1.0590 - 稀疏類別準確度: 0.5909
1012/Unknown 271 秒 263 毫秒/步 - 損失: 1.0588 - 稀疏類別準確度: 0.5909
1013/Unknown 272 秒 263 毫秒/步 - 損失: 1.0586 - 稀疏類別準確度: 0.5910
1014/Unknown 272 秒 263 毫秒/步 - 損失: 1.0584 - 稀疏類別準確度: 0.5911
1015/Unknown 272 秒 263 毫秒/步 - 損失: 1.0582 - 稀疏類別準確度: 0.5912
1016/Unknown 272 秒 263 毫秒/步 - 損失: 1.0580 - 稀疏類別準確度: 0.5912
1017/Unknown 273 秒 263 毫秒/步 - 損失: 1.0578 - 稀疏類別準確度: 0.5913
1018/Unknown 273 秒 263 毫秒/步 - 損失: 1.0575 - 稀疏類別準確度: 0.5914
1019/Unknown 273 秒 263 毫秒/步 - 損失: 1.0573 - 稀疏類別準確度: 0.5914
1020/Unknown 273 秒 263 毫秒/步 - 損失: 1.0571 - 稀疏類別準確度: 0.5915
1021/Unknown 274 秒 263 毫秒/步 - 損失: 1.0569 - 稀疏類別準確度: 0.5916
1022/Unknown 274 秒 263 毫秒/步 - 損失: 1.0567 - 稀疏類別準確度: 0.5916
1023/Unknown 274 秒 263 毫秒/步 - 損失: 1.0565 - 稀疏類別準確度: 0.5917
1024/Unknown 275 秒 263 毫秒/步 - 損失: 1.0563 - 稀疏類別準確度: 0.5918
1025/Unknown 275 秒 263 毫秒/步 - 損失: 1.0561 - 稀疏類別準確度: 0.5918
1026/Unknown 275 秒 263 毫秒/步 - 損失: 1.0559 - 稀疏類別準確度: 0.5919
1027/Unknown 275 秒 263 毫秒/步 - 損失: 1.0556 - 稀疏類別準確度: 0.5920
1028/Unknown 276 秒 263 毫秒/步 - 損失: 1.0554 - 稀疏類別準確度: 0.5920
1029/Unknown 276 秒 263 毫秒/步 - 損失: 1.0552 - 稀疏類別準確度: 0.5921
1030/Unknown 276 秒 263 毫秒/步 - 損失: 1.0550 - 稀疏類別準確度: 0.5922
1031/Unknown 276 秒 263 毫秒/步 - 損失: 1.0548 - 稀疏類別準確度: 0.5922
1032/Unknown 277 秒 263 毫秒/步 - 損失: 1.0546 - 稀疏類別準確度: 0.5923
1033/Unknown 277 秒 263 毫秒/步 - 損失: 1.0544 - 稀疏類別準確度: 0.5924
1034/Unknown 277 秒 263 毫秒/步 - 損失: 1.0542 - 稀疏類別準確度: 0.5924
1035/Unknown 278 秒 263 毫秒/步 - 損失: 1.0540 - 稀疏類別準確度: 0.5925
1036/Unknown 278 秒 263 毫秒/步 - 損失: 1.0538 - 稀疏類別準確度: 0.5926
1037/Unknown 278 秒 263 毫秒/步 - 損失: 1.0536 - 稀疏類別準確度: 0.5926
1038/Unknown 278 秒 263 毫秒/步 - 損失: 1.0533 - 稀疏類別準確度: 0.5927
1039/Unknown 279 秒 263 毫秒/步 - 損失: 1.0531 - 稀疏類別準確度: 0.5928
1040/Unknown 279 秒 263 毫秒/步 - 損失: 1.0529 - 稀疏類別準確度: 0.5928
1041/Unknown 279 秒 263 毫秒/步 - 損失: 1.0527 - 稀疏類別準確度: 0.5929
1042/Unknown 280 秒 263 毫秒/步 - 損失: 1.0525 - 稀疏類別準確度: 0.5930
1043/Unknown 280 秒 263 毫秒/步 - 損失: 1.0523 - 稀疏類別準確度: 0.5930
1044/Unknown 280 秒 263 毫秒/步 - 損失: 1.0521 - 稀疏類別準確度: 0.5931
1045/Unknown 280 秒 263 毫秒/步 - 損失: 1.0519 - 稀疏類別準確度: 0.5932
1046/Unknown 281 秒 263 毫秒/步 - 損失: 1.0517 - 稀疏類別準確度: 0.5932
1047/Unknown 281 秒 263 毫秒/步 - 損失: 1.0515 - 稀疏類別準確度: 0.5933
1048/Unknown 281 秒 263 毫秒/步 - 損失: 1.0513 - 稀疏類別準確度: 0.5934
1049/Unknown 282 秒 263 毫秒/步 - 損失: 1.0511 - 稀疏類別準確度: 0.5934
1050/Unknown 282 秒 263 毫秒/步 - 損失: 1.0509 - 稀疏類別準確度: 0.5935
1051/Unknown 282 秒 263 毫秒/步 - 損失: 1.0507 - 稀疏類別準確度: 0.5935
1052/Unknown 283 秒 263 毫秒/步 - 損失: 1.0505 - 稀疏類別準確度: 0.5936
1053/Unknown 283 秒 263 毫秒/步 - 損失: 1.0503 - 稀疏類別準確度: 0.5937
1054/Unknown 283 秒 263 毫秒/步 - 損失: 1.0501 - 稀疏類別準確度: 0.5937
1055/Unknown 283 秒 263 毫秒/步 - 損失: 1.0499 - 稀疏類別準確度: 0.5938
1056/Unknown 284 秒 263 毫秒/步 - 損失: 1.0497 - 稀疏類別準確度: 0.5939
1057/Unknown 284 秒 263 毫秒/步 - 損失: 1.0495 - 稀疏類別準確度: 0.5939
1058/Unknown 284 秒 263 毫秒/步 - 損失: 1.0493 - 稀疏類別準確度: 0.5940
1059/Unknown 285 秒 263 毫秒/步 - 損失: 1.0491 - 稀疏類別準確度: 0.5941
1060/Unknown 285 秒 263 毫秒/步 - 損失: 1.0489 - 稀疏類別準確度: 0.5941
1061/Unknown 285 秒 263 毫秒/步 - 損失: 1.0487 - 稀疏類別準確度: 0.5942
1062/Unknown 285 秒 263 毫秒/步 - 損失: 1.0485 - 稀疏類別準確度: 0.5943
1063/Unknown 285 秒 263 毫秒/步 - 損失: 1.0483 - 稀疏類別準確度: 0.5943
1064/Unknown 286 秒 263 毫秒/步 - 損失: 1.0481 - 稀疏類別準確度: 0.5944
1065/Unknown 286 秒 263 毫秒/步 - 損失: 1.0479 - 稀疏類別準確度: 0.5944
1066/Unknown 286 秒 263 毫秒/步 - 損失: 1.0477 - 稀疏類別準確度: 0.5945
1067/Unknown 286 秒 263 毫秒/步 - 損失: 1.0475 - 稀疏類別準確度: 0.5946
1068/Unknown 287 秒 263 毫秒/步 - 損失: 1.0473 - 稀疏類別準確度: 0.5946
1069/Unknown 287 秒 263 毫秒/步 - 損失: 1.0471 - 稀疏類別準確度: 0.5947
1070/Unknown 287 秒 263 毫秒/步 - 損失: 1.0469 - 稀疏類別準確度: 0.5948
1071/Unknown 287 秒 263 毫秒/步 - 損失: 1.0467 - 稀疏類別準確度: 0.5948
1072/Unknown 288 秒 263 毫秒/步 - 損失: 1.0465 - 稀疏類別準確度: 0.5949
1073/Unknown 288 秒 263 毫秒/步 - 損失: 1.0463 - 稀疏類別準確度: 0.5949
1074/Unknown 288 秒 263 毫秒/步 - 損失: 1.0461 - 稀疏類別準確度: 0.5950
1075/Unknown 289 秒 263 毫秒/步 - 損失: 1.0459 - 稀疏類別準確度: 0.5951
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1078/Unknown 290 秒 264 毫秒/步 - 損失: 1.0453 - 稀疏類別準確度: 0.5953
1079/Unknown 290 秒 264 毫秒/步 - 損失: 1.0451 - 稀疏類別準確度: 0.5953
1080/Unknown 290 秒 264 毫秒/步 - 損失: 1.0449 - 稀疏類別準確度: 0.5954
1081/Unknown 291 秒 264 毫秒/步 - 損失: 1.0447 - 稀疏類別準確度: 0.5954
1082/Unknown 291 秒 264 毫秒/步 - 損失: 1.0445 - 稀疏類別準確度: 0.5955
1083/Unknown 291 秒 264 毫秒/步 - 損失: 1.0443 - 稀疏類別準確度: 0.5956
1084/Unknown 291 秒 264 毫秒/步 - 損失: 1.0441 - 稀疏類別準確度: 0.5956
1085/Unknown 292 秒 264 毫秒/步 - 損失: 1.0439 - 稀疏類別準確度: 0.5957
1086/Unknown 292 秒 264 毫秒/步 - 損失: 1.0437 - 稀疏類別準確度: 0.5957
1087/Unknown 292 秒 264 毫秒/步 - 損失: 1.0436 - 稀疏類別準確度: 0.5958
1088/Unknown 293 秒 264 毫秒/步 - 損失: 1.0434 - 稀疏類別準確度: 0.5959
1089/Unknown 293 秒 264 毫秒/步 - 損失: 1.0432 - 稀疏類別準確度: 0.5959
1090/Unknown 293 秒 264 毫秒/步 - 損失: 1.0430 - 稀疏類別準確度: 0.5960
1091/Unknown 293 秒 264 毫秒/步 - 損失: 1.0428 - 稀疏類別準確度: 0.5961
1092/Unknown 294 秒 264 毫秒/步 - 損失: 1.0426 - 稀疏類別準確度: 0.5961
1093/Unknown 294 秒 264 毫秒/步 - 損失: 1.0424 - 稀疏類別準確度: 0.5962
1094/Unknown 294 秒 264 毫秒/步 - 損失: 1.0422 - 稀疏類別準確度: 0.5962
1095/Unknown 294 秒 264 毫秒/步 - 損失: 1.0420 - 稀疏類別準確度: 0.5963
1096/Unknown 295 秒 264 毫秒/步 - 損失: 1.0418 - 稀疏類別準確度: 0.5964
1097/Unknown 295 秒 264 毫秒/步 - 損失: 1.0416 - 稀疏類別準確度: 0.5964
1098/Unknown 295 秒 264 毫秒/步 - 損失: 1.0414 - 稀疏類別準確度: 0.5965
1099/Unknown 295 秒 264 毫秒/步 - 損失: 1.0413 - 稀疏類別準確度: 0.5965
1100/Unknown 296 秒 264 毫秒/步 - 損失: 1.0411 - 稀疏類別準確度: 0.5966
1101/Unknown 296 秒 264 毫秒/步 - 損失: 1.0409 - 稀疏類別準確度: 0.5967
1102/Unknown 296 秒 264 毫秒/步 - 損失: 1.0407 - 稀疏類別準確度: 0.5967
1103/Unknown 296 秒 264 毫秒/步 - 損失: 1.0405 - 稀疏類別準確度: 0.5968
1104/Unknown 297 秒 264 毫秒/步 - 損失: 1.0403 - 稀疏類別準確度: 0.5968
1105/Unknown 297 秒 264 毫秒/步 - 損失: 1.0401 - 稀疏類別準確度: 0.5969
1106/Unknown 297 秒 264 毫秒/步 - 損失: 1.0399 - 稀疏類別準確度: 0.5970
1107/Unknown 298 秒 264 毫秒/步 - 損失: 1.0397 - 稀疏類別準確度: 0.5970
1108/Unknown 298 秒 264 毫秒/步 - 損失: 1.0396 - 稀疏類別準確度: 0.5971
1109/Unknown 298 秒 264 毫秒/步 - 損失: 1.0394 - 稀疏類別準確度: 0.5971
1110/Unknown 299 秒 264 毫秒/步 - 損失: 1.0392 - 稀疏類別準確度: 0.5972
1111/Unknown 299 秒 264 毫秒/步 - 損失: 1.0390 - 稀疏類別準確度: 0.5973
1112/Unknown 299 秒 264 毫秒/步 - 損失: 1.0388 - 稀疏類別準確度: 0.5973
1113/Unknown 299 秒 264 毫秒/步 - 損失: 1.0386 - 稀疏類別準確度: 0.5974
1114/Unknown 300 秒 264 毫秒/步 - 損失: 1.0384 - 稀疏類別準確度: 0.5974
1115/Unknown 300 秒 264 毫秒/步 - 損失: 1.0382 - 稀疏類別準確度: 0.5975
1116/Unknown 300 秒 264 毫秒/步 - 損失: 1.0381 - 稀疏類別準確度: 0.5976
1117/Unknown 300 秒 264 毫秒/步 - 損失: 1.0379 - 稀疏類別準確度: 0.5976
1118/Unknown 301 秒 264 毫秒/步 - 損失: 1.0377 - 稀疏類別準確度: 0.5977
1119/Unknown 301 秒 264 毫秒/步 - 損失: 1.0375 - 稀疏類別準確度: 0.5977
1120/Unknown 301 秒 264 毫秒/步 - 損失: 1.0373 - 稀疏類別準確度: 0.5978
1121/Unknown 301 秒 264 毫秒/步 - 損失: 1.0371 - 稀疏類別準確度: 0.5978
1122/Unknown 302 秒 264 毫秒/步 - 損失: 1.0369 - 稀疏類別準確度: 0.5979
1123/Unknown 302 秒 264 毫秒/步 - 損失: 1.0368 - 稀疏類別準確度: 0.5980
1124/Unknown 302 秒 264 毫秒/步 - 損失: 1.0366 - 稀疏類別準確度: 0.5980
1125/Unknown 302 秒 264 毫秒/步 - 損失: 1.0364 - 稀疏類別準確度: 0.5981
1126/Unknown 303 秒 264 毫秒/步 - 損失: 1.0362 - 稀疏類別準確度: 0.5981
1127/Unknown 303 秒 264 毫秒/步 - 損失: 1.0360 - 稀疏類別準確度: 0.5982
1128/Unknown 303 秒 264 毫秒/步 - 損失: 1.0358 - 稀疏類別準確度: 0.5983
1129/Unknown 303 秒 264 毫秒/步 - 損失: 1.0357 - 稀疏類別準確度: 0.5983
1130/Unknown 304 秒 264 毫秒/步 - 損失: 1.0355 - 稀疏類別準確度: 0.5984
1131/Unknown 304 秒 264 毫秒/步 - 損失: 1.0353 - 稀疏類別準確度: 0.5984
1132/Unknown 304 秒 264 毫秒/步 - 損失: 1.0351 - 稀疏類別準確度: 0.5985
1133/Unknown 305 秒 264 毫秒/步 - 損失: 1.0349 - 稀疏類別準確度: 0.5985
1134/Unknown 305 秒 264 毫秒/步 - 損失: 1.0347 - 稀疏類別準確度: 0.5986
1135/Unknown 305 秒 264 毫秒/步 - 損失: 1.0346 - 稀疏類別準確度: 0.5987
1136/Unknown 306 秒 264 毫秒/步 - 損失: 1.0344 - 稀疏類別準確度: 0.5987
1137/Unknown 306 秒 264 毫秒/步 - 損失: 1.0342 - 稀疏類別準確度: 0.5988
1138/Unknown 306 秒 264 毫秒/步 - 損失: 1.0340 - 稀疏類別準確度: 0.5988
1139/Unknown 306 秒 264 毫秒/步 - 損失: 1.0338 - 稀疏類別準確度: 0.5989
1140/Unknown 307 秒 264 毫秒/步 - 損失: 1.0337 - 稀疏類別準確度: 0.5990
1141/Unknown 307 秒 264 毫秒/步 - 損失: 1.0335 - 稀疏類別準確度: 0.5990
1142/Unknown 307 秒 264 毫秒/步 - 損失: 1.0333 - 稀疏類別準確度: 0.5991
1143/Unknown 308 秒 264 毫秒/步 - 損失: 1.0331 - 稀疏類別準確度: 0.5991
1144/Unknown 308 秒 264 毫秒/步 - 損失: 1.0329 - 稀疏類別準確度: 0.5992
1145/Unknown 308 秒 264 毫秒/步 - 損失: 1.0328 - 稀疏類別準確度: 0.5992
1146/Unknown 308 秒 264 毫秒/步 - 損失: 1.0326 - 稀疏類別準確度: 0.5993
1147/Unknown 309 秒 264 毫秒/步 - 損失: 1.0324 - 稀疏類別準確度: 0.5993
1148/Unknown 309 秒 264 毫秒/步 - 損失: 1.0322 - 稀疏類別準確度: 0.5994
1149/Unknown 309 秒 264 毫秒/步 - 損失: 1.0320 - 稀疏類別準確度: 0.5995
1150/Unknown 310 秒 264 毫秒/步 - 損失: 1.0319 - 稀疏類別準確度: 0.5995
1151/Unknown 310 秒 264 毫秒/步 - 損失: 1.0317 - 稀疏類別準確度: 0.5996
1152/Unknown 310 秒 264 毫秒/步 - 損失: 1.0315 - 稀疏類別準確度: 0.5996
1153/Unknown 310 秒 264 毫秒/步 - 損失: 1.0313 - 稀疏類別準確度: 0.5997
1154/Unknown 311 秒 264 毫秒/步 - 損失: 1.0311 - 稀疏類別準確度: 0.5997
1155/Unknown 311 秒 265 毫秒/步 - 損失: 1.0310 - 稀疏類別準確度: 0.5998
1156/Unknown 311 秒 265 毫秒/步 - 損失: 1.0308 - 稀疏類別準確度: 0.5999
1157/Unknown 312 秒 265 毫秒/步 - 損失: 1.0306 - 稀疏類別準確度: 0.5999
1158/Unknown 312 秒 265 毫秒/步 - 損失: 1.0304 - 稀疏類別準確度: 0.6000
1159/Unknown 312 秒 265 毫秒/步 - 損失: 1.0303 - 稀疏類別準確度: 0.6000
1160/Unknown 312 秒 265 毫秒/步 - 損失: 1.0301 - 稀疏類別準確度: 0.6001
1161/Unknown 313 秒 265 毫秒/步 - 損失: 1.0299 - 稀疏類別準確度: 0.6001
1162/Unknown 313 秒 265 毫秒/步 - 損失: 1.0297 - 稀疏類別準確度: 0.6002
1163/Unknown 313 秒 265 毫秒/步 - 損失: 1.0296 - 稀疏類別準確度: 0.6003
1164/Unknown 314 秒 265 毫秒/步 - 損失: 1.0294 - 稀疏類別準確度: 0.6003
1165/Unknown 314 秒 265 毫秒/步 - 損失: 1.0292 - 稀疏類別準確度: 0.6004
1166/Unknown 314 秒 265 毫秒/步 - 損失: 1.0290 - 稀疏類別準確度: 0.6004
1167/Unknown 314 秒 265 毫秒/步 - 損失: 1.0289 - 稀疏類別準確度: 0.6005
1168/Unknown 315 秒 265 毫秒/步 - 損失: 1.0287 - 稀疏類別準確度: 0.6005
1169/Unknown 315 秒 265 毫秒/步 - 損失: 1.0285 - 稀疏類別準確度: 0.6006
1170/Unknown 315 秒 265 毫秒/步 - 損失: 1.0283 - 稀疏類別準確度: 0.6006
1171/Unknown 315 秒 265 毫秒/步 - 損失: 1.0282 - 稀疏類別準確度: 0.6007
1172/Unknown 316 秒 264 毫秒/步 - 損失: 1.0280 - 稀疏類別準確度: 0.6008
1173/Unknown 316 秒 264 毫秒/步 - 損失: 1.0278 - 稀疏類別準確度: 0.6008
1174/Unknown 316 秒 264 毫秒/步 - 損失: 1.0276 - 稀疏類別準確度: 0.6009
1175/Unknown 316 秒 264 毫秒/步 - 損失: 1.0275 - 稀疏類別準確度: 0.6009
1176/Unknown 316 秒 264 毫秒/步 - 損失: 1.0273 - 稀疏類別準確度: 0.6010
1177/Unknown 317 秒 264 毫秒/步 - 損失: 1.0271 - 稀疏類別準確度: 0.6010
1178/Unknown 317 秒 264 毫秒/步 - 損失: 1.0269 - 稀疏類別準確度: 0.6011
1179/Unknown 317 秒 264 毫秒/步 - 損失: 1.0268 - 稀疏類別準確度: 0.6011
1180/Unknown 317 秒 264 毫秒/步 - 損失: 1.0266 - 稀疏類別準確度: 0.6012
1181/Unknown 318 秒 264 毫秒/步 - 損失: 1.0264 - 稀疏類別準確度: 0.6012
1182/Unknown 318 秒 264 毫秒/步 - 損失: 1.0263 - 稀疏類別準確度: 0.6013
1183/Unknown 318 秒 264 毫秒/步 - 損失: 1.0261 - 稀疏類別準確度: 0.6014
1184/Unknown 318 秒 264 毫秒/步 - 損失: 1.0259 - 稀疏類別準確度: 0.6014
1185/Unknown 319 秒 264 毫秒/步 - 損失: 1.0257 - 稀疏類別準確度: 0.6015
1186/Unknown 319 秒 264 毫秒/步 - 損失: 1.0256 - 稀疏類別準確度: 0.6015
1187/Unknown 319 秒 264 毫秒/步 - 損失: 1.0254 - 稀疏類別準確度: 0.6016
1188/Unknown 319 秒 264 毫秒/步 - 損失: 1.0252 - 稀疏類別準確度: 0.6016
1189/Unknown 320 秒 264 毫秒/步 - 損失: 1.0251 - 稀疏類別準確度: 0.6017
1190/Unknown 320 秒 264 毫秒/步 - 損失: 1.0249 - 稀疏類別準確度: 0.6017
1191/Unknown 320 秒 264 毫秒/步 - 損失: 1.0247 - 稀疏類別準確度: 0.6018
1192/Unknown 320 秒 264 毫秒/步 - 損失: 1.0245 - 稀疏類別準確度: 0.6018
1193/Unknown 321 秒 264 毫秒/步 - 損失: 1.0244 - 稀疏類別準確度: 0.6019
1194/Unknown 321 秒 264 毫秒/步 - 損失: 1.0242 - 稀疏類別準確度: 0.6019
1195/Unknown 321 秒 264 毫秒/步 - 損失: 1.0240 - 稀疏類別準確度: 0.6020
1196/Unknown 321 秒 264 毫秒/步 - 損失: 1.0239 - 稀疏類別準確度: 0.6021
1197/Unknown 322 秒 264 毫秒/步 - 損失: 1.0237 - 稀疏類別準確度: 0.6021
1198/Unknown 322 秒 264 毫秒/步 - 損失: 1.0235 - 稀疏類別準確度: 0.6022
1199/Unknown 322 秒 264 毫秒/步 - 損失: 1.0234 - 稀疏類別準確度: 0.6022
1200/Unknown 322 秒 264 毫秒/步 - 損失: 1.0232 - 稀疏類別準確度: 0.6023
1201/Unknown 323 秒 264 毫秒/步 - 損失: 1.0230 - 稀疏類別準確度: 0.6023
1202/Unknown 323 秒 264 毫秒/步 - 損失: 1.0229 - 稀疏類別準確度: 0.6024
1203/Unknown 323 秒 264 毫秒/步 - 損失: 1.0227 - 稀疏類別準確度: 0.6024
1204/Unknown 323 秒 264 毫秒/步 - 損失: 1.0225 - 稀疏類別準確度: 0.6025
1205/Unknown 324 秒 264 毫秒/步 - 損失: 1.0224 - 稀疏類別準確度: 0.6025
1206/Unknown 324 秒 264 毫秒/步 - 損失: 1.0222 - 稀疏類別準確度: 0.6026
1207/Unknown 324 秒 264 毫秒/步 - 損失: 1.0220 - 稀疏類別準確度: 0.6026
1208/Unknown 324 秒 264 毫秒/步 - 損失: 1.0219 - 稀疏類別準確度: 0.6027
1209/Unknown 325 秒 264 毫秒/步 - 損失: 1.0217 - 稀疏類別準確度: 0.6027
1210/Unknown 325 秒 264 毫秒/步 - 損失: 1.0215 - 稀疏類別準確度: 0.6028
1211/Unknown 325 秒 264 毫秒/步 - 損失: 1.0214 - 稀疏類別準確度: 0.6029
1212/Unknown 326 秒 264 毫秒/步 - 損失: 1.0212 - 稀疏類別準確度: 0.6029
1213/Unknown 326 秒 264 毫秒/步 - 損失: 1.0210 - 稀疏類別準確度: 0.6030
1214/Unknown 326 秒 264 毫秒/步 - 損失: 1.0209 - 稀疏類別準確度: 0.6030
1215/Unknown 327 秒 264 毫秒/步 - 損失: 1.0207 - 稀疏類別準確度: 0.6031
1216/Unknown 327 秒 264 毫秒/步 - 損失: 1.0205 - 稀疏類別準確度: 0.6031
1217/Unknown 327 秒 264 毫秒/步 - 損失: 1.0204 - 稀疏類別準確度: 0.6032
1218/Unknown 327 秒 264 毫秒/步 - 損失: 1.0202 - 稀疏類別準確度: 0.6032
1219/Unknown 328 秒 264 毫秒/步 - 損失: 1.0200 - 稀疏類別準確度: 0.6033
1220/Unknown 328 秒 264 毫秒/步 - 損失: 1.0199 - 稀疏類別準確度: 0.6033
1221/Unknown 328 秒 264 毫秒/步 - 損失: 1.0197 - 稀疏類別準確度: 0.6034
1222/Unknown 328 秒 264 毫秒/步 - 損失: 1.0196 - 稀疏類別準確度: 0.6034
1223/Unknown 329 秒 264 毫秒/步 - 損失: 1.0194 - 稀疏類別準確度: 0.6035
1224/Unknown 329 秒 264 毫秒/步 - 損失: 1.0192 - 稀疏類別準確度: 0.6035
1225/Unknown 329 秒 264 毫秒/步 - 損失: 1.0191 - 稀疏類別準確度: 0.6036
1226/Unknown 329 秒 264 毫秒/步 - 損失: 1.0189 - 稀疏類別準確度: 0.6036
1227/Unknown 330 秒 264 毫秒/步 - 損失: 1.0187 - 稀疏類別準確度: 0.6037
1228/Unknown 330 秒 264 毫秒/步 - 損失: 1.0186 - 稀疏類別準確度: 0.6037
1229/Unknown 330 秒 264 毫秒/步 - 損失: 1.0184 - 稀疏類別準確度: 0.6038
1230/Unknown 330 秒 264 毫秒/步 - 損失: 1.0183 - 稀疏類別準確度: 0.6038
1231/Unknown 331 秒 264 毫秒/步 - 損失: 1.0181 - 稀疏類別準確度: 0.6039
1232/Unknown 331 秒 264 毫秒/步 - 損失: 1.0179 - 稀疏類別準確度: 0.6039
1233/Unknown 331 秒 264 毫秒/步 - 損失: 1.0178 - 稀疏類別準確度: 0.6040
1234/Unknown 331 秒 264 毫秒/步 - 損失: 1.0176 - 稀疏類別準確度: 0.6040
1235/Unknown 332 秒 264 毫秒/步 - 損失: 1.0174 - 稀疏類別準確度: 0.6041
1236/Unknown 332 秒 264 毫秒/步 - 損失: 1.0173 - 稀疏類別準確度: 0.6041
1237/Unknown 332 秒 264 毫秒/步 - 損失: 1.0171 - 稀疏類別準確度: 0.6042
1238/Unknown 332 秒 264 毫秒/步 - 損失: 1.0170 - 稀疏類別準確度: 0.6042
1239/Unknown 333 秒 264 毫秒/步 - 損失: 1.0168 - 稀疏類別準確度: 0.6043
1240/Unknown 333 秒 264 毫秒/步 - 損失: 1.0166 - 稀疏類別準確度: 0.6043
1241/Unknown 334 秒 264 毫秒/步 - 損失: 1.0165 - 稀疏類別準確度: 0.6044
1242/Unknown 334 秒 264 毫秒/步 - 損失: 1.0163 - 稀疏類別準確度: 0.6044
1243/Unknown 334 秒 264 毫秒/步 - 損失: 1.0162 - 稀疏類別準確度: 0.6045
1244/Unknown 335 秒 265 毫秒/步 - 損失: 1.0160 - 稀疏類別準確度: 0.6045
1245/Unknown 335 秒 265 毫秒/步 - 損失: 1.0158 - 稀疏類別準確度: 0.6046
1246/Unknown 335 秒 265 毫秒/步 - 損失: 1.0157 - 稀疏類別準確度: 0.6046
1247/Unknown 335 秒 265 毫秒/步 - 損失: 1.0155 - 稀疏類別準確度: 0.6047
1248/Unknown 336 秒 265 毫秒/步 - 損失: 1.0154 - 稀疏類別準確度: 0.6048
1249/Unknown 336 秒 265 毫秒/步 - 損失: 1.0152 - 稀疏類別準確度: 0.6048
1250/Unknown 336 秒 265 毫秒/步 - 損失: 1.0150 - 稀疏類別準確度: 0.6049
1251/Unknown 337 秒 265 毫秒/步 - 損失: 1.0149 - 稀疏類別準確度: 0.6049
1252/Unknown 337 秒 265 毫秒/步 - 損失: 1.0147 - 稀疏類別準確度: 0.6050
1253/Unknown 337 秒 265 毫秒/步 - 損失: 1.0146 - 稀疏類別準確度: 0.6050
1254/Unknown 337 秒 265 毫秒/步 - 損失: 1.0144 - 稀疏類別準確度: 0.6051
1255/Unknown 338 秒 265 毫秒/步 - 損失: 1.0143 - 稀疏類別準確度: 0.6051
1256/Unknown 338 秒 265 毫秒/步 - 損失: 1.0141 - 稀疏類別準確度: 0.6052
1257/Unknown 338 秒 264 毫秒/步 - 損失: 1.0139 - 稀疏類別準確度: 0.6052
1258/Unknown 338 秒 264 毫秒/步 - 損失: 1.0138 - 稀疏類別準確度: 0.6053
1259/Unknown 338 秒 264 毫秒/步 - 損失: 1.0136 - 稀疏類別準確度: 0.6053
1260/Unknown 339 秒 264 毫秒/步 - 損失: 1.0135 - 稀疏類別準確度: 0.6054
1261/Unknown 339 秒 264 毫秒/步 - 損失: 1.0133 - 稀疏類別準確度: 0.6054
1262/Unknown 339 秒 264 毫秒/步 - 損失: 1.0132 - 稀疏類別準確度: 0.6055
1263/Unknown 339 秒 264 毫秒/步 - 損失: 1.0130 - 稀疏類別準確度: 0.6055
1264/Unknown 340 秒 264 毫秒/步 - 損失: 1.0128 - 稀疏類別準確度: 0.6055
1265/Unknown 340 秒 264 毫秒/步 - 損失: 1.0127 - 稀疏類別準確度: 0.6056
1266/Unknown 340 秒 264 毫秒/步 - 損失: 1.0125 - 稀疏類別準確度: 0.6056
1267/Unknown 340 秒 264 毫秒/步 - 損失: 1.0124 - 稀疏類別準確度: 0.6057
1268/Unknown 341 秒 264 毫秒/步 - 損失: 1.0122 - 稀疏類別準確度: 0.6057
1269/Unknown 341 秒 264 毫秒/步 - 損失: 1.0121 - 稀疏類別準確度: 0.6058
1270/Unknown 341 秒 264 毫秒/步 - 損失: 1.0119 - 稀疏類別準確度: 0.6058
1271/Unknown 341 秒 264 毫秒/步 - 損失: 1.0118 - 稀疏類別準確度: 0.6059
1272/Unknown 342 秒 264 毫秒/步 - 損失: 1.0116 - 稀疏類別準確度: 0.6059
1273/Unknown 342 秒 264 毫秒/步 - 損失: 1.0114 - 稀疏類別準確度: 0.6060
1274/Unknown 342 秒 264 毫秒/步 - 損失: 1.0113 - 稀疏類別準確度: 0.6060
1275/Unknown 342 秒 264 毫秒/步 - 損失: 1.0111 - 稀疏類別準確度: 0.6061
1276/Unknown 343 秒 264 毫秒/步 - 損失: 1.0110 - 稀疏類別準確度: 0.6061
1277/Unknown 343 秒 264 毫秒/步 - 損失: 1.0108 - 稀疏類別準確度: 0.6062
1278/Unknown 343 秒 264 毫秒/步 - 損失: 1.0107 - 稀疏類別準確度: 0.6062
1279/Unknown 344 秒 264 毫秒/步 - 損失: 1.0105 - 稀疏類別準確度: 0.6063
1280/Unknown 344 秒 264 毫秒/步 - 損失: 1.0104 - 稀疏類別準確度: 0.6063
1281/Unknown 344 秒 264 毫秒/步 - 損失: 1.0102 - 稀疏類別準確度: 0.6064
1282/Unknown 345 秒 264 毫秒/步 - 損失: 1.0101 - 稀疏類別準確度: 0.6064
1283/Unknown 345 秒 265 毫秒/步 - 損失: 1.0099 - 稀疏類別準確度: 0.6065
1284/Unknown 345 秒 265 毫秒/步 - 損失: 1.0098 - 稀疏類別準確度: 0.6065
1285/Unknown 345 秒 265 毫秒/步 - 損失: 1.0096 - 稀疏類別準確度: 0.6066
1286/Unknown 346 秒 265 毫秒/步 - 損失: 1.0095 - 稀疏類別準確度: 0.6066
1287/Unknown 346 秒 265 毫秒/步 - 損失: 1.0093 - 稀疏類別準確度: 0.6067
1288/Unknown 346 秒 265 毫秒/步 - 損失: 1.0092 - 稀疏類別準確度: 0.6067
1289/Unknown 347 秒 265 毫秒/步 - 損失: 1.0090 - 稀疏類別準確度: 0.6068
1290/Unknown 347 秒 265 毫秒/步 - 損失: 1.0088 - 稀疏類別準確度: 0.6068
1291/Unknown 347 秒 265 毫秒/步 - 損失: 1.0087 - 稀疏類別準確度: 0.6069
1292/Unknown 347 秒 265 毫秒/步 - 損失: 1.0085 - 稀疏類別準確度: 0.6069
1293/Unknown 348 秒 265 毫秒/步 - 損失: 1.0084 - 稀疏類別準確度: 0.6070
1294/Unknown 348 秒 265 毫秒/步 - 損失: 1.0082 - 稀疏類別準確度: 0.6070
1295/Unknown 348 秒 265 毫秒/步 - 損失: 1.0081 - 稀疏類別準確度: 0.6071
1296/Unknown 349 秒 265 毫秒/步 - 損失: 1.0079 - 稀疏類別準確度: 0.6071
1297/Unknown 349 秒 265 毫秒/步 - 損失: 1.0078 - 稀疏類別準確度: 0.6071
1298/Unknown 349 秒 265 毫秒/步 - 損失: 1.0076 - 稀疏類別準確度: 0.6072
1299/Unknown 350 秒 265 毫秒/步 - 損失: 1.0075 - 稀疏類別準確度: 0.6072
1300/Unknown 350 秒 265 毫秒/步 - 損失: 1.0073 - 稀疏類別準確度: 0.6073
1301/Unknown 350 秒 265 毫秒/步 - 損失: 1.0072 - 稀疏類別準確度: 0.6073
1302/Unknown 350 秒 265 毫秒/步 - 損失: 1.0070 - 稀疏類別準確度: 0.6074
1303/Unknown 351 秒 265 毫秒/步 - 損失: 1.0069 - 稀疏類別準確度: 0.6074
1304/Unknown 351 秒 265 毫秒/步 - 損失: 1.0067 - 稀疏類別準確度: 0.6075
1305/Unknown 351 秒 265 毫秒/步 - 損失: 1.0066 - 稀疏類別準確度: 0.6075
1306/Unknown 351 秒 265 毫秒/步 - 損失: 1.0064 - 稀疏類別準確度: 0.6076
1307/Unknown 352 秒 265 毫秒/步 - 損失: 1.0063 - 稀疏類別準確度: 0.6076
1308/Unknown 352 秒 265 毫秒/步 - 損失: 1.0061 - 稀疏類別準確度: 0.6077
1309/Unknown 352 秒 265 毫秒/步 - 損失: 1.0060 - 稀疏類別準確度: 0.6077
1310/Unknown 353 秒 265 毫秒/步 - 損失: 1.0059 - 稀疏類別準確度: 0.6078
1311/Unknown 353 秒 265 毫秒/步 - 損失: 1.0057 - 稀疏類別準確度: 0.6078
1312/Unknown 353 秒 265 毫秒/步 - 損失: 1.0056 - 稀疏類別準確度: 0.6079
1313/Unknown 354 秒 265 毫秒/步 - 損失: 1.0054 - 稀疏類別準確度: 0.6079
1314/Unknown 354 秒 265 毫秒/步 - 損失: 1.0053 - 稀疏類別準確度: 0.6079
1315/Unknown 354 秒 265 毫秒/步 - 損失: 1.0051 - 稀疏類別準確度: 0.6080
1316/Unknown 354 秒 265 毫秒/步 - 損失: 1.0050 - 稀疏類別準確度: 0.6080
1317/未知 355秒 265毫秒/步 - 損失: 1.0048 - 稀疏類別準確度: 0.6081
1318/未知 355秒 265毫秒/步 - 損失: 1.0047 - 稀疏類別準確度: 0.6081
1319/未知 355秒 265毫秒/步 - 損失: 1.0045 - 稀疏類別準確度: 0.6082
1320/未知 356秒 265毫秒/步 - 損失: 1.0044 - 稀疏類別準確度: 0.6082
1321/未知 356秒 265毫秒/步 - 損失: 1.0042 - 稀疏類別準確度: 0.6083
1322/未知 356秒 265毫秒/步 - 損失: 1.0041 - 稀疏類別準確度: 0.6083
1323/未知 356秒 265毫秒/步 - 損失: 1.0039 - 稀疏類別準確度: 0.6084
1324/未知 357秒 265毫秒/步 - 損失: 1.0038 - 稀疏類別準確度: 0.6084
1325/未知 357秒 265毫秒/步 - 損失: 1.0036 - 稀疏類別準確度: 0.6085
1326/未知 357秒 265毫秒/步 - 損失: 1.0035 - 稀疏類別準確度: 0.6085
1327/未知 358秒 265毫秒/步 - 損失: 1.0034 - 稀疏類別準確度: 0.6086
1328/未知 358秒 265毫秒/步 - 損失: 1.0032 - 稀疏類別準確度: 0.6086
1329/未知 358秒 265毫秒/步 - 損失: 1.0031 - 稀疏類別準確度: 0.6086
1330/未知 358秒 265毫秒/步 - 損失: 1.0029 - 稀疏類別準確度: 0.6087
1331/未知 359秒 265毫秒/步 - 損失: 1.0028 - 稀疏類別準確度: 0.6087
1332/未知 359秒 265毫秒/步 - 損失: 1.0026 - 稀疏類別準確度: 0.6088
1333/未知 359秒 265毫秒/步 - 損失: 1.0025 - 稀疏類別準確度: 0.6088
1334/未知 359秒 265毫秒/步 - 損失: 1.0023 - 稀疏類別準確度: 0.6089
1335/未知 360秒 265毫秒/步 - 損失: 1.0022 - 稀疏類別準確度: 0.6089
1336/未知 360秒 265毫秒/步 - 損失: 1.0021 - 稀疏類別準確度: 0.6090
1337/未知 360秒 265毫秒/步 - 損失: 1.0019 - 稀疏類別準確度: 0.6090
1338/未知 360秒 265毫秒/步 - 損失: 1.0018 - 稀疏類別準確度: 0.6091
1339/未知 361秒 265毫秒/步 - 損失: 1.0016 - 稀疏類別準確度: 0.6091
1340/未知 361秒 265毫秒/步 - 損失: 1.0015 - 稀疏類別準確度: 0.6091
1341/未知 361秒 265毫秒/步 - 損失: 1.0013 - 稀疏類別準確度: 0.6092
1342/未知 361秒 265毫秒/步 - 損失: 1.0012 - 稀疏類別準確度: 0.6092
1343/未知 362秒 265毫秒/步 - 損失: 1.0010 - 稀疏類別準確度: 0.6093
1344/未知 362秒 265毫秒/步 - 損失: 1.0009 - 稀疏類別準確度: 0.6093
1345/未知 362秒 265毫秒/步 - 損失: 1.0008 - 稀疏類別準確度: 0.6094
1346/未知 363秒 265毫秒/步 - 損失: 1.0006 - 稀疏類別準確度: 0.6094
1347/未知 363秒 265毫秒/步 - 損失: 1.0005 - 稀疏類別準確度: 0.6095
1348/未知 363秒 265毫秒/步 - 損失: 1.0003 - 稀疏類別準確度: 0.6095
1349/未知 364秒 265毫秒/步 - 損失: 1.0002 - 稀疏類別準確度: 0.6096
1350/未知 364秒 265毫秒/步 - 損失: 1.0000 - 稀疏類別準確度: 0.6096
1351/未知 364秒 265毫秒/步 - 損失: 0.9999 - 稀疏類別準確度: 0.6096
1352/未知 364秒 265毫秒/步 - 損失: 0.9998 - 稀疏類別準確度: 0.6097
1353/未知 365秒 265毫秒/步 - 損失: 0.9996 - 稀疏類別準確度: 0.6097
1354/未知 365秒 265毫秒/步 - 損失: 0.9995 - 稀疏類別準確度: 0.6098
1355/未知 365秒 265毫秒/步 - 損失: 0.9993 - 稀疏類別準確度: 0.6098
1356/未知 366秒 265毫秒/步 - 損失: 0.9992 - 稀疏類別準確度: 0.6099
1357/未知 366秒 266毫秒/步 - 損失: 0.9991 - 稀疏類別準確度: 0.6099
1358/未知 366秒 266毫秒/步 - 損失: 0.9989 - 稀疏類別準確度: 0.6100
1359/未知 366秒 266毫秒/步 - 損失: 0.9988 - 稀疏類別準確度: 0.6100
1360/未知 367秒 266毫秒/步 - 損失: 0.9986 - 稀疏類別準確度: 0.6100
1361/未知 367秒 266毫秒/步 - 損失: 0.9985 - 稀疏類別準確度: 0.6101
1362/未知 367秒 265毫秒/步 - 損失: 0.9984 - 稀疏類別準確度: 0.6101
1363/未知 367秒 265毫秒/步 - 損失: 0.9982 - 稀疏類別準確度: 0.6102
1364/未知 368秒 266毫秒/步 - 損失: 0.9981 - 稀疏類別準確度: 0.6102
1365/未知 368秒 266毫秒/步 - 損失: 0.9979 - 稀疏類別準確度: 0.6103
1366/未知 368秒 266毫秒/步 - 損失: 0.9978 - 稀疏類別準確度: 0.6103
1367/未知 369秒 266毫秒/步 - 損失: 0.9977 - 稀疏類別準確度: 0.6104
1368/未知 369秒 266毫秒/步 - 損失: 0.9975 - 稀疏類別準確度: 0.6104
1369/未知 369秒 266毫秒/步 - 損失: 0.9974 - 稀疏類別準確度: 0.6104
1370/未知 369秒 266毫秒/步 - 損失: 0.9972 - 稀疏類別準確度: 0.6105
1371/未知 370秒 266毫秒/步 - 損失: 0.9971 - 稀疏類別準確度: 0.6105
1372/未知 370秒 266毫秒/步 - 損失: 0.9970 - 稀疏類別準確度: 0.6106
1373/未知 370秒 266毫秒/步 - 損失: 0.9968 - 稀疏類別準確度: 0.6106
1374/未知 371秒 266毫秒/步 - 損失: 0.9967 - 稀疏類別準確度: 0.6107
1375/未知 371秒 266毫秒/步 - 損失: 0.9965 - 稀疏類別準確度: 0.6107
1376/未知 371秒 266毫秒/步 - 損失: 0.9964 - 稀疏類別準確度: 0.6107
1377/未知 372秒 266毫秒/步 - 損失: 0.9963 - 稀疏類別準確度: 0.6108
1378/未知 372秒 266毫秒/步 - 損失: 0.9961 - 稀疏類別準確度: 0.6108
1379/未知 372秒 266毫秒/步 - 損失: 0.9960 - 稀疏類別準確度: 0.6109
1380/未知 372秒 266毫秒/步 - 損失: 0.9959 - 稀疏類別準確度: 0.6109
1381/未知 373秒 266毫秒/步 - 損失: 0.9957 - 稀疏類別準確度: 0.6110
1382/未知 373秒 266毫秒/步 - 損失: 0.9956 - 稀疏類別準確度: 0.6110
1383/未知 373秒 266毫秒/步 - 損失: 0.9954 - 稀疏類別準確度: 0.6111
1384/未知 374秒 266毫秒/步 - 損失: 0.9953 - 稀疏類別準確度: 0.6111
1385/未知 374秒 266毫秒/步 - 損失: 0.9952 - 稀疏類別準確度: 0.6111
1386/未知 374秒 266毫秒/步 - 損失: 0.9950 - 稀疏類別準確度: 0.6112
1387/未知 374秒 266毫秒/步 - 損失: 0.9949 - 稀疏類別準確度: 0.6112
1388/未知 375秒 266毫秒/步 - 損失: 0.9948 - 稀疏類別準確度: 0.6113
1389/未知 375秒 266毫秒/步 - 損失: 0.9946 - 稀疏類別準確度: 0.6113
1390/未知 375秒 266毫秒/步 - 損失: 0.9945 - 稀疏類別準確度: 0.6114
1391/未知 376秒 266毫秒/步 - 損失: 0.9943 - 稀疏類別準確度: 0.6114
1392/未知 376秒 266毫秒/步 - 損失: 0.9942 - 稀疏類別準確度: 0.6114
1393/未知 376秒 266毫秒/步 - 損失: 0.9941 - 稀疏類別準確度: 0.6115
1394/未知 377秒 266毫秒/步 - 損失: 0.9939 - 稀疏類別準確度: 0.6115
1395/未知 377秒 266毫秒/步 - 損失: 0.9938 - 稀疏類別準確度: 0.6116
1396/未知 377秒 266毫秒/步 - 損失: 0.9937 - 稀疏類別準確度: 0.6116
1397/未知 378秒 266毫秒/步 - 損失: 0.9935 - 稀疏類別準確度: 0.6117
1398/未知 378秒 266毫秒/步 - 損失: 0.9934 - 稀疏類別準確度: 0.6117
1399/未知 378秒 266毫秒/步 - 損失: 0.9933 - 稀疏類別準確度: 0.6117
1400/未知 378秒 266毫秒/步 - 損失: 0.9931 - 稀疏類別準確度: 0.6118
1401/未知 379秒 266毫秒/步 - 損失: 0.9930 - 稀疏類別準確度: 0.6118
1402/未知 379秒 266毫秒/步 - 損失: 0.9929 - 稀疏類別準確度: 0.6119
1403/未知 379秒 266毫秒/步 - 損失: 0.9927 - 稀疏類別準確度: 0.6119
1404/未知 379秒 266毫秒/步 - 損失: 0.9926 - 稀疏類別準確度: 0.6120
1405/未知 380秒 266毫秒/步 - 損失: 0.9925 - 稀疏類別準確度: 0.6120
1406/未知 380秒 266毫秒/步 - 損失: 0.9923 - 稀疏類別準確度: 0.6120
1407/未知 380秒 266毫秒/步 - 損失: 0.9922 - 稀疏類別準確度: 0.6121
1408/未知 380秒 266毫秒/步 - 損失: 0.9921 - 稀疏類別準確度: 0.6121
1409/未知 381秒 266毫秒/步 - 損失: 0.9919 - 稀疏類別準確度: 0.6122
1410/未知 381秒 266毫秒/步 - 損失: 0.9918 - 稀疏類別準確度: 0.6122
1411/未知 381秒 266毫秒/步 - 損失: 0.9917 - 稀疏類別準確度: 0.6122
1412/未知 382秒 266毫秒/步 - 損失: 0.9915 - 稀疏類別準確度: 0.6123
1413/未知 382秒 266毫秒/步 - 損失: 0.9914 - 稀疏類別準確度: 0.6123
1414/未知 382秒 266毫秒/步 - 損失: 0.9913 - 稀疏類別準確度: 0.6124
1415/未知 382秒 266毫秒/步 - 損失: 0.9911 - 稀疏類別準確度: 0.6124
1416/未知 383秒 266毫秒/步 - 損失: 0.9910 - 稀疏類別準確度: 0.6125
1417/未知 383秒 266毫秒/步 - 損失: 0.9909 - 稀疏類別準確度: 0.6125
1418/未知 383秒 266毫秒/步 - 損失: 0.9907 - 稀疏類別準確度: 0.6125
1419/未知 384秒 266毫秒/步 - 損失: 0.9906 - 稀疏類別準確度: 0.6126
1420/未知 384秒 267毫秒/步 - 損失: 0.9905 - 稀疏類別準確度: 0.6126
1421/未知 384秒 267毫秒/步 - 損失: 0.9903 - 稀疏類別準確度: 0.6127
1422/未知 385秒 267毫秒/步 - 損失: 0.9902 - 稀疏類別準確度: 0.6127
1423/未知 385秒 267毫秒/步 - 損失: 0.9901 - 稀疏類別準確度: 0.6127
1424/未知 386秒 267毫秒/步 - 損失: 0.9899 - 稀疏類別準確度: 0.6128
1425/未知 386秒 267毫秒/步 - 損失: 0.9898 - 稀疏類別準確度: 0.6128
1426/未知 386秒 267毫秒/步 - 損失: 0.9897 - 稀疏類別準確度: 0.6129
1427/未知 386秒 267毫秒/步 - 損失: 0.9895 - 稀疏類別準確度: 0.6129
1428/未知 387秒 267毫秒/步 - 損失: 0.9894 - 稀疏類別準確度: 0.6130
1429/未知 387秒 267毫秒/步 - 損失: 0.9893 - 稀疏類別準確度: 0.6130
1430/未知 387秒 267毫秒/步 - 損失: 0.9891 - 稀疏類別準確度: 0.6130
1431/未知 388秒 267毫秒/步 - 損失: 0.9890 - 稀疏類別準確度: 0.6131
1432/未知 388秒 267毫秒/步 - 損失: 0.9889 - 稀疏類別準確度: 0.6131
1433/未知 388秒 267毫秒/步 - 損失: 0.9888 - 稀疏類別準確度: 0.6132
1434/未知 388秒 267毫秒/步 - 損失: 0.9886 - 稀疏類別準確度: 0.6132
1435/未知 389秒 267毫秒/步 - 損失: 0.9885 - 稀疏類別準確度: 0.6132
1436/未知 389秒 267毫秒/步 - 損失: 0.9884 - 稀疏類別準確度: 0.6133
1437/未知 389秒 267毫秒/步 - 損失: 0.9882 - 稀疏類別準確度: 0.6133
1438/未知 390秒 267毫秒/步 - 損失: 0.9881 - 稀疏類別準確度: 0.6134
1439/未知 390秒 267毫秒/步 - 損失: 0.9880 - 稀疏類別準確度: 0.6134
1440/未知 390秒 267毫秒/步 - 損失: 0.9878 - 稀疏類別準確度: 0.6134
1441/未知 391秒 267毫秒/步 - 損失: 0.9877 - 稀疏類別準確度: 0.6135
1442/未知 391秒 267毫秒/步 - 損失: 0.9876 - 稀疏類別準確度: 0.6135
1443/未知 391秒 267毫秒/步 - 損失: 0.9875 - 稀疏類別準確度: 0.6136
1444/未知 391秒 267毫秒/步 - 損失: 0.9873 - 稀疏類別準確度: 0.6136
1445/未知 392秒 267毫秒/步 - 損失: 0.9872 - 稀疏類別準確度: 0.6137
1446/未知 392秒 267毫秒/步 - 損失: 0.9871 - 稀疏類別準確度: 0.6137
1447/未知 392秒 267毫秒/步 - 損失: 0.9869 - 稀疏類別準確度: 0.6137
1448/未知 393秒 267毫秒/步 - 損失: 0.9868 - 稀疏類別準確度: 0.6138
1449/未知 393秒 268毫秒/步 - 損失: 0.9867 - 稀疏類別準確度: 0.6138
1450/未知 394秒 268毫秒/步 - 損失: 0.9866 - 稀疏類別準確度: 0.6139
1451/未知 394秒 268毫秒/步 - 損失: 0.9864 - 稀疏類別準確度: 0.6139
1452/未知 394秒 268毫秒/步 - 損失: 0.9863 - 稀疏類別準確度: 0.6139
1453/未知 395秒 268毫秒/步 - 損失: 0.9862 - 稀疏類別準確度: 0.6140
1454/未知 395秒 268毫秒/步 - 損失: 0.9861 - 稀疏類別準確度: 0.6140
1455/未知 395秒 268毫秒/步 - 損失: 0.9859 - 稀疏類別準確度: 0.6141
1456/未知 396秒 268毫秒/步 - 損失: 0.9858 - 稀疏類別準確度: 0.6141
1457/未知 396秒 268毫秒/步 - 損失: 0.9857 - 稀疏類別準確度: 0.6141
1458/未知 396秒 268毫秒/步 - 損失: 0.9855 - 稀疏類別準確度: 0.6142
1459/未知 396秒 268毫秒/步 - 損失: 0.9854 - 稀疏類別準確度: 0.6142
1460/未知 397秒 268毫秒/步 - 損失: 0.9853 - 稀疏類別準確度: 0.6143
1461/未知 397秒 268毫秒/步 - 損失: 0.9852 - 稀疏類別準確度: 0.6143
1462/未知 397秒 268毫秒/步 - 損失: 0.9850 - 稀疏類別準確度: 0.6143
1463/未知 397秒 268毫秒/步 - 損失: 0.9849 - 稀疏類別準確度: 0.6144
1464/未知 398秒 268毫秒/步 - 損失: 0.9848 - 稀疏類別準確度: 0.6144
1465/未知 398秒 268毫秒/步 - 損失: 0.9847 - 稀疏類別準確度: 0.6145
1466/未知 398秒 268毫秒/步 - 損失: 0.9845 - 稀疏類別準確度: 0.6145
1467/未知 399秒 268毫秒/步 - 損失: 0.9844 - 稀疏類別準確度: 0.6145
1468/未知 399秒 268毫秒/步 - 損失: 0.9843 - 稀疏類別準確度: 0.6146
1469/未知 399秒 268毫秒/步 - 損失: 0.9842 - 稀疏類別準確度: 0.6146
1470/未知 399秒 268毫秒/步 - 損失: 0.9840 - 稀疏類別準確度: 0.6147
1471/未知 400秒 268毫秒/步 - 損失: 0.9839 - 稀疏類別準確度: 0.6147
1472/未知 400秒 268毫秒/步 - 損失: 0.9838 - 稀疏類別準確度: 0.6147
1473/未知 400秒 268毫秒/步 - 損失: 0.9837 - 稀疏類別準確度: 0.6148
1474/未知 401秒 268毫秒/步 - 損失: 0.9835 - 稀疏類別準確度: 0.6148
1475/未知 401秒 268毫秒/步 - 損失: 0.9834 - 稀疏類別準確度: 0.6149
1476/未知 401秒 268毫秒/步 - 損失: 0.9833 - 稀疏類別準確度: 0.6149
1477/未知 401秒 268毫秒/步 - 損失: 0.9832 - 稀疏類別準確度: 0.6149
1478/未知 402秒 268毫秒/步 - 損失: 0.9830 - 稀疏類別準確度: 0.6150
1479/未知 402秒 268毫秒/步 - 損失: 0.9829 - 稀疏類別準確度: 0.6150
1480/未知 402秒 268毫秒/步 - 損失: 0.9828 - 稀疏類別準確度: 0.6150
1481/未知 403秒 268毫秒/步 - 損失: 0.9827 - 稀疏類別準確度: 0.6151
1482/未知 403秒 268毫秒/步 - 損失: 0.9825 - 稀疏類別準確度: 0.6151
1483/未知 403秒 268毫秒/步 - 損失: 0.9824 - 稀疏類別準確度: 0.6152
1484/未知 404秒 268毫秒/步 - 損失: 0.9823 - 稀疏類別準確度: 0.6152
1485/未知 404秒 268毫秒/步 - 損失: 0.9822 - 稀疏類別準確度: 0.6152
1486/未知 404秒 268毫秒/步 - 損失: 0.9820 - 稀疏類別準確度: 0.6153
1487/未知 404秒 268毫秒/步 - 損失: 0.9819 - 稀疏類別準確度: 0.6153
1488/未知 405秒 268毫秒/步 - 損失: 0.9818 - 稀疏類別準確度: 0.6154
1489/未知 405秒 268毫秒/步 - 損失: 0.9817 - 稀疏類別準確度: 0.6154
1490/未知 405秒 268毫秒/步 - 損失: 0.9815 - 稀疏類別準確度: 0.6154
1491/未知 406秒 268毫秒/步 - 損失: 0.9814 - 稀疏類別準確度: 0.6155
1492/未知 406秒 268毫秒/步 - 損失: 0.9813 - 稀疏類別準確度: 0.6155
1493/未知 406秒 268毫秒/步 - 損失: 0.9812 - 稀疏類別準確度: 0.6156
1494/未知 406秒 268毫秒/步 - 損失: 0.9810 - 稀疏類別準確度: 0.6156
1495/未知 407秒 268毫秒/步 - 損失: 0.9809 - 稀疏類別準確度: 0.6156
1496/未知 407秒 268毫秒/步 - 損失: 0.9808 - 稀疏類別準確度: 0.6157
1497/未知 407秒 268毫秒/步 - 損失: 0.9807 - 稀疏類別準確度: 0.6157
1498/未知 408秒 268毫秒/步 - 損失: 0.9806 - 稀疏類別準確度: 0.6157
1499/未知 408秒 268毫秒/步 - 損失: 0.9804 - 稀疏類別準確度: 0.6158
1500/未知 408秒 268毫秒/步 - 損失: 0.9803 - 稀疏類別準確度: 0.6158
1501/未知 408秒 268毫秒/步 - 損失: 0.9802 - 稀疏類別準確度: 0.6159
1502/未知 409秒 268毫秒/步 - 損失: 0.9801 - 稀疏類別準確度: 0.6159
1503/未知 409秒 268毫秒/步 - 損失: 0.9800 - 稀疏類別準確度: 0.6159
1504/未知 409秒 268毫秒/步 - 損失: 0.9798 - 稀疏類別準確度: 0.6160
1505/未知 410秒 268毫秒/步 - 損失: 0.9797 - 稀疏類別準確度: 0.6160
1506/未知 410秒 269毫秒/步 - 損失: 0.9796 - 稀疏類別準確度: 0.6161
1507/未知 410秒 269毫秒/步 - 損失: 0.9795 - 稀疏類別準確度: 0.6161
1508/未知 411秒 269毫秒/步 - 損失: 0.9793 - 稀疏類別準確度: 0.6161
1509/未知 411秒 269毫秒/步 - 損失: 0.9792 - 稀疏類別準確度: 0.6162
1510/未知 411秒 269毫秒/步 - 損失: 0.9791 - 稀疏類別準確度: 0.6162
1511/未知 411秒 269毫秒/步 - 損失: 0.9790 - 稀疏類別準確度: 0.6162
1512/未知 412秒 269毫秒/步 - 損失: 0.9789 - 稀疏類別準確度: 0.6163
1513/未知 412秒 269毫秒/步 - 損失: 0.9787 - 稀疏類別準確度: 0.6163
1514/未知 412秒 269毫秒/步 - 損失: 0.9786 - 稀疏類別準確度: 0.6164
1515/未知 413秒 269毫秒/步 - 損失: 0.9785 - 稀疏類別準確度: 0.6164
1516/未知 413秒 269毫秒/步 - 損失: 0.9784 - 稀疏類別準確度: 0.6164
1517/未知 413秒 269毫秒/步 - 損失: 0.9783 - 稀疏類別準確度: 0.6165
1518/未知 413秒 269毫秒/步 - 損失: 0.9781 - 稀疏類別準確度: 0.6165
1519/未知 414秒 269毫秒/步 - 損失: 0.9780 - 稀疏類別準確度: 0.6166
1520/未知 414秒 269毫秒/步 - 損失: 0.9779 - 稀疏類別準確度: 0.6166
1521/未知 414秒 269毫秒/步 - 損失: 0.9778 - 稀疏類別準確度: 0.6166
1522/未知 415秒 269毫秒/步 - 損失: 0.9777 - 稀疏類別準確度: 0.6167
1523/未知 415秒 269毫秒/步 - 損失: 0.9775 - 稀疏類別準確度: 0.6167
1524/未知 415秒 269毫秒/步 - 損失: 0.9774 - 稀疏類別準確度: 0.6167
1525/未知 415秒 269毫秒/步 - 損失: 0.9773 - 稀疏類別準確度: 0.6168
1526/未知 416秒 269毫秒/步 - 損失: 0.9772 - 稀疏類別準確度: 0.6168
1527/未知 416秒 269毫秒/步 - 損失: 0.9771 - 稀疏類別準確度: 0.6169
1528/未知 416秒 269毫秒/步 - 損失: 0.9769 - 稀疏類別準確度: 0.6169
1529/未知 417秒 269毫秒/步 - 損失: 0.9768 - 稀疏類別準確度: 0.6169
1530/未知 417秒 269毫秒/步 - 損失: 0.9767 - 稀疏類別準確度: 0.6170
1531/未知 417秒 269毫秒/步 - 損失: 0.9766 - 稀疏類別準確度: 0.6170
1532/未知 417秒 269毫秒/步 - 損失: 0.9765 - 稀疏類別準確度: 0.6170
1533/未知 418秒 269毫秒/步 - 損失: 0.9764 - 稀疏類別準確度: 0.6171
1534/未知 418秒 269毫秒/步 - 損失: 0.9762 - 稀疏類別準確度: 0.6171
1535/未知 418秒 269毫秒/步 - 損失: 0.9761 - 稀疏類別準確度: 0.6172
1536/未知 418秒 269毫秒/步 - 損失: 0.9760 - 稀疏類別準確度: 0.6172
1537/未知 419秒 269毫秒/步 - 損失: 0.9759 - 稀疏類別準確度: 0.6172
1538/未知 419秒 269毫秒/步 - 損失: 0.9758 - 稀疏類別準確度: 0.6173
1539/未知 419秒 269毫秒/步 - 損失: 0.9756 - 稀疏類別準確度: 0.6173
1540/未知 420秒 269毫秒/步 - 損失: 0.9755 - 稀疏類別準確度: 0.6173
1541/未知 420秒 269毫秒/步 - 損失: 0.9754 - 稀疏類別準確度: 0.6174
1542/未知 420秒 269毫秒/步 - 損失: 0.9753 - 稀疏類別準確度: 0.6174
1543/未知 420秒 269毫秒/步 - 損失: 0.9752 - 稀疏類別準確度: 0.6174
1544/未知 421秒 269毫秒/步 - 損失: 0.9751 - 稀疏類別準確度: 0.6175
1545/未知 421秒 269毫秒/步 - 損失: 0.9749 - 稀疏類別準確度: 0.6175
1546/未知 421秒 269毫秒/步 - 損失: 0.9748 - 稀疏類別準確度: 0.6176
1547/未知 422秒 269毫秒/步 - 損失: 0.9747 - 稀疏類別準確度: 0.6176
1548/未知 422秒 269毫秒/步 - 損失: 0.9746 - 稀疏類別準確度: 0.6176
1549/未知 422秒 269毫秒/步 - 損失: 0.9745 - 稀疏類別準確度: 0.6177
1550/未知 422秒 269毫秒/步 - 損失: 0.9744 - 稀疏類別準確度: 0.6177
1551/未知 423秒 269毫秒/步 - 損失: 0.9742 - 稀疏類別準確度: 0.6177
1552/未知 423秒 269毫秒/步 - 損失: 0.9741 - 稀疏類別準確度: 0.6178
1553/未知 423秒 269毫秒/步 - 損失: 0.9740 - 稀疏類別準確度: 0.6178
1554/未知 424秒 269毫秒/步 - 損失: 0.9739 - 稀疏類別準確度: 0.6179
1555/未知 424秒 269毫秒/步 - 損失: 0.9738 - 稀疏類別準確度: 0.6179
1556/未知 424秒 269毫秒/步 - 損失: 0.9737 - 稀疏類別準確度: 0.6179
1557/未知 424秒 269毫秒/步 - 損失: 0.9736 - 稀疏類別準確度: 0.6180
1558/未知 425秒 269毫秒/步 - 損失: 0.9734 - 稀疏類別準確度: 0.6180
1559/未知 425秒 269毫秒/步 - 損失: 0.9733 - 稀疏類別準確度: 0.6180
1560/未知 425秒 269毫秒/步 - 損失: 0.9732 - 稀疏類別準確度: 0.6181
1561/未知 426秒 269毫秒/步 - 損失: 0.9731 - 稀疏類別準確度: 0.6181
1562/未知 426秒 269毫秒/步 - 損失: 0.9730 - 稀疏類別準確度: 0.6181
1563/未知 426秒 269毫秒/步 - 損失: 0.9729 - 稀疏類別準確度: 0.6182
1564/未知 427秒 269毫秒/步 - 損失: 0.9727 - 稀疏類別準確度: 0.6182
1565/未知 427秒 269毫秒/步 - 損失: 0.9726 - 稀疏類別準確度: 0.6182
1566/未知 427秒 269毫秒/步 - 損失: 0.9725 - 稀疏類別準確度: 0.6183
1567/未知 427秒 269毫秒/步 - 損失: 0.9724 - 稀疏類別準確度: 0.6183
1568/未知 428秒 269毫秒/步 - 損失: 0.9723 - 稀疏類別準確度: 0.6184
1569/未知 428秒 269毫秒/步 - 損失: 0.9722 - 稀疏類別準確度: 0.6184
1570/未知 428秒 269毫秒/步 - 損失: 0.9721 - 稀疏類別準確度: 0.6184
1571/未知 428秒 269毫秒/步 - 損失: 0.9719 - 稀疏類別準確度: 0.6185
1572/未知 429秒 269毫秒/步 - 損失: 0.9718 - 稀疏類別準確度: 0.6185
1573/未知 429秒 269毫秒/步 - 損失: 0.9717 - 稀疏類別準確度: 0.6185
1574/未知 429秒 269毫秒/步 - 損失: 0.9716 - 稀疏類別準確度: 0.6186
1575/未知 430秒 269毫秒/步 - 損失: 0.9715 - 稀疏類別準確度: 0.6186
1576/未知 430秒 269毫秒/步 - 損失: 0.9714 - 稀疏類別準確度: 0.6186
1577/未知 430秒 269毫秒/步 - 損失: 0.9713 - 稀疏類別準確度: 0.6187
1578/未知 430秒 269毫秒/步 - 損失: 0.9712 - 稀疏類別準確度: 0.6187
1579/未知 431秒 269毫秒/步 - 損失: 0.9710 - 稀疏類別準確度: 0.6188
1580/未知 431秒 269毫秒/步 - 損失: 0.9709 - 稀疏類別準確度: 0.6188
1581/未知 431秒 269毫秒/步 - 損失: 0.9708 - 稀疏類別準確度: 0.6188
1582/未知 432秒 269毫秒/步 - 損失: 0.9707 - 稀疏類別準確度: 0.6189
1583/未知 432秒 269毫秒/步 - 損失: 0.9706 - 稀疏類別準確度: 0.6189
1584/未知 432秒 269毫秒/步 - 損失: 0.9705 - 稀疏類別準確度: 0.6189
1585/未知 433秒 269毫秒/步 - 損失: 0.9704 - 稀疏類別準確度: 0.6190
1586/未知 433秒 269毫秒/步 - 損失: 0.9702 - 稀疏類別準確度: 0.6190
1587/未知 433秒 269毫秒/步 - 損失: 0.9701 - 稀疏類別準確度: 0.6190
1588/未知 433秒 269毫秒/步 - 損失: 0.9700 - 稀疏類別準確度: 0.6191
1589/未知 434秒 269毫秒/步 - 損失: 0.9699 - 稀疏類別準確度: 0.6191
1590/未知 434秒 269毫秒/步 - 損失: 0.9698 - 稀疏類別準確度: 0.6191
1591/未知 434秒 269毫秒/步 - 損失: 0.9697 - 稀疏類別準確度: 0.6192
1592/未知 435秒 270毫秒/步 - 損失: 0.9696 - 稀疏類別準確度: 0.6192
1593/未知 435秒 270毫秒/步 - 損失: 0.9695 - 稀疏類別準確度: 0.6192
1594/未知 435秒 270毫秒/步 - 損失: 0.9694 - 稀疏類別準確度: 0.6193
1595/未知 435秒 270毫秒/步 - 損失: 0.9692 - 稀疏類別準確度: 0.6193
1596/未知 436秒 270毫秒/步 - 損失: 0.9691 - 稀疏類別準確度: 0.6194
1597/未知 436秒 270毫秒/步 - 損失: 0.9690 - 稀疏類別準確度: 0.6194
1598/未知 436秒 270毫秒/步 - 損失: 0.9689 - 稀疏類別準確度: 0.6194
1599/未知 437秒 270毫秒/步 - 損失: 0.9688 - 稀疏類別準確度: 0.6195
1600/未知 437秒 270毫秒/步 - 損失: 0.9687 - 稀疏類別準確度: 0.6195
1601/未知 437秒 270毫秒/步 - 損失: 0.9686 - 稀疏類別準確度: 0.6195
1602/未知 437秒 270毫秒/步 - 損失: 0.9685 - 稀疏類別準確度: 0.6196
1603/未知 438秒 270毫秒/步 - 損失: 0.9684 - 稀疏類別準確度: 0.6196
1604/未知 438秒 270毫秒/步 - 損失: 0.9682 - 稀疏類別準確度: 0.6196
1605/未知 438秒 270毫秒/步 - 損失: 0.9681 - 稀疏類別準確度: 0.6197
1606/未知 439秒 270毫秒/步 - 損失: 0.9680 - 稀疏類別準確度: 0.6197
1607/未知 439秒 270毫秒/步 - 損失: 0.9679 - 稀疏類別準確度: 0.6197
1608/未知 439秒 270毫秒/步 - 損失: 0.9678 - 稀疏類別準確度: 0.6198
1609/未知 439秒 270毫秒/步 - 損失: 0.9677 - 稀疏類別準確度: 0.6198
1610/未知 440秒 270毫秒/步 - 損失: 0.9676 - 稀疏類別準確度: 0.6198
1611/未知 440秒 270毫秒/步 - 損失: 0.9675 - 稀疏類別準確度: 0.6199
1612/未知 440秒 270毫秒/步 - 損失: 0.9674 - 稀疏類別準確度: 0.6199
1613/未知 441秒 270毫秒/步 - 損失: 0.9673 - 稀疏類別準確度: 0.6199
1614/未知 441秒 270毫秒/步 - 損失: 0.9671 - 稀疏類別準確度: 0.6200
1615/未知 441秒 270毫秒/步 - 損失: 0.9670 - 稀疏類別準確度: 0.6200
1616/未知 442秒 270毫秒/步 - 損失: 0.9669 - 稀疏類別準確度: 0.6200
1617/未知 442秒 270毫秒/步 - 損失: 0.9668 - 稀疏類別準確度: 0.6201
1618/未知 442秒 270毫秒/步 - 損失: 0.9667 - 稀疏類別準確度: 0.6201
1619/未知 442秒 270毫秒/步 - 損失: 0.9666 - 稀疏類別準確度: 0.6202
1620/未知 443秒 270毫秒/步 - 損失: 0.9665 - 稀疏類別準確度: 0.6202
1621/未知 443秒 270毫秒/步 - 損失: 0.9664 - 稀疏類別準確度: 0.6202
1622/未知 443秒 270毫秒/步 - 損失: 0.9663 - 稀疏類別準確度: 0.6203
1623/未知 444秒 270毫秒/步 - 損失: 0.9662 - 稀疏類別準確度: 0.6203
1624/未知 444秒 270毫秒/步 - 損失: 0.9661 - 稀疏類別準確度: 0.6203
1625/未知 444秒 270毫秒/步 - 損失: 0.9659 - 稀疏類別準確度: 0.6204
1626/未知 445秒 270毫秒/步 - 損失: 0.9658 - 稀疏類別準確度: 0.6204
1627/未知 445秒 270毫秒/步 - 損失: 0.9657 - 稀疏類別準確度: 0.6204
1628/未知 445秒 270毫秒/步 - 損失: 0.9656 - 稀疏類別準確度: 0.6205
1629/未知 446秒 270毫秒/步 - 損失: 0.9655 - 稀疏類別準確度: 0.6205
1630/未知 446秒 270毫秒/步 - 損失: 0.9654 - 稀疏類別準確度: 0.6205
1631/未知 446秒 270毫秒/步 - 損失: 0.9653 - 稀疏類別準確度: 0.6206
1632/未知 447秒 270毫秒/步 - 損失: 0.9652 - 稀疏類別準確度: 0.6206
1633/未知 447秒 270毫秒/步 - 損失: 0.9651 - 稀疏類別準確度: 0.6206
1634/未知 447秒 270毫秒/步 - 損失: 0.9650 - 稀疏類別準確度: 0.6207
1635/未知 448秒 271毫秒/步 - 損失: 0.9649 - 稀疏類別準確度: 0.6207
1636/未知 448秒 271毫秒/步 - 損失: 0.9648 - 稀疏類別準確度: 0.6207
1637/未知 448秒 271毫秒/步 - 損失: 0.9646 - 稀疏類別準確度: 0.6208
1638/未知 449秒 271毫秒/步 - 損失: 0.9645 - 稀疏類別準確度: 0.6208
1639/未知 449秒 271毫秒/步 - 損失: 0.9644 - 稀疏類別準確度: 0.6208
1640/未知 449秒 271毫秒/步 - 損失: 0.9643 - 稀疏類別準確度: 0.6209
1641/未知 450秒 271毫秒/步 - 損失: 0.9642 - 稀疏類別準確度: 0.6209
1642/未知 450秒 271毫秒/步 - 損失: 0.9641 - 稀疏類別準確度: 0.6209
1643/未知 450秒 271毫秒/步 - 損失: 0.9640 - 稀疏類別準確度: 0.6210
1644/未知 450秒 271毫秒/步 - 損失: 0.9639 - 稀疏類別準確度: 0.6210
1645/未知 451秒 271毫秒/步 - 損失: 0.9638 - 稀疏類別準確度: 0.6210
1646/未知 451秒 271毫秒/步 - 損失: 0.9637 - 稀疏類別準確度: 0.6211
1647/未知 451秒 271毫秒/步 - 損失: 0.9636 - 稀疏類別準確度: 0.6211
1648/未知 452秒 271毫秒/步 - 損失: 0.9635 - 稀疏類別準確度: 0.6211
1649/未知 452秒 271毫秒/步 - 損失: 0.9634 - 稀疏類別準確度: 0.6212
1650/未知 452秒 271毫秒/步 - 損失: 0.9633 - 稀疏類別準確度: 0.6212
1651/未知 452秒 271毫秒/步 - 損失: 0.9632 - 稀疏類別準確度: 0.6212
1652/未知 453秒 271毫秒/步 - 損失: 0.9631 - 稀疏類別準確度: 0.6213
1653/未知 453秒 271毫秒/步 - 損失: 0.9629 - 稀疏類別準確度: 0.6213
1654/未知 453秒 271毫秒/步 - 損失: 0.9628 - 稀疏類別準確度: 0.6213
1655/未知 454秒 271毫秒/步 - 損失: 0.9627 - 稀疏類別準確度: 0.6214
1656/未知 454秒 271毫秒/步 - 損失: 0.9626 - 稀疏類別準確度: 0.6214
1657/未知 454秒 271毫秒/步 - 損失: 0.9625 - 稀疏類別準確度: 0.6214
1658/未知 455秒 271毫秒/步 - 損失: 0.9624 - 稀疏類別準確度: 0.6215
1659/未知 455秒 271毫秒/步 - 損失: 0.9623 - 稀疏類別準確度: 0.6215
1660/未知 455秒 271毫秒/步 - 損失: 0.9622 - 稀疏類別準確度: 0.6215
1661/未知 455秒 271毫秒/步 - 損失: 0.9621 - 稀疏類別準確度: 0.6216
1662/未知 456秒 271毫秒/步 - 損失: 0.9620 - 稀疏類別準確度: 0.6216
1663/未知 456秒 271毫秒/步 - 損失: 0.9619 - 稀疏類別準確度: 0.6216
1664/未知 456秒 271毫秒/步 - 損失: 0.9618 - 稀疏類別準確度: 0.6217
1665/未知 457秒 271毫秒/步 - 損失: 0.9617 - 稀疏類別準確度: 0.6217
1666/未知 457秒 271毫秒/步 - 損失: 0.9616 - 稀疏類別準確度: 0.6217
1667/未知 457秒 271毫秒/步 - 損失: 0.9615 - 稀疏類別準確度: 0.6218
1668/未知 457秒 271毫秒/步 - 損失: 0.9614 - 稀疏類別準確度: 0.6218
1669/未知 458秒 271毫秒/步 - 損失: 0.9613 - 稀疏類別準確度: 0.6218
1670/未知 458秒 271毫秒/步 - 損失: 0.9612 - 稀疏類別準確度: 0.6219
1671/未知 458秒 271毫秒/步 - 損失: 0.9611 - 稀疏類別準確度: 0.6219
1672/未知 459秒 271毫秒/步 - 損失: 0.9610 - 稀疏類別準確度: 0.6219
1673/未知 459秒 271毫秒/步 - 損失: 0.9609 - 稀疏類別準確度: 0.6220
1674/未知 459秒 271毫秒/步 - 損失: 0.9607 - 稀疏類別準確度: 0.6220
1675/未知 460秒 271毫秒/步 - 損失: 0.9606 - 稀疏類別準確度: 0.6220
1676/未知 460秒 271毫秒/步 - 損失: 0.9605 - 稀疏類別準確度: 0.6221
1677/未知 460秒 271毫秒/步 - 損失: 0.9604 - 稀疏類別準確度: 0.6221
1678/未知 460秒 271毫秒/步 - 損失: 0.9603 - 稀疏類別準確度: 0.6221
1679/未知 461秒 271毫秒/步 - 損失: 0.9602 - 稀疏類別準確度: 0.6222
1680/未知 461秒 271毫秒/步 - 損失: 0.9601 - 稀疏類別準確度: 0.6222
1681/未知 461秒 271毫秒/步 - 損失: 0.9600 - 稀疏類別準確度: 0.6222
1682/未知 462秒 271毫秒/步 - 損失: 0.9599 - 稀疏類別準確度: 0.6223
1683/未知 462秒 271毫秒/步 - 損失: 0.9598 - 稀疏類別準確度: 0.6223
1684/未知 462秒 271毫秒/步 - 損失: 0.9597 - 稀疏類別準確度: 0.6223
1685/未知 462秒 271毫秒/步 - 損失: 0.9596 - 稀疏類別準確度: 0.6224
1686/未知 463秒 271毫秒/步 - 損失: 0.9595 - 稀疏類別準確度: 0.6224
1687/未知 463秒 271毫秒/步 - 損失: 0.9594 - 稀疏類別準確度: 0.6224
1688/未知 463秒 271毫秒/步 - 損失: 0.9593 - 稀疏類別準確度: 0.6224
1689/未知 463秒 271毫秒/步 - 損失: 0.9592 - 稀疏類別準確度: 0.6225
1690/未知 464秒 271毫秒/步 - 損失: 0.9591 - 稀疏類別準確度: 0.6225
1691/未知 464秒 271毫秒/步 - 損失: 0.9590 - 稀疏類別準確度: 0.6225
1692/未知 464秒 271毫秒/步 - 損失: 0.9589 - 稀疏類別準確度: 0.6226
1693/未知 464秒 271毫秒/步 - 損失: 0.9588 - 稀疏類別準確度: 0.6226
1694/未知 465秒 271毫秒/步 - 損失: 0.9587 - 稀疏類別準確度: 0.6226
1695/未知 465秒 271毫秒/步 - 損失: 0.9586 - 稀疏類別準確度: 0.6227
1696/未知 465秒 271毫秒/步 - 損失: 0.9585 - 稀疏類別準確度: 0.6227
1697/未知 465秒 271毫秒/步 - 損失: 0.9584 - 稀疏類別準確度: 0.6227
1698/未知 466秒 271毫秒/步 - 損失: 0.9583 - 稀疏類別準確度: 0.6228
1699/未知 466秒 271毫秒/步 - 損失: 0.9582 - 稀疏類別準確度: 0.6228
1700/未知 466秒 271毫秒/步 - 損失: 0.9581 - 稀疏類別準確度: 0.6228
1701/未知 466秒 271毫秒/步 - 損失: 0.9580 - 稀疏類別準確度: 0.6229
1702/未知 467秒 271毫秒/步 - 損失: 0.9579 - 稀疏類別準確度: 0.6229
1703/未知 467秒 271毫秒/步 - 損失: 0.9578 - 稀疏類別準確度: 0.6229
1704/未知 467秒 271毫秒/步 - 損失: 0.9577 - 稀疏類別準確度: 0.6230
1705/未知 468秒 271毫秒/步 - 損失: 0.9576 - 稀疏類別準確度: 0.6230
1706/未知 468秒 271毫秒/步 - 損失: 0.9575 - 稀疏類別準確度: 0.6230
1707/未知 468秒 271毫秒/步 - 損失: 0.9574 - 稀疏類別準確度: 0.6231
1708/未知 469秒 271毫秒/步 - 損失: 0.9573 - 稀疏類別準確度: 0.6231
1709/未知 469秒 271毫秒/步 - 損失: 0.9572 - 稀疏類別準確度: 0.6231
1710/未知 469秒 271毫秒/步 - 損失: 0.9571 - 稀疏類別準確度: 0.6232
1711/未知 470秒 271毫秒/步 - 損失: 0.9570 - 稀疏類別準確度: 0.6232
1712/未知 470秒 271毫秒/步 - 損失: 0.9569 - 稀疏類別準確度: 0.6232
1713/未知 470秒 271毫秒/步 - 損失: 0.9568 - 稀疏類別準確度: 0.6232
1714/未知 470秒 271毫秒/步 - 損失: 0.9567 - 稀疏類別準確度: 0.6233
1715/未知 471秒 271毫秒/步 - 損失: 0.9566 - 稀疏類別準確度: 0.6233
1716/未知 471秒 271毫秒/步 - 損失: 0.9565 - 稀疏類別準確度: 0.6233
1717/未知 471秒 271毫秒/步 - 損失: 0.9564 - 稀疏類別準確度: 0.6234
1718/未知 471秒 271毫秒/步 - 損失: 0.9563 - 稀疏類別準確度: 0.6234
1719/Unknown 472秒 271毫秒/步 - 損失: 0.9562 - 稀疏類別準確度: 0.6234
1720/Unknown 472秒 271毫秒/步 - 損失: 0.9561 - 稀疏類別準確度: 0.6235
1721/Unknown 472秒 271毫秒/步 - 損失: 0.9560 - 稀疏類別準確度: 0.6235
1722/Unknown 472秒 271毫秒/步 - 損失: 0.9559 - 稀疏類別準確度: 0.6235
1723/Unknown 473秒 271毫秒/步 - 損失: 0.9558 - 稀疏類別準確度: 0.6236
1724/Unknown 473秒 271毫秒/步 - 損失: 0.9557 - 稀疏類別準確度: 0.6236
1725/Unknown 473秒 271毫秒/步 - 損失: 0.9556 - 稀疏類別準確度: 0.6236
1726/Unknown 473秒 271毫秒/步 - 損失: 0.9555 - 稀疏類別準確度: 0.6237
1727/Unknown 474秒 271毫秒/步 - 損失: 0.9554 - 稀疏類別準確度: 0.6237
1728/Unknown 474秒 271毫秒/步 - 損失: 0.9553 - 稀疏類別準確度: 0.6237
1729/Unknown 474秒 271毫秒/步 - 損失: 0.9552 - 稀疏類別準確度: 0.6237
1730/Unknown 474秒 271毫秒/步 - 損失: 0.9551 - 稀疏類別準確度: 0.6238
1731/Unknown 475秒 271毫秒/步 - 損失: 0.9550 - 稀疏類別準確度: 0.6238
1732/Unknown 475秒 271毫秒/步 - 損失: 0.9549 - 稀疏類別準確度: 0.6238
1733/Unknown 476秒 271毫秒/步 - 損失: 0.9548 - 稀疏類別準確度: 0.6239
1734/Unknown 476秒 271毫秒/步 - 損失: 0.9547 - 稀疏類別準確度: 0.6239
1735/Unknown 476秒 271毫秒/步 - 損失: 0.9546 - 稀疏類別準確度: 0.6239
1736/Unknown 477秒 271毫秒/步 - 損失: 0.9545 - 稀疏類別準確度: 0.6240
1737/Unknown 477秒 271毫秒/步 - 損失: 0.9544 - 稀疏類別準確度: 0.6240
1738/Unknown 477秒 271毫秒/步 - 損失: 0.9543 - 稀疏類別準確度: 0.6240
1739/Unknown 478秒 272毫秒/步 - 損失: 0.9542 - 稀疏類別準確度: 0.6241
1740/Unknown 478秒 272毫秒/步 - 損失: 0.9541 - 稀疏類別準確度: 0.6241
1741/Unknown 478秒 272毫秒/步 - 損失: 0.9540 - 稀疏類別準確度: 0.6241
1742/Unknown 479秒 272毫秒/步 - 損失: 0.9539 - 稀疏類別準確度: 0.6242
1743/Unknown 479秒 272毫秒/步 - 損失: 0.9538 - 稀疏類別準確度: 0.6242
1744/Unknown 479秒 272毫秒/步 - 損失: 0.9537 - 稀疏類別準確度: 0.6242
1745/Unknown 480秒 272毫秒/步 - 損失: 0.9536 - 稀疏類別準確度: 0.6242
1746/Unknown 480秒 272毫秒/步 - 損失: 0.9535 - 稀疏類別準確度: 0.6243
1747/Unknown 480秒 272毫秒/步 - 損失: 0.9534 - 稀疏類別準確度: 0.6243
1748/Unknown 481秒 272毫秒/步 - 損失: 0.9533 - 稀疏類別準確度: 0.6243
1749/Unknown 481秒 272毫秒/步 - 損失: 0.9532 - 稀疏類別準確度: 0.6244
1750/Unknown 481秒 272毫秒/步 - 損失: 0.9531 - 稀疏類別準確度: 0.6244
1751/Unknown 481秒 272毫秒/步 - 損失: 0.9530 - 稀疏類別準確度: 0.6244
1752/Unknown 482秒 272毫秒/步 - 損失: 0.9529 - 稀疏類別準確度: 0.6245
1753/Unknown 482秒 272毫秒/步 - 損失: 0.9528 - 稀疏類別準確度: 0.6245
1754/Unknown 482秒 272毫秒/步 - 損失: 0.9527 - 稀疏類別準確度: 0.6245
1755/Unknown 483秒 272毫秒/步 - 損失: 0.9526 - 稀疏類別準確度: 0.6246
1756/Unknown 483秒 272毫秒/步 - 損失: 0.9525 - 稀疏類別準確度: 0.6246
1757/Unknown 483秒 272毫秒/步 - 損失: 0.9524 - 稀疏類別準確度: 0.6246
1758/Unknown 484秒 272毫秒/步 - 損失: 0.9523 - 稀疏類別準確度: 0.6246
1759/Unknown 484秒 272毫秒/步 - 損失: 0.9522 - 稀疏類別準確度: 0.6247
1760/Unknown 484秒 272毫秒/步 - 損失: 0.9521 - 稀疏類別準確度: 0.6247
1761/Unknown 484秒 272毫秒/步 - 損失: 0.9520 - 稀疏類別準確度: 0.6247
1762/Unknown 485秒 272毫秒/步 - 損失: 0.9519 - 稀疏類別準確度: 0.6248
1763/Unknown 485秒 272毫秒/步 - 損失: 0.9519 - 稀疏類別準確度: 0.6248
1764/Unknown 485秒 272毫秒/步 - 損失: 0.9518 - 稀疏類別準確度: 0.6248
1765/Unknown 486秒 272毫秒/步 - 損失: 0.9517 - 稀疏類別準確度: 0.6249
1766/Unknown 486秒 272毫秒/步 - 損失: 0.9516 - 稀疏類別準確度: 0.6249
1767/Unknown 486秒 272毫秒/步 - 損失: 0.9515 - 稀疏類別準確度: 0.6249
1768/Unknown 487秒 272毫秒/步 - 損失: 0.9514 - 稀疏類別準確度: 0.6249
1769/Unknown 487秒 272毫秒/步 - 損失: 0.9513 - 稀疏類別準確度: 0.6250
1770/Unknown 488秒 272毫秒/步 - 損失: 0.9512 - 稀疏類別準確度: 0.6250
1771/Unknown 488秒 272毫秒/步 - 損失: 0.9511 - 稀疏類別準確度: 0.6250
1772/Unknown 488秒 272毫秒/步 - 損失: 0.9510 - 稀疏類別準確度: 0.6251
1773/Unknown 489秒 272毫秒/步 - 損失: 0.9509 - 稀疏類別準確度: 0.6251
1774/Unknown 489秒 273毫秒/步 - 損失: 0.9508 - 稀疏類別準確度: 0.6251
1775/Unknown 489秒 273毫秒/步 - 損失: 0.9507 - 稀疏類別準確度: 0.6252
1776/Unknown 490秒 273毫秒/步 - 損失: 0.9506 - 稀疏類別準確度: 0.6252
1777/Unknown 490秒 273毫秒/步 - 損失: 0.9505 - 稀疏類別準確度: 0.6252
1778/Unknown 490秒 273毫秒/步 - 損失: 0.9504 - 稀疏類別準確度: 0.6252
1779/Unknown 491秒 273毫秒/步 - 損失: 0.9503 - 稀疏類別準確度: 0.6253
1780/Unknown 491秒 273毫秒/步 - 損失: 0.9502 - 稀疏類別準確度: 0.6253
1781/Unknown 492秒 273毫秒/步 - 損失: 0.9501 - 稀疏類別準確度: 0.6253
1782/Unknown 492秒 273毫秒/步 - 損失: 0.9500 - 稀疏類別準確度: 0.6254
1783/Unknown 492秒 273毫秒/步 - 損失: 0.9499 - 稀疏類別準確度: 0.6254
1784/Unknown 493秒 273毫秒/步 - 損失: 0.9498 - 稀疏類別準確度: 0.6254
1785/Unknown 493秒 273毫秒/步 - 損失: 0.9498 - 稀疏類別準確度: 0.6255
1786/Unknown 493秒 273毫秒/步 - 損失: 0.9497 - 稀疏類別準確度: 0.6255
1787/Unknown 494秒 273毫秒/步 - 損失: 0.9496 - 稀疏類別準確度: 0.6255
1788/Unknown 494秒 273毫秒/步 - 損失: 0.9495 - 稀疏類別準確度: 0.6255
1789/Unknown 494秒 273毫秒/步 - 損失: 0.9494 - 稀疏類別準確度: 0.6256
1790/Unknown 495秒 273毫秒/步 - 損失: 0.9493 - 稀疏類別準確度: 0.6256
1791/Unknown 495秒 273毫秒/步 - 損失: 0.9492 - 稀疏類別準確度: 0.6256
1792/Unknown 495秒 273毫秒/步 - 損失: 0.9491 - 稀疏類別準確度: 0.6257
1793/Unknown 496秒 273毫秒/步 - 損失: 0.9490 - 稀疏類別準確度: 0.6257
1794/Unknown 496秒 273毫秒/步 - 損失: 0.9489 - 稀疏類別準確度: 0.6257
1795/Unknown 496秒 273毫秒/步 - 損失: 0.9488 - 稀疏類別準確度: 0.6258
1796/Unknown 497秒 273毫秒/步 - 損失: 0.9487 - 稀疏類別準確度: 0.6258
1797/Unknown 497秒 274毫秒/步 - 損失: 0.9486 - 稀疏類別準確度: 0.6258
1798/Unknown 497秒 274毫秒/步 - 損失: 0.9485 - 稀疏類別準確度: 0.6258
1799/Unknown 498秒 274毫秒/步 - 損失: 0.9484 - 稀疏類別準確度: 0.6259
1800/Unknown 498秒 274毫秒/步 - 損失: 0.9483 - 稀疏類別準確度: 0.6259
1801/Unknown 498秒 274毫秒/步 - 損失: 0.9482 - 稀疏類別準確度: 0.6259
1802/Unknown 499秒 274毫秒/步 - 損失: 0.9482 - 稀疏類別準確度: 0.6260
1803/Unknown 499秒 274毫秒/步 - 損失: 0.9481 - 稀疏類別準確度: 0.6260
1804/Unknown 499秒 274毫秒/步 - 損失: 0.9480 - 稀疏類別準確度: 0.6260
1805/Unknown 500秒 274毫秒/步 - 損失: 0.9479 - 稀疏類別準確度: 0.6260
1806/Unknown 500秒 274毫秒/步 - 損失: 0.9478 - 稀疏類別準確度: 0.6261
1807/Unknown 500秒 274毫秒/步 - 損失: 0.9477 - 稀疏類別準確度: 0.6261
1808/Unknown 501秒 274毫秒/步 - 損失: 0.9476 - 稀疏類別準確度: 0.6261
1809/Unknown 501秒 274毫秒/步 - 損失: 0.9475 - 稀疏類別準確度: 0.6262
1810/Unknown 501秒 274毫秒/步 - 損失: 0.9474 - 稀疏類別準確度: 0.6262
1811/Unknown 502秒 274毫秒/步 - 損失: 0.9473 - 稀疏類別準確度: 0.6262
1812/Unknown 502秒 274毫秒/步 - 損失: 0.9472 - 稀疏類別準確度: 0.6263
1813/Unknown 502秒 274毫秒/步 - 損失: 0.9471 - 稀疏類別準確度: 0.6263
1814/Unknown 503秒 274毫秒/步 - 損失: 0.9470 - 稀疏類別準確度: 0.6263
1815/Unknown 503秒 274毫秒/步 - 損失: 0.9469 - 稀疏類別準確度: 0.6263
1816/Unknown 503秒 274毫秒/步 - 損失: 0.9469 - 稀疏類別準確度: 0.6264
1817/Unknown 504秒 274毫秒/步 - 損失: 0.9468 - 稀疏類別準確度: 0.6264
1818/Unknown 504秒 274毫秒/步 - 損失: 0.9467 - 稀疏類別準確度: 0.6264
1819/Unknown 504秒 274毫秒/步 - 損失: 0.9466 - 稀疏類別準確度: 0.6265
1820/Unknown 505秒 274毫秒/步 - 損失: 0.9465 - 稀疏類別準確度: 0.6265
1821/Unknown 505秒 274毫秒/步 - 損失: 0.9464 - 稀疏類別準確度: 0.6265
1822/Unknown 505秒 274毫秒/步 - 損失: 0.9463 - 稀疏類別準確度: 0.6265
1823/Unknown 505秒 274毫秒/步 - 損失: 0.9462 - 稀疏類別準確度: 0.6266
1824/Unknown 506秒 274毫秒/步 - 損失: 0.9461 - 稀疏類別準確度: 0.6266
1825/Unknown 506秒 274毫秒/步 - 損失: 0.9460 - 稀疏類別準確度: 0.6266
1826/Unknown 506秒 274毫秒/步 - 損失: 0.9459 - 稀疏類別準確度: 0.6267
1827/Unknown 507秒 274毫秒/步 - 損失: 0.9458 - 稀疏類別準確度: 0.6267
1828/Unknown 507秒 274毫秒/步 - 損失: 0.9458 - 稀疏類別準確度: 0.6267
1829/Unknown 507秒 274毫秒/步 - 損失: 0.9457 - 稀疏類別準確度: 0.6267
1830/Unknown 508秒 274毫秒/步 - 損失: 0.9456 - 稀疏類別準確度: 0.6268
1831/Unknown 508秒 274毫秒/步 - 損失: 0.9455 - 稀疏類別準確度: 0.6268
1832/Unknown 508秒 274毫秒/步 - 損失: 0.9454 - 稀疏類別準確度: 0.6268
1833/Unknown 509秒 274毫秒/步 - 損失: 0.9453 - 稀疏類別準確度: 0.6269
1834/Unknown 509秒 274毫秒/步 - 損失: 0.9452 - 稀疏類別準確度: 0.6269
1835/Unknown 509秒 275毫秒/步 - 損失: 0.9451 - 稀疏類別準確度: 0.6269
1836/Unknown 510秒 275毫秒/步 - 損失: 0.9450 - 稀疏類別準確度: 0.6269
1837/Unknown 510秒 275毫秒/步 - 損失: 0.9449 - 稀疏類別準確度: 0.6270
1838/Unknown 510秒 275毫秒/步 - 損失: 0.9448 - 稀疏類別準確度: 0.6270
1839/Unknown 511秒 275毫秒/步 - 損失: 0.9448 - 稀疏類別準確度: 0.6270
1840/Unknown 511秒 275毫秒/步 - 損失: 0.9447 - 稀疏類別準確度: 0.6271
1841/Unknown 511秒 275毫秒/步 - 損失: 0.9446 - 稀疏類別準確度: 0.6271
1842/Unknown 512秒 275毫秒/步 - 損失: 0.9445 - 稀疏類別準確度: 0.6271
1843/Unknown 512秒 275毫秒/步 - 損失: 0.9444 - 稀疏類別準確度: 0.6271
1844/Unknown 513秒 275毫秒/步 - 損失: 0.9443 - 稀疏類別準確度: 0.6272
1845/Unknown 513秒 275毫秒/步 - 損失: 0.9442 - 稀疏類別準確度: 0.6272
1846/Unknown 513秒 275毫秒/步 - 損失: 0.9441 - 稀疏類別準確度: 0.6272
1847/Unknown 513秒 275毫秒/步 - 損失: 0.9440 - 稀疏類別準確度: 0.6273
1848/Unknown 514秒 275毫秒/步 - 損失: 0.9439 - 稀疏類別準確度: 0.6273
1849/Unknown 514秒 275毫秒/步 - 損失: 0.9439 - 稀疏類別準確度: 0.6273
1850/Unknown 514秒 275毫秒/步 - 損失: 0.9438 - 稀疏類別準確度: 0.6273
1851/Unknown 514秒 275毫秒/步 - 損失: 0.9437 - 稀疏類別準確度: 0.6274
1852/Unknown 515秒 275毫秒/步 - 損失: 0.9436 - 稀疏類別準確度: 0.6274
1853/Unknown 515秒 275毫秒/步 - 損失: 0.9435 - 稀疏類別準確度: 0.6274
1854/Unknown 515秒 275毫秒/步 - 損失: 0.9434 - 稀疏類別準確度: 0.6275
1855/Unknown 516秒 275毫秒/步 - 損失: 0.9433 - 稀疏類別準確度: 0.6275
1856/Unknown 516秒 275毫秒/步 - 損失: 0.9432 - 稀疏類別準確度: 0.6275
1857/Unknown 516秒 275毫秒/步 - 損失: 0.9431 - 稀疏類別準確度: 0.6275
1858/Unknown 517秒 275毫秒/步 - 損失: 0.9431 - 稀疏類別準確度: 0.6276
1859/Unknown 517秒 275毫秒/步 - 損失: 0.9430 - 稀疏類別準確度: 0.6276
1860/Unknown 517秒 275毫秒/步 - 損失: 0.9429 - 稀疏類別準確度: 0.6276
1861/Unknown 517秒 275毫秒/步 - 損失: 0.9428 - 稀疏類別準確度: 0.6277
1862/Unknown 518秒 275毫秒/步 - 損失: 0.9427 - 稀疏類別準確度: 0.6277
1863/Unknown 518秒 275毫秒/步 - 損失: 0.9426 - 稀疏類別準確度: 0.6277
1864/Unknown 518秒 275毫秒/步 - 損失: 0.9425 - 稀疏類別準確度: 0.6277
1865/Unknown 519秒 275毫秒/步 - 損失: 0.9424 - 稀疏類別準確度: 0.6278
1865/1865 ━━━━━━━━━━━━━━━━━━━━ 519秒 275毫秒/步 - 損失: 0.9423 - 稀疏類別準確度: 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%
基準線性模型達到約 76% 的測試準確度。
實驗 2：廣度與深度模型
在第二個實驗中，我們建立了一個廣度與深度模型。模型中的廣度部分是線性模型，而深度部分是多層前饋網路。
在模型的廣度部分使用輸入特徵的稀疏表示法，在模型的深度部分使用輸入特徵的密集表示法。
請注意，每個輸入特徵都以不同的表示法貢獻於模型的兩個部分。
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")
讓我們執行它
run_experiment(wide_and_deep_model)
Start training the model...
1/Unknown 3s 3s/step - loss: 2.0684 - sparse_categorical_accuracy: 0.2566
2/Unknown 4s 453ms/step - loss: 2.1023 - sparse_categorical_accuracy: 0.2575
3/Unknown 4s 434ms/step - loss: 2.1031 - sparse_categorical_accuracy: 0.2618
4/Unknown 5s 456ms/step - loss: 2.1023 - sparse_categorical_accuracy: 0.2648
5/Unknown 5s 455ms/step - loss: 2.0994 - sparse_categorical_accuracy: 0.2678
6/Unknown 5s 452ms/step - loss: 2.0958 - sparse_categorical_accuracy: 0.2694
7/Unknown 6s 455ms/step - loss: 2.0920 - sparse_categorical_accuracy: 0.2710
8/Unknown 6s 470ms/step - loss: 2.0869 - sparse_categorical_accuracy: 0.2727
9/Unknown 7s 479ms/step - loss: 2.0817 - sparse_categorical_accuracy: 0.2742
10/Unknown 7s 481ms/step - loss: 2.0759 - sparse_categorical_accuracy: 0.2760
11/Unknown 8s 477ms/step - loss: 2.0698 - sparse_categorical_accuracy: 0.2781
12/Unknown 8s 481ms/step - loss: 2.0635 - sparse_categorical_accuracy: 0.2802
13/Unknown 9s 476ms/step - loss: 2.0573 - sparse_categorical_accuracy: 0.2821
14/Unknown 9s 479ms/step - loss: 2.0513 - sparse_categorical_accuracy: 0.2839
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17/Unknown 11s 477ms/step - loss: 2.0326 - sparse_categorical_accuracy: 0.2897
18/Unknown 11s 478ms/step - loss: 2.0264 - sparse_categorical_accuracy: 0.2918
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26/Unknown 15s 478ms/step - loss: 1.9790 - sparse_categorical_accuracy: 0.3071
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30/Unknown 17s 478ms/step - loss: 1.9575 - sparse_categorical_accuracy: 0.3136
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1007/Unknown 416秒 410毫秒/步 - 損失: 0.9866 - 稀疏類別準確度: 0.6093
1008/Unknown 416秒 410毫秒/步 - 損失: 0.9864 - 稀疏類別準確度: 0.6093
1009/Unknown 416秒 410毫秒/步 - 損失: 0.9862 - 稀疏類別準確度: 0.6094
1010/Unknown 416秒 410毫秒/步 - 損失: 0.9860 - 稀疏類別準確度: 0.6095
1011/Unknown 417秒 410毫秒/步 - 損失: 0.9857 - 稀疏類別準確度: 0.6096
1012/Unknown 417秒 409毫秒/步 - 損失: 0.9855 - 稀疏類別準確度: 0.6096
1013/Unknown 417秒 409毫秒/步 - 損失: 0.9853 - 稀疏類別準確度: 0.6097
1014/Unknown 418秒 409毫秒/步 - 損失: 0.9851 - 稀疏類別準確度: 0.6098
1015/Unknown 418秒 409毫秒/步 - 損失: 0.9849 - 稀疏類別準確度: 0.6099
1016/Unknown 418秒 409毫秒/步 - 損失: 0.9847 - 稀疏類別準確度: 0.6099
1017/Unknown 419秒 409毫秒/步 - 損失: 0.9844 - 稀疏類別準確度: 0.6100
1018/Unknown 419秒 409毫秒/步 - 損失: 0.9842 - 稀疏類別準確度: 0.6101
1019/Unknown 419秒 409毫秒/步 - 損失: 0.9840 - 稀疏類別準確度: 0.6101
1020/Unknown 420秒 409毫秒/步 - 損失: 0.9838 - 稀疏類別準確度: 0.6102
1021/Unknown 420秒 409毫秒/步 - 損失: 0.9836 - 稀疏類別準確度: 0.6103
1022/Unknown 421秒 409毫秒/步 - 損失: 0.9834 - 稀疏類別準確度: 0.6104
1023/Unknown 421秒 409毫秒/步 - 損失: 0.9831 - 稀疏類別準確度: 0.6104
1024/Unknown 422秒 409毫秒/步 - 損失: 0.9829 - 稀疏類別準確度: 0.6105
1025/Unknown 422秒 409毫秒/步 - 損失: 0.9827 - 稀疏類別準確度: 0.6106
1026/Unknown 423秒 409毫秒/步 - 損失: 0.9825 - 稀疏類別準確度: 0.6106
1027/Unknown 423秒 409毫秒/步 - 損失: 0.9823 - 稀疏類別準確度: 0.6107
1028/Unknown 423秒 409毫秒/步 - 損失: 0.9821 - 稀疏類別準確度: 0.6108
1029/Unknown 424秒 409毫秒/步 - 損失: 0.9819 - 稀疏類別準確度: 0.6109
1030/Unknown 424秒 409毫秒/步 - 損失: 0.9816 - 稀疏類別準確度: 0.6109
1031/Unknown 425秒 409毫秒/步 - 損失: 0.9814 - 稀疏類別準確度: 0.6110
1032/Unknown 425秒 409毫秒/步 - 損失: 0.9812 - 稀疏類別準確度: 0.6111
1033/Unknown 425秒 409毫秒/步 - 損失: 0.9810 - 稀疏類別準確度: 0.6111
1034/Unknown 426秒 409毫秒/步 - 損失: 0.9808 - 稀疏類別準確度: 0.6112
1035/Unknown 426秒 409毫秒/步 - 損失: 0.9806 - 稀疏類別準確度: 0.6113
1036/Unknown 427秒 409毫秒/步 - 損失: 0.9804 - 稀疏類別準確度: 0.6113
1037/Unknown 427秒 409毫秒/步 - 損失: 0.9802 - 稀疏類別準確度: 0.6114
1038/Unknown 427秒 409毫秒/步 - 損失: 0.9799 - 稀疏類別準確度: 0.6115
1039/Unknown 428秒 409毫秒/步 - 損失: 0.9797 - 稀疏類別準確度: 0.6116
1040/Unknown 428秒 409毫秒/步 - 損失: 0.9795 - 稀疏類別準確度: 0.6116
1041/Unknown 428秒 409毫秒/步 - 損失: 0.9793 - 稀疏類別準確度: 0.6117
1042/Unknown 429秒 409毫秒/步 - 損失: 0.9791 - 稀疏類別準確度: 0.6118
1043/Unknown 429秒 409毫秒/步 - 損失: 0.9789 - 稀疏類別準確度: 0.6118
1044/Unknown 430秒 409毫秒/步 - 損失: 0.9787 - 稀疏類別準確度: 0.6119
1045/Unknown 430秒 409毫秒/步 - 損失: 0.9785 - 稀疏類別準確度: 0.6120
1046/Unknown 430秒 409毫秒/步 - 損失: 0.9783 - 稀疏類別準確度: 0.6120
1047/Unknown 431秒 409毫秒/步 - 損失: 0.9781 - 稀疏類別準確度: 0.6121
1048/Unknown 431秒 409毫秒/步 - 損失: 0.9779 - 稀疏類別準確度: 0.6122
1049/Unknown 432秒 409毫秒/步 - 損失: 0.9777 - 稀疏類別準確度: 0.6122
1050/Unknown 432秒 409毫秒/步 - 損失: 0.9774 - 稀疏類別準確度: 0.6123
1051/Unknown 433秒 409毫秒/步 - 損失: 0.9772 - 稀疏類別準確度: 0.6124
1052/Unknown 433秒 409毫秒/步 - 損失: 0.9770 - 稀疏類別準確度: 0.6125
1053/Unknown 433秒 409毫秒/步 - 損失: 0.9768 - 稀疏類別準確度: 0.6125
1054/Unknown 434秒 409毫秒/步 - 損失: 0.9766 - 稀疏類別準確度: 0.6126
1055/Unknown 434秒 409毫秒/步 - 損失: 0.9764 - 稀疏類別準確度: 0.6127
1056/Unknown 435秒 409毫秒/步 - 損失: 0.9762 - 稀疏類別準確度: 0.6127
1057/Unknown 435秒 409毫秒/步 - 損失: 0.9760 - 稀疏類別準確度: 0.6128
1058/Unknown 435秒 409毫秒/步 - 損失: 0.9758 - 稀疏類別準確度: 0.6129
1059/Unknown 436秒 409毫秒/步 - 損失: 0.9756 - 稀疏類別準確度: 0.6129
1060/Unknown 436秒 409毫秒/步 - 損失: 0.9754 - 稀疏類別準確度: 0.6130
1061/Unknown 436秒 409毫秒/步 - 損失: 0.9752 - 稀疏類別準確度: 0.6131
1062/Unknown 437秒 409毫秒/步 - 損失: 0.9750 - 稀疏類別準確度: 0.6131
1063/Unknown 437秒 409毫秒/步 - 損失: 0.9748 - 稀疏類別準確度: 0.6132
1064/Unknown 438秒 409毫秒/步 - 損失: 0.9746 - 稀疏類別準確度: 0.6133
1065/Unknown 438秒 409毫秒/步 - 損失: 0.9744 - 稀疏類別準確度: 0.6133
1066/Unknown 439秒 409毫秒/步 - 損失: 0.9742 - 稀疏類別準確度: 0.6134
1067/Unknown 439秒 409毫秒/步 - 損失: 0.9740 - 稀疏類別準確度: 0.6135
1068/Unknown 440秒 409毫秒/步 - 損失: 0.9738 - 稀疏類別準確度: 0.6135
1069/Unknown 440秒 409毫秒/步 - 損失: 0.9736 - 稀疏類別準確度: 0.6136
1070/Unknown 441秒 409毫秒/步 - 損失: 0.9734 - 稀疏類別準確度: 0.6137
1071/Unknown 441秒 409毫秒/步 - 損失: 0.9732 - 稀疏類別準確度: 0.6137
1072/Unknown 442秒 409毫秒/步 - 損失: 0.9730 - 稀疏類別準確度: 0.6138
1073/Unknown 442秒 409毫秒/步 - 損失: 0.9728 - 稀疏類別準確度: 0.6139
1074/Unknown 443秒 410毫秒/步 - 損失: 0.9726 - 稀疏類別準確度: 0.6139
1075/Unknown 443秒 410毫秒/步 - 損失: 0.9723 - 稀疏類別準確度: 0.6140
1076/Unknown 444秒 410毫秒/步 - 損失: 0.9721 - 稀疏類別準確度: 0.6141
1077/Unknown 444秒 410毫秒/步 - 損失: 0.9719 - 稀疏類別準確度: 0.6141
1078/Unknown 445秒 410毫秒/步 - 損失: 0.9717 - 稀疏類別準確度: 0.6142
1079/Unknown 445秒 410毫秒/步 - 損失: 0.9716 - 稀疏類別準確度: 0.6143
1080/Unknown 445秒 410毫秒/步 - 損失: 0.9714 - 稀疏類別準確度: 0.6143
1081/Unknown 446秒 410毫秒/步 - 損失: 0.9712 - 稀疏類別準確度: 0.6144
1082/Unknown 446秒 410毫秒/步 - 損失: 0.9710 - 稀疏類別準確度: 0.6145
1083/Unknown 447秒 410毫秒/步 - 損失: 0.9708 - 稀疏類別準確度: 0.6145
1084/Unknown 447秒 410毫秒/步 - 損失: 0.9706 - 稀疏類別準確度: 0.6146
1085/Unknown 448秒 410毫秒/步 - 損失: 0.9704 - 稀疏類別準確度: 0.6147
1086/Unknown 448秒 410毫秒/步 - 損失: 0.9702 - 稀疏類別準確度: 0.6147
1087/Unknown 449秒 410毫秒/步 - 損失: 0.9700 - 稀疏類別準確度: 0.6148
1088/Unknown 449秒 410毫秒/步 - 損失: 0.9698 - 稀疏類別準確度: 0.6149
1089/Unknown 449秒 410毫秒/步 - 損失: 0.9696 - 稀疏類別準確度: 0.6149
1090/Unknown 450秒 410毫秒/步 - 損失: 0.9694 - 稀疏類別準確度: 0.6150
1091/Unknown 450秒 410毫秒/步 - 損失: 0.9692 - 稀疏類別準確度: 0.6150
1092/Unknown 451秒 410毫秒/步 - 損失: 0.9690 - 稀疏類別準確度: 0.6151
1093/Unknown 451秒 411毫秒/步 - 損失: 0.9688 - 稀疏類別準確度: 0.6152
1094/Unknown 452秒 411毫秒/步 - 損失: 0.9686 - 稀疏類別準確度: 0.6152
1095/Unknown 452秒 411毫秒/步 - 損失: 0.9684 - 稀疏類別準確度: 0.6153
1096/Unknown 453秒 411毫秒/步 - 損失: 0.9682 - 稀疏類別準確度: 0.6154
1097/Unknown 453秒 411毫秒/步 - 損失: 0.9680 - 稀疏類別準確度: 0.6154
1098/Unknown 454秒 411毫秒/步 - 損失: 0.9678 - 稀疏類別準確度: 0.6155
1099/Unknown 454秒 411毫秒/步 - 損失: 0.9676 - 稀疏類別準確度: 0.6156
1100/Unknown 455秒 411毫秒/步 - 損失: 0.9674 - 稀疏類別準確度: 0.6156
1101/Unknown 455秒 411毫秒/步 - 損失: 0.9672 - 稀疏類別準確度: 0.6157
1102/Unknown 456秒 411毫秒/步 - 損失: 0.9670 - 稀疏類別準確度: 0.6158
1103/Unknown 456秒 411毫秒/步 - 損失: 0.9668 - 稀疏類別準確度: 0.6158
1104/Unknown 457秒 411毫秒/步 - 損失: 0.9667 - 稀疏類別準確度: 0.6159
1105/Unknown 457秒 411毫秒/步 - 損失: 0.9665 - 稀疏類別準確度: 0.6159
1106/Unknown 457秒 411毫秒/步 - 損失: 0.9663 - 稀疏類別準確度: 0.6160
1107/Unknown 458秒 411毫秒/步 - 損失: 0.9661 - 稀疏類別準確度: 0.6161
1108/Unknown 458秒 411毫秒/步 - 損失: 0.9659 - 稀疏類別準確度: 0.6161
1109/Unknown 459秒 411毫秒/步 - 損失: 0.9657 - 稀疏類別準確度: 0.6162
1110/Unknown 459秒 411毫秒/步 - 損失: 0.9655 - 稀疏類別準確度: 0.6163
1111/Unknown 459秒 411毫秒/步 - 損失: 0.9653 - 稀疏類別準確度: 0.6163
1112/Unknown 460秒 411毫秒/步 - 損失: 0.9651 - 稀疏類別準確度: 0.6164
1113/Unknown 460秒 411毫秒/步 - 損失: 0.9649 - 稀疏類別準確度: 0.6165
1114/Unknown 461秒 411毫秒/步 - 損失: 0.9647 - 稀疏類別準確度: 0.6165
1115/Unknown 461秒 411毫秒/步 - 損失: 0.9645 - 稀疏類別準確度: 0.6166
1116/Unknown 462秒 411毫秒/步 - 損失: 0.9644 - 稀疏類別準確度: 0.6166
1117/Unknown 462秒 412毫秒/步 - 損失: 0.9642 - 稀疏類別準確度: 0.6167
1118/Unknown 463秒 412毫秒/步 - 損失: 0.9640 - 稀疏類別準確度: 0.6168
1119/Unknown 463秒 412毫秒/步 - 損失: 0.9638 - 稀疏類別準確度: 0.6168
1120/Unknown 464秒 412毫秒/步 - 損失: 0.9636 - 稀疏類別準確度: 0.6169
1121/Unknown 464秒 412毫秒/步 - 損失: 0.9634 - 稀疏類別準確度: 0.6170
1122/Unknown 465秒 412毫秒/步 - 損失: 0.9632 - 稀疏類別準確度: 0.6170
1123/Unknown 465秒 412毫秒/步 - 損失: 0.9630 - 稀疏類別準確度: 0.6171
1124/Unknown 466秒 412毫秒/步 - 損失: 0.9628 - 稀疏類別準確度: 0.6171
1125/Unknown 466秒 412毫秒/步 - 損失: 0.9627 - 稀疏類別準確度: 0.6172
1126/Unknown 467秒 412毫秒/步 - 損失: 0.9625 - 稀疏類別準確度: 0.6173
1127/Unknown 467秒 412毫秒/步 - 損失: 0.9623 - 稀疏類別準確度: 0.6173
1128/Unknown 468秒 412毫秒/步 - 損失: 0.9621 - 稀疏類別準確度: 0.6174
1129/Unknown 468秒 412毫秒/步 - 損失: 0.9619 - 稀疏類別準確度: 0.6174
1130/Unknown 469秒 412毫秒/步 - 損失: 0.9617 - 稀疏類別準確度: 0.6175
1131/Unknown 469秒 412毫秒/步 - 損失: 0.9615 - 稀疏類別準確度: 0.6176
1132/Unknown 470秒 412毫秒/步 - 損失: 0.9614 - 稀疏類別準確度: 0.6176
1133/Unknown 470秒 412毫秒/步 - 損失: 0.9612 - 稀疏類別準確度: 0.6177
1134/Unknown 471秒 413毫秒/步 - 損失: 0.9610 - 稀疏類別準確度: 0.6178
1135/Unknown 471秒 413毫秒/步 - 損失: 0.9608 - 稀疏類別準確度: 0.6178
1136/Unknown 471秒 413毫秒/步 - 損失: 0.9606 - 稀疏類別準確度: 0.6179
1137/Unknown 472秒 413毫秒/步 - 損失: 0.9604 - 稀疏類別準確度: 0.6179
1138/Unknown 472秒 413毫秒/步 - 損失: 0.9602 - 稀疏類別準確度: 0.6180
1139/Unknown 473秒 413毫秒/步 - 損失: 0.9601 - 稀疏類別準確度: 0.6181
1140/Unknown 473秒 413毫秒/步 - 損失: 0.9599 - 稀疏類別準確度: 0.6181
1141/Unknown 474秒 413毫秒/步 - 損失: 0.9597 - 稀疏類別準確度: 0.6182
1142/Unknown 474秒 413毫秒/步 - 損失: 0.9595 - 稀疏類別準確度: 0.6182
1143/Unknown 475秒 413毫秒/步 - 損失: 0.9593 - 稀疏類別準確度: 0.6183
1144/Unknown 475秒 413毫秒/步 - 損失: 0.9591 - 稀疏類別準確度: 0.6184
1145/Unknown 476秒 413毫秒/步 - 損失: 0.9590 - 稀疏類別準確度: 0.6184
1146/Unknown 476秒 413毫秒/步 - 損失: 0.9588 - 稀疏類別準確度: 0.6185
1147/Unknown 477秒 413毫秒/步 - 損失: 0.9586 - 稀疏類別準確度: 0.6185
1148/Unknown 477秒 413毫秒/步 - 損失: 0.9584 - 稀疏類別準確度: 0.6186
1149/Unknown 478秒 413毫秒/步 - 損失: 0.9582 - 稀疏類別準確度: 0.6187
1150/Unknown 478秒 413毫秒/步 - 損失: 0.9580 - 稀疏類別準確度: 0.6187
1151/Unknown 479秒 413毫秒/步 - 損失: 0.9579 - 稀疏類別準確度: 0.6188
1152/Unknown 479秒 413毫秒/步 - 損失: 0.9577 - 稀疏類別準確度: 0.6188
1153/Unknown 479秒 413毫秒/步 - 損失: 0.9575 - 稀疏類別準確度: 0.6189
1154/Unknown 480秒 413毫秒/步 - 損失: 0.9573 - 稀疏類別準確度: 0.6190
1155/Unknown 480秒 413毫秒/步 - 損失: 0.9571 - 稀疏類別準確度: 0.6190
1156/Unknown 480秒 413毫秒/步 - 損失: 0.9570 - 稀疏類別準確度: 0.6191
1157/Unknown 481秒 413毫秒/步 - 損失: 0.9568 - 稀疏類別準確度: 0.6191
1158/Unknown 481秒 413毫秒/步 - 損失: 0.9566 - 稀疏類別準確度: 0.6192
1159/Unknown 482秒 413毫秒/步 - 損失: 0.9564 - 稀疏類別準確度: 0.6193
1160/Unknown 482秒 413毫秒/步 - 損失: 0.9562 - 稀疏類別準確度: 0.6193
1161/Unknown 482秒 413毫秒/步 - 損失: 0.9561 - 稀疏類別準確度: 0.6194
1162/Unknown 483秒 413毫秒/步 - 損失: 0.9559 - 稀疏類別準確度: 0.6194
1163/Unknown 483秒 413毫秒/步 - 損失: 0.9557 - 稀疏類別準確度: 0.6195
1164/Unknown 484秒 413毫秒/步 - 損失: 0.9555 - 稀疏類別準確度: 0.6196
1165/Unknown 484秒 413毫秒/步 - 損失: 0.9554 - 稀疏類別準確度: 0.6196
1166/Unknown 484秒 413毫秒/步 - 損失: 0.9552 - 稀疏類別準確度: 0.6197
1167/Unknown 485秒 413毫秒/步 - 損失: 0.9550 - 稀疏類別準確度: 0.6197
1168/Unknown 485秒 413毫秒/步 - 損失: 0.9548 - 稀疏類別準確度: 0.6198
1169/Unknown 486秒 413毫秒/步 - 損失: 0.9546 - 稀疏類別準確度: 0.6199
1170/Unknown 486秒 413毫秒/步 - 損失: 0.9545 - 稀疏類別準確度: 0.6199
1171/Unknown 487秒 413毫秒/步 - 損失: 0.9543 - 稀疏類別準確度: 0.6200
1172/Unknown 487秒 413毫秒/步 - 損失: 0.9541 - 稀疏類別準確度: 0.6200
1173/Unknown 488秒 413毫秒/步 - 損失: 0.9539 - 稀疏類別準確度: 0.6201
1174/Unknown 488秒 413毫秒/步 - 損失: 0.9538 - 稀疏類別準確度: 0.6201
1175/Unknown 489秒 413毫秒/步 - 損失: 0.9536 - 稀疏類別準確度: 0.6202
1176/Unknown 489秒 413毫秒/步 - 損失: 0.9534 - 稀疏類別準確度: 0.6203
1177/Unknown 489秒 413毫秒/步 - 損失: 0.9532 - 稀疏類別準確度: 0.6203
1178/Unknown 490秒 413毫秒/步 - 損失: 0.9531 - 稀疏類別準確度: 0.6204
1179/Unknown 490秒 413毫秒/步 - 損失: 0.9529 - 稀疏類別準確度: 0.6204
1180/Unknown 491秒 414毫秒/步 - 損失: 0.9527 - 稀疏類別準確度: 0.6205
1181/Unknown 491秒 414毫秒/步 - 損失: 0.9525 - 稀疏類別準確度: 0.6206
1182/Unknown 492秒 414毫秒/步 - 損失: 0.9524 - 稀疏類別準確度: 0.6206
1183/Unknown 492秒 414毫秒/步 - 損失: 0.9522 - 稀疏類別準確度: 0.6207
1184/Unknown 492秒 414毫秒/步 - 損失: 0.9520 - 稀疏類別準確度: 0.6207
1185/Unknown 493秒 414毫秒/步 - 損失: 0.9518 - 稀疏類別準確度: 0.6208
1186/Unknown 493秒 413毫秒/步 - 損失: 0.9517 - 稀疏類別準確度: 0.6208
1187/Unknown 493秒 413毫秒/步 - 損失: 0.9515 - 稀疏類別準確度: 0.6209
1188/Unknown 494秒 413毫秒/步 - 損失: 0.9513 - 稀疏類別準確度: 0.6210
1189/Unknown 494秒 413毫秒/步 - 損失: 0.9511 - 稀疏類別準確度: 0.6210
1190/Unknown 495秒 413毫秒/步 - 損失: 0.9510 - 稀疏類別準確度: 0.6211
1191/Unknown 495秒 413毫秒/步 - 損失: 0.9508 - 稀疏類別準確度: 0.6211
1192/Unknown 495秒 413毫秒/步 - 損失: 0.9506 - 稀疏類別準確度: 0.6212
1193/Unknown 496秒 413毫秒/步 - 損失: 0.9504 - 稀疏類別準確度: 0.6212
1194/Unknown 496秒 413毫秒/步 - 損失: 0.9503 - 稀疏類別準確度: 0.6213
1195/Unknown 496秒 413毫秒/步 - 損失: 0.9501 - 稀疏類別準確度: 0.6214
1196/Unknown 497秒 413毫秒/步 - 損失: 0.9499 - 稀疏類別準確度: 0.6214
1197/Unknown 497秒 413毫秒/步 - 損失: 0.9498 - 稀疏類別準確度: 0.6215
1198/Unknown 498秒 413毫秒/步 - 損失: 0.9496 - 稀疏類別準確度: 0.6215
1199/Unknown 498秒 413毫秒/步 - 損失: 0.9494 - 稀疏類別準確度: 0.6216
1200/Unknown 499秒 413毫秒/步 - 損失: 0.9492 - 稀疏類別準確度: 0.6216
1201/Unknown 499秒 413毫秒/步 - 損失: 0.9491 - 稀疏類別準確度: 0.6217
1202/Unknown 500秒 413毫秒/步 - 損失: 0.9489 - 稀疏類別準確度: 0.6218
1203/Unknown 500秒 413毫秒/步 - 損失: 0.9487 - 稀疏類別準確度: 0.6218
1204/Unknown 500秒 413毫秒/步 - 損失: 0.9486 - 稀疏類別準確度: 0.6219
1205/Unknown 501秒 413毫秒/步 - 損失: 0.9484 - 稀疏類別準確度: 0.6219
1206/Unknown 501秒 413毫秒/步 - 損失: 0.9482 - 稀疏類別準確度: 0.6220
1207/Unknown 501秒 413毫秒/步 - 損失: 0.9481 - 稀疏類別準確度: 0.6220
1208/Unknown 502秒 413毫秒/步 - 損失: 0.9479 - 稀疏類別準確度: 0.6221
1209/Unknown 502秒 413毫秒/步 - 損失: 0.9477 - 稀疏類別準確度: 0.6221
1210/Unknown 503秒 413毫秒/步 - 損失: 0.9476 - 稀疏類別準確度: 0.6222
1211/Unknown 503秒 413毫秒/步 - 損失: 0.9474 - 稀疏類別準確度: 0.6223
1212/Unknown 503秒 413毫秒/步 - 損失: 0.9472 - 稀疏類別準確度: 0.6223
1213/Unknown 504秒 413毫秒/步 - 損失: 0.9470 - 稀疏類別準確度: 0.6224
1214/Unknown 504秒 413毫秒/步 - 損失: 0.9469 - 稀疏類別準確度: 0.6224
1215/Unknown 505秒 413毫秒/步 - 損失: 0.9467 - 稀疏類別準確度: 0.6225
1216/Unknown 505秒 413毫秒/步 - 損失: 0.9465 - 稀疏類別準確度: 0.6225
1217/Unknown 506秒 413毫秒/步 - 損失: 0.9464 - 稀疏類別準確度: 0.6226
1218/Unknown 506秒 413毫秒/步 - 損失: 0.9462 - 稀疏類別準確度: 0.6226
1219/Unknown 506秒 413毫秒/步 - 損失: 0.9460 - 稀疏類別準確度: 0.6227
1220/未知 507秒 413毫秒/步 - 損失: 0.9459 - 稀疏類別準確度: 0.6228
1221/未知 507秒 413毫秒/步 - 損失: 0.9457 - 稀疏類別準確度: 0.6228
1222/未知 508秒 413毫秒/步 - 損失: 0.9455 - 稀疏類別準確度: 0.6229
1223/未知 508秒 413毫秒/步 - 損失: 0.9454 - 稀疏類別準確度: 0.6229
1224/未知 509秒 413毫秒/步 - 損失: 0.9452 - 稀疏類別準確度: 0.6230
1225/未知 509秒 413毫秒/步 - 損失: 0.9450 - 稀疏類別準確度: 0.6230
1226/未知 509秒 413毫秒/步 - 損失: 0.9449 - 稀疏類別準確度: 0.6231
1227/未知 510秒 413毫秒/步 - 損失: 0.9447 - 稀疏類別準確度: 0.6231
1228/未知 510秒 413毫秒/步 - 損失: 0.9446 - 稀疏類別準確度: 0.6232
1229/未知 511秒 413毫秒/步 - 損失: 0.9444 - 稀疏類別準確度: 0.6233
1230/未知 511秒 413毫秒/步 - 損失: 0.9442 - 稀疏類別準確度: 0.6233
1231/未知 512秒 413毫秒/步 - 損失: 0.9441 - 稀疏類別準確度: 0.6234
1232/未知 512秒 414毫秒/步 - 損失: 0.9439 - 稀疏類別準確度: 0.6234
1233/未知 513秒 414毫秒/步 - 損失: 0.9437 - 稀疏類別準確度: 0.6235
1234/未知 513秒 414毫秒/步 - 損失: 0.9436 - 稀疏類別準確度: 0.6235
1235/未知 513秒 414毫秒/步 - 損失: 0.9434 - 稀疏類別準確度: 0.6236
1236/未知 514秒 414毫秒/步 - 損失: 0.9432 - 稀疏類別準確度: 0.6236
1237/未知 514秒 414毫秒/步 - 損失: 0.9431 - 稀疏類別準確度: 0.6237
1238/未知 515秒 414毫秒/步 - 損失: 0.9429 - 稀疏類別準確度: 0.6237
1239/未知 515秒 414毫秒/步 - 損失: 0.9427 - 稀疏類別準確度: 0.6238
1240/未知 516秒 414毫秒/步 - 損失: 0.9426 - 稀疏類別準確度: 0.6239
1241/未知 516秒 414毫秒/步 - 損失: 0.9424 - 稀疏類別準確度: 0.6239
1242/未知 517秒 414毫秒/步 - 損失: 0.9423 - 稀疏類別準確度: 0.6240
1243/未知 517秒 414毫秒/步 - 損失: 0.9421 - 稀疏類別準確度: 0.6240
1244/未知 518秒 414毫秒/步 - 損失: 0.9419 - 稀疏類別準確度: 0.6241
1245/未知 518秒 414毫秒/步 - 損失: 0.9418 - 稀疏類別準確度: 0.6241
1246/未知 519秒 414毫秒/步 - 損失: 0.9416 - 稀疏類別準確度: 0.6242
1247/未知 519秒 414毫秒/步 - 損失: 0.9415 - 稀疏類別準確度: 0.6242
1248/未知 519秒 414毫秒/步 - 損失: 0.9413 - 稀疏類別準確度: 0.6243
1249/未知 520秒 414毫秒/步 - 損失: 0.9411 - 稀疏類別準確度: 0.6243
1250/未知 520秒 414毫秒/步 - 損失: 0.9410 - 稀疏類別準確度: 0.6244
1251/未知 521秒 414毫秒/步 - 損失: 0.9408 - 稀疏類別準確度: 0.6244
1252/未知 521秒 414毫秒/步 - 損失: 0.9406 - 稀疏類別準確度: 0.6245
1253/未知 521秒 414毫秒/步 - 損失: 0.9405 - 稀疏類別準確度: 0.6245
1254/未知 522秒 414毫秒/步 - 損失: 0.9403 - 稀疏類別準確度: 0.6246
1255/未知 522秒 414毫秒/步 - 損失: 0.9402 - 稀疏類別準確度: 0.6247
1256/未知 522秒 414毫秒/步 - 損失: 0.9400 - 稀疏類別準確度: 0.6247
1257/未知 523秒 414毫秒/步 - 損失: 0.9398 - 稀疏類別準確度: 0.6248
1258/未知 523秒 414毫秒/步 - 損失: 0.9397 - 稀疏類別準確度: 0.6248
1259/未知 524秒 414毫秒/步 - 損失: 0.9395 - 稀疏類別準確度: 0.6249
1260/未知 524秒 414毫秒/步 - 損失: 0.9394 - 稀疏類別準確度: 0.6249
1261/未知 524秒 414毫秒/步 - 損失: 0.9392 - 稀疏類別準確度: 0.6250
1262/未知 525秒 414毫秒/步 - 損失: 0.9391 - 稀疏類別準確度: 0.6250
1263/未知 525秒 414毫秒/步 - 損失: 0.9389 - 稀疏類別準確度: 0.6251
1264/未知 526秒 414毫秒/步 - 損失: 0.9387 - 稀疏類別準確度: 0.6251
1265/未知 526秒 414毫秒/步 - 損失: 0.9386 - 稀疏類別準確度: 0.6252
1266/未知 527秒 414毫秒/步 - 損失: 0.9384 - 稀疏類別準確度: 0.6252
1267/未知 527秒 414毫秒/步 - 損失: 0.9383 - 稀疏類別準確度: 0.6253
1268/未知 527秒 414毫秒/步 - 損失: 0.9381 - 稀疏類別準確度: 0.6253
1269/未知 528秒 414毫秒/步 - 損失: 0.9380 - 稀疏類別準確度: 0.6254
1270/未知 528秒 414毫秒/步 - 損失: 0.9378 - 稀疏類別準確度: 0.6254
1271/未知 529秒 414毫秒/步 - 損失: 0.9376 - 稀疏類別準確度: 0.6255
1272/未知 529秒 414毫秒/步 - 損失: 0.9375 - 稀疏類別準確度: 0.6255
1273/未知 530秒 414毫秒/步 - 損失: 0.9373 - 稀疏類別準確度: 0.6256
1274/未知 530秒 414毫秒/步 - 損失: 0.9372 - 稀疏類別準確度: 0.6256
1275/未知 531秒 414毫秒/步 - 損失: 0.9370 - 稀疏類別準確度: 0.6257
1276/未知 531秒 414毫秒/步 - 損失: 0.9369 - 稀疏類別準確度: 0.6257
1277/未知 532秒 414毫秒/步 - 損失: 0.9367 - 稀疏類別準確度: 0.6258
1278/未知 532秒 414毫秒/步 - 損失: 0.9365 - 稀疏類別準確度: 0.6259
1279/未知 532秒 414毫秒/步 - 損失: 0.9364 - 稀疏類別準確度: 0.6259
1280/未知 533秒 414毫秒/步 - 損失: 0.9362 - 稀疏類別準確度: 0.6260
1281/未知 533秒 414毫秒/步 - 損失: 0.9361 - 稀疏類別準確度: 0.6260
1282/未知 534秒 414毫秒/步 - 損失: 0.9359 - 稀疏類別準確度: 0.6261
1283/未知 534秒 414毫秒/步 - 損失: 0.9358 - 稀疏類別準確度: 0.6261
1284/未知 535秒 414毫秒/步 - 損失: 0.9356 - 稀疏類別準確度: 0.6262
1285/未知 535秒 414毫秒/步 - 損失: 0.9355 - 稀疏類別準確度: 0.6262
1286/未知 535秒 414毫秒/步 - 損失: 0.9353 - 稀疏類別準確度: 0.6263
1287/未知 536秒 414毫秒/步 - 損失: 0.9352 - 稀疏類別準確度: 0.6263
1288/未知 536秒 414毫秒/步 - 損失: 0.9350 - 稀疏類別準確度: 0.6264
1289/未知 537秒 414毫秒/步 - 損失: 0.9348 - 稀疏類別準確度: 0.6264
1290/未知 537秒 414毫秒/步 - 損失: 0.9347 - 稀疏類別準確度: 0.6265
1291/未知 537秒 414毫秒/步 - 損失: 0.9345 - 稀疏類別準確度: 0.6265
1292/未知 538秒 414毫秒/步 - 損失: 0.9344 - 稀疏類別準確度: 0.6266
1293/未知 538秒 414毫秒/步 - 損失: 0.9342 - 稀疏類別準確度: 0.6266
1294/未知 539秒 414毫秒/步 - 損失: 0.9341 - 稀疏類別準確度: 0.6267
1295/未知 539秒 414毫秒/步 - 損失: 0.9339 - 稀疏類別準確度: 0.6267
1296/未知 539秒 414毫秒/步 - 損失: 0.9338 - 稀疏類別準確度: 0.6268
1297/未知 540秒 414毫秒/步 - 損失: 0.9336 - 稀疏類別準確度: 0.6268
1298/未知 540秒 414毫秒/步 - 損失: 0.9335 - 稀疏類別準確度: 0.6269
1299/未知 540秒 414毫秒/步 - 損失: 0.9333 - 稀疏類別準確度: 0.6269
1300/未知 541秒 414毫秒/步 - 損失: 0.9332 - 稀疏類別準確度: 0.6270
1301/未知 541秒 414毫秒/步 - 損失: 0.9330 - 稀疏類別準確度: 0.6270
1302/未知 542秒 414毫秒/步 - 損失: 0.9329 - 稀疏類別準確度: 0.6271
1303/未知 542秒 414毫秒/步 - 損失: 0.9327 - 稀疏類別準確度: 0.6271
1304/未知 542秒 414毫秒/步 - 損失: 0.9326 - 稀疏類別準確度: 0.6272
1305/未知 543秒 414毫秒/步 - 損失: 0.9324 - 稀疏類別準確度: 0.6272
1306/未知 543秒 414毫秒/步 - 損失: 0.9323 - 稀疏類別準確度: 0.6273
1307/未知 544秒 414毫秒/步 - 損失: 0.9321 - 稀疏類別準確度: 0.6273
1308/未知 544秒 414毫秒/步 - 損失: 0.9320 - 稀疏類別準確度: 0.6274
1309/未知 544秒 414毫秒/步 - 損失: 0.9318 - 稀疏類別準確度: 0.6274
1310/未知 545秒 414毫秒/步 - 損失: 0.9317 - 稀疏類別準確度: 0.6275
1311/未知 545秒 414毫秒/步 - 損失: 0.9315 - 稀疏類別準確度: 0.6275
1312/未知 546秒 414毫秒/步 - 損失: 0.9314 - 稀疏類別準確度: 0.6276
1313/未知 546秒 414毫秒/步 - 損失: 0.9312 - 稀疏類別準確度: 0.6276
1314/未知 547秒 414毫秒/步 - 損失: 0.9311 - 稀疏類別準確度: 0.6277
1315/未知 547秒 414毫秒/步 - 損失: 0.9309 - 稀疏類別準確度: 0.6277
1316/未知 548秒 414毫秒/步 - 損失: 0.9308 - 稀疏類別準確度: 0.6278
1317/未知 548秒 414毫秒/步 - 損失: 0.9306 - 稀疏類別準確度: 0.6278
1318/未知 549秒 414毫秒/步 - 損失: 0.9305 - 稀疏類別準確度: 0.6279
1319/未知 549秒 414毫秒/步 - 損失: 0.9303 - 稀疏類別準確度: 0.6279
1320/未知 550秒 414毫秒/步 - 損失: 0.9302 - 稀疏類別準確度: 0.6280
1321/未知 550秒 414毫秒/步 - 損失: 0.9300 - 稀疏類別準確度: 0.6280
1322/未知 551秒 414毫秒/步 - 損失: 0.9299 - 稀疏類別準確度: 0.6281
1323/未知 551秒 415毫秒/步 - 損失: 0.9297 - 稀疏類別準確度: 0.6281
1324/未知 552秒 415毫秒/步 - 損失: 0.9296 - 稀疏類別準確度: 0.6282
1325/未知 552秒 415毫秒/步 - 損失: 0.9294 - 稀疏類別準確度: 0.6282
1326/未知 553秒 415毫秒/步 - 損失: 0.9293 - 稀疏類別準確度: 0.6283
1327/未知 553秒 415毫秒/步 - 損失: 0.9291 - 稀疏類別準確度: 0.6283
1328/未知 553秒 415毫秒/步 - 損失: 0.9290 - 稀疏類別準確度: 0.6284
1329/未知 554秒 415毫秒/步 - 損失: 0.9288 - 稀疏類別準確度: 0.6284
1330/未知 554秒 415毫秒/步 - 損失: 0.9287 - 稀疏類別準確度: 0.6285
1331/未知 555秒 415毫秒/步 - 損失: 0.9285 - 稀疏類別準確度: 0.6285
1332/未知 555秒 415毫秒/步 - 損失: 0.9284 - 稀疏類別準確度: 0.6285
1333/未知 556秒 415毫秒/步 - 損失: 0.9283 - 稀疏類別準確度: 0.6286
1334/未知 556秒 415毫秒/步 - 損失: 0.9281 - 稀疏類別準確度: 0.6286
1335/未知 556秒 415毫秒/步 - 損失: 0.9280 - 稀疏類別準確度: 0.6287
1336/未知 557秒 415毫秒/步 - 損失: 0.9278 - 稀疏類別準確度: 0.6287
1337/未知 557秒 415毫秒/步 - 損失: 0.9277 - 稀疏類別準確度: 0.6288
1338/未知 558秒 415毫秒/步 - 損失: 0.9275 - 稀疏類別準確度: 0.6288
1339/未知 558秒 415毫秒/步 - 損失: 0.9274 - 稀疏類別準確度: 0.6289
1340/未知 559秒 415毫秒/步 - 損失: 0.9272 - 稀疏類別準確度: 0.6289
1341/未知 559秒 415毫秒/步 - 損失: 0.9271 - 稀疏類別準確度: 0.6290
1342/未知 560秒 415毫秒/步 - 損失: 0.9269 - 稀疏類別準確度: 0.6290
1343/未知 560秒 415毫秒/步 - 損失: 0.9268 - 稀疏類別準確度: 0.6291
1344/未知 561秒 415毫秒/步 - 損失: 0.9267 - 稀疏類別準確度: 0.6291
1345/未知 561秒 415毫秒/步 - 損失: 0.9265 - 稀疏類別準確度: 0.6292
1346/未知 561秒 415毫秒/步 - 損失: 0.9264 - 稀疏類別準確度: 0.6292
1347/未知 562秒 415毫秒/步 - 損失: 0.9262 - 稀疏類別準確度: 0.6293
1348/未知 562秒 415毫秒/步 - 損失: 0.9261 - 稀疏類別準確度: 0.6293
1349/未知 563秒 415毫秒/步 - 損失: 0.9259 - 稀疏類別準確度: 0.6294
1350/未知 563秒 415毫秒/步 - 損失: 0.9258 - 稀疏類別準確度: 0.6294
1351/未知 564秒 415毫秒/步 - 損失: 0.9256 - 稀疏類別準確度: 0.6295
1352/未知 564秒 415毫秒/步 - 損失: 0.9255 - 稀疏類別準確度: 0.6295
1353/未知 564秒 415毫秒/步 - 損失: 0.9254 - 稀疏類別準確度: 0.6296
1354/未知 565秒 415毫秒/步 - 損失: 0.9252 - 稀疏類別準確度: 0.6296
1355/未知 565秒 415毫秒/步 - 損失: 0.9251 - 稀疏類別準確度: 0.6296
1356/未知 565秒 415毫秒/步 - 損失: 0.9249 - 稀疏類別準確度: 0.6297
1357/未知 566秒 415毫秒/步 - 損失: 0.9248 - 稀疏類別準確度: 0.6297
1358/未知 566秒 415毫秒/步 - 損失: 0.9246 - 稀疏類別準確度: 0.6298
1359/未知 566秒 415毫秒/步 - 損失: 0.9245 - 稀疏類別準確度: 0.6298
1360/未知 567秒 415毫秒/步 - 損失: 0.9244 - 稀疏類別準確度: 0.6299
1361/未知 567秒 415毫秒/步 - 損失: 0.9242 - 稀疏類別準確度: 0.6299
1362/未知 568秒 415毫秒/步 - 損失: 0.9241 - 稀疏類別準確度: 0.6300
1363/未知 568秒 415毫秒/步 - 損失: 0.9239 - 稀疏類別準確度: 0.6300
1364/未知 568秒 415毫秒/步 - 損失: 0.9238 - 稀疏類別準確度: 0.6301
1365/未知 569秒 415毫秒/步 - 損失: 0.9237 - 稀疏類別準確度: 0.6301
1366/未知 569秒 415毫秒/步 - 損失: 0.9235 - 稀疏類別準確度: 0.6302
1367/未知 570秒 415毫秒/步 - 損失: 0.9234 - 稀疏類別準確度: 0.6302
1368/未知 570秒 415毫秒/步 - 損失: 0.9232 - 稀疏類別準確度: 0.6303
1369/未知 571秒 415毫秒/步 - 損失: 0.9231 - 稀疏類別準確度: 0.6303
1370/未知 571秒 415毫秒/步 - 損失: 0.9229 - 稀疏類別準確度: 0.6304
1371/未知 572秒 415毫秒/步 - 損失: 0.9228 - 稀疏類別準確度: 0.6304
1372/未知 572秒 415毫秒/步 - 損失: 0.9227 - 稀疏類別準確度: 0.6304
1373/未知 573秒 415毫秒/步 - 損失: 0.9225 - 稀疏類別準確度: 0.6305
1374/未知 573秒 415毫秒/步 - 損失: 0.9224 - 稀疏類別準確度: 0.6305
1375/未知 574秒 415毫秒/步 - 損失: 0.9222 - 稀疏類別準確度: 0.6306
1376/未知 574秒 415毫秒/步 - 損失: 0.9221 - 稀疏類別準確度: 0.6306
1377/未知 574秒 415毫秒/步 - 損失: 0.9220 - 稀疏類別準確度: 0.6307
1378/未知 575秒 415毫秒/步 - 損失: 0.9218 - 稀疏類別準確度: 0.6307
1379/未知 575秒 415毫秒/步 - 損失: 0.9217 - 稀疏類別準確度: 0.6308
1380/未知 575秒 415毫秒/步 - 損失: 0.9215 - 稀疏類別準確度: 0.6308
1381/未知 576秒 415毫秒/步 - 損失: 0.9214 - 稀疏類別準確度: 0.6309
1382/未知 576秒 415毫秒/步 - 損失: 0.9213 - 稀疏類別準確度: 0.6309
1383/未知 576秒 415毫秒/步 - 損失: 0.9211 - 稀疏類別準確度: 0.6309
1384/未知 577秒 415毫秒/步 - 損失: 0.9210 - 稀疏類別準確度: 0.6310
1385/未知 577秒 415毫秒/步 - 損失: 0.9209 - 稀疏類別準確度: 0.6310
1386/未知 578秒 415毫秒/步 - 損失: 0.9207 - 稀疏類別準確度: 0.6311
1387/未知 578秒 415毫秒/步 - 損失: 0.9206 - 稀疏類別準確度: 0.6311
1388/未知 578秒 415毫秒/步 - 損失: 0.9204 - 稀疏類別準確度: 0.6312
1389/未知 579秒 415毫秒/步 - 損失: 0.9203 - 稀疏類別準確度: 0.6312
1390/未知 579秒 415毫秒/步 - 損失: 0.9202 - 稀疏類別準確度: 0.6313
1391/未知 580秒 415毫秒/步 - 損失: 0.9200 - 稀疏類別準確度: 0.6313
1392/未知 580秒 415毫秒/步 - 損失: 0.9199 - 稀疏類別準確度: 0.6314
1393/未知 580秒 415毫秒/步 - 損失: 0.9198 - 稀疏類別準確度: 0.6314
1394/未知 581秒 415毫秒/步 - 損失: 0.9196 - 稀疏類別準確度: 0.6315
1395/未知 581秒 415毫秒/步 - 損失: 0.9195 - 稀疏類別準確度: 0.6315
1396/未知 582秒 415毫秒/步 - 損失: 0.9193 - 稀疏類別準確度: 0.6315
1397/未知 582秒 415毫秒/步 - 損失: 0.9192 - 稀疏類別準確度: 0.6316
1398/未知 583秒 415毫秒/步 - 損失: 0.9191 - 稀疏類別準確度: 0.6316
1399/未知 583秒 415毫秒/步 - 損失: 0.9189 - 稀疏類別準確度: 0.6317
1400/未知 583秒 415毫秒/步 - 損失: 0.9188 - 稀疏類別準確度: 0.6317
1401/未知 584秒 415毫秒/步 - 損失: 0.9187 - 稀疏類別準確度: 0.6318
1402/未知 584秒 415毫秒/步 - 損失: 0.9185 - 稀疏類別準確度: 0.6318
1403/未知 585秒 415毫秒/步 - 損失: 0.9184 - 稀疏類別準確度: 0.6319
1404/未知 585秒 415毫秒/步 - 損失: 0.9183 - 稀疏類別準確度: 0.6319
1405/未知 586秒 415毫秒/步 - 損失: 0.9181 - 稀疏類別準確度: 0.6319
1406/未知 586秒 415毫秒/步 - 損失: 0.9180 - 稀疏類別準確度: 0.6320
1407/未知 587秒 415毫秒/步 - 損失: 0.9178 - 稀疏類別準確度: 0.6320
1408/未知 587秒 415毫秒/步 - 損失: 0.9177 - 稀疏類別準確度: 0.6321
1409/未知 588秒 415毫秒/步 - 損失: 0.9176 - 稀疏類別準確度: 0.6321
1410/未知 588秒 415毫秒/步 - 損失: 0.9174 - 稀疏類別準確度: 0.6322
1411/未知 589秒 415毫秒/步 - 損失: 0.9173 - 稀疏類別準確度: 0.6322
1412/未知 589秒 415毫秒/步 - 損失: 0.9172 - 稀疏類別準確度: 0.6323
1413/未知 590秒 415毫秒/步 - 損失: 0.9170 - 稀疏類別準確度: 0.6323
1414/未知 590秒 415毫秒/步 - 損失: 0.9169 - 稀疏類別準確度: 0.6323
1415/未知 591秒 415毫秒/步 - 損失: 0.9168 - 稀疏類別準確度: 0.6324
1416/未知 591秒 415毫秒/步 - 損失: 0.9166 - 稀疏類別準確度: 0.6324
1417/未知 591秒 415毫秒/步 - 損失: 0.9165 - 稀疏類別準確度: 0.6325
1418/未知 592秒 415毫秒/步 - 損失: 0.9164 - 稀疏類別準確度: 0.6325
1419/未知 592秒 415毫秒/步 - 損失: 0.9162 - 稀疏類別準確度: 0.6326
1420/未知 592秒 415毫秒/步 - 損失: 0.9161 - 稀疏類別準確度: 0.6326
1421/未知 593秒 415毫秒/步 - 損失: 0.9160 - 稀疏類別準確度: 0.6327
1422/未知 593秒 415毫秒/步 - 損失: 0.9158 - 稀疏類別準確度: 0.6327
1423/未知 594秒 415毫秒/步 - 損失: 0.9157 - 稀疏類別準確度: 0.6327
1424/未知 594秒 415毫秒/步 - 損失: 0.9156 - 稀疏類別準確度: 0.6328
1425/未知 594秒 415毫秒/步 - 損失: 0.9154 - 稀疏類別準確度: 0.6328
1426/未知 595秒 415毫秒/步 - 損失: 0.9153 - 稀疏類別準確度: 0.6329
1427/未知 595秒 415毫秒/步 - 損失: 0.9152 - 稀疏類別準確度: 0.6329
1428/未知 596秒 415毫秒/步 - 損失: 0.9150 - 稀疏類別準確度: 0.6330
1429/未知 596秒 415毫秒/步 - 損失: 0.9149 - 稀疏類別準確度: 0.6330
1430/未知 596秒 415毫秒/步 - 損失: 0.9148 - 稀疏類別準確度: 0.6331
1431/未知 597秒 415毫秒/步 - 損失: 0.9146 - 稀疏類別準確度: 0.6331
1432/未知 597秒 415毫秒/步 - 損失: 0.9145 - 稀疏類別準確度: 0.6331
1433/未知 598秒 415毫秒/步 - 損失: 0.9144 - 稀疏類別準確度: 0.6332
1434/未知 598秒 415毫秒/步 - 損失: 0.9142 - 稀疏類別準確度: 0.6332
1435/未知 599秒 415毫秒/步 - 損失: 0.9141 - 稀疏類別準確度: 0.6333
1436/未知 599秒 415毫秒/步 - 損失: 0.9140 - 稀疏類別準確度: 0.6333
1437/未知 599秒 415毫秒/步 - 損失: 0.9139 - 稀疏類別準確度: 0.6334
1438/未知 600秒 415毫秒/步 - 損失: 0.9137 - 稀疏類別準確度: 0.6334
1439/未知 600秒 415毫秒/步 - 損失: 0.9136 - 稀疏類別準確度: 0.6334
1440/未知 601秒 415毫秒/步 - 損失: 0.9135 - 稀疏類別準確度: 0.6335
1441/未知 601秒 415毫秒/步 - 損失: 0.9133 - 稀疏類別準確度: 0.6335
1442/未知 602秒 416毫秒/步 - 損失: 0.9132 - 稀疏類別準確度: 0.6336
1443/未知 602秒 416毫秒/步 - 損失: 0.9131 - 稀疏類別準確度: 0.6336
1444/未知 603秒 416毫秒/步 - 損失: 0.9129 - 稀疏類別準確度: 0.6337
1445/未知 603秒 416毫秒/步 - 損失: 0.9128 - 稀疏類別準確度: 0.6337
1446/未知 604秒 416毫秒/步 - 損失: 0.9127 - 稀疏類別準確度: 0.6337
1447/未知 604秒 416毫秒/步 - 損失: 0.9126 - 稀疏類別準確度: 0.6338
1448/未知 605秒 416毫秒/步 - 損失: 0.9124 - 稀疏類別準確度: 0.6338
1449/未知 605秒 416毫秒/步 - 損失: 0.9123 - 稀疏類別準確度: 0.6339
1450/未知 606秒 416毫秒/步 - 損失: 0.9122 - 稀疏類別準確度: 0.6339
1451/未知 606秒 416毫秒/步 - 損失: 0.9120 - 稀疏類別準確度: 0.6340
1452/未知 606秒 416毫秒/步 - 損失: 0.9119 - 稀疏類別準確度: 0.6340
1453/未知 607秒 416毫秒/步 - 損失: 0.9118 - 稀疏類別準確度: 0.6340
1454/未知 607秒 416毫秒/步 - 損失: 0.9116 - 稀疏類別準確度: 0.6341
1455/未知 608秒 416毫秒/步 - 損失: 0.9115 - 稀疏類別準確度: 0.6341
1456/未知 608秒 416毫秒/步 - 損失: 0.9114 - 稀疏類別準確度: 0.6342
1457/未知 609秒 416毫秒/步 - 損失: 0.9113 - 稀疏類別準確度: 0.6342
1458/未知 609秒 416毫秒/步 - 損失: 0.9111 - 稀疏類別準確度: 0.6343
1459/未知 610秒 416毫秒/步 - 損失: 0.9110 - 稀疏類別準確度: 0.6343
1460/未知 610秒 416毫秒/步 - 損失: 0.9109 - 稀疏類別準確度: 0.6343
1461/未知 610秒 416毫秒/步 - 損失: 0.9108 - 稀疏類別準確度: 0.6344
1462/未知 611秒 416毫秒/步 - 損失: 0.9106 - 稀疏類別準確度: 0.6344
1463/未知 611秒 416毫秒/步 - 損失: 0.9105 - 稀疏類別準確度: 0.6345
1464/未知 612秒 416毫秒/步 - 損失: 0.9104 - 稀疏類別準確度: 0.6345
1465/未知 612秒 416毫秒/步 - 損失: 0.9102 - 稀疏類別準確度: 0.6345
1466/未知 613秒 416毫秒/步 - 損失: 0.9101 - 稀疏類別準確度: 0.6346
1467/未知 613秒 416毫秒/步 - 損失: 0.9100 - 稀疏類別準確度: 0.6346
1468/未知 613秒 416毫秒/步 - 損失: 0.9099 - 稀疏類別準確度: 0.6347
1469/未知 614秒 416毫秒/步 - 損失: 0.9097 - 稀疏類別準確度: 0.6347
1470/未知 614秒 416毫秒/步 - 損失: 0.9096 - 稀疏類別準確度: 0.6348
1471/未知 614秒 416毫秒/步 - 損失: 0.9095 - 稀疏類別準確度: 0.6348
1472/未知 615秒 416毫秒/步 - 損失: 0.9094 - 稀疏類別準確度: 0.6348
1473/未知 615秒 416毫秒/步 - 損失: 0.9092 - 稀疏類別準確度: 0.6349
1474/未知 615秒 416毫秒/步 - 損失: 0.9091 - 稀疏類別準確度: 0.6349
1475/未知 616秒 416毫秒/步 - 損失: 0.9090 - 稀疏類別準確度: 0.6350
1476/未知 616秒 416毫秒/步 - 損失: 0.9089 - 稀疏類別準確度: 0.6350
1477/未知 616秒 416毫秒/步 - 損失: 0.9087 - 稀疏類別準確度: 0.6350
1478/未知 617秒 415毫秒/步 - 損失: 0.9086 - 稀疏類別準確度: 0.6351
1479/未知 617秒 415毫秒/步 - 損失: 0.9085 - 稀疏類別準確度: 0.6351
1480/未知 617秒 415毫秒/步 - 損失: 0.9083 - 稀疏類別準確度: 0.6352
1481/未知 618秒 415毫秒/步 - 損失: 0.9082 - 稀疏類別準確度: 0.6352
1482/未知 618秒 415毫秒/步 - 損失: 0.9081 - 稀疏類別準確度: 0.6353
1483/未知 619秒 415毫秒/步 - 損失: 0.9080 - 稀疏類別準確度: 0.6353
1484/未知 619秒 415毫秒/步 - 損失: 0.9078 - 稀疏類別準確度: 0.6353
1485/未知 620秒 415毫秒/步 - 損失: 0.9077 - 稀疏類別準確度: 0.6354
1486/未知 620秒 415毫秒/步 - 損失: 0.9076 - 稀疏類別準確度: 0.6354
1487/未知 620秒 415毫秒/步 - 損失: 0.9075 - 稀疏類別準確度: 0.6355
1488/未知 621秒 416毫秒/步 - 損失: 0.9073 - 稀疏類別準確度: 0.6355
1489/未知 621秒 416毫秒/步 - 損失: 0.9072 - 稀疏類別準確度: 0.6355
1490/未知 622秒 416毫秒/步 - 損失: 0.9071 - 稀疏類別準確度: 0.6356
1491/未知 622秒 416毫秒/步 - 損失: 0.9070 - 稀疏類別準確度: 0.6356
1492/未知 623秒 416毫秒/步 - 損失: 0.9069 - 稀疏類別準確度: 0.6357
1493/未知 623秒 416毫秒/步 - 損失: 0.9067 - 稀疏類別準確度: 0.6357
1494/未知 624秒 416毫秒/步 - 損失: 0.9066 - 稀疏類別準確度: 0.6358
1495/未知 624秒 416毫秒/步 - 損失: 0.9065 - 稀疏類別準確度: 0.6358
1496/未知 624秒 416毫秒/步 - 損失: 0.9064 - 稀疏類別準確度: 0.6358
1497/未知 625秒 416毫秒/步 - 損失: 0.9062 - 稀疏類別準確度: 0.6359
1498/未知 625秒 416毫秒/步 - 損失: 0.9061 - 稀疏類別準確度: 0.6359
1499/未知 626秒 416毫秒/步 - 損失: 0.9060 - 稀疏類別準確度: 0.6360
1500/未知 626秒 416毫秒/步 - 損失: 0.9059 - 稀疏類別準確度: 0.6360
1501/未知 627秒 416毫秒/步 - 損失: 0.9057 - 稀疏類別準確度: 0.6360
1502/未知 627秒 416毫秒/步 - 損失: 0.9056 - 稀疏類別準確度: 0.6361
1503/未知 628秒 416毫秒/步 - 損失: 0.9055 - 稀疏類別準確度: 0.6361
1504/未知 628秒 416毫秒/步 - 損失: 0.9054 - 稀疏類別準確度: 0.6362
1505/未知 628秒 416毫秒/步 - 損失: 0.9053 - 稀疏類別準確度: 0.6362
1506/未知 629秒 416毫秒/步 - 損失: 0.9051 - 稀疏類別準確度: 0.6362
1507/未知 629秒 416毫秒/步 - 損失: 0.9050 - 稀疏類別準確度: 0.6363
1508/未知 630秒 416毫秒/步 - 損失: 0.9049 - 稀疏類別準確度: 0.6363
1509/未知 630秒 416毫秒/步 - 損失: 0.9048 - 稀疏類別準確度: 0.6364
1510/未知 631秒 416毫秒/步 - 損失: 0.9046 - 稀疏類別準確度: 0.6364
1511/未知 631秒 416毫秒/步 - 損失: 0.9045 - 稀疏類別準確度: 0.6364
1512/未知 631秒 416毫秒/步 - 損失: 0.9044 - 稀疏類別準確度: 0.6365
1513/未知 632秒 416毫秒/步 - 損失: 0.9043 - 稀疏類別準確度: 0.6365
1514/未知 632秒 416毫秒/步 - 損失: 0.9042 - 稀疏類別準確度: 0.6366
1515/未知 632秒 416毫秒/步 - 損失: 0.9040 - 稀疏類別準確度: 0.6366
1516/未知 633秒 416毫秒/步 - 損失: 0.9039 - 稀疏類別準確度: 0.6366
1517/未知 633秒 416毫秒/步 - 損失: 0.9038 - 稀疏類別準確度: 0.6367
1518/未知 634秒 416毫秒/步 - 損失: 0.9037 - 稀疏類別準確度: 0.6367
1519/未知 634秒 416毫秒/步 - 損失: 0.9036 - 稀疏類別準確度: 0.6368
1520/未知 634秒 415毫秒/步 - 損失: 0.9034 - 稀疏類別準確度: 0.6368
1521/未知 635秒 415毫秒/步 - 損失: 0.9033 - 稀疏類別準確度: 0.6368
1522/未知 635秒 415毫秒/步 - 損失: 0.9032 - 稀疏類別準確度: 0.6369
1523/未知 635秒 415毫秒/步 - 損失: 0.9031 - 稀疏類別準確度: 0.6369
1524/未知 636秒 415毫秒/步 - 損失: 0.9029 - 稀疏類別準確度: 0.6370
1525/未知 636秒 415毫秒/步 - 損失: 0.9028 - 稀疏類別準確度: 0.6370
1526/未知 637秒 415毫秒/步 - 損失: 0.9027 - 稀疏類別準確度: 0.6370
1527/未知 637秒 415毫秒/步 - 損失: 0.9026 - 稀疏類別準確度: 0.6371
1528/未知 638秒 416毫秒/步 - 損失: 0.9025 - 稀疏類別準確度: 0.6371
1529/未知 638秒 416毫秒/步 - 損失: 0.9023 - 稀疏類別準確度: 0.6372
1530/未知 639秒 416毫秒/步 - 損失: 0.9022 - 稀疏類別準確度: 0.6372
1531/未知 639秒 416毫秒/步 - 損失: 0.9021 - 稀疏類別準確度: 0.6372
1532/未知 640秒 416毫秒/步 - 損失: 0.9020 - 稀疏類別準確度: 0.6373
1533/未知 640秒 416毫秒/步 - 損失: 0.9019 - 稀疏類別準確度: 0.6373
1534/未知 641秒 416毫秒/步 - 損失: 0.9018 - 稀疏類別準確度: 0.6374
1535/未知 641秒 416毫秒/步 - 損失: 0.9016 - 稀疏類別準確度: 0.6374
1536/未知 641秒 416毫秒/步 - 損失: 0.9015 - 稀疏類別準確度: 0.6374
1537/未知 642秒 416毫秒/步 - 損失: 0.9014 - 稀疏類別準確度: 0.6375
1538/未知 642秒 416毫秒/步 - 損失: 0.9013 - 稀疏類別準確度: 0.6375
1539/未知 643秒 416毫秒/步 - 損失: 0.9012 - 稀疏類別準確度: 0.6376
1540/未知 643秒 416毫秒/步 - 損失: 0.9010 - 稀疏類別準確度: 0.6376
1541/未知 644秒 416毫秒/步 - 損失: 0.9009 - 稀疏類別準確度: 0.6376
1542/未知 644秒 416毫秒/步 - 損失: 0.9008 - 稀疏類別準確度: 0.6377
1543/未知 645秒 416毫秒/步 - 損失: 0.9007 - 稀疏類別準確度: 0.6377
1544/未知 645秒 416毫秒/步 - 損失: 0.9006 - 稀疏類別準確度: 0.6378
1545/未知 645秒 416毫秒/步 - 損失: 0.9004 - 稀疏類別準確度: 0.6378
1546/未知 646秒 416毫秒/步 - 損失: 0.9003 - 稀疏類別準確度: 0.6378
1547/未知 646秒 416毫秒/步 - 損失: 0.9002 - 稀疏類別準確度: 0.6379
1548/未知 646秒 416毫秒/步 - 損失: 0.9001 - 稀疏類別準確度: 0.6379
1549/未知 647秒 416毫秒/步 - 損失: 0.9000 - 稀疏類別準確度: 0.6379
1550/未知 647秒 416毫秒/步 - 損失: 0.8999 - 稀疏類別準確度: 0.6380
1551/未知 648秒 416毫秒/步 - 損失: 0.8997 - 稀疏類別準確度: 0.6380
1552/未知 648秒 416毫秒/步 - 損失: 0.8996 - 稀疏類別準確度: 0.6381
1553/未知 648秒 416毫秒/步 - 損失: 0.8995 - 稀疏類別準確度: 0.6381
1554/未知 649秒 416毫秒/步 - 損失: 0.8994 - 稀疏類別準確度: 0.6381
1555/未知 649秒 416毫秒/步 - 損失: 0.8993 - 稀疏類別準確度: 0.6382
1556/未知 650秒 416毫秒/步 - 損失: 0.8992 - 稀疏類別準確度: 0.6382
1557/未知 650秒 416毫秒/步 - 損失: 0.8990 - 稀疏類別準確度: 0.6383
1558/未知 650秒 416毫秒/步 - 損失: 0.8989 - 稀疏類別準確度: 0.6383
1559/未知 651秒 416毫秒/步 - 損失: 0.8988 - 稀疏類別準確度: 0.6383
1560/未知 651秒 416毫秒/步 - 損失: 0.8987 - 稀疏類別準確度: 0.6384
1561/未知 652秒 416毫秒/步 - 損失: 0.8986 - 稀疏類別準確度: 0.6384
1562/未知 652秒 416毫秒/步 - 損失: 0.8985 - 稀疏類別準確度: 0.6385
1563/未知 653秒 416毫秒/步 - 損失: 0.8983 - 稀疏類別準確度: 0.6385
1564/未知 653秒 416毫秒/步 - 損失: 0.8982 - 稀疏類別準確度: 0.6385
1565/未知 654秒 416毫秒/步 - 損失: 0.8981 - 稀疏類別準確度: 0.6386
1566/未知 654秒 416毫秒/步 - 損失: 0.8980 - 稀疏類別準確度: 0.6386
1567/未知 655秒 416毫秒/步 - 損失: 0.8979 - 稀疏類別準確度: 0.6386
1568/未知 655秒 416毫秒/步 - 損失: 0.8978 - 稀疏類別準確度: 0.6387
1569/未知 656秒 416毫秒/步 - 損失: 0.8977 - 稀疏類別準確度: 0.6387
1570/未知 656秒 416毫秒/步 - 損失: 0.8975 - 稀疏類別準確度: 0.6388
1571/未知 656秒 416毫秒/步 - 損失: 0.8974 - 稀疏類別準確度: 0.6388
1572/未知 657秒 416毫秒/步 - 損失: 0.8973 - 稀疏類別準確度: 0.6388
1573/未知 657秒 416毫秒/步 - 損失: 0.8972 - 稀疏類別準確度: 0.6389
1574/未知 658秒 416毫秒/步 - 損失: 0.8971 - 稀疏類別準確度: 0.6389
1575/未知 658秒 416毫秒/步 - 損失: 0.8970 - 稀疏類別準確度: 0.6389
1576/未知 659秒 416毫秒/步 - 損失: 0.8969 - 稀疏類別準確度: 0.6390
1577/未知 659秒 416毫秒/步 - 損失: 0.8967 - 稀疏類別準確度: 0.6390
1578/未知 660秒 416毫秒/步 - 損失: 0.8966 - 稀疏類別準確度: 0.6391
1579/未知 660秒 416毫秒/步 - 損失: 0.8965 - 稀疏類別準確度: 0.6391
1580/未知 661秒 416毫秒/步 - 損失: 0.8964 - 稀疏類別準確度: 0.6391
1581/未知 661秒 416毫秒/步 - 損失: 0.8963 - 稀疏類別準確度: 0.6392
1582/未知 662秒 416毫秒/步 - 損失: 0.8962 - 稀疏類別準確度: 0.6392
1583/未知 662秒 417毫秒/步 - 損失: 0.8961 - 稀疏類別準確度: 0.6392
1584/未知 662秒 417毫秒/步 - 損失: 0.8959 - 稀疏類別準確度: 0.6393
1585/未知 663秒 417毫秒/步 - 損失: 0.8958 - 稀疏類別準確度: 0.6393
1586/未知 663秒 417毫秒/步 - 損失: 0.8957 - 稀疏類別準確度: 0.6394
1587/未知 664秒 417毫秒/步 - 損失: 0.8956 - 稀疏類別準確度: 0.6394
1588/未知 664秒 417毫秒/步 - 損失: 0.8955 - 稀疏類別準確度: 0.6394
1589/未知 665秒 417毫秒/步 - 損失: 0.8954 - 稀疏類別準確度: 0.6395
1590/未知 665秒 417毫秒/步 - 損失: 0.8953 - 稀疏類別準確度: 0.6395
1591/未知 666秒 417毫秒/步 - 損失: 0.8952 - 稀疏類別準確度: 0.6395
1592/未知 666秒 417毫秒/步 - 損失: 0.8950 - 稀疏類別準確度: 0.6396
1593/未知 666秒 417毫秒/步 - 損失: 0.8949 - 稀疏類別準確度: 0.6396
1594/未知 667秒 417毫秒/步 - 損失: 0.8948 - 稀疏類別準確度: 0.6397
1595/未知 667秒 417毫秒/步 - 損失: 0.8947 - 稀疏類別準確度: 0.6397
1596/未知 668秒 417毫秒/步 - 損失: 0.8946 - 稀疏類別準確度: 0.6397
1597/未知 668秒 417毫秒/步 - 損失: 0.8945 - 稀疏類別準確度: 0.6398
1598/未知 669秒 417毫秒/步 - 損失: 0.8944 - 稀疏類別準確度: 0.6398
1599/未知 669秒 417毫秒/步 - 損失: 0.8943 - 稀疏類別準確度: 0.6398
1600/未知 669秒 417毫秒/步 - 損失: 0.8941 - 稀疏類別準確度: 0.6399
1601/未知 670秒 417毫秒/步 - 損失: 0.8940 - 稀疏類別準確度: 0.6399
1602/未知 670秒 417毫秒/步 - 損失: 0.8939 - 稀疏類別準確度: 0.6400
1603/未知 671秒 417毫秒/步 - 損失: 0.8938 - 稀疏類別準確度: 0.6400
1604/未知 671秒 417毫秒/步 - 損失: 0.8937 - 稀疏類別準確度: 0.6400
1605/未知 672秒 417毫秒/步 - 損失: 0.8936 - 稀疏類別準確度: 0.6401
1606/未知 672秒 417毫秒/步 - 損失: 0.8935 - 稀疏類別準確度: 0.6401
1607/未知 673秒 417毫秒/步 - 損失: 0.8934 - 稀疏類別準確度: 0.6401
1608/未知 673秒 417毫秒/步 - 損失: 0.8933 - 稀疏類別準確度: 0.6402
1609/未知 673秒 417毫秒/步 - 損失: 0.8931 - 稀疏類別準確度: 0.6402
1610/未知 674秒 417毫秒/步 - 損失: 0.8930 - 稀疏類別準確度: 0.6403
1611/未知 674秒 417毫秒/步 - 損失: 0.8929 - 稀疏類別準確度: 0.6403
1612/未知 675秒 417毫秒/步 - 損失: 0.8928 - 稀疏類別準確度: 0.6403
1613/未知 675秒 417毫秒/步 - 損失: 0.8927 - 稀疏類別準確度: 0.6404
1614/未知 675秒 417毫秒/步 - 損失: 0.8926 - 稀疏類別準確度: 0.6404
1615/未知 676秒 417毫秒/步 - 損失: 0.8925 - 稀疏類別準確度: 0.6404
1616/未知 676秒 417毫秒/步 - 損失: 0.8924 - 稀疏類別準確度: 0.6405
1617/未知 677秒 417毫秒/步 - 損失: 0.8923 - 稀疏類別準確度: 0.6405
1618/未知 677秒 417毫秒/步 - 損失: 0.8922 - 稀疏類別準確度: 0.6405
1619/未知 677秒 417毫秒/步 - 損失: 0.8920 - 稀疏類別準確度: 0.6406
1620/未知 678秒 417毫秒/步 - 損失: 0.8919 - 稀疏類別準確度: 0.6406
1621/未知 678秒 417毫秒/步 - 損失: 0.8918 - 稀疏類別準確度: 0.6407
1622/未知 678秒 417毫秒/步 - 損失: 0.8917 - 稀疏類別準確度: 0.6407
1623/未知 679秒 417毫秒/步 - 損失: 0.8916 - 稀疏類別準確度: 0.6407
1624/未知 679秒 417毫秒/步 - 損失: 0.8915 - 稀疏類別準確度: 0.6408
1625/未知 679秒 416毫秒/步 - 損失: 0.8914 - 稀疏類別準確度: 0.6408
1626/未知 680秒 416毫秒/步 - 損失: 0.8913 - 稀疏類別準確度: 0.6408
1627/未知 680秒 417毫秒/步 - 損失: 0.8912 - 稀疏類別準確度: 0.6409
1628/未知 681秒 417毫秒/步 - 損失: 0.8911 - 稀疏類別準確度: 0.6409
1629/未知 681秒 417毫秒/步 - 損失: 0.8909 - 稀疏類別準確度: 0.6409
1630/未知 682秒 417毫秒/步 - 損失: 0.8908 - 稀疏類別準確度: 0.6410
1631/未知 682秒 417毫秒/步 - 損失: 0.8907 - 稀疏類別準確度: 0.6410
1632/未知 683秒 417毫秒/步 - 損失: 0.8906 - 稀疏類別準確度: 0.6411
1633/未知 683秒 417毫秒/步 - 損失: 0.8905 - 稀疏類別準確度: 0.6411
1634/未知 684秒 417毫秒/步 - 損失: 0.8904 - 稀疏類別準確度: 0.6411
1635/未知 684秒 417毫秒/步 - 損失: 0.8903 - 稀疏類別準確度: 0.6412
1636/未知 685秒 417毫秒/步 - 損失: 0.8902 - 稀疏類別準確度: 0.6412
1637/未知 685秒 417毫秒/步 - 損失: 0.8901 - 稀疏類別準確度: 0.6412
1638/未知 686秒 417毫秒/步 - 損失: 0.8900 - 稀疏類別準確度: 0.6413
1639/未知 686秒 417毫秒/步 - 損失: 0.8899 - 稀疏類別準確度: 0.6413
1640/未知 686秒 417毫秒/步 - 損失: 0.8898 - 稀疏類別準確度: 0.6413
1641/未知 687秒 417毫秒/步 - 損失: 0.8897 - 稀疏類別準確度: 0.6414
1642/未知 687秒 417毫秒/步 - 損失: 0.8895 - 稀疏類別準確度: 0.6414
1643/未知 688秒 417毫秒/步 - 損失: 0.8894 - 稀疏類別準確度: 0.6414
1644/未知 688秒 417毫秒/步 - 損失: 0.8893 - 稀疏類別準確度: 0.6415
1645/未知 689秒 417毫秒/步 - 損失: 0.8892 - 稀疏類別準確度: 0.6415
1646/未知 689秒 417毫秒/步 - 損失: 0.8891 - 稀疏類別準確度: 0.6416
1647/未知 690秒 417毫秒/步 - 損失: 0.8890 - 稀疏類別準確度: 0.6416
1648/未知 690秒 417毫秒/步 - 損失: 0.8889 - 稀疏類別準確度: 0.6416
1649/未知 690秒 417毫秒/步 - 損失: 0.8888 - 稀疏類別準確度: 0.6417
1650/未知 691秒 417毫秒/步 - 損失: 0.8887 - 稀疏類別準確度: 0.6417
1651/未知 691秒 417毫秒/步 - 損失: 0.8886 - 稀疏類別準確度: 0.6417
1652/未知 692秒 417毫秒/步 - 損失: 0.8885 - 稀疏類別準確度: 0.6418
1653/未知 692秒 417毫秒/步 - 損失: 0.8884 - 稀疏類別準確度: 0.6418
1654/未知 693秒 417毫秒/步 - 損失: 0.8883 - 稀疏類別準確度: 0.6418
1655/未知 693秒 417毫秒/步 - 損失: 0.8882 - 稀疏類別準確度: 0.6419
1656/未知 693秒 417毫秒/步 - 損失: 0.8880 - 稀疏類別準確度: 0.6419
1657/未知 694秒 417毫秒/步 - 損失: 0.8879 - 稀疏類別準確度: 0.6419
1658/未知 694秒 417毫秒/步 - 損失: 0.8878 - 稀疏類別準確度: 0.6420
1659/未知 695秒 417毫秒/步 - 損失: 0.8877 - 稀疏類別準確度: 0.6420
1660/未知 695秒 417毫秒/步 - 損失: 0.8876 - 稀疏類別準確度: 0.6420
1661/未知 695秒 417毫秒/步 - 損失: 0.8875 - 稀疏類別準確度: 0.6421
1662/未知 696秒 417毫秒/步 - 損失: 0.8874 - 稀疏類別準確度: 0.6421
1663/未知 696秒 417毫秒/步 - 損失: 0.8873 - 稀疏類別準確度: 0.6422
1664/未知 696秒 417毫秒/步 - 損失: 0.8872 - 稀疏類別準確度: 0.6422
1665/未知 697秒 417毫秒/步 - 損失: 0.8871 - 稀疏類別準確度: 0.6422
1666/未知 697秒 417毫秒/步 - 損失: 0.8870 - 稀疏類別準確度: 0.6423
1667/未知 698秒 417毫秒/步 - 損失: 0.8869 - 稀疏類別準確度: 0.6423
1668/未知 698秒 417毫秒/步 - 損失: 0.8868 - 稀疏類別準確度: 0.6423
1669/未知 698秒 417毫秒/步 - 損失: 0.8867 - 稀疏類別準確度: 0.6424
1670/未知 699秒 417毫秒/步 - 損失: 0.8866 - 稀疏類別準確度: 0.6424
1671/未知 699秒 417毫秒/步 - 損失: 0.8865 - 稀疏類別準確度: 0.6424
1672/未知 700秒 417毫秒/步 - 損失: 0.8864 - 稀疏類別準確度: 0.6425
1673/未知 700秒 417毫秒/步 - 損失: 0.8863 - 稀疏類別準確度: 0.6425
1674/未知 700秒 417毫秒/步 - 損失: 0.8862 - 稀疏類別準確度: 0.6425
1675/未知 701秒 417毫秒/步 - 損失: 0.8861 - 稀疏類別準確度: 0.6426
1676/未知 701秒 417毫秒/步 - 損失: 0.8859 - 稀疏類別準確度: 0.6426
1677/未知 702秒 417毫秒/步 - 損失: 0.8858 - 稀疏類別準確度: 0.6426
1678/未知 702秒 417毫秒/步 - 損失: 0.8857 - 稀疏類別準確度: 0.6427
1679/未知 703秒 417毫秒/步 - 損失: 0.8856 - 稀疏類別準確度: 0.6427
1680/未知 703秒 417毫秒/步 - 損失: 0.8855 - 稀疏類別準確度: 0.6427
1681/未知 704秒 417毫秒/步 - 損失: 0.8854 - 稀疏類別準確度: 0.6428
1682/未知 704秒 417毫秒/步 - 損失: 0.8853 - 稀疏類別準確度: 0.6428
1683/未知 705秒 417毫秒/步 - 損失: 0.8852 - 稀疏類別準確度: 0.6428
1684/未知 705秒 417毫秒/步 - 損失: 0.8851 - 稀疏類別準確度: 0.6429
1685/未知 706秒 417毫秒/步 - 損失: 0.8850 - 稀疏類別準確度: 0.6429
1686/未知 706秒 417毫秒/步 - 損失: 0.8849 - 稀疏類別準確度: 0.6429
1687/未知 706秒 417毫秒/步 - 損失: 0.8848 - 稀疏類別準確度: 0.6430
1688/未知 707秒 417毫秒/步 - 損失: 0.8847 - 稀疏類別準確度: 0.6430
1689/未知 707秒 417毫秒/步 - 損失: 0.8846 - 稀疏類別準確度: 0.6431
1690/未知 708秒 417毫秒/步 - 損失: 0.8845 - 稀疏類別準確度: 0.6431
1691/未知 708秒 417毫秒/步 - 損失: 0.8844 - 稀疏類別準確度: 0.6431
1692/未知 709秒 417毫秒/步 - 損失: 0.8843 - 稀疏類別準確度: 0.6432
1693/未知 709秒 417毫秒/步 - 損失: 0.8842 - 稀疏類別準確度: 0.6432
1694/未知 709秒 417毫秒/步 - 損失: 0.8841 - 稀疏類別準確度: 0.6432
1695/未知 710秒 417毫秒/步 - 損失: 0.8840 - 稀疏類別準確度: 0.6433
1696/未知 710秒 417毫秒/步 - 損失: 0.8839 - 稀疏類別準確度: 0.6433
1697/未知 711秒 417毫秒/步 - 損失: 0.8838 - 稀疏類別準確度: 0.6433
1698/未知 711秒 417毫秒/步 - 損失: 0.8837 - 稀疏類別準確度: 0.6434
1699/未知 711秒 417毫秒/步 - 損失: 0.8836 - 稀疏類別準確度: 0.6434
1700/未知 712秒 417毫秒/步 - 損失: 0.8835 - 稀疏類別準確度: 0.6434
1701/未知 712秒 417毫秒/步 - 損失: 0.8834 - 稀疏類別準確度: 0.6435
1702/未知 713秒 417毫秒/步 - 損失: 0.8833 - 稀疏類別準確度: 0.6435
1703/未知 713秒 417毫秒/步 - 損失: 0.8832 - 稀疏類別準確度: 0.6435
1704/未知 713秒 417毫秒/步 - 損失: 0.8831 - 稀疏類別準確度: 0.6436
1705/未知 714秒 417毫秒/步 - 損失: 0.8830 - 稀疏類別準確度: 0.6436
1706/未知 714秒 417毫秒/步 - 損失: 0.8829 - 稀疏類別準確度: 0.6436
1707/未知 714秒 417毫秒/步 - 損失: 0.8828 - 稀疏類別準確度: 0.6437
1708/未知 715秒 417毫秒/步 - 損失: 0.8827 - 稀疏類別準確度: 0.6437
1709/未知 715秒 417毫秒/步 - 損失: 0.8826 - 稀疏類別準確度: 0.6437
1710/未知 716秒 417毫秒/步 - 損失: 0.8825 - 稀疏類別準確度: 0.6438
1711/未知 716秒 417毫秒/步 - 損失: 0.8824 - 稀疏類別準確度: 0.6438
1712/未知 717秒 417毫秒/步 - 損失: 0.8823 - 稀疏類別準確度: 0.6438
1713/未知 717秒 417毫秒/步 - 損失: 0.8822 - 稀疏類別準確度: 0.6439
1714/未知 718秒 417毫秒/步 - 損失: 0.8821 - 稀疏類別準確度: 0.6439
1715/未知 718秒 417毫秒/步 - 損失: 0.8820 - 稀疏類別準確度: 0.6439
1716/未知 719秒 417毫秒/步 - 損失: 0.8818 - 稀疏類別準確度: 0.6440
1717/未知 719秒 417毫秒/步 - 損失: 0.8817 - 稀疏類別準確度: 0.6440
1718/未知 719秒 417毫秒/步 - 損失: 0.8816 - 稀疏類別準確度: 0.6440
1719/未知 720秒 417毫秒/步 - 損失: 0.8815 - 稀疏類別準確度: 0.6441
1720/未知 720秒 417毫秒/步 - 損失: 0.8814 - 稀疏類別準確度: 0.6441
1721/未知 720秒 417毫秒/步 - 損失: 0.8813 - 稀疏類別準確度: 0.6441
1722/未知 721秒 417毫秒/步 - 損失: 0.8812 - 稀疏類別準確度: 0.6442
1723/未知 721秒 417毫秒/步 - 損失: 0.8811 - 稀疏類別準確度: 0.6442
1724/未知 722秒 417毫秒/步 - 損失: 0.8810 - 稀疏類別準確度: 0.6442
1725/未知 722秒 417毫秒/步 - 損失: 0.8809 - 稀疏類別準確度: 0.6443
1726/未知 722秒 417毫秒/步 - 損失: 0.8808 - 稀疏類別準確度: 0.6443
1727/未知 723秒 417毫秒/步 - 損失: 0.8807 - 稀疏類別準確度: 0.6443
1728/未知 723秒 417毫秒/步 - 損失: 0.8806 - 稀疏類別準確度: 0.6444
1729/未知 723秒 417毫秒/步 - 損失: 0.8805 - 稀疏類別準確度: 0.6444
1730/未知 724秒 417毫秒/步 - 損失: 0.8804 - 稀疏類別準確度: 0.6444
1731/未知 724秒 417毫秒/步 - 損失: 0.8804 - 稀疏類別準確度: 0.6445
1732/未知 725秒 417毫秒/步 - 損失: 0.8803 - 稀疏類別準確度: 0.6445
1733/未知 725秒 417毫秒/步 - 損失: 0.8802 - 稀疏類別準確度: 0.6445
1734/未知 726秒 417毫秒/步 - 損失: 0.8801 - 稀疏類別準確度: 0.6446
1735/未知 726秒 417毫秒/步 - 損失: 0.8800 - 稀疏類別準確度: 0.6446
1736/未知 727秒 417毫秒/步 - 損失: 0.8799 - 稀疏類別準確度: 0.6446
1737/未知 727秒 417毫秒/步 - 損失: 0.8798 - 稀疏類別準確度: 0.6447
1738/未知 727秒 417毫秒/步 - 損失: 0.8797 - 稀疏類別準確度: 0.6447
1739/未知 728秒 417毫秒/步 - 損失: 0.8796 - 稀疏類別準確度: 0.6447
1740/未知 728秒 417毫秒/步 - 損失: 0.8795 - 稀疏類別準確度: 0.6448
1741/未知 729秒 417毫秒/步 - 損失: 0.8794 - 稀疏類別準確度: 0.6448
1742/未知 729秒 417毫秒/步 - 損失: 0.8793 - 稀疏類別準確度: 0.6448
1743/未知 730秒 417毫秒/步 - 損失: 0.8792 - 稀疏類別準確度: 0.6449
1744/未知 730秒 417毫秒/步 - 損失: 0.8791 - 稀疏類別準確度: 0.6449
1745/未知 730秒 417毫秒/步 - 損失: 0.8790 - 稀疏類別準確度: 0.6449
1746/未知 731秒 417毫秒/步 - 損失: 0.8789 - 稀疏類別準確度: 0.6450
1747/未知 731秒 417毫秒/步 - 損失: 0.8788 - 稀疏類別準確度: 0.6450
1748/未知 731秒 417毫秒/步 - 損失: 0.8787 - 稀疏類別準確度: 0.6450
1749/未知 732秒 417毫秒/步 - 損失: 0.8786 - 稀疏類別準確度: 0.6451
1750/未知 732秒 417毫秒/步 - 損失: 0.8785 - 稀疏類別準確度: 0.6451
1751/未知 733秒 417毫秒/步 - 損失: 0.8784 - 稀疏類別準確度: 0.6451
1752/未知 733秒 417毫秒/步 - 損失: 0.8783 - 稀疏類別準確度: 0.6452
1753/未知 733秒 417毫秒/步 - 損失: 0.8782 - 稀疏類別準確度: 0.6452
1754/未知 734秒 417毫秒/步 - 損失: 0.8781 - 稀疏類別準確度: 0.6452
1755/未知 734秒 417毫秒/步 - 損失: 0.8780 - 稀疏類別準確度: 0.6453
1756/未知 735秒 417毫秒/步 - 損失: 0.8779 - 稀疏類別準確度: 0.6453
1757/未知 735秒 417毫秒/步 - 損失: 0.8778 - 稀疏類別準確度: 0.6453
1758/未知 736秒 417毫秒/步 - 損失: 0.8777 - 稀疏類別準確度: 0.6453
1759/未知 736秒 417毫秒/步 - 損失: 0.8776 - 稀疏類別準確度: 0.6454
1760/未知 737秒 417毫秒/步 - 損失: 0.8775 - 稀疏類別準確度: 0.6454
1761/未知 737秒 417毫秒/步 - 損失: 0.8774 - 稀疏類別準確度: 0.6454
1762/未知 738秒 417毫秒/步 - 損失: 0.8773 - 稀疏類別準確度: 0.6455
1763/未知 738秒 417毫秒/步 - 損失: 0.8772 - 稀疏類別準確度: 0.6455
1764/未知 738秒 417毫秒/步 - 損失: 0.8771 - 稀疏類別準確度: 0.6455
1765/未知 739秒 417毫秒/步 - 損失: 0.8770 - 稀疏類別準確度: 0.6456
1766/未知 739秒 417毫秒/步 - 損失: 0.8769 - 稀疏類別準確度: 0.6456
1767/未知 739秒 417毫秒/步 - 損失: 0.8768 - 稀疏類別準確度: 0.6456
1768/未知 740秒 417毫秒/步 - 損失: 0.8767 - 稀疏類別準確度: 0.6457
1769/未知 740秒 417毫秒/步 - 損失: 0.8766 - 稀疏類別準確度: 0.6457
1770/未知 741秒 417毫秒/步 - 損失: 0.8765 - 稀疏類別準確度: 0.6457
1771/未知 741秒 417毫秒/步 - 損失: 0.8764 - 稀疏類別準確度: 0.6458
1772/未知 741秒 417毫秒/步 - 損失: 0.8763 - 稀疏類別準確度: 0.6458
1773/未知 742秒 417毫秒/步 - 損失: 0.8763 - 稀疏類別準確度: 0.6458
1774/未知 742秒 417毫秒/步 - 損失: 0.8762 - 稀疏類別準確度: 0.6459
1775/未知 743秒 417毫秒/步 - 損失: 0.8761 - 稀疏類別準確度: 0.6459
1776/未知 743秒 417毫秒/步 - 損失: 0.8760 - 稀疏類別準確度: 0.6459
1777/未知 743秒 417毫秒/步 - 損失: 0.8759 - 稀疏類別準確度: 0.6460
1778/未知 744秒 417毫秒/步 - 損失: 0.8758 - 稀疏類別準確度: 0.6460
1779/未知 744秒 417毫秒/步 - 損失: 0.8757 - 稀疏類別準確度: 0.6460
1780/未知 745秒 417毫秒/步 - 損失: 0.8756 - 稀疏類別準確度: 0.6461
1781/未知 745秒 417毫秒/步 - 損失: 0.8755 - 稀疏類別準確度: 0.6461
1782/未知 746秒 417毫秒/步 - 損失: 0.8754 - 稀疏類別準確度: 0.6461
1783/未知 746秒 417毫秒/步 - 損失: 0.8753 - 稀疏類別準確度: 0.6461
1784/未知 747秒 417毫秒/步 - 損失: 0.8752 - 稀疏類別準確度: 0.6462
1785/未知 747秒 417毫秒/步 - 損失: 0.8751 - 稀疏類別準確度: 0.6462
1786/未知 747秒 417毫秒/步 - 損失: 0.8750 - 稀疏類別準確度: 0.6462
1787/未知 748秒 417毫秒/步 - 損失: 0.8749 - 稀疏類別準確度: 0.6463
1788/未知 748秒 417毫秒/步 - 損失: 0.8748 - 稀疏類別準確度: 0.6463
1789/未知 749秒 417毫秒/步 - 損失: 0.8747 - 稀疏類別準確度: 0.6463
1790/未知 749秒 417毫秒/步 - 損失: 0.8746 - 稀疏類別準確度: 0.6464
1791/未知 750秒 417毫秒/步 - 損失: 0.8745 - 稀疏類別準確度: 0.6464
1792/未知 750秒 417毫秒/步 - 損失: 0.8744 - 稀疏類別準確度: 0.6464
1793/未知 751秒 417毫秒/步 - 損失: 0.8743 - 稀疏類別準確度: 0.6465
1794/未知 751秒 417毫秒/步 - 損失: 0.8743 - 稀疏類別準確度: 0.6465
1795/未知 752秒 417毫秒/步 - 損失: 0.8742 - 稀疏類別準確度: 0.6465
1796/未知 752秒 417毫秒/步 - 損失: 0.8741 - 稀疏類別準確度: 0.6466
1797/未知 753秒 417毫秒/步 - 損失: 0.8740 - 稀疏類別準確度: 0.6466
1798/未知 753秒 417毫秒/步 - 損失: 0.8739 - 稀疏類別準確度: 0.6466
1799/未知 753秒 417毫秒/步 - 損失: 0.8738 - 稀疏類別準確度: 0.6466
1800/未知 754秒 417毫秒/步 - 損失: 0.8737 - 稀疏類別準確度: 0.6467
1801/未知 754秒 417毫秒/步 - 損失: 0.8736 - 稀疏類別準確度: 0.6467
1802/未知 755秒 417毫秒/步 - 損失: 0.8735 - 稀疏類別準確度: 0.6467
1803/未知 755秒 417毫秒/步 - 損失: 0.8734 - 稀疏類別準確度: 0.6468
1804/未知 756秒 417毫秒/步 - 損失: 0.8733 - 稀疏類別準確度: 0.6468
1805/未知 756秒 417毫秒/步 - 損失: 0.8732 - 稀疏類別準確度: 0.6468
1806/未知 757秒 417毫秒/步 - 損失: 0.8731 - 稀疏類別準確度: 0.6469
1807/未知 757秒 417毫秒/步 - 損失: 0.8730 - 稀疏類別準確度: 0.6469
1808/未知 757秒 417毫秒/步 - 損失: 0.8729 - 稀疏類別準確度: 0.6469
1809/未知 758秒 417毫秒/步 - 損失: 0.8729 - 稀疏類別準確度: 0.6470
1810/未知 758秒 417毫秒/步 - 損失: 0.8728 - 稀疏類別準確度: 0.6470
1811/未知 758秒 417毫秒/步 - 損失: 0.8727 - 稀疏類別準確度: 0.6470
1812/未知 759秒 417毫秒/步 - 損失: 0.8726 - 稀疏類別準確度: 0.6471
1813/未知 759秒 417毫秒/步 - 損失: 0.8725 - 稀疏類別準確度: 0.6471
1814/未知 760秒 417毫秒/步 - 損失: 0.8724 - 稀疏類別準確度: 0.6471
1815/未知 760秒 417毫秒/步 - 損失: 0.8723 - 稀疏類別準確度: 0.6471
1816/未知 760秒 417毫秒/步 - 損失: 0.8722 - 稀疏類別準確度: 0.6472
1817/未知 761秒 417毫秒/步 - 損失: 0.8721 - 稀疏類別準確度: 0.6472
1818/未知 761秒 417毫秒/步 - 損失: 0.8720 - 稀疏類別準確度: 0.6472
1819/未知 761秒 417毫秒/步 - 損失: 0.8719 - 稀疏類別準確度: 0.6473
1820/未知 762秒 417毫秒/步 - 損失: 0.8718 - 稀疏類別準確度: 0.6473
1821/未知 762秒 417毫秒/步 - 損失: 0.8717 - 稀疏類別準確度: 0.6473
1822/未知 763秒 417毫秒/步 - 損失: 0.8717 - 稀疏類別準確度: 0.6474
1823/未知 763秒 417毫秒/步 - 損失: 0.8716 - 稀疏類別準確度: 0.6474
1824/未知 764秒 417毫秒/步 - 損失: 0.8715 - 稀疏類別準確度: 0.6474
1825/未知 764秒 417毫秒/步 - 損失: 0.8714 - 稀疏類別準確度: 0.6475
1826/未知 765秒 417毫秒/步 - 損失: 0.8713 - 稀疏類別準確度: 0.6475
1827/未知 765秒 417毫秒/步 - 損失: 0.8712 - 稀疏類別準確度: 0.6475
1828/未知 766秒 417毫秒/步 - 損失: 0.8711 - 稀疏類別準確度: 0.6475
1829/未知 766秒 417毫秒/步 - 損失: 0.8710 - 稀疏類別準確度: 0.6476
1830/未知 767秒 417毫秒/步 - 損失: 0.8709 - 稀疏類別準確度: 0.6476
1831/未知 767秒 417毫秒/步 - 損失: 0.8708 - 稀疏類別準確度: 0.6476
1832/未知 767秒 417毫秒/步 - 損失: 0.8707 - 稀疏類別準確度: 0.6477
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1840/未知 771秒 418毫秒/步 - 損失: 0.8700 - 稀疏類別準確度: 0.6479
1841/未知 771秒 417毫秒/步 - 損失: 0.8699 - 稀疏類別準確度: 0.6479
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1855/未知 777秒 417毫秒/步 - 損失: 0.8687 - 稀疏類別準確度: 0.6484
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1858/未知 778秒 417毫秒/步 - 損失: 0.8684 - 稀疏類別準確度: 0.6484
1859/未知 779秒 417毫秒/步 - 損失: 0.8683 - 稀疏類別準確度: 0.6485
1860/未知 779秒 417毫秒/步 - 損失: 0.8682 - 稀疏類別準確度: 0.6485
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1862/未知 780秒 417毫秒/步 - 損失: 0.8680 - 稀疏類別準確度: 0.6486
1863/未知 780秒 417毫秒/步 - 損失: 0.8679 - 稀疏類別準確度: 0.6486
1864/未知 781秒 417毫秒/步 - 損失: 0.8679 - 稀疏類別準確度: 0.6486
1865/未知 781秒 417毫秒/步 - 損失: 0.8678 - 稀疏類別準確度: 0.6486
1865/1865 ━━━━━━━━━━━━━━━━━━━━ 781秒 417毫秒/步 - 損失: 0.8677 - 稀疏類別準確度: 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 模型達到約 79% 測試準確度。
實驗 3：深度與交叉模型
在第三個實驗中，我們建立了一個深度與交叉模型。這個模型的深度部分與前一個實驗中建立的深度部分相同。交叉部分的主要概念是以有效的方式應用顯式的特徵交叉，其中交叉特徵的程度隨著層深度而增加。
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")
讓我們執行它
run_experiment(deep_and_cross_model)
Start training the model...
1/Unknown 1s 993ms/step - loss: 2.4838 - sparse_categorical_accuracy: 0.1057
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1000/未知 459秒 458毫秒/步 - 損失: 0.9624 - 稀疏類別準確度: 0.6280
1001/未知 459秒 458毫秒/步 - 損失: 0.9622 - 稀疏類別準確度: 0.6281
1002/未知 460秒 458毫秒/步 - 損失: 0.9620 - 稀疏類別準確度: 0.6282
1003/未知 460秒 458毫秒/步 - 損失: 0.9618 - 稀疏類別準確度: 0.6282
1004/未知 461秒 458毫秒/步 - 損失: 0.9616 - 稀疏類別準確度: 0.6283
1005/未知 461秒 458毫秒/步 - 損失: 0.9614 - 稀疏類別準確度: 0.6284
1006/未知 462秒 458毫秒/步 - 損失: 0.9612 - 稀疏類別準確度: 0.6284
1007/未知 462秒 458毫秒/步 - 損失: 0.9609 - 稀疏類別準確度: 0.6285
1008/未知 462秒 458毫秒/步 - 損失: 0.9607 - 稀疏類別準確度: 0.6286
1009/未知 463秒 458毫秒/步 - 損失: 0.9605 - 稀疏類別準確度: 0.6286
1010/未知 463秒 458毫秒/步 - 損失: 0.9603 - 稀疏類別準確度: 0.6287
1011/未知 464秒 458毫秒/步 - 損失: 0.9601 - 稀疏類別準確度: 0.6287
1012/未知 465秒 458毫秒/步 - 損失: 0.9599 - 稀疏類別準確度: 0.6288
1013/未知 465秒 458毫秒/步 - 損失: 0.9597 - 稀疏類別準確度: 0.6289
1014/未知 465秒 459毫秒/步 - 損失: 0.9595 - 稀疏類別準確度: 0.6289
1015/未知 466秒 459毫秒/步 - 損失: 0.9593 - 稀疏類別準確度: 0.6290
1016/未知 466秒 459毫秒/步 - 損失: 0.9591 - 稀疏類別準確度: 0.6291
1017/未知 467秒 459毫秒/步 - 損失: 0.9589 - 稀疏類別準確度: 0.6291
1018/未知 467秒 459毫秒/步 - 損失: 0.9587 - 稀疏類別準確度: 0.6292
1019/未知 468秒 459毫秒/步 - 損失: 0.9584 - 稀疏類別準確度: 0.6293
1020/未知 468秒 459毫秒/步 - 損失: 0.9582 - 稀疏類別準確度: 0.6293
1021/未知 469秒 459毫秒/步 - 損失: 0.9580 - 稀疏類別準確度: 0.6294
1022/未知 469秒 459毫秒/步 - 損失: 0.9578 - 稀疏類別準確度: 0.6295
1023/未知 470秒 459毫秒/步 - 損失: 0.9576 - 稀疏類別準確度: 0.6295
1024/未知 470秒 459毫秒/步 - 損失: 0.9574 - 稀疏類別準確度: 0.6296
1025/未知 471秒 459毫秒/步 - 損失: 0.9572 - 稀疏類別準確度: 0.6297
1026/未知 471秒 459毫秒/步 - 損失: 0.9570 - 稀疏類別準確度: 0.6297
1027/未知 472秒 459毫秒/步 - 損失: 0.9568 - 稀疏類別準確度: 0.6298
1028/未知 472秒 459毫秒/步 - 損失: 0.9566 - 稀疏類別準確度: 0.6298
1029/未知 473秒 459毫秒/步 - 損失: 0.9564 - 稀疏類別準確度: 0.6299
1030/未知 473秒 459毫秒/步 - 損失: 0.9562 - 稀疏類別準確度: 0.6300
1031/未知 474秒 459毫秒/步 - 損失: 0.9560 - 稀疏類別準確度: 0.6300
1032/未知 474秒 459毫秒/步 - 損失: 0.9558 - 稀疏類別準確度: 0.6301
1033/未知 475秒 459毫秒/步 - 損失: 0.9556 - 稀疏類別準確度: 0.6302
1034/未知 475秒 459毫秒/步 - 損失: 0.9554 - 稀疏類別準確度: 0.6302
1035/未知 476秒 459毫秒/步 - 損失: 0.9552 - 稀疏類別準確度: 0.6303
1036/未知 476秒 459毫秒/步 - 損失: 0.9550 - 稀疏類別準確度: 0.6304
1037/未知 477秒 459毫秒/步 - 損失: 0.9548 - 稀疏類別準確度: 0.6304
1038/未知 477秒 459毫秒/步 - 損失: 0.9546 - 稀疏類別準確度: 0.6305
1039/未知 478秒 459毫秒/步 - 損失: 0.9544 - 稀疏類別準確度: 0.6305
1040/未知 478秒 459毫秒/步 - 損失: 0.9542 - 稀疏類別準確度: 0.6306
1041/未知 479秒 459毫秒/步 - 損失: 0.9540 - 稀疏類別準確度: 0.6307
1042/未知 479秒 459毫秒/步 - 損失: 0.9538 - 稀疏類別準確度: 0.6307
1043/未知 480秒 459毫秒/步 - 損失: 0.9536 - 稀疏類別準確度: 0.6308
1044/未知 480秒 459毫秒/步 - 損失: 0.9534 - 稀疏類別準確度: 0.6309
1045/未知 481秒 459毫秒/步 - 損失: 0.9532 - 稀疏類別準確度: 0.6309
1046/未知 481秒 459毫秒/步 - 損失: 0.9530 - 稀疏類別準確度: 0.6310
1047/未知 482秒 459毫秒/步 - 損失: 0.9528 - 稀疏類別準確度: 0.6310
1048/未知 482秒 459毫秒/步 - 損失: 0.9526 - 稀疏類別準確度: 0.6311
1049/未知 483秒 459毫秒/步 - 損失: 0.9524 - 稀疏類別準確度: 0.6312
1050/未知 483秒 460毫秒/步 - 損失: 0.9522 - 稀疏類別準確度: 0.6312
1051/未知 484秒 460毫秒/步 - 損失: 0.9520 - 稀疏類別準確度: 0.6313
1052/未知 484秒 460毫秒/步 - 損失: 0.9518 - 稀疏類別準確度: 0.6314
1053/未知 484秒 460毫秒/步 - 損失: 0.9516 - 稀疏類別準確度: 0.6314
1054/未知 485秒 460毫秒/步 - 損失: 0.9514 - 稀疏類別準確度: 0.6315
1055/未知 485秒 460毫秒/步 - 損失: 0.9512 - 稀疏類別準確度: 0.6315
1056/未知 486秒 460毫秒/步 - 損失: 0.9510 - 稀疏類別準確度: 0.6316
1057/未知 486秒 460毫秒/步 - 損失: 0.9508 - 稀疏類別準確度: 0.6317
1058/未知 487秒 460毫秒/步 - 損失: 0.9506 - 稀疏類別準確度: 0.6317
1059/未知 487秒 460毫秒/步 - 損失: 0.9504 - 稀疏類別準確度: 0.6318
1060/未知 488秒 460毫秒/步 - 損失: 0.9502 - 稀疏類別準確度: 0.6318
1061/未知 488秒 460毫秒/步 - 損失: 0.9500 - 稀疏類別準確度: 0.6319
1062/未知 489秒 460毫秒/步 - 損失: 0.9498 - 稀疏類別準確度: 0.6320
1063/未知 489秒 460毫秒/步 - 損失: 0.9496 - 稀疏類別準確度: 0.6320
1064/未知 490秒 460毫秒/步 - 損失: 0.9495 - 稀疏類別準確度: 0.6321
1065/未知 490秒 460毫秒/步 - 損失: 0.9493 - 稀疏類別準確度: 0.6321
1066/未知 491秒 460毫秒/步 - 損失: 0.9491 - 稀疏類別準確度: 0.6322
1067/未知 491秒 460毫秒/步 - 損失: 0.9489 - 稀疏類別準確度: 0.6323
1068/未知 492秒 460毫秒/步 - 損失: 0.9487 - 稀疏類別準確度: 0.6323
1069/未知 492秒 460毫秒/步 - 損失: 0.9485 - 稀疏類別準確度: 0.6324
1070/未知 493秒 460毫秒/步 - 損失: 0.9483 - 稀疏類別準確度: 0.6324
1071/未知 493秒 460毫秒/步 - 損失: 0.9481 - 稀疏類別準確度: 0.6325
1072/未知 494秒 460毫秒/步 - 損失: 0.9479 - 稀疏類別準確度: 0.6326
1073/未知 494秒 460毫秒/步 - 損失: 0.9477 - 稀疏類別準確度: 0.6326
1074/未知 495秒 460毫秒/步 - 損失: 0.9475 - 稀疏類別準確度: 0.6327
1075/未知 495秒 460毫秒/步 - 損失: 0.9473 - 稀疏類別準確度: 0.6327
1076/未知 496秒 460毫秒/步 - 損失: 0.9471 - 稀疏類別準確度: 0.6328
1077/未知 496秒 460毫秒/步 - 損失: 0.9470 - 稀疏類別準確度: 0.6329
1078/未知 496秒 460毫秒/步 - 損失: 0.9468 - 稀疏類別準確度: 0.6329
1079/未知 497秒 460毫秒/步 - 損失: 0.9466 - 稀疏類別準確度: 0.6330
1080/未知 497秒 460毫秒/步 - 損失: 0.9464 - 稀疏類別準確度: 0.6330
1081/未知 498秒 460毫秒/步 - 損失: 0.9462 - 稀疏類別準確度: 0.6331
1082/未知 498秒 460毫秒/步 - 損失: 0.9460 - 稀疏類別準確度: 0.6332
1083/未知 499秒 460毫秒/步 - 損失: 0.9458 - 稀疏類別準確度: 0.6332
1084/未知 499秒 460毫秒/步 - 損失: 0.9456 - 稀疏類別準確度: 0.6333
1085/未知 500秒 460毫秒/步 - 損失: 0.9454 - 稀疏類別準確度: 0.6333
1086/未知 500秒 460毫秒/步 - 損失: 0.9453 - 稀疏類別準確度: 0.6334
1087/未知 501秒 460毫秒/步 - 損失: 0.9451 - 稀疏類別準確度: 0.6335
1088/未知 501秒 460毫秒/步 - 損失: 0.9449 - 稀疏類別準確度: 0.6335
1089/未知 502秒 460毫秒/步 - 損失: 0.9447 - 稀疏類別準確度: 0.6336
1090/未知 502秒 460毫秒/步 - 損失: 0.9445 - 稀疏類別準確度: 0.6336
1091/未知 503秒 460毫秒/步 - 損失: 0.9443 - 稀疏類別準確度: 0.6337
1092/未知 503秒 460毫秒/步 - 損失: 0.9441 - 稀疏類別準確度: 0.6337
1093/未知 503秒 460毫秒/步 - 損失: 0.9439 - 稀疏類別準確度: 0.6338
1094/未知 504秒 460毫秒/步 - 損失: 0.9438 - 稀疏類別準確度: 0.6339
1095/未知 504秒 460毫秒/步 - 損失: 0.9436 - 稀疏類別準確度: 0.6339
1096/未知 505秒 460毫秒/步 - 損失: 0.9434 - 稀疏類別準確度: 0.6340
1097/未知 505秒 460毫秒/步 - 損失: 0.9432 - 稀疏類別準確度: 0.6340
1098/未知 506秒 460毫秒/步 - 損失: 0.9430 - 稀疏類別準確度: 0.6341
1099/未知 506秒 460毫秒/步 - 損失: 0.9428 - 稀疏類別準確度: 0.6342
1100/未知 507秒 460毫秒/步 - 損失: 0.9427 - 稀疏類別準確度: 0.6342
1101/未知 507秒 460毫秒/步 - 損失: 0.9425 - 稀疏類別準確度: 0.6343
1102/未知 508秒 460毫秒/步 - 損失: 0.9423 - 稀疏類別準確度: 0.6343
1103/未知 508秒 460毫秒/步 - 損失: 0.9421 - 稀疏類別準確度: 0.6344
1104/未知 508秒 460毫秒/步 - 損失: 0.9419 - 稀疏類別準確度: 0.6344
1105/未知 509秒 460毫秒/步 - 損失: 0.9417 - 稀疏類別準確度: 0.6345
1106/未知 509秒 460毫秒/步 - 損失: 0.9416 - 稀疏類別準確度: 0.6346
1107/未知 510秒 460毫秒/步 - 損失: 0.9414 - 稀疏類別準確度: 0.6346
1108/未知 510秒 460毫秒/步 - 損失: 0.9412 - 稀疏類別準確度: 0.6347
1109/未知 510秒 460毫秒/步 - 損失: 0.9410 - 稀疏類別準確度: 0.6347
1110/未知 511秒 459毫秒/步 - 損失: 0.9408 - 稀疏類別準確度: 0.6348
1111/未知 511秒 459毫秒/步 - 損失: 0.9406 - 稀疏類別準確度: 0.6348
1112/未知 511秒 459毫秒/步 - 損失: 0.9405 - 稀疏類別準確度: 0.6349
1113/未知 512秒 459毫秒/步 - 損失: 0.9403 - 稀疏類別準確度: 0.6349
1114/未知 512秒 459毫秒/步 - 損失: 0.9401 - 稀疏類別準確度: 0.6350
1115/未知 512秒 459毫秒/步 - 損失: 0.9399 - 稀疏類別準確度: 0.6351
1116/未知 513秒 459毫秒/步 - 損失: 0.9397 - 稀疏類別準確度: 0.6351
1117/未知 513秒 459毫秒/步 - 損失: 0.9396 - 稀疏類別準確度: 0.6352
1118/未知 513秒 459毫秒/步 - 損失: 0.9394 - 稀疏類別準確度: 0.6352
1119/未知 514秒 459毫秒/步 - 損失: 0.9392 - 稀疏類別準確度: 0.6353
1120/未知 514秒 458毫秒/步 - 損失: 0.9390 - 稀疏類別準確度: 0.6353
1121/未知 515秒 458毫秒/步 - 損失: 0.9388 - 稀疏類別準確度: 0.6354
1122/未知 515秒 459毫秒/步 - 損失: 0.9387 - 稀疏類別準確度: 0.6355
1123/未知 515秒 459毫秒/步 - 損失: 0.9385 - 稀疏類別準確度: 0.6355
1124/未知 516秒 459毫秒/步 - 損失: 0.9383 - 稀疏類別準確度: 0.6356
1125/未知 516秒 459毫秒/步 - 損失: 0.9381 - 稀疏類別準確度: 0.6356
1126/未知 517秒 458毫秒/步 - 損失: 0.9379 - 稀疏類別準確度: 0.6357
1127/未知 517秒 458毫秒/步 - 損失: 0.9378 - 稀疏類別準確度: 0.6357
1128/未知 518秒 458毫秒/步 - 損失: 0.9376 - 稀疏類別準確度: 0.6358
1129/未知 518秒 458毫秒/步 - 損失: 0.9374 - 稀疏類別準確度: 0.6358
1130/未知 519秒 458毫秒/步 - 損失: 0.9372 - 稀疏類別準確度: 0.6359
1131/未知 519秒 458毫秒/步 - 損失: 0.9371 - 稀疏類別準確度: 0.6360
1132/未知 519秒 458毫秒/步 - 損失: 0.9369 - 稀疏類別準確度: 0.6360
1133/未知 520秒 458毫秒/步 - 損失: 0.9367 - 稀疏類別準確度: 0.6361
1134/未知 520秒 458毫秒/步 - 損失: 0.9365 - 稀疏類別準確度: 0.6361
1135/未知 521秒 458毫秒/步 - 損失: 0.9364 - 稀疏類別準確度: 0.6362
1136/未知 521秒 458毫秒/步 - 損失: 0.9362 - 稀疏類別準確度: 0.6362
1137/未知 522秒 458毫秒/步 - 損失: 0.9360 - 稀疏類別準確度: 0.6363
1138/未知 522秒 458毫秒/步 - 損失: 0.9358 - 稀疏類別準確度: 0.6363
1139/未知 523秒 458毫秒/步 - 損失: 0.9356 - 稀疏類別準確度: 0.6364
1140/未知 523秒 458毫秒/步 - 損失: 0.9355 - 稀疏類別準確度: 0.6364
1141/未知 524秒 458毫秒/步 - 損失: 0.9353 - 稀疏類別準確度: 0.6365
1142/未知 524秒 458毫秒/步 - 損失: 0.9351 - 稀疏類別準確度: 0.6366
1143/未知 525秒 458毫秒/步 - 損失: 0.9350 - 稀疏類別準確度: 0.6366
1144/未知 525秒 458毫秒/步 - 損失: 0.9348 - 稀疏類別準確度: 0.6367
1145/未知 525秒 458毫秒/步 - 損失: 0.9346 - 稀疏類別準確度: 0.6367
1146/未知 526秒 458毫秒/步 - 損失: 0.9344 - 稀疏類別準確度: 0.6368
1147/未知 526秒 458毫秒/步 - 損失: 0.9343 - 稀疏類別準確度: 0.6368
1148/未知 527秒 458毫秒/步 - 損失: 0.9341 - 稀疏類別準確度: 0.6369
1149/未知 527秒 458毫秒/步 - 損失: 0.9339 - 稀疏類別準確度: 0.6369
1150/未知 528秒 458毫秒/步 - 損失: 0.9337 - 稀疏類別準確度: 0.6370
1151/未知 528秒 458毫秒/步 - 損失: 0.9336 - 稀疏類別準確度: 0.6370
1152/未知 528秒 458毫秒/步 - 損失: 0.9334 - 稀疏類別準確度: 0.6371
1153/未知 529秒 458毫秒/步 - 損失: 0.9332 - 稀疏類別準確度: 0.6372
1154/未知 529秒 458毫秒/步 - 損失: 0.9330 - 稀疏類別準確度: 0.6372
1155/未知 530秒 458毫秒/步 - 損失: 0.9329 - 稀疏類別準確度: 0.6373
1156/未知 530秒 458毫秒/步 - 損失: 0.9327 - 稀疏類別準確度: 0.6373
1157/未知 530秒 458毫秒/步 - 損失: 0.9325 - 稀疏類別準確度: 0.6374
1158/未知 531秒 458毫秒/步 - 損失: 0.9324 - 稀疏類別準確度: 0.6374
1159/未知 531秒 458毫秒/步 - 損失: 0.9322 - 稀疏類別準確度: 0.6375
1160/未知 532秒 458毫秒/步 - 損失: 0.9320 - 稀疏類別準確度: 0.6375
1161/未知 532秒 458毫秒/步 - 損失: 0.9318 - 稀疏類別準確度: 0.6376
1162/未知 532秒 458毫秒/步 - 損失: 0.9317 - 稀疏類別準確度: 0.6376
1163/未知 533秒 458毫秒/步 - 損失: 0.9315 - 稀疏類別準確度: 0.6377
1164/未知 533秒 458毫秒/步 - 損失: 0.9313 - 稀疏類別準確度: 0.6377
1165/未知 534秒 458毫秒/步 - 損失: 0.9312 - 稀疏類別準確度: 0.6378
1166/未知 534秒 458毫秒/步 - 損失: 0.9310 - 稀疏類別準確度: 0.6378
1167/未知 535秒 458毫秒/步 - 損失: 0.9308 - 稀疏類別準確度: 0.6379
1168/未知 535秒 458毫秒/步 - 損失: 0.9307 - 稀疏類別準確度: 0.6380
1169/未知 536秒 458毫秒/步 - 損失: 0.9305 - 稀疏類別準確度: 0.6380
1170/未知 536秒 458毫秒/步 - 損失: 0.9303 - 稀疏類別準確度: 0.6381
1171/未知 537秒 458毫秒/步 - 損失: 0.9302 - 稀疏類別準確度: 0.6381
1172/未知 537秒 458毫秒/步 - 損失: 0.9300 - 稀疏類別準確度: 0.6382
1173/未知 538秒 458毫秒/步 - 損失: 0.9298 - 稀疏類別準確度: 0.6382
1174/未知 538秒 458毫秒/步 - 損失: 0.9297 - 稀疏類別準確度: 0.6383
1175/未知 538秒 458毫秒/步 - 損失: 0.9295 - 稀疏類別準確度: 0.6383
1176/未知 539秒 458毫秒/步 - 損失: 0.9293 - 稀疏類別準確度: 0.6384
1177/未知 539秒 458毫秒/步 - 損失: 0.9292 - 稀疏類別準確度: 0.6384
1178/未知 540秒 458毫秒/步 - 損失: 0.9290 - 稀疏類別準確度: 0.6385
1179/未知 540秒 458毫秒/步 - 損失: 0.9288 - 稀疏類別準確度: 0.6385
1180/未知 541秒 458毫秒/步 - 損失: 0.9287 - 稀疏類別準確度: 0.6386
1181/未知 541秒 458毫秒/步 - 損失: 0.9285 - 稀疏類別準確度: 0.6386
1182/未知 542秒 458毫秒/步 - 損失: 0.9283 - 稀疏類別準確度: 0.6387
1183/未知 542秒 458毫秒/步 - 損失: 0.9282 - 稀疏類別準確度: 0.6387
1184/未知 543秒 458毫秒/步 - 損失: 0.9280 - 稀疏類別準確度: 0.6388
1185/未知 543秒 458毫秒/步 - 損失: 0.9278 - 稀疏類別準確度: 0.6388
1186/未知 543秒 458毫秒/步 - 損失: 0.9277 - 稀疏類別準確度: 0.6389
1187/未知 544秒 458毫秒/步 - 損失: 0.9275 - 稀疏類別準確度: 0.6389
1188/未知 544秒 458毫秒/步驟 - 損失: 0.9273 - 稀疏類別準確度: 0.6390
1189/未知 545秒 458毫秒/步驟 - 損失: 0.9272 - 稀疏類別準確度: 0.6390
1190/未知 545秒 458毫秒/步驟 - 損失: 0.9270 - 稀疏類別準確度: 0.6391
1191/未知 546秒 458毫秒/步驟 - 損失: 0.9268 - 稀疏類別準確度: 0.6391
1192/未知 546秒 458毫秒/步驟 - 損失: 0.9267 - 稀疏類別準確度: 0.6392
1193/未知 547秒 458毫秒/步驟 - 損失: 0.9265 - 稀疏類別準確度: 0.6392
1194/未知 547秒 458毫秒/步驟 - 損失: 0.9263 - 稀疏類別準確度: 0.6393
1195/未知 548秒 458毫秒/步驟 - 損失: 0.9262 - 稀疏類別準確度: 0.6394
1196/未知 548秒 458毫秒/步驟 - 損失: 0.9260 - 稀疏類別準確度: 0.6394
1197/未知 548秒 458毫秒/步驟 - 損失: 0.9259 - 稀疏類別準確度: 0.6395
1198/未知 549秒 458毫秒/步驟 - 損失: 0.9257 - 稀疏類別準確度: 0.6395
1199/未知 549秒 458毫秒/步驟 - 損失: 0.9255 - 稀疏類別準確度: 0.6396
1200/未知 550秒 458毫秒/步驟 - 損失: 0.9254 - 稀疏類別準確度: 0.6396
1201/未知 550秒 458毫秒/步驟 - 損失: 0.9252 - 稀疏類別準確度: 0.6397
1202/未知 551秒 458毫秒/步驟 - 損失: 0.9250 - 稀疏類別準確度: 0.6397
1203/未知 551秒 458毫秒/步驟 - 損失: 0.9249 - 稀疏類別準確度: 0.6398
1204/未知 552秒 458毫秒/步驟 - 損失: 0.9247 - 稀疏類別準確度: 0.6398
1205/未知 552秒 458毫秒/步驟 - 損失: 0.9246 - 稀疏類別準確度: 0.6399
1206/未知 553秒 458毫秒/步驟 - 損失: 0.9244 - 稀疏類別準確度: 0.6399
1207/未知 553秒 458毫秒/步驟 - 損失: 0.9242 - 稀疏類別準確度: 0.6400
1208/未知 554秒 458毫秒/步驟 - 損失: 0.9241 - 稀疏類別準確度: 0.6400
1209/未知 554秒 458毫秒/步驟 - 損失: 0.9239 - 稀疏類別準確度: 0.6401
1210/未知 555秒 458毫秒/步驟 - 損失: 0.9238 - 稀疏類別準確度: 0.6401
1211/未知 555秒 458毫秒/步驟 - 損失: 0.9236 - 稀疏類別準確度: 0.6402
1212/未知 556秒 458毫秒/步驟 - 損失: 0.9234 - 稀疏類別準確度: 0.6402
1213/未知 556秒 458毫秒/步驟 - 損失: 0.9233 - 稀疏類別準確度: 0.6403
1214/未知 557秒 458毫秒/步驟 - 損失: 0.9231 - 稀疏類別準確度: 0.6403
1215/未知 557秒 458毫秒/步驟 - 損失: 0.9230 - 稀疏類別準確度: 0.6404
1216/未知 558秒 458毫秒/步驟 - 損失: 0.9228 - 稀疏類別準確度: 0.6404
1217/未知 558秒 458毫秒/步驟 - 損失: 0.9226 - 稀疏類別準確度: 0.6405
1218/未知 559秒 458毫秒/步驟 - 損失: 0.9225 - 稀疏類別準確度: 0.6405
1219/未知 559秒 458毫秒/步驟 - 損失: 0.9223 - 稀疏類別準確度: 0.6406
1220/未知 560秒 458毫秒/步驟 - 損失: 0.9222 - 稀疏類別準確度: 0.6406
1221/未知 560秒 458毫秒/步驟 - 損失: 0.9220 - 稀疏類別準確度: 0.6407
1222/未知 560秒 458毫秒/步驟 - 損失: 0.9218 - 稀疏類別準確度: 0.6407
1223/未知 561秒 458毫秒/步驟 - 損失: 0.9217 - 稀疏類別準確度: 0.6408
1224/未知 561秒 458毫秒/步驟 - 損失: 0.9215 - 稀疏類別準確度: 0.6408
1225/未知 562秒 458毫秒/步驟 - 損失: 0.9214 - 稀疏類別準確度: 0.6409
1226/未知 562秒 458毫秒/步驟 - 損失: 0.9212 - 稀疏類別準確度: 0.6409
1227/未知 563秒 458毫秒/步驟 - 損失: 0.9211 - 稀疏類別準確度: 0.6410
1228/未知 563秒 458毫秒/步驟 - 損失: 0.9209 - 稀疏類別準確度: 0.6410
1229/未知 564秒 458毫秒/步驟 - 損失: 0.9207 - 稀疏類別準確度: 0.6410
1230/未知 564秒 458毫秒/步驟 - 損失: 0.9206 - 稀疏類別準確度: 0.6411
1231/未知 565秒 458毫秒/步驟 - 損失: 0.9204 - 稀疏類別準確度: 0.6411
1232/未知 565秒 458毫秒/步驟 - 損失: 0.9203 - 稀疏類別準確度: 0.6412
1233/未知 566秒 458毫秒/步驟 - 損失: 0.9201 - 稀疏類別準確度: 0.6412
1234/未知 566秒 458毫秒/步驟 - 損失: 0.9200 - 稀疏類別準確度: 0.6413
1235/未知 567秒 458毫秒/步驟 - 損失: 0.9198 - 稀疏類別準確度: 0.6413
1236/未知 567秒 458毫秒/步驟 - 損失: 0.9197 - 稀疏類別準確度: 0.6414
1237/未知 568秒 458毫秒/步驟 - 損失: 0.9195 - 稀疏類別準確度: 0.6414
1238/未知 568秒 458毫秒/步驟 - 損失: 0.9193 - 稀疏類別準確度: 0.6415
1239/未知 569秒 458毫秒/步驟 - 損失: 0.9192 - 稀疏類別準確度: 0.6415
1240/未知 569秒 458毫秒/步驟 - 損失: 0.9190 - 稀疏類別準確度: 0.6416
1241/未知 569秒 458毫秒/步驟 - 損失: 0.9189 - 稀疏類別準確度: 0.6416
1242/未知 570秒 458毫秒/步驟 - 損失: 0.9187 - 稀疏類別準確度: 0.6417
1243/未知 570秒 458毫秒/步驟 - 損失: 0.9186 - 稀疏類別準確度: 0.6417
1244/未知 571秒 458毫秒/步驟 - 損失: 0.9184 - 稀疏類別準確度: 0.6418
1245/未知 571秒 458毫秒/步驟 - 損失: 0.9183 - 稀疏類別準確度: 0.6418
1246/未知 572秒 458毫秒/步驟 - 損失: 0.9181 - 稀疏類別準確度: 0.6419
1247/未知 572秒 458毫秒/步驟 - 損失: 0.9180 - 稀疏類別準確度: 0.6419
1248/未知 573秒 458毫秒/步驟 - 損失: 0.9178 - 稀疏類別準確度: 0.6420
1249/未知 573秒 458毫秒/步驟 - 損失: 0.9177 - 稀疏類別準確度: 0.6420
1250/未知 574秒 458毫秒/步驟 - 損失: 0.9175 - 稀疏類別準確度: 0.6421
1251/未知 574秒 458毫秒/步驟 - 損失: 0.9173 - 稀疏類別準確度: 0.6421
1252/未知 574秒 458毫秒/步驟 - 損失: 0.9172 - 稀疏類別準確度: 0.6422
1253/未知 575秒 458毫秒/步驟 - 損失: 0.9170 - 稀疏類別準確度: 0.6422
1254/未知 575秒 458毫秒/步驟 - 損失: 0.9169 - 稀疏類別準確度: 0.6423
1255/未知 576秒 458毫秒/步驟 - 損失: 0.9167 - 稀疏類別準確度: 0.6423
1256/未知 576秒 458毫秒/步驟 - 損失: 0.9166 - 稀疏類別準確度: 0.6424
1257/未知 577秒 458毫秒/步驟 - 損失: 0.9164 - 稀疏類別準確度: 0.6424
1258/未知 577秒 458毫秒/步驟 - 損失: 0.9163 - 稀疏類別準確度: 0.6424
1259/未知 578秒 458毫秒/步驟 - 損失: 0.9161 - 稀疏類別準確度: 0.6425
1260/未知 578秒 459毫秒/步驟 - 損失: 0.9160 - 稀疏類別準確度: 0.6425
1261/未知 579秒 459毫秒/步驟 - 損失: 0.9158 - 稀疏類別準確度: 0.6426
1262/未知 579秒 458毫秒/步驟 - 損失: 0.9157 - 稀疏類別準確度: 0.6426
1263/未知 580秒 459毫秒/步驟 - 損失: 0.9155 - 稀疏類別準確度: 0.6427
1264/未知 580秒 459毫秒/步驟 - 損失: 0.9154 - 稀疏類別準確度: 0.6427
1265/未知 581秒 459毫秒/步驟 - 損失: 0.9152 - 稀疏類別準確度: 0.6428
1266/未知 581秒 459毫秒/步驟 - 損失: 0.9151 - 稀疏類別準確度: 0.6428
1267/未知 582秒 459毫秒/步驟 - 損失: 0.9149 - 稀疏類別準確度: 0.6429
1268/未知 582秒 459毫秒/步驟 - 損失: 0.9148 - 稀疏類別準確度: 0.6429
1269/未知 583秒 459毫秒/步驟 - 損失: 0.9146 - 稀疏類別準確度: 0.6430
1270/未知 583秒 459毫秒/步驟 - 損失: 0.9145 - 稀疏類別準確度: 0.6430
1271/未知 584秒 459毫秒/步驟 - 損失: 0.9143 - 稀疏類別準確度: 0.6431
1272/未知 584秒 459毫秒/步驟 - 損失: 0.9142 - 稀疏類別準確度: 0.6431
1273/未知 584秒 459毫秒/步驟 - 損失: 0.9140 - 稀疏類別準確度: 0.6432
1274/未知 585秒 459毫秒/步驟 - 損失: 0.9139 - 稀疏類別準確度: 0.6432
1275/未知 585秒 459毫秒/步驟 - 損失: 0.9137 - 稀疏類別準確度: 0.6432
1276/未知 586秒 459毫秒/步驟 - 損失: 0.9136 - 稀疏類別準確度: 0.6433
1277/未知 586秒 459毫秒/步驟 - 損失: 0.9134 - 稀疏類別準確度: 0.6433
1278/未知 587秒 459毫秒/步驟 - 損失: 0.9133 - 稀疏類別準確度: 0.6434
1279/未知 587秒 459毫秒/步驟 - 損失: 0.9131 - 稀疏類別準確度: 0.6434
1280/未知 588秒 459毫秒/步驟 - 損失: 0.9130 - 稀疏類別準確度: 0.6435
1281/未知 588秒 459毫秒/步驟 - 損失: 0.9128 - 稀疏類別準確度: 0.6435
1282/未知 589秒 459毫秒/步驟 - 損失: 0.9127 - 稀疏類別準確度: 0.6436
1283/未知 589秒 459毫秒/步驟 - 損失: 0.9126 - 稀疏類別準確度: 0.6436
1284/未知 589秒 459毫秒/步驟 - 損失: 0.9124 - 稀疏類別準確度: 0.6437
1285/未知 590秒 458毫秒/步驟 - 損失: 0.9123 - 稀疏類別準確度: 0.6437
1286/未知 590秒 458毫秒/步驟 - 損失: 0.9121 - 稀疏類別準確度: 0.6438
1287/未知 591秒 458毫秒/步驟 - 損失: 0.9120 - 稀疏類別準確度: 0.6438
1288/未知 591秒 458毫秒/步驟 - 損失: 0.9118 - 稀疏類別準確度: 0.6438
1289/未知 591秒 458毫秒/步驟 - 損失: 0.9117 - 稀疏類別準確度: 0.6439
1290/未知 592秒 458毫秒/步驟 - 損失: 0.9115 - 稀疏類別準確度: 0.6439
1291/未知 592秒 458毫秒/步驟 - 損失: 0.9114 - 稀疏類別準確度: 0.6440
1292/未知 593秒 458毫秒/步驟 - 損失: 0.9112 - 稀疏類別準確度: 0.6440
1293/未知 593秒 458毫秒/步驟 - 損失: 0.9111 - 稀疏類別準確度: 0.6441
1294/未知 594秒 458毫秒/步驟 - 損失: 0.9109 - 稀疏類別準確度: 0.6441
1295/未知 594秒 458毫秒/步驟 - 損失: 0.9108 - 稀疏類別準確度: 0.6442
1296/未知 595秒 458毫秒/步驟 - 損失: 0.9107 - 稀疏類別準確度: 0.6442
1297/未知 595秒 458毫秒/步驟 - 損失: 0.9105 - 稀疏類別準確度: 0.6443
1298/未知 596秒 458毫秒/步驟 - 損失: 0.9104 - 稀疏類別準確度: 0.6443
1299/未知 596秒 458毫秒/步驟 - 損失: 0.9102 - 稀疏類別準確度: 0.6443
1300/未知 596秒 458毫秒/步驟 - 損失: 0.9101 - 稀疏類別準確度: 0.6444
1301/未知 597秒 458毫秒/步驟 - 損失: 0.9099 - 稀疏類別準確度: 0.6444
1302/未知 597秒 458毫秒/步驟 - 損失: 0.9098 - 稀疏類別準確度: 0.6445
1303/未知 598秒 458毫秒/步驟 - 損失: 0.9096 - 稀疏類別準確度: 0.6445
1304/未知 598秒 458毫秒/步驟 - 損失: 0.9095 - 稀疏類別準確度: 0.6446
1305/未知 599秒 458毫秒/步驟 - 損失: 0.9094 - 稀疏類別準確度: 0.6446
1306/未知 599秒 458毫秒/步驟 - 損失: 0.9092 - 稀疏類別準確度: 0.6447
1307/未知 600秒 458毫秒/步驟 - 損失: 0.9091 - 稀疏類別準確度: 0.6447
1308/未知 600秒 458毫秒/步驟 - 損失: 0.9089 - 稀疏類別準確度: 0.6448
1309/未知 601秒 458毫秒/步驟 - 損失: 0.9088 - 稀疏類別準確度: 0.6448
1310/未知 601秒 458毫秒/步驟 - 損失: 0.9086 - 稀疏類別準確度: 0.6448
1311/未知 602秒 458毫秒/步驟 - 損失: 0.9085 - 稀疏類別準確度: 0.6449
1312/未知 602秒 458毫秒/步驟 - 損失: 0.9084 - 稀疏類別準確度: 0.6449
1313/未知 602秒 458毫秒/步驟 - 損失: 0.9082 - 稀疏類別準確度: 0.6450
1314/未知 603秒 458毫秒/步驟 - 損失: 0.9081 - 稀疏類別準確度: 0.6450
1315/未知 603秒 458毫秒/步驟 - 損失: 0.9079 - 稀疏類別準確度: 0.6451
1316/未知 604秒 458毫秒/步驟 - 損失: 0.9078 - 稀疏類別準確度: 0.6451
1317/未知 604秒 458毫秒/步驟 - 損失: 0.9076 - 稀疏類別準確度: 0.6452
1318/未知 604秒 458毫秒/步驟 - 損失: 0.9075 - 稀疏類別準確度: 0.6452
1319/未知 605秒 458毫秒/步驟 - 損失: 0.9074 - 稀疏類別準確度: 0.6452
1320/未知 605秒 458毫秒/步驟 - 損失: 0.9072 - 稀疏類別準確度: 0.6453
1321/未知 605秒 458毫秒/步驟 - 損失: 0.9071 - 稀疏類別準確度: 0.6453
1322/未知 606秒 458毫秒/步驟 - 損失: 0.9069 - 稀疏類別準確度: 0.6454
1323/未知 606秒 458毫秒/步驟 - 損失: 0.9068 - 稀疏類別準確度: 0.6454
1324/未知 607秒 458毫秒/步驟 - 損失: 0.9067 - 稀疏類別準確度: 0.6455
1325/未知 607秒 458毫秒/步驟 - 損失: 0.9065 - 稀疏類別準確度: 0.6455
1326/未知 608秒 458毫秒/步驟 - 損失: 0.9064 - 稀疏類別準確度: 0.6455
1327/未知 608秒 458毫秒/步驟 - 損失: 0.9062 - 稀疏類別準確度: 0.6456
1328/未知 609秒 458毫秒/步驟 - 損失: 0.9061 - 稀疏類別準確度: 0.6456
1329/未知 609秒 458毫秒/步驟 - 損失: 0.9060 - 稀疏類別準確度: 0.6457
1330/未知 609秒 458毫秒/步驟 - 損失: 0.9058 - 稀疏類別準確度: 0.6457
1331/未知 610秒 458毫秒/步驟 - 損失: 0.9057 - 稀疏類別準確度: 0.6458
1332/未知 610秒 458毫秒/步驟 - 損失: 0.9055 - 稀疏類別準確度: 0.6458
1333/未知 611秒 458毫秒/步驟 - 損失: 0.9054 - 稀疏類別準確度: 0.6459
1334/未知 611秒 458毫秒/步驟 - 損失: 0.9053 - 稀疏類別準確度: 0.6459
1335/未知 612秒 458毫秒/步驟 - 損失: 0.9051 - 稀疏類別準確度: 0.6459
1336/未知 612秒 458毫秒/步驟 - 損失: 0.9050 - 稀疏類別準確度: 0.6460
1337/未知 613秒 458毫秒/步驟 - 損失: 0.9048 - 稀疏類別準確度: 0.6460
1338/未知 613秒 458毫秒/步驟 - 損失: 0.9047 - 稀疏類別準確度: 0.6461
1339/未知 614秒 458毫秒/步驟 - 損失: 0.9046 - 稀疏類別準確度: 0.6461
1340/未知 614秒 458毫秒/步驟 - 損失: 0.9044 - 稀疏類別準確度: 0.6462
1341/未知 614秒 458毫秒/步驟 - 損失: 0.9043 - 稀疏類別準確度: 0.6462
1342/未知 615秒 458毫秒/步驟 - 損失: 0.9042 - 稀疏類別準確度: 0.6462
1343/未知 615秒 458毫秒/步驟 - 損失: 0.9040 - 稀疏類別準確度: 0.6463
1344/未知 615秒 458毫秒/步驟 - 損失: 0.9039 - 稀疏類別準確度: 0.6463
1345/未知 616秒 458毫秒/步驟 - 損失: 0.9037 - 稀疏類別準確度: 0.6464
1346/未知 616秒 458毫秒/步驟 - 損失: 0.9036 - 稀疏類別準確度: 0.6464
1347/未知 617秒 457毫秒/步驟 - 損失: 0.9035 - 稀疏類別準確度: 0.6465
1348/未知 617秒 457毫秒/步驟 - 損失: 0.9033 - 稀疏類別準確度: 0.6465
1349/未知 618秒 457毫秒/步驟 - 損失: 0.9032 - 稀疏類別準確度: 0.6465
1350/未知 618秒 457毫秒/步驟 - 損失: 0.9031 - 稀疏類別準確度: 0.6466
1351/未知 618秒 457毫秒/步驟 - 損失: 0.9029 - 稀疏類別準確度: 0.6466
1352/未知 619秒 457毫秒/步驟 - 損失: 0.9028 - 稀疏類別準確度: 0.6467
1353/未知 619秒 457毫秒/步驟 - 損失: 0.9026 - 稀疏類別準確度: 0.6467
1354/未知 620秒 457毫秒/步驟 - 損失: 0.9025 - 稀疏類別準確度: 0.6468
1355/未知 620秒 457毫秒/步驟 - 損失: 0.9024 - 稀疏類別準確度: 0.6468
1356/未知 621秒 457毫秒/步驟 - 損失: 0.9022 - 稀疏類別準確度: 0.6468
1357/未知 621秒 457毫秒/步驟 - 損失: 0.9021 - 稀疏類別準確度: 0.6469
1358/未知 622秒 457毫秒/步驟 - 損失: 0.9020 - 稀疏類別準確度: 0.6469
1359/未知 622秒 457毫秒/步驟 - 損失: 0.9018 - 稀疏類別準確度: 0.6470
1360/未知 623秒 457毫秒/步驟 - 損失: 0.9017 - 稀疏類別準確度: 0.6470
1361/未知 623秒 457毫秒/步驟 - 損失: 0.9016 - 稀疏類別準確度: 0.6471
1362/未知 624秒 457毫秒/步驟 - 損失: 0.9014 - 稀疏類別準確度: 0.6471
1363/未知 624秒 457毫秒/步驟 - 損失: 0.9013 - 稀疏類別準確度: 0.6471
1364/未知 624秒 457毫秒/步驟 - 損失: 0.9012 - 稀疏類別準確度: 0.6472
1365/未知 625秒 457毫秒/步驟 - 損失: 0.9010 - 稀疏類別準確度: 0.6472
1366/未知 625秒 457毫秒/步驟 - 損失: 0.9009 - 稀疏類別準確度: 0.6473
1367/未知 625秒 457毫秒/步驟 - 損失: 0.9008 - 稀疏類別準確度: 0.6473
1368/未知 626秒 457毫秒/步驟 - 損失: 0.9006 - 稀疏類別準確度: 0.6474
1369/未知 626秒 457毫秒/步驟 - 損失: 0.9005 - 稀疏類別準確度: 0.6474
1370/未知 627秒 457毫秒/步驟 - 損失: 0.9004 - 稀疏類別準確度: 0.6474
1371/未知 627秒 457毫秒/步驟 - 損失: 0.9002 - 稀疏類別準確度: 0.6475
1372/未知 627秒 457毫秒/步驟 - 損失: 0.9001 - 稀疏類別準確度: 0.6475
1373/未知 628秒 457毫秒/步驟 - 損失: 0.9000 - 稀疏類別準確度: 0.6476
1374/未知 628秒 457毫秒/步驟 - 損失: 0.8998 - 稀疏類別準確度: 0.6476
1375/未知 629秒 457毫秒/步驟 - 損失: 0.8997 - 稀疏類別準確度: 0.6476
1376/未知 629秒 457毫秒/步驟 - 損失: 0.8996 - 稀疏類別準確度: 0.6477
1377/未知 630秒 457毫秒/步驟 - 損失: 0.8994 - 稀疏類別準確度: 0.6477
1378/未知 630秒 457毫秒/步驟 - 損失: 0.8993 - 稀疏類別準確度: 0.6478
1379/未知 631秒 457毫秒/步驟 - 損失: 0.8992 - 稀疏類別準確度: 0.6478
1380/未知 631秒 457毫秒/步驟 - 損失: 0.8990 - 稀疏類別準確度: 0.6479
1381/未知 632秒 457毫秒/步驟 - 損失: 0.8989 - 稀疏類別準確度: 0.6479
1382/未知 632秒 457毫秒/步驟 - 損失: 0.8988 - 稀疏類別準確度: 0.6479
1383/未知 633秒 457毫秒/步驟 - 損失: 0.8986 - 稀疏類別準確度: 0.6480
1384/未知 633秒 457毫秒/步驟 - 損失: 0.8985 - 稀疏類別準確度: 0.6480
1385/未知 633秒 457毫秒/步驟 - 損失: 0.8984 - 稀疏類別準確度: 0.6481
1386/未知 634秒 457毫秒/步驟 - 損失: 0.8982 - 稀疏類別準確度: 0.6481
1387/未知 634秒 457毫秒/步驟 - 損失: 0.8981 - 稀疏類別準確度: 0.6481
1388/未知 634秒 457毫秒/步驟 - 損失: 0.8980 - 稀疏類別準確度: 0.6482
1389/未知 635秒 457毫秒/步驟 - 損失: 0.8978 - 稀疏類別準確度: 0.6482
1390/未知 635秒 457毫秒/步驟 - 損失: 0.8977 - 稀疏類別準確度: 0.6483
1391/未知 636秒 457毫秒/步驟 - 損失: 0.8976 - 稀疏類別準確度: 0.6483
1392/未知 636秒 457毫秒/步驟 - 損失: 0.8974 - 稀疏類別準確度: 0.6483
1393/未知 636秒 456毫秒/步驟 - 損失: 0.8973 - 稀疏類別準確度: 0.6484
1394/未知 637秒 456毫秒/步驟 - 損失: 0.8972 - 稀疏類別準確度: 0.6484
1395/未知 637秒 456毫秒/步驟 - 損失: 0.8971 - 稀疏類別準確度: 0.6485
1396/未知 638秒 456毫秒/步驟 - 損失: 0.8969 - 稀疏類別準確度: 0.6485
1397/未知 638秒 456毫秒/步驟 - 損失: 0.8968 - 稀疏類別準確度: 0.6485
1398/未知 639秒 456毫秒/步驟 - 損失: 0.8967 - 稀疏類別準確度: 0.6486
1399/未知 639秒 456毫秒/步驟 - 損失: 0.8965 - 稀疏類別準確度: 0.6486
1400/未知 640秒 456毫秒/步驟 - 損失: 0.8964 - 稀疏類別準確度: 0.6487
1401/未知 640秒 456毫秒/步驟 - 損失: 0.8963 - 稀疏類別準確度: 0.6487
1402/未知 640秒 456毫秒/步驟 - 損失: 0.8962 - 稀疏類別準確度: 0.6488
1403/未知 641秒 456毫秒/步驟 - 損失: 0.8960 - 稀疏類別準確度: 0.6488
1404/未知 641秒 456毫秒/步驟 - 損失: 0.8959 - 稀疏類別準確度: 0.6488
1405/未知 642秒 456毫秒/步驟 - 損失: 0.8958 - 稀疏類別準確度: 0.6489
1406/未知 642秒 456毫秒/步驟 - 損失: 0.8956 - 稀疏類別準確度: 0.6489
1407/未知 643秒 456毫秒/步驟 - 損失: 0.8955 - 稀疏類別準確度: 0.6490
1408/未知 643秒 456毫秒/步驟 - 損失: 0.8954 - 稀疏類別準確度: 0.6490
1409/未知 644秒 457毫秒/步驟 - 損失: 0.8953 - 稀疏類別準確度: 0.6490
1410/未知 644秒 457毫秒/步驟 - 損失: 0.8951 - 稀疏類別準確度: 0.6491
1411/未知 645秒 457毫秒/步驟 - 損失: 0.8950 - 稀疏類別準確度: 0.6491
1412/未知 645秒 457毫秒/步驟 - 損失: 0.8949 - 稀疏類別準確度: 0.6492
1413/未知 646秒 457毫秒/步驟 - 損失: 0.8947 - 稀疏類別準確度: 0.6492
1414/未知 646秒 457毫秒/步驟 - 損失: 0.8946 - 稀疏類別準確度: 0.6492
1415/未知 647秒 457毫秒/步驟 - 損失: 0.8945 - 稀疏類別準確度: 0.6493
1416/未知 647秒 457毫秒/步驟 - 損失: 0.8944 - 稀疏類別準確度: 0.6493
1417/未知 647秒 457毫秒/步驟 - 損失: 0.8942 - 稀疏類別準確度: 0.6494
1418/未知 648秒 457毫秒/步驟 - 損失: 0.8941 - 稀疏類別準確度: 0.6494
1419/未知 648秒 457毫秒/步驟 - 損失: 0.8940 - 稀疏類別準確度: 0.6494
1420/未知 649秒 456毫秒/步驟 - 損失: 0.8939 - 稀疏類別準確度: 0.6495
1421/未知 649秒 456毫秒/步驟 - 損失: 0.8937 - 稀疏類別準確度: 0.6495
1422/未知 650秒 456毫秒/步驟 - 損失: 0.8936 - 稀疏類別準確度: 0.6495
1423/未知 650秒 456毫秒/步驟 - 損失: 0.8935 - 稀疏類別準確度: 0.6496
1424/未知 651秒 456毫秒/步驟 - 損失: 0.8933 - 稀疏類別準確度: 0.6496
1425/未知 651秒 456毫秒/步驟 - 損失: 0.8932 - 稀疏類別準確度: 0.6497
1426/未知 651秒 456毫秒/步驟 - 損失: 0.8931 - 稀疏類別準確度: 0.6497
1427/未知 652秒 456毫秒/步驟 - 損失: 0.8930 - 稀疏類別準確度: 0.6497
1428/未知 652秒 456毫秒/步驟 - 損失: 0.8928 - 稀疏類別準確度: 0.6498
1429/未知 653秒 456毫秒/步驟 - 損失: 0.8927 - 稀疏類別準確度: 0.6498
1430/未知 653秒 456毫秒/步驟 - 損失: 0.8926 - 稀疏類別準確度: 0.6499
1431/未知 653秒 456毫秒/步驟 - 損失: 0.8925 - 稀疏類別準確度: 0.6499
1432/未知 654秒 456毫秒/步驟 - 損失: 0.8923 - 稀疏類別準確度: 0.6499
1433/未知 654秒 456毫秒/步驟 - 損失: 0.8922 - 稀疏類別準確度: 0.6500
1434/未知 655秒 456毫秒/步驟 - 損失: 0.8921 - 稀疏類別準確度: 0.6500
1435/未知 655秒 456毫秒/步驟 - 損失: 0.8920 - 稀疏類別準確度: 0.6501
1436/未知 655秒 456毫秒/步驟 - 損失: 0.8918 - 稀疏類別準確度: 0.6501
1437/未知 656秒 456毫秒/步驟 - 損失: 0.8917 - 稀疏類別準確度: 0.6501
1438/未知 656秒 456毫秒/步驟 - 損失: 0.8916 - 稀疏類別準確度: 0.6502
1439/未知 657秒 456毫秒/步驟 - 損失: 0.8915 - 稀疏類別準確度: 0.6502
1440/未知 657秒 456毫秒/步驟 - 損失: 0.8913 - 稀疏類別準確度: 0.6503
1441/未知 657秒 456毫秒/步驟 - 損失: 0.8912 - 稀疏類別準確度: 0.6503
1442/未知 658秒 456毫秒/步驟 - 損失: 0.8911 - 稀疏類別準確度: 0.6503
1443/未知 658秒 456毫秒/步驟 - 損失: 0.8910 - 稀疏類別準確度: 0.6504
1444/未知 659秒 456毫秒/步驟 - 損失: 0.8909 - 稀疏類別準確度: 0.6504
1445/未知 659秒 456毫秒/步驟 - 損失: 0.8907 - 稀疏類別準確度: 0.6504
1446/未知 660秒 456毫秒/步驟 - 損失: 0.8906 - 稀疏類別準確度: 0.6505
1447/未知 660秒 456毫秒/步驟 - 損失: 0.8905 - 稀疏類別準確度: 0.6505
1448/未知 661秒 456毫秒/步驟 - 損失: 0.8904 - 稀疏類別準確度: 0.6506
1449/未知 661秒 456毫秒/步驟 - 損失: 0.8902 - 稀疏類別準確度: 0.6506
1450/未知 662秒 456毫秒/步驟 - 損失: 0.8901 - 稀疏類別準確度: 0.6506
1451/未知 662秒 456毫秒/步驟 - 損失: 0.8900 - 稀疏類別準確度: 0.6507
1452/未知 662秒 456毫秒/步驟 - 損失: 0.8899 - 稀疏類別準確度: 0.6507
1453/未知 663秒 456毫秒/步驟 - 損失: 0.8897 - 稀疏類別準確度: 0.6508
1454/未知 663秒 456毫秒/步驟 - 損失: 0.8896 - 稀疏類別準確度: 0.6508
1455/未知 664秒 456毫秒/步驟 - 損失: 0.8895 - 稀疏類別準確度: 0.6508
1456/未知 664秒 456毫秒/步驟 - 損失: 0.8894 - 稀疏類別準確度: 0.6509
1457/未知 665秒 456毫秒/步驟 - 損失: 0.8893 - 稀疏類別準確度: 0.6509
1458/未知 665秒 456毫秒/步驟 - 損失: 0.8891 - 稀疏類別準確度: 0.6509
1459/未知 665秒 456毫秒/步驟 - 損失: 0.8890 - 稀疏類別準確度: 0.6510
1460/未知 666秒 456毫秒/步驟 - 損失: 0.8889 - 稀疏類別準確度: 0.6510
1461/未知 666秒 456毫秒/步驟 - 損失: 0.8888 - 稀疏類別準確度: 0.6511
1462/未知 667秒 456毫秒/步驟 - 損失: 0.8887 - 稀疏類別準確度: 0.6511
1463/未知 667秒 455毫秒/步驟 - 損失: 0.8885 - 稀疏類別準確度: 0.6511
1464/未知 667秒 455毫秒/步驟 - 損失: 0.8884 - 稀疏類別準確度: 0.6512
1465/未知 668秒 455毫秒/步驟 - 損失: 0.8883 - 稀疏類別準確度: 0.6512
1466/未知 668秒 455毫秒/步驟 - 損失: 0.8882 - 稀疏類別準確度: 0.6512
1467/未知 669秒 455毫秒/步驟 - 損失: 0.8880 - 稀疏類別準確度: 0.6513
1468/未知 669秒 455毫秒/步驟 - 損失: 0.8879 - 稀疏類別準確度: 0.6513
1469/未知 669秒 455毫秒/步驟 - 損失: 0.8878 - 稀疏類別準確度: 0.6514
1470/未知 670秒 455毫秒/步驟 - 損失: 0.8877 - 稀疏類別準確度: 0.6514
1471/未知 670秒 455毫秒/步驟 - 損失: 0.8876 - 稀疏類別準確度: 0.6514
1472/未知 671秒 455毫秒/步驟 - 損失: 0.8874 - 稀疏類別準確度: 0.6515
1473/未知 671秒 455毫秒/步驟 - 損失: 0.8873 - 稀疏類別準確度: 0.6515
1474/未知 672秒 455毫秒/步驟 - 損失: 0.8872 - 稀疏類別準確度: 0.6515
1475/未知 672秒 455毫秒/步驟 - 損失: 0.8871 - 稀疏類別準確度: 0.6516
1476/未知 673秒 455毫秒/步驟 - 損失: 0.8870 - 稀疏類別準確度: 0.6516
1477/未知 673秒 455毫秒/步驟 - 損失: 0.8868 - 稀疏類別準確度: 0.6517
1478/未知 673秒 455毫秒/步驟 - 損失: 0.8867 - 稀疏類別準確度: 0.6517
1479/未知 674秒 455毫秒/步驟 - 損失: 0.8866 - 稀疏類別準確度: 0.6517
1480/未知 674秒 455毫秒/步驟 - 損失: 0.8865 - 稀疏類別準確度: 0.6518
1481/未知 674秒 455毫秒/步驟 - 損失: 0.8864 - 稀疏類別準確度: 0.6518
1482/未知 675秒 455毫秒/步驟 - 損失: 0.8863 - 稀疏類別準確度: 0.6518
1483/未知 675秒 455毫秒/步驟 - 損失: 0.8861 - 稀疏類別準確度: 0.6519
1484/未知 676秒 455毫秒/步驟 - 損失: 0.8860 - 稀疏類別準確度: 0.6519
1485/未知 676秒 455毫秒/步驟 - 損失: 0.8859 - 稀疏類別準確度: 0.6520
1486/未知 677秒 455毫秒/步驟 - 損失: 0.8858 - 稀疏類別準確度: 0.6520
1487/未知 677秒 455毫秒/步驟 - 損失: 0.8857 - 稀疏類別準確度: 0.6520
1488/未知 677秒 455毫秒/步驟 - 損失: 0.8855 - 稀疏類別準確度: 0.6521
1489/未知 678秒 455毫秒/步驟 - 損失: 0.8854 - 稀疏類別準確度: 0.6521
1490/未知 678秒 455毫秒/步驟 - 損失: 0.8853 - 稀疏類別準確度: 0.6521
1491/未知 679秒 455毫秒/步驟 - 損失: 0.8852 - 稀疏類別準確度: 0.6522
1492/未知 679秒 455毫秒/步驟 - 損失: 0.8851 - 稀疏類別準確度: 0.6522
1493/未知 679秒 455毫秒/步驟 - 損失: 0.8850 - 稀疏類別準確度: 0.6523
1494/未知 680秒 455毫秒/步驟 - 損失: 0.8848 - 稀疏類別準確度: 0.6523
1495/未知 680秒 455毫秒/步驟 - 損失: 0.8847 - 稀疏類別準確度: 0.6523
1496/未知 681秒 455毫秒/步驟 - 損失: 0.8846 - 稀疏類別準確度: 0.6524
1497/未知 681秒 455毫秒/步驟 - 損失: 0.8845 - 稀疏類別準確度: 0.6524
1498/未知 682秒 455毫秒/步驟 - 損失: 0.8844 - 稀疏類別準確度: 0.6524
1499/未知 682秒 455毫秒/步驟 - 損失: 0.8843 - 稀疏類別準確度: 0.6525
1500/未知 683秒 455毫秒/步驟 - 損失: 0.8841 - 稀疏類別準確度: 0.6525
1501/未知 683秒 455毫秒/步驟 - 損失: 0.8840 - 稀疏類別準確度: 0.6525
1502/未知 684秒 455毫秒/步驟 - 損失: 0.8839 - 稀疏類別準確度: 0.6526
1503/未知 684秒 455毫秒/步驟 - 損失: 0.8838 - 稀疏類別準確度: 0.6526
1504/未知 685秒 455毫秒/步驟 - 損失: 0.8837 - 稀疏類別準確度: 0.6527
1505/未知 685秒 455毫秒/步驟 - 損失: 0.8836 - 稀疏類別準確度: 0.6527
1506/未知 685秒 455毫秒/步驟 - 損失: 0.8834 - 稀疏類別準確度: 0.6527
1507/未知 686秒 455毫秒/步驟 - 損失: 0.8833 - 稀疏類別準確度: 0.6528
1508/未知 686秒 455毫秒/步驟 - 損失: 0.8832 - 稀疏類別準確度: 0.6528
1509/未知 687秒 455毫秒/步驟 - 損失: 0.8831 - 稀疏類別準確度: 0.6528
1510/未知 687秒 455毫秒/步驟 - 損失: 0.8830 - 稀疏類別準確度: 0.6529
1511/未知 687秒 455毫秒/步驟 - 損失: 0.8829 - 稀疏類別準確度: 0.6529
1512/未知 688秒 454毫秒/步驟 - 損失: 0.8827 - 稀疏類別準確度: 0.6529
1513/未知 688秒 454毫秒/步驟 - 損失: 0.8826 - 稀疏類別準確度: 0.6530
1514/未知 688秒 454毫秒/步驟 - 損失: 0.8825 - 稀疏類別準確度: 0.6530
1515/未知 689秒 454毫秒/步驟 - 損失: 0.8824 - 稀疏類別準確度: 0.6531
1516/未知 689秒 454毫秒/步驟 - 損失: 0.8823 - 稀疏類別準確度: 0.6531
1517/未知 690秒 454毫秒/步驟 - 損失: 0.8822 - 稀疏類別準確度: 0.6531
1518/未知 690秒 454毫秒/步驟 - 損失: 0.8821 - 稀疏類別準確度: 0.6532
1519/未知 690秒 454毫秒/步驟 - 損失: 0.8819 - 稀疏類別準確度: 0.6532
1520/未知 691秒 454毫秒/步驟 - 損失: 0.8818 - 稀疏類別準確度: 0.6532
1521/未知 691秒 454毫秒/步驟 - 損失: 0.8817 - 稀疏類別準確度: 0.6533
1522/未知 692秒 454毫秒/步驟 - 損失: 0.8816 - 稀疏類別準確度: 0.6533
1523/未知 692秒 454毫秒/步驟 - 損失: 0.8815 - 稀疏類別準確度: 0.6533
1524/未知 693秒 454毫秒/步驟 - 損失: 0.8814 - 稀疏類別準確度: 0.6534
1525/未知 693秒 454毫秒/步驟 - 損失: 0.8813 - 稀疏類別準確度: 0.6534
1526/未知 694秒 454毫秒/步驟 - 損失: 0.8811 - 稀疏類別準確度: 0.6534
1527/未知 694秒 454毫秒/步驟 - 損失: 0.8810 - 稀疏類別準確度: 0.6535
1528/未知 695秒 454毫秒/步驟 - 損失: 0.8809 - 稀疏類別準確度: 0.6535
1529/未知 695秒 454毫秒/步驟 - 損失: 0.8808 - 稀疏類別準確度: 0.6536
1530/未知 695秒 454毫秒/步驟 - 損失: 0.8807 - 稀疏類別準確度: 0.6536
1531/未知 696秒 454毫秒/步驟 - 損失: 0.8806 - 稀疏類別準確度: 0.6536
1532/未知 696秒 454毫秒/步驟 - 損失: 0.8805 - 稀疏類別準確度: 0.6537
1533/未知 697秒 454毫秒/步驟 - 損失: 0.8803 - 稀疏類別準確度: 0.6537
1534/未知 697秒 454毫秒/步驟 - 損失: 0.8802 - 稀疏類別準確度: 0.6537
1535/未知 697秒 454毫秒/步驟 - 損失: 0.8801 - 稀疏類別準確度: 0.6538
1536/未知 698秒 454毫秒/步驟 - 損失: 0.8800 - 稀疏類別準確度: 0.6538
1537/未知 698秒 454毫秒/步驟 - 損失: 0.8799 - 稀疏類別準確度: 0.6538
1538/未知 699秒 454毫秒/步驟 - 損失: 0.8798 - 稀疏類別準確度: 0.6539
1539/未知 699秒 454毫秒/步驟 - 損失: 0.8797 - 稀疏類別準確度: 0.6539
1540/未知 699秒 454毫秒/步驟 - 損失: 0.8796 - 稀疏類別準確度: 0.6539
1541/未知 700秒 454毫秒/步驟 - 損失: 0.8794 - 稀疏類別準確度: 0.6540
1542/Unknown 700秒 454毫秒/步 - 損失: 0.8793 - 稀疏分類準確度: 0.6540
1543/Unknown 701秒 454毫秒/步 - 損失: 0.8792 - 稀疏分類準確度: 0.6540
1544/Unknown 701秒 454毫秒/步 - 損失: 0.8791 - 稀疏分類準確度: 0.6541
1545/Unknown 702秒 454毫秒/步 - 損失: 0.8790 - 稀疏分類準確度: 0.6541
1546/Unknown 702秒 454毫秒/步 - 損失: 0.8789 - 稀疏分類準確度: 0.6542
1547/Unknown 702秒 454毫秒/步 - 損失: 0.8788 - 稀疏分類準確度: 0.6542
1548/Unknown 703秒 454毫秒/步 - 損失: 0.8787 - 稀疏分類準確度: 0.6542
1549/Unknown 703秒 454毫秒/步 - 損失: 0.8786 - 稀疏分類準確度: 0.6543
1550/Unknown 704秒 454毫秒/步 - 損失: 0.8784 - 稀疏分類準確度: 0.6543
1551/Unknown 704秒 454毫秒/步 - 損失: 0.8783 - 稀疏分類準確度: 0.6543
1552/Unknown 705秒 454毫秒/步 - 損失: 0.8782 - 稀疏分類準確度: 0.6544
1553/Unknown 705秒 454毫秒/步 - 損失: 0.8781 - 稀疏分類準確度: 0.6544
1554/Unknown 705秒 454毫秒/步 - 損失: 0.8780 - 稀疏分類準確度: 0.6544
1555/Unknown 706秒 453毫秒/步 - 損失: 0.8779 - 稀疏分類準確度: 0.6545
1556/Unknown 706秒 453毫秒/步 - 損失: 0.8778 - 稀疏分類準確度: 0.6545
1557/Unknown 706秒 453毫秒/步 - 損失: 0.8777 - 稀疏分類準確度: 0.6545
1558/Unknown 707秒 453毫秒/步 - 損失: 0.8776 - 稀疏分類準確度: 0.6546
1559/Unknown 707秒 453毫秒/步 - 損失: 0.8774 - 稀疏分類準確度: 0.6546
1560/Unknown 708秒 453毫秒/步 - 損失: 0.8773 - 稀疏分類準確度: 0.6546
1561/Unknown 708秒 453毫秒/步 - 損失: 0.8772 - 稀疏分類準確度: 0.6547
1562/Unknown 708秒 453毫秒/步 - 損失: 0.8771 - 稀疏分類準確度: 0.6547
1563/Unknown 709秒 453毫秒/步 - 損失: 0.8770 - 稀疏分類準確度: 0.6547
1564/Unknown 709秒 453毫秒/步 - 損失: 0.8769 - 稀疏分類準確度: 0.6548
1565/Unknown 710秒 453毫秒/步 - 損失: 0.8768 - 稀疏分類準確度: 0.6548
1566/Unknown 710秒 453毫秒/步 - 損失: 0.8767 - 稀疏分類準確度: 0.6548
1567/Unknown 711秒 453毫秒/步 - 損失: 0.8766 - 稀疏分類準確度: 0.6549
1568/Unknown 711秒 453毫秒/步 - 損失: 0.8765 - 稀疏分類準確度: 0.6549
1569/Unknown 711秒 453毫秒/步 - 損失: 0.8763 - 稀疏分類準確度: 0.6549
1570/Unknown 712秒 453毫秒/步 - 損失: 0.8762 - 稀疏分類準確度: 0.6550
1571/Unknown 712秒 453毫秒/步 - 損失: 0.8761 - 稀疏分類準確度: 0.6550
1572/Unknown 713秒 453毫秒/步 - 損失: 0.8760 - 稀疏分類準確度: 0.6550
1573/Unknown 713秒 453毫秒/步 - 損失: 0.8759 - 稀疏分類準確度: 0.6551
1574/Unknown 714秒 453毫秒/步 - 損失: 0.8758 - 稀疏分類準確度: 0.6551
1575/Unknown 714秒 453毫秒/步 - 損失: 0.8757 - 稀疏分類準確度: 0.6552
1576/Unknown 715秒 453毫秒/步 - 損失: 0.8756 - 稀疏分類準確度: 0.6552
1577/Unknown 715秒 453毫秒/步 - 損失: 0.8755 - 稀疏分類準確度: 0.6552
1578/Unknown 715秒 453毫秒/步 - 損失: 0.8754 - 稀疏分類準確度: 0.6553
1579/Unknown 716秒 453毫秒/步 - 損失: 0.8753 - 稀疏分類準確度: 0.6553
1580/Unknown 716秒 453毫秒/步 - 損失: 0.8752 - 稀疏分類準確度: 0.6553
1581/Unknown 716秒 453毫秒/步 - 損失: 0.8750 - 稀疏分類準確度: 0.6554
1582/Unknown 717秒 453毫秒/步 - 損失: 0.8749 - 稀疏分類準確度: 0.6554
1583/Unknown 717秒 453毫秒/步 - 損失: 0.8748 - 稀疏分類準確度: 0.6554
1584/Unknown 718秒 453毫秒/步 - 損失: 0.8747 - 稀疏分類準確度: 0.6555
1585/Unknown 718秒 453毫秒/步 - 損失: 0.8746 - 稀疏分類準確度: 0.6555
1586/Unknown 718秒 453毫秒/步 - 損失: 0.8745 - 稀疏分類準確度: 0.6555
1587/Unknown 719秒 453毫秒/步 - 損失: 0.8744 - 稀疏分類準確度: 0.6556
1588/Unknown 719秒 453毫秒/步 - 損失: 0.8743 - 稀疏分類準確度: 0.6556
1589/Unknown 720秒 453毫秒/步 - 損失: 0.8742 - 稀疏分類準確度: 0.6556
1590/Unknown 720秒 453毫秒/步 - 損失: 0.8741 - 稀疏分類準確度: 0.6557
1591/Unknown 721秒 453毫秒/步 - 損失: 0.8740 - 稀疏分類準確度: 0.6557
1592/Unknown 721秒 452毫秒/步 - 損失: 0.8739 - 稀疏分類準確度: 0.6557
1593/Unknown 721秒 452毫秒/步 - 損失: 0.8738 - 稀疏分類準確度: 0.6558
1594/Unknown 722秒 452毫秒/步 - 損失: 0.8737 - 稀疏分類準確度: 0.6558
1595/Unknown 722秒 452毫秒/步 - 損失: 0.8735 - 稀疏分類準確度: 0.6558
1596/Unknown 723秒 452毫秒/步 - 損失: 0.8734 - 稀疏分類準確度: 0.6559
1597/Unknown 723秒 453毫秒/步 - 損失: 0.8733 - 稀疏分類準確度: 0.6559
1598/Unknown 724秒 453毫秒/步 - 損失: 0.8732 - 稀疏分類準確度: 0.6559
1599/Unknown 724秒 453毫秒/步 - 損失: 0.8731 - 稀疏分類準確度: 0.6560
1600/Unknown 725秒 453毫秒/步 - 損失: 0.8730 - 稀疏分類準確度: 0.6560
1601/Unknown 725秒 452毫秒/步 - 損失: 0.8729 - 稀疏分類準確度: 0.6560
1602/Unknown 725秒 452毫秒/步 - 損失: 0.8728 - 稀疏分類準確度: 0.6561
1603/Unknown 726秒 452毫秒/步 - 損失: 0.8727 - 稀疏分類準確度: 0.6561
1604/Unknown 726秒 452毫秒/步 - 損失: 0.8726 - 稀疏分類準確度: 0.6561
1605/Unknown 726秒 452毫秒/步 - 損失: 0.8725 - 稀疏分類準確度: 0.6562
1606/Unknown 727秒 452毫秒/步 - 損失: 0.8724 - 稀疏分類準確度: 0.6562
1607/Unknown 727秒 452毫秒/步 - 損失: 0.8723 - 稀疏分類準確度: 0.6562
1608/Unknown 728秒 452毫秒/步 - 損失: 0.8722 - 稀疏分類準確度: 0.6563
1609/Unknown 728秒 452毫秒/步 - 損失: 0.8721 - 稀疏分類準確度: 0.6563
1610/Unknown 728秒 452毫秒/步 - 損失: 0.8720 - 稀疏分類準確度: 0.6563
1611/Unknown 729秒 452毫秒/步 - 損失: 0.8719 - 稀疏分類準確度: 0.6564
1612/Unknown 729秒 452毫秒/步 - 損失: 0.8717 - 稀疏分類準確度: 0.6564
1613/Unknown 730秒 452毫秒/步 - 損失: 0.8716 - 稀疏分類準確度: 0.6564
1614/Unknown 730秒 452毫秒/步 - 損失: 0.8715 - 稀疏分類準確度: 0.6565
1615/Unknown 730秒 452毫秒/步 - 損失: 0.8714 - 稀疏分類準確度: 0.6565
1616/Unknown 731秒 452毫秒/步 - 損失: 0.8713 - 稀疏分類準確度: 0.6565
1617/Unknown 731秒 452毫秒/步 - 損失: 0.8712 - 稀疏分類準確度: 0.6566
1618/Unknown 732秒 452毫秒/步 - 損失: 0.8711 - 稀疏分類準確度: 0.6566
1619/Unknown 732秒 452毫秒/步 - 損失: 0.8710 - 稀疏分類準確度: 0.6566
1620/Unknown 733秒 452毫秒/步 - 損失: 0.8709 - 稀疏分類準確度: 0.6567
1621/Unknown 733秒 452毫秒/步 - 損失: 0.8708 - 稀疏分類準確度: 0.6567
1622/Unknown 734秒 452毫秒/步 - 損失: 0.8707 - 稀疏分類準確度: 0.6567
1623/Unknown 734秒 452毫秒/步 - 損失: 0.8706 - 稀疏分類準確度: 0.6567
1624/Unknown 734秒 452毫秒/步 - 損失: 0.8705 - 稀疏分類準確度: 0.6568
1625/Unknown 735秒 452毫秒/步 - 損失: 0.8704 - 稀疏分類準確度: 0.6568
1626/Unknown 735秒 452毫秒/步 - 損失: 0.8703 - 稀疏分類準確度: 0.6568
1627/Unknown 736秒 452毫秒/步 - 損失: 0.8702 - 稀疏分類準確度: 0.6569
1628/Unknown 736秒 452毫秒/步 - 損失: 0.8701 - 稀疏分類準確度: 0.6569
1629/Unknown 736秒 452毫秒/步 - 損失: 0.8700 - 稀疏分類準確度: 0.6569
1630/Unknown 737秒 452毫秒/步 - 損失: 0.8699 - 稀疏分類準確度: 0.6570
1631/Unknown 737秒 452毫秒/步 - 損失: 0.8698 - 稀疏分類準確度: 0.6570
1632/Unknown 738秒 452毫秒/步 - 損失: 0.8697 - 稀疏分類準確度: 0.6570
1633/Unknown 738秒 452毫秒/步 - 損失: 0.8696 - 稀疏分類準確度: 0.6571
1634/Unknown 738秒 451毫秒/步 - 損失: 0.8695 - 稀疏分類準確度: 0.6571
1635/Unknown 739秒 451毫秒/步 - 損失: 0.8694 - 稀疏分類準確度: 0.6571
1636/Unknown 739秒 451毫秒/步 - 損失: 0.8693 - 稀疏分類準確度: 0.6572
1637/Unknown 739秒 451毫秒/步 - 損失: 0.8692 - 稀疏分類準確度: 0.6572
1638/Unknown 740秒 451毫秒/步 - 損失: 0.8690 - 稀疏分類準確度: 0.6572
1639/Unknown 740秒 451毫秒/步 - 損失: 0.8689 - 稀疏分類準確度: 0.6573
1640/Unknown 741秒 451毫秒/步 - 損失: 0.8688 - 稀疏分類準確度: 0.6573
1641/Unknown 741秒 451毫秒/步 - 損失: 0.8687 - 稀疏分類準確度: 0.6573
1642/Unknown 742秒 451毫秒/步 - 損失: 0.8686 - 稀疏分類準確度: 0.6574
1643/Unknown 742秒 451毫秒/步 - 損失: 0.8685 - 稀疏分類準確度: 0.6574
1644/Unknown 743秒 451毫秒/步 - 損失: 0.8684 - 稀疏分類準確度: 0.6574
1645/Unknown 743秒 451毫秒/步 - 損失: 0.8683 - 稀疏分類準確度: 0.6575
1646/Unknown 743秒 451毫秒/步 - 損失: 0.8682 - 稀疏分類準確度: 0.6575
1647/Unknown 744秒 451毫秒/步 - 損失: 0.8681 - 稀疏分類準確度: 0.6575
1648/Unknown 744秒 451毫秒/步 - 損失: 0.8680 - 稀疏分類準確度: 0.6576
1649/Unknown 744秒 451毫秒/步 - 損失: 0.8679 - 稀疏分類準確度: 0.6576
1650/Unknown 745秒 451毫秒/步 - 損失: 0.8678 - 稀疏分類準確度: 0.6576
1651/Unknown 745秒 451毫秒/步 - 損失: 0.8677 - 稀疏分類準確度: 0.6577
1652/Unknown 746秒 451毫秒/步 - 損失: 0.8676 - 稀疏分類準確度: 0.6577
1653/Unknown 746秒 451毫秒/步 - 損失: 0.8675 - 稀疏分類準確度: 0.6577
1654/Unknown 746秒 451毫秒/步 - 損失: 0.8674 - 稀疏分類準確度: 0.6577
1655/Unknown 747秒 451毫秒/步 - 損失: 0.8673 - 稀疏分類準確度: 0.6578
1656/Unknown 747秒 451毫秒/步 - 損失: 0.8672 - 稀疏分類準確度: 0.6578
1657/Unknown 748秒 451毫秒/步 - 損失: 0.8671 - 稀疏分類準確度: 0.6578
1658/Unknown 748秒 451毫秒/步 - 損失: 0.8670 - 稀疏分類準確度: 0.6579
1659/Unknown 749秒 451毫秒/步 - 損失: 0.8669 - 稀疏分類準確度: 0.6579
1660/Unknown 749秒 451毫秒/步 - 損失: 0.8668 - 稀疏分類準確度: 0.6579
1661/Unknown 749秒 451毫秒/步 - 損失: 0.8667 - 稀疏分類準確度: 0.6580
1662/Unknown 750秒 451毫秒/步 - 損失: 0.8666 - 稀疏分類準確度: 0.6580
1663/Unknown 750秒 451毫秒/步 - 損失: 0.8665 - 稀疏分類準確度: 0.6580
1664/Unknown 750秒 451毫秒/步 - 損失: 0.8664 - 稀疏分類準確度: 0.6581
1665/Unknown 751秒 451毫秒/步 - 損失: 0.8663 - 稀疏分類準確度: 0.6581
1666/Unknown 751秒 451毫秒/步 - 損失: 0.8662 - 稀疏分類準確度: 0.6581
1667/Unknown 752秒 451毫秒/步 - 損失: 0.8661 - 稀疏分類準確度: 0.6582
1668/Unknown 752秒 451毫秒/步 - 損失: 0.8660 - 稀疏分類準確度: 0.6582
1669/Unknown 753秒 451毫秒/步 - 損失: 0.8659 - 稀疏分類準確度: 0.6582
1670/Unknown 753秒 451毫秒/步 - 損失: 0.8658 - 稀疏分類準確度: 0.6583
1671/Unknown 754秒 451毫秒/步 - 損失: 0.8657 - 稀疏分類準確度: 0.6583
1672/Unknown 754秒 451毫秒/步 - 損失: 0.8656 - 稀疏分類準確度: 0.6583
1673/Unknown 755秒 451毫秒/步 - 損失: 0.8655 - 稀疏分類準確度: 0.6583
1674/Unknown 755秒 451毫秒/步 - 損失: 0.8654 - 稀疏分類準確度: 0.6584
1675/Unknown 755秒 451毫秒/步 - 損失: 0.8653 - 稀疏分類準確度: 0.6584
1676/Unknown 756秒 451毫秒/步 - 損失: 0.8652 - 稀疏分類準確度: 0.6584
1677/Unknown 756秒 451毫秒/步 - 損失: 0.8651 - 稀疏分類準確度: 0.6585
1678/Unknown 757秒 451毫秒/步 - 損失: 0.8650 - 稀疏分類準確度: 0.6585
1679/Unknown 757秒 450毫秒/步 - 損失: 0.8649 - 稀疏分類準確度: 0.6585
1680/Unknown 757秒 450毫秒/步 - 損失: 0.8648 - 稀疏分類準確度: 0.6586
1681/Unknown 758秒 450毫秒/步 - 損失: 0.8647 - 稀疏分類準確度: 0.6586
1682/Unknown 758秒 450毫秒/步 - 損失: 0.8646 - 稀疏分類準確度: 0.6586
1683/Unknown 758秒 450毫秒/步 - 損失: 0.8645 - 稀疏分類準確度: 0.6587
1684/Unknown 759秒 450毫秒/步 - 損失: 0.8644 - 稀疏分類準確度: 0.6587
1685/Unknown 759秒 450毫秒/步 - 損失: 0.8643 - 稀疏分類準確度: 0.6587
1686/Unknown 760秒 450毫秒/步 - 損失: 0.8642 - 稀疏分類準確度: 0.6587
1687/Unknown 760秒 450毫秒/步 - 損失: 0.8641 - 稀疏分類準確度: 0.6588
1688/Unknown 760秒 450毫秒/步 - 損失: 0.8640 - 稀疏分類準確度: 0.6588
1689/Unknown 761秒 450毫秒/步 - 損失: 0.8639 - 稀疏分類準確度: 0.6588
1690/Unknown 761秒 450毫秒/步 - 損失: 0.8638 - 稀疏分類準確度: 0.6589
1691/Unknown 762秒 450毫秒/步 - 損失: 0.8637 - 稀疏分類準確度: 0.6589
1692/Unknown 762秒 450毫秒/步 - 損失: 0.8636 - 稀疏分類準確度: 0.6589
1693/Unknown 762秒 450毫秒/步 - 損失: 0.8635 - 稀疏分類準確度: 0.6590
1694/Unknown 763秒 450毫秒/步 - 損失: 0.8634 - 稀疏分類準確度: 0.6590
1695/Unknown 763秒 450毫秒/步 - 損失: 0.8633 - 稀疏分類準確度: 0.6590
1696/Unknown 764秒 450毫秒/步 - 損失: 0.8632 - 稀疏分類準確度: 0.6591
1697/Unknown 764秒 450毫秒/步 - 損失: 0.8632 - 稀疏分類準確度: 0.6591
1698/Unknown 765秒 450毫秒/步 - 損失: 0.8631 - 稀疏分類準確度: 0.6591
1699/Unknown 765秒 450毫秒/步 - 損失: 0.8630 - 稀疏分類準確度: 0.6591
1700/Unknown 766秒 450毫秒/步 - 損失: 0.8629 - 稀疏分類準確度: 0.6592
1701/Unknown 766秒 450毫秒/步 - 損失: 0.8628 - 稀疏分類準確度: 0.6592
1702/Unknown 766秒 450毫秒/步 - 損失: 0.8627 - 稀疏分類準確度: 0.6592
1703/Unknown 767秒 450毫秒/步 - 損失: 0.8626 - 稀疏分類準確度: 0.6593
1704/Unknown 767秒 450毫秒/步 - 損失: 0.8625 - 稀疏分類準確度: 0.6593
1705/Unknown 768秒 450毫秒/步 - 損失: 0.8624 - 稀疏分類準確度: 0.6593
1706/Unknown 768秒 450毫秒/步 - 損失: 0.8623 - 稀疏分類準確度: 0.6594
1707/Unknown 768秒 450毫秒/步 - 損失: 0.8622 - 稀疏分類準確度: 0.6594
1708/Unknown 769秒 450毫秒/步 - 損失: 0.8621 - 稀疏分類準確度: 0.6594
1709/Unknown 769秒 450毫秒/步 - 損失: 0.8620 - 稀疏分類準確度: 0.6595
1710/Unknown 770秒 450毫秒/步 - 損失: 0.8619 - 稀疏分類準確度: 0.6595
1711/Unknown 770秒 450毫秒/步 - 損失: 0.8618 - 稀疏分類準確度: 0.6595
1712/Unknown 770秒 450毫秒/步 - 損失: 0.8617 - 稀疏分類準確度: 0.6595
1713/Unknown 771秒 450毫秒/步 - 損失: 0.8616 - 稀疏分類準確度: 0.6596
1714/Unknown 771秒 450毫秒/步 - 損失: 0.8615 - 稀疏分類準確度: 0.6596
1715/Unknown 772秒 450毫秒/步 - 損失: 0.8614 - 稀疏分類準確度: 0.6596
1716/Unknown 772秒 450毫秒/步 - 損失: 0.8613 - 稀疏分類準確度: 0.6597
1717/Unknown 773秒 450毫秒/步 - 損失: 0.8612 - 稀疏分類準確度: 0.6597
1718/Unknown 773秒 450毫秒/步 - 損失: 0.8611 - 稀疏分類準確度: 0.6597
1719/Unknown 774秒 450毫秒/步 - 損失: 0.8610 - 稀疏分類準確度: 0.6598
1720/Unknown 774秒 450毫秒/步 - 損失: 0.8609 - 稀疏分類準確度: 0.6598
1721/Unknown 774秒 450毫秒/步 - 損失: 0.8608 - 稀疏分類準確度: 0.6598
1722/Unknown 775秒 450毫秒/步 - 損失: 0.8607 - 稀疏分類準確度: 0.6598
1723/Unknown 775秒 450毫秒/步 - 損失: 0.8606 - 稀疏分類準確度: 0.6599
1724/Unknown 776秒 450毫秒/步 - 損失: 0.8606 - 稀疏分類準確度: 0.6599
1725/Unknown 776秒 450毫秒/步 - 損失: 0.8605 - 稀疏分類準確度: 0.6599
1726/Unknown 777秒 450毫秒/步 - 損失: 0.8604 - 稀疏分類準確度: 0.6600
1727/Unknown 777秒 450毫秒/步 - 損失: 0.8603 - 稀疏分類準確度: 0.6600
1728/Unknown 777秒 450毫秒/步 - 損失: 0.8602 - 稀疏分類準確度: 0.6600
1729/Unknown 778秒 450毫秒/步 - 損失: 0.8601 - 稀疏分類準確度: 0.6601
1730/Unknown 778秒 449毫秒/步 - 損失: 0.8600 - 稀疏分類準確度: 0.6601
1731/Unknown 779秒 449毫秒/步 - 損失: 0.8599 - 稀疏分類準確度: 0.6601
1732/Unknown 779秒 449毫秒/步 - 損失: 0.8598 - 稀疏分類準確度: 0.6601
1733/Unknown 779秒 449毫秒/步 - 損失: 0.8597 - 稀疏分類準確度: 0.6602
1734/Unknown 780秒 449毫秒/步 - 損失: 0.8596 - 稀疏分類準確度: 0.6602
1735/Unknown 780秒 449毫秒/步 - 損失: 0.8595 - 稀疏分類準確度: 0.6602
1736/Unknown 780秒 449毫秒/步 - 損失: 0.8594 - 稀疏分類準確度: 0.6603
1737/Unknown 781秒 449毫秒/步 - 損失: 0.8593 - 稀疏分類準確度: 0.6603
1738/Unknown 781秒 449毫秒/步 - 損失: 0.8592 - 稀疏分類準確度: 0.6603
1739/Unknown 782秒 449毫秒/步 - 損失: 0.8591 - 稀疏分類準確度: 0.6603
1740/Unknown 782秒 449毫秒/步 - 損失: 0.8590 - 稀疏分類準確度: 0.6604
1741/Unknown 782秒 449毫秒/步 - 損失: 0.8589 - 稀疏分類準確度: 0.6604
1742/Unknown 783秒 449毫秒/步 - 損失: 0.8589 - 稀疏分類準確度: 0.6604
1743/Unknown 783秒 449毫秒/步 - 損失: 0.8588 - 稀疏分類準確度: 0.6605
1744/Unknown 784秒 449毫秒/步 - 損失: 0.8587 - 稀疏分類準確度: 0.6605
1745/Unknown 784秒 449毫秒/步 - 損失: 0.8586 - 稀疏分類準確度: 0.6605
1746/Unknown 785秒 449毫秒/步 - 損失: 0.8585 - 稀疏分類準確度: 0.6606
1747/Unknown 785秒 449毫秒/步 - 損失: 0.8584 - 稀疏分類準確度: 0.6606
1748/Unknown 786秒 449毫秒/步 - 損失: 0.8583 - 稀疏分類準確度: 0.6606
1749/Unknown 786秒 449毫秒/步 - 損失: 0.8582 - 稀疏分類準確度: 0.6606
1750/Unknown 787秒 449毫秒/步 - 損失: 0.8581 - 稀疏分類準確度: 0.6607
1751/Unknown 787秒 449毫秒/步 - 損失: 0.8580 - 稀疏分類準確度: 0.6607
1752/Unknown 787秒 449毫秒/步 - 損失: 0.8579 - 稀疏分類準確度: 0.6607
1753/Unknown 788秒 449毫秒/步 - 損失: 0.8578 - 稀疏分類準確度: 0.6608
1754/Unknown 788秒 449毫秒/步 - 損失: 0.8577 - 稀疏分類準確度: 0.6608
1755/Unknown 789秒 449毫秒/步 - 損失: 0.8576 - 稀疏分類準確度: 0.6608
1756/Unknown 789秒 449毫秒/步 - 損失: 0.8576 - 稀疏分類準確度: 0.6608
1757/Unknown 789秒 449毫秒/步 - 損失: 0.8575 - 稀疏分類準確度: 0.6609
1758/Unknown 790秒 449毫秒/步 - 損失: 0.8574 - 稀疏分類準確度: 0.6609
1759/Unknown 790秒 449毫秒/步 - 損失: 0.8573 - 稀疏分類準確度: 0.6609
1760/Unknown 791秒 449毫秒/步 - 損失: 0.8572 - 稀疏分類準確度: 0.6610
1761/Unknown 791秒 449毫秒/步 - 損失: 0.8571 - 稀疏分類準確度: 0.6610
1762/Unknown 792秒 449毫秒/步 - 損失: 0.8570 - 稀疏分類準確度: 0.6610
1763/Unknown 792秒 449毫秒/步 - 損失: 0.8569 - 稀疏分類準確度: 0.6610
1764/Unknown 792秒 449毫秒/步 - 損失: 0.8568 - 稀疏分類準確度: 0.6611
1765/Unknown 793秒 449毫秒/步 - 損失: 0.8567 - 稀疏分類準確度: 0.6611
1766/Unknown 793秒 449毫秒/步 - 損失: 0.8566 - 稀疏分類準確度: 0.6611
1767/Unknown 794秒 449毫秒/步 - 損失: 0.8565 - 稀疏分類準確度: 0.6612
1768/Unknown 794秒 449毫秒/步 - 損失: 0.8564 - 稀疏分類準確度: 0.6612
1769/Unknown 795秒 449毫秒/步 - 損失: 0.8564 - 稀疏分類準確度: 0.6612
1770/Unknown 795秒 449毫秒/步 - 損失: 0.8563 - 稀疏分類準確度: 0.6612
1771/Unknown 796秒 449毫秒/步 - 損失: 0.8562 - 稀疏分類準確度: 0.6613
1772/Unknown 796秒 449毫秒/步 - 損失: 0.8561 - 稀疏分類準確度: 0.6613
1773/Unknown 796秒 449毫秒/步 - 損失: 0.8560 - 稀疏分類準確度: 0.6613
1774/Unknown 797秒 449毫秒/步 - 損失: 0.8559 - 稀疏分類準確度: 0.6614
1775/Unknown 797秒 449毫秒/步 - 損失: 0.8558 - 稀疏分類準確度: 0.6614
1776/Unknown 797秒 449毫秒/步 - 損失: 0.8557 - 稀疏分類準確度: 0.6614
1777/Unknown 798秒 449毫秒/步 - 損失: 0.8556 - 稀疏分類準確度: 0.6614
1778/Unknown 798秒 449毫秒/步 - 損失: 0.8555 - 稀疏分類準確度: 0.6615
1779/Unknown 799秒 449毫秒/步 - 損失: 0.8554 - 稀疏分類準確度: 0.6615
1780/Unknown 799秒 449毫秒/步 - 損失: 0.8554 - 稀疏分類準確度: 0.6615
1781/Unknown 799秒 449毫秒/步 - 損失: 0.8553 - 稀疏分類準確度: 0.6616
1782/Unknown 800秒 449毫秒/步 - 損失: 0.8552 - 稀疏分類準確度: 0.6616
1783/Unknown 800秒 448毫秒/步 - 損失: 0.8551 - 稀疏分類準確度: 0.6616
1784/Unknown 801秒 448毫秒/步 - 損失: 0.8550 - 稀疏分類準確度: 0.6616
1785/Unknown 801秒 448毫秒/步 - 損失: 0.8549 - 稀疏分類準確度: 0.6617
1786/Unknown 801秒 448毫秒/步 - 損失: 0.8548 - 稀疏分類準確度: 0.6617
1787/Unknown 802秒 448毫秒/步 - 損失: 0.8547 - 稀疏分類準確度: 0.6617
1788/Unknown 802秒 448毫秒/步 - 損失: 0.8546 - 稀疏分類準確度: 0.6618
1789/Unknown 803秒 448毫秒/步 - 損失: 0.8545 - 稀疏分類準確度: 0.6618
1790/Unknown 803秒 448毫秒/步 - 損失: 0.8545 - 稀疏分類準確度: 0.6618
1791/Unknown 803秒 448毫秒/步 - 損失: 0.8544 - 稀疏分類準確度: 0.6618
1792/Unknown 804秒 448毫秒/步 - 損失: 0.8543 - 稀疏分類準確度: 0.6619
1793/Unknown 804秒 448毫秒/步 - 損失: 0.8542 - 稀疏分類準確度: 0.6619
1794/Unknown 805秒 448毫秒/步 - 損失: 0.8541 - 稀疏分類準確度: 0.6619
1795/Unknown 805秒 448毫秒/步 - 損失: 0.8540 - 稀疏分類準確度: 0.6620
1796/Unknown 805秒 448毫秒/步 - 損失: 0.8539 - 稀疏分類準確度: 0.6620
1797/Unknown 806秒 448毫秒/步 - 損失: 0.8538 - 稀疏分類準確度: 0.6620
1798/Unknown 806秒 448毫秒/步 - 損失: 0.8537 - 稀疏分類準確度: 0.6620
1799/Unknown 807秒 448毫秒/步 - 損失: 0.8536 - 稀疏分類準確度: 0.6621
1800/Unknown 807秒 448毫秒/步 - 損失: 0.8536 - 稀疏分類準確度: 0.6621
1801/Unknown 808秒 448毫秒/步 - 損失: 0.8535 - 稀疏分類準確度: 0.6621
1802/Unknown 808秒 448毫秒/步 - 損失: 0.8534 - 稀疏分類準確度: 0.6622
1803/Unknown 808秒 448毫秒/步 - 損失: 0.8533 - 稀疏分類準確度: 0.6622
1804/Unknown 809秒 448毫秒/步 - 損失: 0.8532 - 稀疏分類準確度: 0.6622
1805/Unknown 809秒 448毫秒/步 - 損失: 0.8531 - 稀疏分類準確度: 0.6622
1806/Unknown 810秒 448毫秒/步 - 損失: 0.8530 - 稀疏分類準確度: 0.6623
1807/Unknown 810秒 448毫秒/步 - 損失: 0.8529 - 稀疏分類準確度: 0.6623
1808/Unknown 811秒 448毫秒/步 - 損失: 0.8528 - 稀疏分類準確度: 0.6623
1809/Unknown 811秒 448毫秒/步 - 損失: 0.8528 - 稀疏分類準確度: 0.6623
1810/Unknown 811秒 448毫秒/步 - 損失: 0.8527 - 稀疏分類準確度: 0.6624
1811/Unknown 812秒 448毫秒/步 - 損失: 0.8526 - 稀疏分類準確度: 0.6624
1812/Unknown 812秒 448毫秒/步 - 損失: 0.8525 - 稀疏分類準確度: 0.6624
1813/Unknown 812秒 448毫秒/步 - 損失: 0.8524 - 稀疏分類準確度: 0.6625
1814/Unknown 813秒 448毫秒/步 - 損失: 0.8523 - 稀疏分類準確度: 0.6625
1815/Unknown 813秒 448毫秒/步 - 損失: 0.8522 - 稀疏分類準確度: 0.6625
1816/Unknown 814秒 448毫秒/步 - 損失: 0.8521 - 稀疏分類準確度: 0.6625
1817/Unknown 814秒 448毫秒/步 - 損失: 0.8520 - 稀疏分類準確度: 0.6626
1818/Unknown 814秒 448毫秒/步 - 損失: 0.8520 - 稀疏分類準確度: 0.6626
1819/Unknown 815秒 448毫秒/步 - 損失: 0.8519 - 稀疏分類準確度: 0.6626
1820/Unknown 815秒 448毫秒/步 - 損失: 0.8518 - 稀疏分類準確度: 0.6627
1821/Unknown 816秒 448毫秒/步 - 損失: 0.8517 - 稀疏分類準確度: 0.6627
1822/Unknown 816秒 448毫秒/步 - 損失: 0.8516 - 稀疏分類準確度: 0.6627
1823/Unknown 817秒 448毫秒/步 - 損失: 0.8515 - 稀疏分類準確度: 0.6627
1824/Unknown 817秒 448毫秒/步 - 損失: 0.8514 - 稀疏分類準確度: 0.6628
1825/Unknown 818秒 448毫秒/步 - 損失: 0.8513 - 稀疏分類準確度: 0.6628
1826/Unknown 818秒 448毫秒/步 - 損失: 0.8513 - 稀疏分類準確度: 0.6628
1827/Unknown 819秒 448毫秒/步 - 損失: 0.8512 - 稀疏分類準確度: 0.6628
1828/Unknown 819秒 448毫秒/步 - 損失: 0.8511 - 稀疏分類準確度: 0.6629
1829/Unknown 819秒 448毫秒/步 - 損失: 0.8510 - 稀疏分類準確度: 0.6629
1830/Unknown 820秒 448毫秒/步 - 損失: 0.8509 - 稀疏分類準確度: 0.6629
1831/Unknown 820秒 448毫秒/步 - 損失: 0.8508 - 稀疏分類準確度: 0.6630
1832/Unknown 821秒 448毫秒/步 - 損失: 0.8507 - 稀疏分類準確度: 0.6630
1833/Unknown 821秒 448毫秒/步 - 損失: 0.8507 - 稀疏分類準確度: 0.6630
1834/Unknown 821秒 448毫秒/步 - 損失: 0.8506 - 稀疏分類準確度: 0.6630
1835/Unknown 822秒 447毫秒/步 - 損失: 0.8505 - 稀疏分類準確度: 0.6631
1836/Unknown 822秒 447毫秒/步 - 損失: 0.8504 - 稀疏分類準確度: 0.6631
1837/Unknown 822秒 447毫秒/步 - 損失: 0.8503 - 稀疏分類準確度: 0.6631
1838/Unknown 823秒 447毫秒/步 - 損失: 0.8502 - 稀疏分類準確度: 0.6631
1839/Unknown 823秒 447毫秒/步 - 損失: 0.8501 - 稀疏分類準確度: 0.6632
1840/Unknown 823秒 447毫秒/步 - 損失: 0.8500 - 稀疏分類準確度: 0.6632
1841/Unknown 824秒 447毫秒/步 - 損失: 0.8500 - 稀疏分類準確度: 0.6632
1842/Unknown 824秒 447毫秒/步 - 損失: 0.8499 - 稀疏分類準確度: 0.6633
1843/Unknown 825秒 447毫秒/步 - 損失: 0.8498 - 稀疏分類準確度: 0.6633
1844/Unknown 825秒 447毫秒/步 - 損失: 0.8497 - 稀疏分類準確度: 0.6633
1845/Unknown 825秒 447毫秒/步 - 損失: 0.8496 - 稀疏分類準確度: 0.6633
1846/Unknown 826秒 447毫秒/步 - 損失: 0.8495 - 稀疏分類準確度: 0.6634
1847/Unknown 826秒 447毫秒/步 - 損失: 0.8494 - 稀疏分類準確度: 0.6634
1848/Unknown 827秒 447毫秒/步 - 損失: 0.8494 - 稀疏分類準確度: 0.6634
1849/Unknown 827秒 447毫秒/步 - 損失: 0.8493 - 稀疏分類準確度: 0.6634
1850/Unknown 828秒 447毫秒/步 - 損失: 0.8492 - 稀疏分類準確度: 0.6635
1851/Unknown 828秒 447毫秒/步 - 損失: 0.8491 - 稀疏分類準確度: 0.6635
1852/Unknown 828秒 447毫秒/步 - 損失: 0.8490 - 稀疏分類準確度: 0.6635
1853/Unknown 829秒 447毫秒/步 - 損失: 0.8489 - 稀疏分類準確度: 0.6636
1854/Unknown 829秒 447毫秒/步 - 損失: 0.8488 - 稀疏分類準確度: 0.6636
1855/Unknown 830秒 447毫秒/步 - 損失: 0.8488 - 稀疏分類準確度: 0.6636
1856/Unknown 830秒 447毫秒/步 - 損失: 0.8487 - 稀疏分類準確度: 0.6636
1857/Unknown 830秒 447毫秒/步 - 損失: 0.8486 - 稀疏分類準確度: 0.6637
1858/Unknown 831秒 447毫秒/步 - 損失: 0.8485 - 稀疏分類準確度: 0.6637
1859/Unknown 831秒 447毫秒/步 - 損失: 0.8484 - 稀疏分類準確度: 0.6637
1860/Unknown 832秒 447毫秒/步 - 損失: 0.8483 - 稀疏分類準確度: 0.6637
1861/Unknown 832秒 447毫秒/步 - 損失: 0.8482 - 稀疏分類準確度: 0.6638
1862/Unknown 832秒 447毫秒/步 - 損失: 0.8482 - 稀疏分類準確度: 0.6638
1863/Unknown 833秒 447毫秒/步 - 損失: 0.8481 - 稀疏分類準確度: 0.6638
1864/Unknown 833秒 447毫秒/步 - 損失: 0.8480 - 稀疏分類準確度: 0.6638
1865/Unknown 834秒 447毫秒/步 - 損失: 0.8479 - 稀疏分類準確度: 0.6639
1865/1865 ━━━━━━━━━━━━━━━━━━━━ 834秒 447毫秒/步 - 損失: 0.8478 - 稀疏分類準確度: 0.6639
Model training finished
/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/trainers/epoch_iterator.py:151: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.
self._interrupted_warning()
Test accuracy: 75.0%
深度與交叉模型達到約 81% 的測試準確度。
結論
您可以使用 Keras 預處理層輕鬆處理具有不同編碼機制的類別特徵，包括獨熱編碼和特徵嵌入。此外，不同的模型架構（例如寬、深和交叉網路）在不同的資料集屬性方面具有不同的優勢。您可以探索獨立使用它們或將它們組合起來，以為您的資料集實現最佳結果。