トレーニング用データ:
>>> train_data = packed_train_data.shuffle(500)
>>> test_data = packed_test_data
モデルの訓練
ここで「モデルの訓練」と定めるものは, 「train_data に model を fit させる」である。
──Model.fit メソッドは損失を最小化するようにモデルのパラメータを調整する。
モデルの訓練の進行とともに、損失値と正解率が表示される
>>> model.fit(train_data, epochs=20)
WARNING:tensorflow:From /home/pi/venv/lib/python3.7/site-packages/
tensorflow_core/python/feature_column/feature_column_v2.py:4266:
IndicatorColumn._variable_shape
(from tensorflow.python.feature_column.feature_column_v2)
is deprecated and will be removed in a future version.
Instructions for updating:
The old _FeatureColumn APIs are being deprecated.
Please use the new FeatureColumn APIs instead.
WARNING:tensorflow:From /home/pi/venv/lib/python3.7/site-packages/
tensorflow_core/python/feature_column/feature_column_v2.py:4321:
VocabularyListCategoricalColumn._num_buckets
(from tensorflow.python.feature_column.feature_column_v2)
is deprecated and will be removed in a future version.
Instructions for updating:
The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.
Epoch 1/20
126/Unknown - 8s 66ms/step - loss: 0.4839 - acc: 0.7368
2021-04-23 12:40:34.532338:
W tensorflow/core/common_runtime/base_collective_executor.cc:217]
BaseCollectiveExecutor::StartAbort Out of range: End of sequence
[[{{node IteratorGetNext}}]]
126/126 [==============================] - 8s 66ms/step - loss: 0.4839 - acc: 0.7368
Epoch 2/20
126/126 [==============================] - 2s 12ms/step - loss: 0.4148 - acc: 0.8214
Epoch 3/20
126/126 [==============================] - 2s 13ms/step - loss: 0.3956 - acc: 0.8357
Epoch 4/20
126/126 [==============================] - 2s 13ms/step - loss: 0.3834 - acc: 0.8341
Epoch 5/20
126/126 [==============================] - 2s 13ms/step - loss: 0.3739 - acc: 0.8405
Epoch 6/20
126/126 [==============================] - 2s 12ms/step - loss: 0.3658 - acc: 0.8437
Epoch 7/20
126/126 [==============================] - 2s 13ms/step - loss: 0.3590 - acc: 0.8469
Epoch 8/20
126/126 [==============================] - 2s 13ms/step - loss: 0.3538 - acc: 0.8517
Epoch 9/20
126/126 [==============================] - 2s 12ms/step - loss: 0.3480 - acc: 0.8533
Epoch 10/20
126/126 [==============================] - 2s 12ms/step - loss: 0.3420 - acc: 0.8533
Epoch 11/20
126/126 [==============================] - 2s 13ms/step - loss: 0.3369 - acc: 0.8533
Epoch 12/20
126/126 [==============================] - 2s 13ms/step - loss: 0.3323 - acc: 0.8517
Epoch 13/20
126/126 [==============================] - 2s 13ms/step - loss: 0.3284 - acc: 0.8549
Epoch 14/20
126/126 [==============================] - 2s 12ms/step - loss: 0.3245 - acc: 0.8565
Epoch 15/20
126/126 [==============================] - 2s 13ms/step - loss: 0.3200 - acc: 0.8596
Epoch 16/20
126/126 [==============================] - 2s 12ms/step - loss: 0.3163 - acc: 0.8628
Epoch 17/20
126/126 [==============================] - 2s 12ms/step - loss: 0.3126 - acc: 0.8628
Epoch 18/20
126/126 [==============================] - 2s 12ms/step - loss: 0.3096 - acc: 0.8612
Epoch 19/20
126/126 [==============================] - 2s 12ms/step - loss: 0.3059 - acc: 0.8612
Epoch 20/20
126/126 [==============================] - 2s 13ms/step - loss: 0.3031 - acc: 0.8612
>>>
epochs は,学習の繰り返しの数。
繰り返すごとに,正解率が全体的に上がっている。
このモデルの場合、訓練用データでは 0.8612(すなわち86.12%)の正解率に達した。
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