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a guest Feb 16th, 2019 63 Never
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  1. model.fit(train_images, train_labels, validation_data=(test_images, test_labels), epochs=5)
  2.  
  3.  
  4. Train on 60000 samples, validate on 10000 samples
  5. Epoch 1/5
  6. 60000/60000 [==============================] - 40s 660us/step - loss: 0.2486 - acc: 0.9273 - val_loss: 0.1027 - val_acc: 0.9660
  7. Epoch 2/5
  8. 60000/60000 [==============================] - 38s 638us/step - loss: 0.0853 - acc: 0.9739 - val_loss: 0.0666 - val_acc: 0.9791
  9. Epoch 3/5
  10. 60000/60000 [==============================] - 38s 632us/step - loss: 0.0614 - acc: 0.9813 - val_loss: 0.0513 - val_acc: 0.9840
  11. Epoch 4/5
  12. 60000/60000 [==============================] - 38s 631us/step - loss: 0.0496 - acc: 0.9842 - val_loss: 0.0429 - val_acc: 0.9870
  13. Epoch 5/5
  14. 60000/60000 [==============================] - 38s 638us/step - loss: 0.0423 - acc: 0.9867 - val_loss: 0.0373 - val_acc: 0.9882
  15. <keras.callbacks.History at 0x7f7a5d883668>
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