python - 如何更新我的模型 acc 和 val_acc？

Question

我目前正在开展一个创建单词预测模型的项目。有80万个数据集，但单独使用0.5%作为原型，训练数据量如下。我想知道为什么 loss 和 val_loss 在训练过程中会减少，但是 acc 和 val_acc 保持不变。

Train Data Set : 31471

我的模型参数

epochs=0
optimizer = tensorflow.keras.optimizers.SGD(lr=0.01)
loss_func = 'categorical_crossentropy'
hidden_1_neural = 128
hidden_2_neural = 64
hidden_1_dropout = 0.1
hidden_2_dropout = 0
activation = 'relu'
out_put_activation='softmax'
embedding_dim = 10

训练：优化器=SGD

Epoch 1/100
1259/1259 [==============================] - 15s 12ms/step - loss: 8.6827 - accuracy: 0.1164 - val_loss: 8.3275 - val_accuracy: 0.1300
Epoch 2/100
1259/1259 [==============================] - 12s 10ms/step - loss: 8.3446 - accuracy: 0.1178 - val_loss: 8.1969 - val_accuracy: 0.1300
Epoch 3/100
1259/1259 [==============================] - 13s 10ms/step - loss: 8.2007 - accuracy: 0.1178 - val_loss: 8.0654 - val_accuracy: 0.1300
Epoch 4/100
1259/1259 [==============================] - 13s 10ms/step - loss: 8.0747 - accuracy: 0.1178 - val_loss: 7.9659 - val_accuracy: 0.1300
Epoch 5/100
1259/1259 [==============================] - 12s 10ms/step - loss: 7.9752 - accuracy: 0.1178 - val_loss: 7.8901 - val_accuracy: 0.1300
Epoch 6/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.8923 - accuracy: 0.1178 - val_loss: 7.8225 - val_accuracy: 0.1300
Epoch 7/100
1259/1259 [==============================] - 13s 11ms/step - loss: 7.8232 - accuracy: 0.1178 - val_loss: 7.7742 - val_accuracy: 0.1300
Epoch 8/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.7664 - accuracy: 0.1178 - val_loss: 7.7329 - val_accuracy: 0.1300
Epoch 9/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.7186 - accuracy: 0.1178 - val_loss: 7.7037 - val_accuracy: 0.1300
Epoch 10/100
1259/1259 [==============================] - 14s 11ms/step - loss: 7.6785 - accuracy: 0.1178 - val_loss: 7.6797 - val_accuracy: 0.1300
Epoch 11/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.6450 - accuracy: 0.1178 - val_loss: 7.6598 - val_accuracy: 0.1300
Epoch 12/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.6165 - accuracy: 0.1178 - val_loss: 7.6524 - val_accuracy: 0.1300
Epoch 13/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.5922 - accuracy: 0.1178 - val_loss: 7.6367 - val_accuracy: 0.1300
Epoch 14/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.5712 - accuracy: 0.1178 - val_loss: 7.6332 - val_accuracy: 0.1300
Epoch 15/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.5534 - accuracy: 0.1178 - val_loss: 7.6280 - val_accuracy: 0.1300
Epoch 16/100
1259/1259 [==============================] - 14s 11ms/step - loss: 7.5373 - accuracy: 0.1178 - val_loss: 7.6238 - val_accuracy: 0.1300
Epoch 17/100
1259/1259 [==============================] - 14s 11ms/step - loss: 7.5234 - accuracy: 0.1178 - val_loss: 7.6239 - val_accuracy: 0.1300
Epoch 18/100
1259/1259 [==============================] - 14s 11ms/step - loss: 7.5107 - accuracy: 0.1178 - val_loss: 7.6246 - val_accuracy: 0.1300
Epoch 19/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.4995 - accuracy: 0.1178 - val_loss: 7.6208 - val_accuracy: 0.1300
Epoch 20/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.4893 - accuracy: 0.1178 - val_loss: 7.6222 - val_accuracy: 0.1300
Epoch 21/100
1259/1259 [==============================] - 12s 10ms/step - loss: 7.4798 - accuracy: 0.1178 - val_loss: 7.6239 - val_accuracy: 0.1300
Epoch 22/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.4710 - accuracy: 0.1178 - val_loss: 7.6246 - val_accuracy: 0.1300
Epoch 23/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.4634 - accuracy: 0.1178 - val_loss: 7.6286 - val_accuracy: 0.1300
Epoch 24/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.4561 - accuracy: 0.1178 - val_loss: 7.6315 - val_accuracy: 0.1300
Epoch 25/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.4492 - accuracy: 0.1178 - val_loss: 7.6363 - val_accuracy: 0.1300
Epoch 26/100
1259/1259 [==============================] - 13s 11ms/step - loss: 7.4432 - accuracy: 0.1178 - val_loss: 7.6363 - val_accuracy: 0.1300
Epoch 27/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.4370 - accuracy: 0.1178 - val_loss: 7.6396 - val_accuracy: 0.1300
Epoch 28/100
1259/1259 [==============================] - 12s 10ms/step - loss: 7.4321 - accuracy: 0.1178 - val_loss: 7.6433 - val_accuracy: 0.1300
Epoch 29/100
1259/1259 [==============================] - 12s 10ms/step - loss: 7.4264 - accuracy: 0.1178 - val_loss: 7.6484 - val_accuracy: 0.1300
Epoch 30/100
1259/1259 [==============================] - 12s 10ms/step - loss: 7.4214 - accuracy: 0.1178 - val_loss: 7.6568 - val_accuracy: 0.1300
Epoch 31/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.4173 - accuracy: 0.1178 - val_loss: 7.6591 - val_accuracy: 0.1300
Epoch 32/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.4122 - accuracy: 0.1178 - val_loss: 7.6672 - val_accuracy: 0.1300
Epoch 33/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.4084 - accuracy: 0.1178 - val_loss: 7.6637 - val_accuracy: 0.1300
Epoch 34/100
1259/1259 [==============================] - 14s 11ms/step - loss: 7.4047 - accuracy: 0.1178 - val_loss: 7.6674 - val_accuracy: 0.1300
Epoch 35/100
1259/1259 [==============================] - 13s 11ms/step - loss: 7.4007 - accuracy: 0.1178 - val_loss: 7.6710 - val_accuracy: 0.1300
Epoch 36/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.3977 - accuracy: 0.1178 - val_loss: 7.6747 - val_accuracy: 0.1300
Epoch 37/100
1259/1259 [==============================] - 14s 11ms/step - loss: 7.3936 - accuracy: 0.1178 - val_loss: 7.6788 - val_accuracy: 0.1300
Epoch 38/100
1259/1259 [==============================] - 13s 11ms/step - loss: 7.3905 - accuracy: 0.1178 - val_loss: 7.6854 - val_accuracy: 0.1300
Epoch 39/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.3874 - accuracy: 0.1178 - val_loss: 7.6879 - val_accuracy: 0.1300
Epoch 40/100
1259/1259 [==============================] - 13s 11ms/step - loss: 7.3848 - accuracy: 0.1178 - val_loss: 7.6914 - val_accuracy: 0.1300
Epoch 41/100
1259/1259 [==============================] - 13s 11ms/step - loss: 7.3819 - accuracy: 0.1178 - val_loss: 7.6973 - val_accuracy: 0.1300
Epoch 42/100
1259/1259 [==============================] - 14s 11ms/step - loss: 7.3787 - accuracy: 0.1178 - val_loss: 7.6993 - val_accuracy: 0.1300
Epoch 43/100
1259/1259 [==============================] - 14s 11ms/step - loss: 7.3762 - accuracy: 0.1178 - val_loss: 7.7056 - val_accuracy: 0.1300
Epoch 44/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.3737 - accuracy: 0.1178 - val_loss: 7.7069 - val_accuracy: 0.1300
Epoch 45/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.3711 - accuracy: 0.1178 - val_loss: 7.7115 - val_accuracy: 0.1300
Epoch 46/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.3683 - accuracy: 0.1178 - val_loss: 7.7161 - val_accuracy: 0.1300
Epoch 47/100
1259/1259 [==============================] - 13s 11ms/step - loss: 7.3662 - accuracy: 0.1178 - val_loss: 7.7211 - val_accuracy: 0.1300
Epoch 48/100
1259/1259 [==============================] - 12s 10ms/step - loss: 7.3643 - accuracy: 0.1178 - val_loss: 7.7230 - val_accuracy: 0.1300
Epoch 49/100
1259/1259 [==============================] - 12s 10ms/step - loss: 7.3619 - accuracy: 0.1178 - val_loss: 7.7278 - val_accuracy: 0.1300
Epoch 50/100
1259/1259 [==============================] - 14s 11ms/step - loss: 7.3597 - accuracy: 0.1178 - val_loss: 7.7334 - val_accuracy: 0.1300
Epoch 51/100
1259/1259 [==============================] - 13s 11ms/step - loss: 7.3579 - accuracy: 0.1178 - val_loss: 7.7357 - val_accuracy: 0.1300
Epoch 52/100
1259/1259 [==============================] - 13s 11ms/step - loss: 7.3560 - accuracy: 0.1178 - val_loss: 7.7445 - val_accuracy: 0.1300
Epoch 53/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.3541 - accuracy: 0.1178 - val_loss: 7.7450 - val_accuracy: 0.1300
Epoch 54/100
1259/1259 [==============================] - 13s 11ms/step - loss: 7.3518 - accuracy: 0.1178 - val_loss: 7.7577 - val_accuracy: 0.1300
Epoch 55/100
1259/1259 [==============================] - 13s 11ms/step - loss: 7.3504 - accuracy: 0.1178 - val_loss: 7.7527 - val_accuracy: 0.1300
Epoch 56/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.3485 - accuracy: 0.1178 - val_loss: 7.7569 - val_accuracy: 0.1300
Epoch 57/100
1259/1259 [==============================] - 13s 11ms/step - loss: 7.3472 - accuracy: 0.1178 - val_loss: 7.7567 - val_accuracy: 0.1300
Epoch 58/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.3462 - accuracy: 0.1178 - val_loss: 7.7610 - val_accuracy: 0.1300
Epoch 59/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.3444 - accuracy: 0.1178 - val_loss: 7.7650 - val_accuracy: 0.1300
Epoch 60/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.3426 - accuracy: 0.1178 - val_loss: 7.7676 - val_accuracy: 0.1300
Epoch 61/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.3406 - accuracy: 0.1178 - val_loss: 7.7711 - val_accuracy: 0.1300
Epoch 62/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.3398 - accuracy: 0.1178 - val_loss: 7.7753 - val_accuracy: 0.1300
Epoch 63/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.3381 - accuracy: 0.1178 - val_loss: 7.7841 - val_accuracy: 0.1300
Epoch 64/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.3375 - accuracy: 0.1178 - val_loss: 7.7857 - val_accuracy: 0.1300
Epoch 65/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.3359 - accuracy: 0.1178 - val_loss: 7.7862 - val_accuracy: 0.1300
Epoch 66/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.3345 - accuracy: 0.1178 - val_loss: 7.7889 - val_accuracy: 0.1300
Epoch 67/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.3336 - accuracy: 0.1178 - val_loss: 7.7951 - val_accuracy: 0.1300
Epoch 68/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.3321 - accuracy: 0.1178 - val_loss: 7.7976 - val_accuracy: 0.1300
Epoch 69/100
1259/1259 [==============================] - 13s 11ms/step - loss: 7.3309 - accuracy: 0.1178 - val_loss: 7.7996 - val_accuracy: 0.1300
Epoch 70/100
1259/1259 [==============================] - 14s 11ms/step - loss: 7.3297 - accuracy: 0.1178 - val_loss: 7.8092 - val_accuracy: 0.1300
Epoch 71/100
1259/1259 [==============================] - 14s 11ms/step - loss: 7.3286 - accuracy: 0.1178 - val_loss: 7.8060 - val_accuracy: 0.1300
Epoch 72/100
1259/1259 [==============================] - 14s 11ms/step - loss: 7.3279 - accuracy: 0.1178 - val_loss: 7.8098 - val_accuracy: 0.1300
Epoch 73/100
1259/1259 [==============================] - 14s 11ms/step - loss: 7.3261 - accuracy: 0.1178 - val_loss: 7.8125 - val_accuracy: 0.1300
Epoch 74/100
1259/1259 [==============================] - 14s 11ms/step - loss: 7.3249 - accuracy: 0.1178 - val_loss: 7.8165 - val_accuracy: 0.1300
Epoch 75/100
1259/1259 [==============================] - 15s 12ms/step - loss: 7.3244 - accuracy: 0.1178 - val_loss: 7.8197 - val_accuracy: 0.1300
Epoch 76/100
1259/1259 [==============================] - 14s 11ms/step - loss: 7.3239 - accuracy: 0.1178 - val_loss: 7.8224 - val_accuracy: 0.1300
Epoch 77/100
1259/1259 [==============================] - 14s 11ms/step - loss: 7.3226 - accuracy: 0.1178 - val_loss: 7.8259 - val_accuracy: 0.1300
Epoch 78/100
1259/1259 [==============================] - 14s 11ms/step - loss: 7.3217 - accuracy: 0.1178 - val_loss: 7.8311 - val_accuracy: 0.1300
Epoch 79/100
1259/1259 [==============================] - 13s 11ms/step - loss: 7.3206 - accuracy: 0.1178 - val_loss: 7.8353 - val_accuracy: 0.1300
Epoch 80/100
1259/1259 [==============================] - 14s 11ms/step - loss: 7.3197 - accuracy: 0.1178 - val_loss: 7.8423 - val_accuracy: 0.1300
Epoch 81/100
1259/1259 [==============================] - 14s 11ms/step - loss: 7.3193 - accuracy: 0.1178 - val_loss: 7.8391 - val_accuracy: 0.1300
Epoch 82/100
1259/1259 [==============================] - 14s 11ms/step - loss: 7.3180 - accuracy: 0.1178 - val_loss: 7.8399 - val_accuracy: 0.1300
Epoch 83/100
1259/1259 [==============================] - 13s 11ms/step - loss: 7.3172 - accuracy: 0.1178 - val_loss: 7.8495 - val_accuracy: 0.1300
Epoch 84/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.3165 - accuracy: 0.1178 - val_loss: 7.8492 - val_accuracy: 0.1300
Epoch 85/100
1259/1259 [==============================] - 13s 11ms/step - loss: 7.3151 - accuracy: 0.1178 - val_loss: 7.8505 - val_accuracy: 0.1300
Epoch 86/100
1259/1259 [==============================] - 14s 11ms/step - loss: 7.3150 - accuracy: 0.1178 - val_loss: 7.8527 - val_accuracy: 0.1300
Epoch 87/100
1259/1259 [==============================] - 14s 11ms/step - loss: 7.3143 - accuracy: 0.1178 - val_loss: 7.8555 - val_accuracy: 0.1300
Epoch 88/100
1259/1259 [==============================] - 16s 13ms/step - loss: 7.3132 - accuracy: 0.1178 - val_loss: 7.8578 - val_accuracy: 0.1300
Epoch 89/100
1259/1259 [==============================] - 15s 12ms/step - loss: 7.3128 - accuracy: 0.1178 - val_loss: 7.8605 - val_accuracy: 0.1300
Epoch 90/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.3125 - accuracy: 0.1178 - val_loss: 7.8639 - val_accuracy: 0.1300
Epoch 91/100
1259/1259 [==============================] - 12s 10ms/step - loss: 7.3114 - accuracy: 0.1178 - val_loss: 7.8733 - val_accuracy: 0.1300
Epoch 92/100
1259/1259 [==============================] - 12s 10ms/step - loss: 7.3108 - accuracy: 0.1178 - val_loss: 7.8717 - val_accuracy: 0.1300
Epoch 93/100
1259/1259 [==============================] - 12s 10ms/step - loss: 7.3097 - accuracy: 0.1178 - val_loss: 7.8742 - val_accuracy: 0.1300
Epoch 94/100
1259/1259 [==============================] - 12s 10ms/step - loss: 7.3095 - accuracy: 0.1178 - val_loss: 7.8750 - val_accuracy: 0.1300
Epoch 95/100
1259/1259 [==============================] - 12s 10ms/step - loss: 7.3086 - accuracy: 0.1178 - val_loss: 7.8805 - val_accuracy: 0.1300
Epoch 96/100
1259/1259 [==============================] - 12s 10ms/step - loss: 7.3083 - accuracy: 0.1178 - val_loss: 7.8804 - val_accuracy: 0.1300
Epoch 97/100
1259/1259 [==============================] - 12s 10ms/step - loss: 7.3077 - accuracy: 0.1178 - val_loss: 7.8858 - val_accuracy: 0.1300
Epoch 98/100
1259/1259 [==============================] - 14s 11ms/step - loss: 7.3070 - accuracy: 0.1178 - val_loss: 7.8868 - val_accuracy: 0.1300
Epoch 99/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.3062 - accuracy: 0.1178 - val_loss: 7.8913 - val_accuracy: 0.1300
Epoch 100/100
1259/1259 [==============================] - 13s 10ms/step - loss: 7.3059 - accuracy: 0.1178 - val_loss: 7.8924 - val_accuracy: 0.1300

如果我使用 adam 作为优化器，损失会减少，acc 会增加，但 val_loss 和 val_acc 会增加。

#1 添加上下文

模型总结：

Model: "functional_23"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_12 (InputLayer)        [(None, 2)]               0         
_________________________________________________________________
embedding_11 (Embedding)     (None, 2, 10)             104270    
_________________________________________________________________
lstm_22 (LSTM)               (None, 2, 128)            71168     
_________________________________________________________________
lstm_23 (LSTM)               (None, 64)                49408     
_________________________________________________________________
dense_11 (Dense)             (None, 10427)             677755    
=================================================================
Total params: 902,601
Trainable params: 902,601
Non-trainable params: 0
_________________________________________________________________

训练：优化器 = Adam

Epoch 1/100
1259/1259 [==============================] - 11s 9ms/step - loss: 8.0481 - accuracy: 0.1177 - val_loss: 7.6862 - val_accuracy: 0.1300
Epoch 2/100
1259/1259 [==============================] - 10s 8ms/step - loss: 7.2587 - accuracy: 0.1178 - val_loss: 8.0457 - val_accuracy: 0.1300
Epoch 3/100
1259/1259 [==============================] - 12s 9ms/step - loss: 7.0693 - accuracy: 0.1178 - val_loss: 8.3413 - val_accuracy: 0.1300
Epoch 4/100
1259/1259 [==============================] - 10s 8ms/step - loss: 6.9767 - accuracy: 0.1178 - val_loss: 8.4930 - val_accuracy: 0.1300
Epoch 5/100
1259/1259 [==============================] - 10s 8ms/step - loss: 6.8866 - accuracy: 0.1178 - val_loss: 9.0810 - val_accuracy: 0.1300
Epoch 6/100
1259/1259 [==============================] - 10s 8ms/step - loss: 6.7718 - accuracy: 0.1178 - val_loss: 9.5166 - val_accuracy: 0.1303
Epoch 7/100
1259/1259 [==============================] - 12s 9ms/step - loss: 6.6101 - accuracy: 0.1204 - val_loss: 10.2690 - val_accuracy: 0.1385
Epoch 8/100
1259/1259 [==============================] - 11s 9ms/step - loss: 6.4294 - accuracy: 0.1291 - val_loss: 10.5882 - val_accuracy: 0.1405
Epoch 9/100
1259/1259 [==============================] - 11s 9ms/step - loss: 6.2603 - accuracy: 0.1316 - val_loss: 10.7328 - val_accuracy: 0.1395
Epoch 10/100
1259/1259 [==============================] - 12s 9ms/step - loss: 6.1231 - accuracy: 0.1351 - val_loss: 11.0442 - val_accuracy: 0.1405
Epoch 11/100
1259/1259 [==============================] - 11s 9ms/step - loss: 6.0100 - accuracy: 0.1366 - val_loss: 11.2861 - val_accuracy: 0.1401
Epoch 12/100
1259/1259 [==============================] - 11s 9ms/step - loss: 5.8962 - accuracy: 0.1378 - val_loss: 11.4858 - val_accuracy: 0.1366
Epoch 13/100
1259/1259 [==============================] - 12s 9ms/step - loss: 5.7899 - accuracy: 0.1389 - val_loss: 11.5724 - val_accuracy: 0.1379
Epoch 14/100
1259/1259 [==============================] - 11s 9ms/step - loss: 5.6857 - accuracy: 0.1397 - val_loss: 12.1945 - val_accuracy: 0.1392
Epoch 15/100
1259/1259 [==============================] - 12s 9ms/step - loss: 5.5770 - accuracy: 0.1416 - val_loss: 12.4677 - val_accuracy: 0.1389
Epoch 16/100
1259/1259 [==============================] - 11s 9ms/step - loss: 5.4650 - accuracy: 0.1436 - val_loss: 13.1879 - val_accuracy: 0.1398
Epoch 17/100
1259/1259 [==============================] - 12s 10ms/step - loss: 5.3608 - accuracy: 0.1448 - val_loss: 13.3614 - val_accuracy: 0.1392
Epoch 18/100
1259/1259 [==============================] - 11s 9ms/step - loss: 5.2428 - accuracy: 0.1468 - val_loss: 13.8756 - val_accuracy: 0.1373
Epoch 19/100
1259/1259 [==============================] - 11s 9ms/step - loss: 5.1173 - accuracy: 0.1506 - val_loss: 14.5616 - val_accuracy: 0.1344
Epoch 20/100
1259/1259 [==============================] - 10s 8ms/step - loss: 4.9850 - accuracy: 0.1519 - val_loss: 15.1821 - val_accuracy: 0.1322
Epoch 21/100
1259/1259 [==============================] - 11s 9ms/step - loss: 4.8699 - accuracy: 0.1563 - val_loss: 15.8595 - val_accuracy: 0.1246
Epoch 22/100
1259/1259 [==============================] - 10s 8ms/step - loss: 4.7625 - accuracy: 0.1609 - val_loss: 16.9606 - val_accuracy: 0.1274
Epoch 23/100
1259/1259 [==============================] - 11s 9ms/step - loss: 4.6529 - accuracy: 0.1648 - val_loss: 17.2735 - val_accuracy: 0.1255
Epoch 24/100
1259/1259 [==============================] - 11s 8ms/step - loss: 4.5586 - accuracy: 0.1665 - val_loss: 17.6336 - val_accuracy: 0.1268
Epoch 25/100
1259/1259 [==============================] - 11s 8ms/step - loss: 4.4696 - accuracy: 0.1719 - val_loss: 18.8503 - val_accuracy: 0.1239
Epoch 26/100
1259/1259 [==============================] - 11s 9ms/step - loss: 4.3908 - accuracy: 0.1768 - val_loss: 18.8996 - val_accuracy: 0.1271
Epoch 27/100
1259/1259 [==============================] - 15s 12ms/step - loss: 4.3114 - accuracy: 0.1809 - val_loss: 20.1614 - val_accuracy: 0.1271
Epoch 28/100
1259/1259 [==============================] - 11s 9ms/step - loss: 4.2313 - accuracy: 0.1856 - val_loss: 19.8104 - val_accuracy: 0.1239
Epoch 29/100
1259/1259 [==============================] - 15s 12ms/step - loss: 4.1639 - accuracy: 0.1898 - val_loss: 21.2305 - val_accuracy: 0.1268
Epoch 30/100
1259/1259 [==============================] - 11s 9ms/step - loss: 4.0977 - accuracy: 0.1964 - val_loss: 22.0776 - val_accuracy: 0.1290
Epoch 31/100
1259/1259 [==============================] - 12s 9ms/step - loss: 4.0339 - accuracy: 0.2020 - val_loss: 22.2132 - val_accuracy: 0.1284
Epoch 32/100
1259/1259 [==============================] - 11s 9ms/step - loss: 3.9690 - accuracy: 0.2041 - val_loss: 22.7188 - val_accuracy: 0.1303
Epoch 33/100
1259/1259 [==============================] - 11s 9ms/step - loss: 3.9047 - accuracy: 0.2060 - val_loss: 23.6534 - val_accuracy: 0.1277
Epoch 34/100
1259/1259 [==============================] - 12s 10ms/step - loss: 3.8326 - accuracy: 0.2119 - val_loss: 24.6426 - val_accuracy: 0.1255
Epoch 35/100
1259/1259 [==============================] - 11s 9ms/step - loss: 3.7886 - accuracy: 0.2203 - val_loss: 23.4429 - val_accuracy: 0.1214
Epoch 36/100
1259/1259 [==============================] - 11s 9ms/step - loss: 3.7441 - accuracy: 0.2277 - val_loss: 23.9890 - val_accuracy: 0.1246
Epoch 37/100
1259/1259 [==============================] - 11s 9ms/step - loss: 3.6865 - accuracy: 0.2305 - val_loss: 25.8336 - val_accuracy: 0.1262
Epoch 38/100
1259/1259 [==============================] - 11s 9ms/step - loss: 3.6346 - accuracy: 0.2368 - val_loss: 26.5063 - val_accuracy: 0.1195
Epoch 39/100
1259/1259 [==============================] - 11s 9ms/step - loss: 3.5873 - accuracy: 0.2434 - val_loss: 26.5917 - val_accuracy: 0.1249
Epoch 40/100
1259/1259 [==============================] - 11s 9ms/step - loss: 3.5522 - accuracy: 0.2452 - val_loss: 26.5287 - val_accuracy: 0.1214
Epoch 41/100
1259/1259 [==============================] - 11s 9ms/step - loss: 3.4908 - accuracy: 0.2509 - val_loss: 27.0090 - val_accuracy: 0.1255
Epoch 42/100
1259/1259 [==============================] - 11s 9ms/step - loss: 3.4511 - accuracy: 0.2560 - val_loss: 27.7853 - val_accuracy: 0.1201
Epoch 43/100
1259/1259 [==============================] - 11s 9ms/step - loss: 3.4017 - accuracy: 0.2629 - val_loss: 27.8698 - val_accuracy: 0.1169
Epoch 44/100
1259/1259 [==============================] - 11s 9ms/step - loss: 3.3732 - accuracy: 0.2718 - val_loss: 28.2814 - val_accuracy: 0.1230
Epoch 45/100
1259/1259 [==============================] - 11s 9ms/step - loss: 3.3030 - accuracy: 0.2763 - val_loss: 29.2292 - val_accuracy: 0.1227
Epoch 46/100
1259/1259 [==============================] - 11s 9ms/step - loss: 3.2584 - accuracy: 0.2841 - val_loss: 28.8271 - val_accuracy: 0.1211
Epoch 47/100
1259/1259 [==============================] - 11s 9ms/step - loss: 3.2145 - accuracy: 0.2907 - val_loss: 30.1880 - val_accuracy: 0.1220
Epoch 48/100
1259/1259 [==============================] - 11s 9ms/step - loss: 3.1666 - accuracy: 0.3000 - val_loss: 29.0877 - val_accuracy: 0.1150
Epoch 49/100
1259/1259 [==============================] - 12s 9ms/step - loss: 3.1291 - accuracy: 0.3031 - val_loss: 30.4579 - val_accuracy: 0.1265
Epoch 50/100
1259/1259 [==============================] - 11s 9ms/step - loss: 3.0989 - accuracy: 0.3113 - val_loss: 30.1047 - val_accuracy: 0.1109
Epoch 51/100
1259/1259 [==============================] - 11s 9ms/step - loss: 3.0430 - accuracy: 0.3180 - val_loss: 30.4653 - val_accuracy: 0.1207
Epoch 52/100
1259/1259 [==============================] - 12s 9ms/step - loss: 3.0016 - accuracy: 0.3242 - val_loss: 29.9269 - val_accuracy: 0.1207
Epoch 53/100
1259/1259 [==============================] - 12s 9ms/step - loss: 2.9472 - accuracy: 0.3358 - val_loss: 30.7540 - val_accuracy: 0.1115
Epoch 54/100
1259/1259 [==============================] - 12s 9ms/step - loss: 2.9289 - accuracy: 0.3397 - val_loss: 31.4299 - val_accuracy: 0.1147
Epoch 55/100
1259/1259 [==============================] - 12s 9ms/step - loss: 2.8597 - accuracy: 0.3513 - val_loss: 31.6839 - val_accuracy: 0.1195
Epoch 56/100
1259/1259 [==============================] - 12s 9ms/step - loss: 2.8454 - accuracy: 0.3586 - val_loss: 32.0642 - val_accuracy: 0.1192
Epoch 57/100
1259/1259 [==============================] - 11s 9ms/step - loss: 2.8153 - accuracy: 0.3668 - val_loss: 32.8230 - val_accuracy: 0.1099
Epoch 58/100
1259/1259 [==============================] - 11s 9ms/step - loss: 2.7687 - accuracy: 0.3722 - val_loss: 33.0815 - val_accuracy: 0.1052
Epoch 59/100
1259/1259 [==============================] - 12s 10ms/step - loss: 2.7297 - accuracy: 0.3837 - val_loss: 32.4366 - val_accuracy: 0.1071
Epoch 60/100
1259/1259 [==============================] - 12s 9ms/step - loss: 2.7077 - accuracy: 0.3884 - val_loss: 32.3653 - val_accuracy: 0.1182
Epoch 61/100
1259/1259 [==============================] - 12s 9ms/step - loss: 2.6574 - accuracy: 0.3970 - val_loss: 32.7342 - val_accuracy: 0.1153
Epoch 62/100
1259/1259 [==============================] - 11s 9ms/step - loss: 2.6173 - accuracy: 0.4048 - val_loss: 33.3435 - val_accuracy: 0.1106
Epoch 63/100
1259/1259 [==============================] - 11s 9ms/step - loss: 2.6145 - accuracy: 0.4094 - val_loss: 32.7989 - val_accuracy: 0.1119
Epoch 64/100
1259/1259 [==============================] - 12s 9ms/step - loss: 2.5724 - accuracy: 0.4115 - val_loss: 32.9530 - val_accuracy: 0.1080
Epoch 65/100
1259/1259 [==============================] - 11s 9ms/step - loss: 2.5247 - accuracy: 0.4273 - val_loss: 33.1921 - val_accuracy: 0.1020
Epoch 66/100
1259/1259 [==============================] - 11s 9ms/step - loss: 2.4935 - accuracy: 0.4287 - val_loss: 33.1907 - val_accuracy: 0.1131
Epoch 67/100
1259/1259 [==============================] - 12s 9ms/step - loss: 2.4738 - accuracy: 0.4344 - val_loss: 33.8599 - val_accuracy: 0.1099
Epoch 68/100
1259/1259 [==============================] - 12s 9ms/step - loss: 2.4751 - accuracy: 0.4383 - val_loss: 34.0607 - val_accuracy: 0.1065
Epoch 69/100
1259/1259 [==============================] - 12s 9ms/step - loss: 2.4106 - accuracy: 0.4451 - val_loss: 33.5866 - val_accuracy: 0.1144
Epoch 70/100
1259/1259 [==============================] - 12s 9ms/step - loss: 2.3821 - accuracy: 0.4553 - val_loss: 33.7491 - val_accuracy: 0.1163
Epoch 71/100
1259/1259 [==============================] - 12s 9ms/step - loss: 2.3647 - accuracy: 0.4584 - val_loss: 34.5417 - val_accuracy: 0.1084
Epoch 72/100
1259/1259 [==============================] - 12s 9ms/step - loss: 2.3422 - accuracy: 0.4631 - val_loss: 34.1619 - val_accuracy: 0.1109
Epoch 73/100
1259/1259 [==============================] - 12s 9ms/step - loss: 2.3076 - accuracy: 0.4702 - val_loss: 34.0050 - val_accuracy: 0.1084
Epoch 74/100
1259/1259 [==============================] - 12s 9ms/step - loss: 2.3071 - accuracy: 0.4740 - val_loss: 34.2133 - val_accuracy: 0.1147
Epoch 75/100
1259/1259 [==============================] - 12s 9ms/step - loss: 2.2503 - accuracy: 0.4759 - val_loss: 33.9111 - val_accuracy: 0.1058
Epoch 76/100
1259/1259 [==============================] - 12s 9ms/step - loss: 2.2167 - accuracy: 0.4925 - val_loss: 35.0675 - val_accuracy: 0.1125
Epoch 77/100
1259/1259 [==============================] - 12s 10ms/step - loss: 2.2121 - accuracy: 0.4908 - val_loss: 35.0796 - val_accuracy: 0.1071
Epoch 78/100
1259/1259 [==============================] - 11s 9ms/step - loss: 2.1943 - accuracy: 0.4936 - val_loss: 34.2224 - val_accuracy: 0.1084
Epoch 79/100
1259/1259 [==============================] - 13s 11ms/step - loss: 2.1579 - accuracy: 0.5009 - val_loss: 34.5191 - val_accuracy: 0.1077
Epoch 80/100
1259/1259 [==============================] - 13s 10ms/step - loss: 2.1489 - accuracy: 0.5049 - val_loss: 35.8632 - val_accuracy: 0.1090
Epoch 81/100
1259/1259 [==============================] - 12s 10ms/step - loss: 2.1266 - accuracy: 0.5052 - val_loss: 34.8432 - val_accuracy: 0.1074
Epoch 82/100
1259/1259 [==============================] - 12s 9ms/step - loss: 2.0830 - accuracy: 0.5130 - val_loss: 35.7247 - val_accuracy: 0.1033
Epoch 83/100
1259/1259 [==============================] - 13s 11ms/step - loss: 2.0682 - accuracy: 0.5209 - val_loss: 35.3208 - val_accuracy: 0.1065
Epoch 84/100
1259/1259 [==============================] - 12s 9ms/step - loss: 2.0702 - accuracy: 0.5256 - val_loss: 35.3447 - val_accuracy: 0.1061
Epoch 85/100
1259/1259 [==============================] - 11s 9ms/step - loss: 2.0445 - accuracy: 0.5174 - val_loss: 34.5911 - val_accuracy: 0.1077

标签： pythonmachine-learningkerasdeep-learning