Respan Dataset Explorer

Can you generate images?

As an AI developed by OpenAI, I don't have the capability to generate images. My primary function is to provide information, answer questions, and help with a variety of text-based tasks. However, OpenAI has developed another AI called DALL-E, which specializes in creating images from textual descriptions. If you have access to DALL-E or a similar AI image generator, you can use text prompts to generate images.

For image generation, I can provide guidance or suggest ideas for prompts that you might use with an AI image-generating service, but I cannot directly create or display images for you. If you need help coming up with a prompt or description for an image you'd like to generate, feel free to ask!

Epoch 1/1000
7680/7680 [==============================] - 182s 23ms/step - loss: 0.7901 - accuracy: 0.5237 - precision: 0.5238 - recall: 0.5217 - val_loss: 0.7325 - val_accuracy: 0.5495 - val_precision: 0.5408 - val_recall: 0.6552 - lr: 1.0000e-04
Epoch 2/1000
7680/7680 [==============================] - 171s 22ms/step - loss: 0.7174 - accuracy: 0.5440 - precision: 0.5445 - recall: 0.5387 - val_loss: 0.7015 - val_accuracy: 0.5575 - val_precision: 0.5527 - val_recall: 0.6037 - lr: 1.0000e-04
Epoch 3/1000
7680/7680 [==============================] - 172s 22ms/step - loss: 0.6977 - accuracy: 0.5515 - precision: 0.5520 - recall: 0.5467 - val_loss: 0.6902 - val_accuracy: 0.5622 - val_precision: 0.5592 - val_recall: 0.5875 - lr: 1.0000e-04
Epoch 4/1000
7680/7680 [==============================] - 173s 23ms/step - loss: 0.6904 - accuracy: 0.5560 - precision: 0.5564 - recall: 0.5517 - val_loss: 0.6856 - val_accuracy: 0.5659 - val_precision: 0.5636 - val_recall: 0.5842 - lr: 1.0000e-04
Epoch 5/1000
7680/7680 [==============================] - 174s 23ms/step - loss: 0.6873 - accuracy: 0.5591 - precision: 0.5595 - recall: 0.5556 - val_loss: 0.6831 - val_accuracy: 0.5684 - val_precision: 0.5650 - val_recall: 0.5947 - lr: 1.0000e-04
Epoch 6/1000
7680/7680 [==============================] - 173s 22ms/step - loss: 0.6853 - accuracy: 0.5614 - precision: 0.5617 - recall: 0.5588 - val_loss: 0.6814 - val_accuracy: 0.5712 - val_precision: 0.5678 - val_recall: 0.5969 - lr: 1.0000e-04
Epoch 7/1000
7680/7680 [==============================] - 173s 23ms/step - loss: 0.6841 - accuracy: 0.5634 - precision: 0.5639 - recall: 0.5592 - val_loss: 0.6798 - val_accuracy: 0.5734 - val_precision: 0.5699 - val_recall: 0.5982 - lr: 1.0000e-04
Epoch 8/1000
7680/7680 [==============================] - 172s 22ms/step - loss: 0.6828 - accuracy: 0.5658 - precision: 0.5662 - recall: 0.5626 - val_loss: 0.6786 - val_accuracy: 0.5755 - val_precision: 0.5718 - val_recall: 0.6011 - lr: 1.0000e-04
Epoch 9/1000
7680/7680 [==============================] - 172s 22ms/step - loss: 0.6822 - accuracy: 0.5675 - precision: 0.5676 - recall: 0.5663 - val_loss: 0.6776 - val_accuracy: 0.5774 - val_precision: 0.5718 - val_recall: 0.6166 - lr: 1.0000e-04
Epoch 10/1000
7680/7680 [==============================] - 172s 22ms/step - loss: 0.6811 - accuracy: 0.5694 - precision: 0.5695 - recall: 0.5687 - val_loss: 0.6766 - val_accuracy: 0.5795 - val_precision: 0.5734 - val_recall: 0.6213 - lr: 1.0000e-04
Epoch 11/1000
7680/7680 [==============================] - 171s 22ms/step - loss: 0.6803 - accuracy: 0.5713 - precision: 0.5715 - recall: 0.5699 - val_loss: 0.6757 - val_accuracy: 0.5807 - val_precision: 0.5768 - val_recall: 0.6060 - lr: 1.0000e-04
Epoch 12/1000
7680/7680 [==============================] - 170s 22ms/step - loss: 0.6794 - accuracy: 0.5733 - precision: 0.5738 - recall: 0.5701 - val_loss: 0.6745 - val_accuracy: 0.5834 - val_precision: 0.5812 - val_recall: 0.5968 - lr: 1.0000e-04
Epoch 13/1000
7680/7680 [==============================] - 170s 22ms/step - loss: 0.6789 - accuracy: 0.5734 - precision: 0.5736 - recall: 0.5719 - val_loss: 0.6744 - val_accuracy: 0.5827 - val_precision: 0.5775 - val_recall: 0.6164 - lr: 1.0000e-04
Epoch 14/1000
7680/7680 [==============================] - 171s 22ms/step - loss: 0.6778 - accuracy: 0.5757 - precision: 0.5760 - recall: 0.5739 - val_loss: 0.6729 - val_accuracy: 0.5852 - val_precision: 0.5826 - val_recall: 0.6010 - lr: 1.0000e-04
Epoch 15/1000
7680/7680 [==============================] - 170s 22ms/step - loss: 0.6771 - accuracy: 0.5777 - precision: 0.5785 - recall: 0.5727 - val_loss: 0.6721 - val_accuracy: 0.5868 - val_precision: 0.5815 - val_recall: 0.6195 - lr: 1.0000e-04
Epoch 16/1000
7680/7680 [==============================] - 172s 22ms/step - loss: 0.6765 - accuracy: 0.5783 - precision: 0.5784 - recall: 0.5774 - val_loss: 0.6713 - val_accuracy: 0.5882 - val_precision: 0.5818 - val_recall: 0.6272 - lr: 1.0000e-04
Epoch 17/1000
7680/7680 [==============================] - 171s 22ms/step - loss: 0.6758 - accuracy: 0.5793 - precision: 0.5796 - recall: 0.5775 - val_loss: 0.6694 - val_accuracy: 0.5924 - val_precision: 0.5906 - val_recall: 0.6023 - lr: 1.0000e-04
Epoch 18/1000
7680/7680 [==============================] - 172s 22ms/step - loss: 0.6752 - accuracy: 0.5803 - precision: 0.5806 - recall: 0.5782 - val_loss: 0.6696 - val_accuracy: 0.5912 - val_precision: 0.5859 - val_recall: 0.6219 - lr: 1.0000e-04
Epoch 19/1000
7680/7680 [==============================] - 173s 23ms/step - loss: 0.6743 - accuracy: 0.5827 - precision: 0.5827 - recall: 0.5829 - val_loss: 0.6678 - val_accuracy: 0.5946 - val_precision: 0.5912 - val_recall: 0.6134 - lr: 1.0000e-04
Epoch 20/1000
7680/7680 [==============================] - 173s 22ms/step - loss: 0.6737 - accuracy: 0.5845 - precision: 0.5850 - recall: 0.5815 - val_loss: 0.6669 - val_accuracy: 0.5946 - val_precision: 0.5911 - val_recall: 0.6138 - lr: 1.0000e-04
Epoch 21/1000
7680/7680 [==============================] - 173s 22ms/step - loss: 0.6727 - accuracy: 0.5850 - precision: 0.5851 - recall: 0.5846 - val_loss: 0.6661 - val_accuracy: 0.5961 - val_precision: 0.5924 - val_recall: 0.6161 - lr: 1.0000e-04
Epoch 22/1000
7680/7680 [==============================] - 173s 23ms/step - loss: 0.6722 - accuracy: 0.5861 - precision: 0.5864 - recall: 0.5842 - val_loss: 0.6659 - val_accuracy: 0.5967 - val_precision: 0.5931 - val_recall: 0.6158 - lr: 1.0000e-04
Epoch 23/1000
7680/7680 [==============================] - 167s 22ms/step - loss: 0.6712 - accuracy: 0.5881 - precision: 0.5885 - recall: 0.5853 - val_loss: 0.6646 - val_accuracy: 0.5983 - val_precision: 0.5942 - val_recall: 0.6199 - lr: 1.0000e-04
Epoch 24/1000
7680/7680 [==============================] - 165s 21ms/step - loss: 0.6711 - accuracy: 0.5880 - precision: 0.5883 - recall: 0.5859 - val_loss: 0.6638 - val_accuracy: 0.5999 - val_precision: 0.5944 - val_recall: 0.6290 - lr: 1.0000e-04
Epoch 25/1000
7680/7680 [==============================] - 165s 22ms/step - loss: 0.6701 - accuracy: 0.5896 - precision: 0.5898 - recall: 0.5889 - val_loss: 0.6625 - val_accuracy: 0.6017 - val_precision: 0.5991 - val_recall: 0.6144 - lr: 1.0000e-04
Epoch 26/1000
7680/7680 [==============================] - 166s 22ms/step - loss: 0.6695 - accuracy: 0.5914 - precision: 0.5915 - recall: 0.5911 - val_loss: 0.6617 - val_accuracy: 0.6036 - val_precision: 0.6004 - val_recall: 0.6197 - lr: 1.0000e-04
Epoch 27/1000
7680/7680 [==============================] - 165s 22ms/step - loss: 0.6689 - accuracy: 0.5914 - precision: 0.5918 - recall: 0.5894 - val_loss: 0.6611 - val_accuracy: 0.6032 - val_precision: 0.6010 - val_recall: 0.6141 - lr: 1.0000e-04
Epoch 28/1000
7680/7680 [==============================] - 166s 22ms/step - loss: 0.6678 - accuracy: 0.5929 - precision: 0.5930 - recall: 0.5919 - val_loss: 0.6605 - val_accuracy: 0.6047 - val_precision: 0.5987 - val_recall: 0.6347 - lr: 1.0000e-04
Epoch 29/1000
7680/7680 [==============================] - 165s 22ms/step - loss: 0.6674 - accuracy: 0.5925 - precision: 0.5924 - recall: 0.5927 - val_loss: 0.6606 - val_accuracy: 0.6046 - val_precision: 0.5998 - val_recall: 0.6286 - lr: 1.0000e-04
Epoch 30/1000
7680/7680 [==============================] - 166s 22ms/step - loss: 0.6668 - accuracy: 0.5949 - precision: 0.5952 - recall: 0.5935 - val_loss: 0.6591 - val_accuracy: 0.6060 - val_precision: 0.6036 - val_recall: 0.6178 - lr: 1.0000e-04
Epoch 31/1000
7680/7680 [==============================] - 167s 22ms/step - loss: 0.6658 - accuracy: 0.5957 - precision: 0.5959 - recall: 0.5943 - val_loss: 0.6574 - val_accuracy: 0.6079 - val_precision: 0.6040 - val_recall: 0.6267 - lr: 1.0000e-04
Epoch 32/1000
7680/7680 [==============================] - 165s 22ms/step - loss: 0.6652 - accuracy: 0.5966 - precision: 0.5967 - recall: 0.5958 - val_loss: 0.6562 - val_accuracy: 0.6092 - val_precision: 0.6076 - val_recall: 0.6163 - lr: 1.0000e-04
Epoch 33/1000
7680/7680 [==============================] - 166s 22ms/step - loss: 0.6646 - accuracy: 0.5980 - precision: 0.5982 - recall: 0.5971 - val_loss: 0.6554 - val_accuracy: 0.6109 - val_precision: 0.6085 - val_recall: 0.6220 - lr: 1.0000e-04
Epoch 34/1000
7680/7680 [==============================] - 171s 22ms/step - loss: 0.6640 - accuracy: 0.5989 - precision: 0.5992 - recall: 0.5974 - val_loss: 0.6556 - val_accuracy: 0.6102 - val_precision: 0.6069 - val_recall: 0.6257 - lr: 1.0000e-04
Epoch 35/1000
7680/7680 [==============================] - 180s 23ms/step - loss: 0.6633 - accuracy: 0.6002 - precision: 0.6006 - recall: 0.5979 - val_loss: 0.6541 - val_accuracy: 0.6120 - val_precision: 0.6115 - val_recall: 0.6143 - lr: 1.0000e-04
Epoch 36/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6629 - accuracy: 0.6002 - precision: 0.6007 - recall: 0.5978 - val_loss: 0.6537 - val_accuracy: 0.6131 - val_precision: 0.6117 - val_recall: 0.6192 - lr: 1.0000e-04
Epoch 37/1000
7680/7680 [==============================] - 175s 23ms/step - loss: 0.6621 - accuracy: 0.6010 - precision: 0.6011 - recall: 0.6003 - val_loss: 0.6523 - val_accuracy: 0.6151 - val_precision: 0.6150 - val_recall: 0.6156 - lr: 1.0000e-04
Epoch 38/1000
7680/7680 [==============================] - 175s 23ms/step - loss: 0.6613 - accuracy: 0.6030 - precision: 0.6038 - recall: 0.5989 - val_loss: 0.6520 - val_accuracy: 0.6155 - val_precision: 0.6131 - val_recall: 0.6258 - lr: 1.0000e-04
Epoch 39/1000
7680/7680 [==============================] - 179s 23ms/step - loss: 0.6608 - accuracy: 0.6030 - precision: 0.6036 - recall: 0.5999 - val_loss: 0.6508 - val_accuracy: 0.6161 - val_precision: 0.6149 - val_recall: 0.6209 - lr: 1.0000e-04
Epoch 40/1000
7680/7680 [==============================] - 176s 23ms/step - loss: 0.6600 - accuracy: 0.6041 - precision: 0.6052 - recall: 0.5992 - val_loss: 0.6503 - val_accuracy: 0.6165 - val_precision: 0.6140 - val_recall: 0.6272 - lr: 1.0000e-04
Epoch 41/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6594 - accuracy: 0.6053 - precision: 0.6068 - recall: 0.5987 - val_loss: 0.6489 - val_accuracy: 0.6188 - val_precision: 0.6212 - val_recall: 0.6088 - lr: 1.0000e-04
Epoch 42/1000
7680/7680 [==============================] - 178s 23ms/step - loss: 0.6589 - accuracy: 0.6061 - precision: 0.6080 - recall: 0.5974 - val_loss: 0.6487 - val_accuracy: 0.6194 - val_precision: 0.6213 - val_recall: 0.6115 - lr: 1.0000e-04
Epoch 43/1000
7680/7680 [==============================] - 178s 23ms/step - loss: 0.6581 - accuracy: 0.6079 - precision: 0.6088 - recall: 0.6037 - val_loss: 0.6470 - val_accuracy: 0.6210 - val_precision: 0.6221 - val_recall: 0.6167 - lr: 1.0000e-04
Epoch 44/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6576 - accuracy: 0.6075 - precision: 0.6091 - recall: 0.6003 - val_loss: 0.6468 - val_accuracy: 0.6214 - val_precision: 0.6181 - val_recall: 0.6352 - lr: 1.0000e-04
Epoch 45/1000
7680/7680 [==============================] - 178s 23ms/step - loss: 0.6572 - accuracy: 0.6083 - precision: 0.6092 - recall: 0.6042 - val_loss: 0.6458 - val_accuracy: 0.6230 - val_precision: 0.6212 - val_recall: 0.6304 - lr: 1.0000e-04
Epoch 46/1000
7680/7680 [==============================] - 178s 23ms/step - loss: 0.6565 - accuracy: 0.6092 - precision: 0.6102 - recall: 0.6046 - val_loss: 0.6451 - val_accuracy: 0.6237 - val_precision: 0.6258 - val_recall: 0.6155 - lr: 1.0000e-04
Epoch 47/1000
7680/7680 [==============================] - 178s 23ms/step - loss: 0.6556 - accuracy: 0.6106 - precision: 0.6113 - recall: 0.6071 - val_loss: 0.6445 - val_accuracy: 0.6247 - val_precision: 0.6257 - val_recall: 0.6206 - lr: 1.0000e-04
Epoch 48/1000
7680/7680 [==============================] - 176s 23ms/step - loss: 0.6557 - accuracy: 0.6101 - precision: 0.6115 - recall: 0.6038 - val_loss: 0.6442 - val_accuracy: 0.6244 - val_precision: 0.6250 - val_recall: 0.6218 - lr: 1.0000e-04
Epoch 49/1000
7680/7680 [==============================] - 178s 23ms/step - loss: 0.6549 - accuracy: 0.6112 - precision: 0.6128 - recall: 0.6042 - val_loss: 0.6425 - val_accuracy: 0.6267 - val_precision: 0.6247 - val_recall: 0.6346 - lr: 1.0000e-04
Epoch 50/1000
7680/7680 [==============================] - 175s 23ms/step - loss: 0.6540 - accuracy: 0.6130 - precision: 0.6145 - recall: 0.6064 - val_loss: 0.6418 - val_accuracy: 0.6279 - val_precision: 0.6255 - val_recall: 0.6374 - lr: 1.0000e-04
Epoch 51/1000
7680/7680 [==============================] - 175s 23ms/step - loss: 0.6535 - accuracy: 0.6143 - precision: 0.6152 - recall: 0.6101 - val_loss: 0.6409 - val_accuracy: 0.6285 - val_precision: 0.6314 - val_recall: 0.6176 - lr: 1.0000e-04
Epoch 52/1000
7680/7680 [==============================] - 175s 23ms/step - loss: 0.6528 - accuracy: 0.6141 - precision: 0.6155 - recall: 0.6081 - val_loss: 0.6402 - val_accuracy: 0.6297 - val_precision: 0.6272 - val_recall: 0.6394 - lr: 1.0000e-04
Epoch 53/1000
7680/7680 [==============================] - 176s 23ms/step - loss: 0.6521 - accuracy: 0.6149 - precision: 0.6164 - recall: 0.6084 - val_loss: 0.6394 - val_accuracy: 0.6302 - val_precision: 0.6338 - val_recall: 0.6169 - lr: 1.0000e-04
Epoch 54/1000
7680/7680 [==============================] - 176s 23ms/step - loss: 0.6518 - accuracy: 0.6161 - precision: 0.6173 - recall: 0.6108 - val_loss: 0.6391 - val_accuracy: 0.6309 - val_precision: 0.6270 - val_recall: 0.6461 - lr: 1.0000e-04
Epoch 55/1000
7680/7680 [==============================] - 178s 23ms/step - loss: 0.6510 - accuracy: 0.6164 - precision: 0.6177 - recall: 0.6106 - val_loss: 0.6379 - val_accuracy: 0.6324 - val_precision: 0.6289 - val_recall: 0.6458 - lr: 1.0000e-04
Epoch 56/1000
7680/7680 [==============================] - 180s 23ms/step - loss: 0.6508 - accuracy: 0.6166 - precision: 0.6182 - recall: 0.6096 - val_loss: 0.6367 - val_accuracy: 0.6341 - val_precision: 0.6336 - val_recall: 0.6363 - lr: 1.0000e-04
Epoch 57/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6499 - accuracy: 0.6185 - precision: 0.6201 - recall: 0.6119 - val_loss: 0.6368 - val_accuracy: 0.6330 - val_precision: 0.6307 - val_recall: 0.6418 - lr: 1.0000e-04
Epoch 58/1000
7680/7680 [==============================] - 179s 23ms/step - loss: 0.6495 - accuracy: 0.6182 - precision: 0.6200 - recall: 0.6105 - val_loss: 0.6361 - val_accuracy: 0.6351 - val_precision: 0.6353 - val_recall: 0.6343 - lr: 1.0000e-04
Epoch 59/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6494 - accuracy: 0.6188 - precision: 0.6206 - recall: 0.6112 - val_loss: 0.6355 - val_accuracy: 0.6356 - val_precision: 0.6355 - val_recall: 0.6358 - lr: 1.0000e-04
Epoch 60/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6486 - accuracy: 0.6192 - precision: 0.6203 - recall: 0.6143 - val_loss: 0.6351 - val_accuracy: 0.6366 - val_precision: 0.6376 - val_recall: 0.6332 - lr: 1.0000e-04
Epoch 61/1000
7680/7680 [==============================] - 176s 23ms/step - loss: 0.6478 - accuracy: 0.6205 - precision: 0.6222 - recall: 0.6136 - val_loss: 0.6329 - val_accuracy: 0.6380 - val_precision: 0.6357 - val_recall: 0.6463 - lr: 1.0000e-04
Epoch 62/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6470 - accuracy: 0.6221 - precision: 0.6236 - recall: 0.6158 - val_loss: 0.6322 - val_accuracy: 0.6385 - val_precision: 0.6351 - val_recall: 0.6514 - lr: 1.0000e-04
Epoch 63/1000
7680/7680 [==============================] - 176s 23ms/step - loss: 0.6466 - accuracy: 0.6227 - precision: 0.6244 - recall: 0.6158 - val_loss: 0.6320 - val_accuracy: 0.6390 - val_precision: 0.6389 - val_recall: 0.6391 - lr: 1.0000e-04
Epoch 64/1000
7680/7680 [==============================] - 179s 23ms/step - loss: 0.6465 - accuracy: 0.6226 - precision: 0.6240 - recall: 0.6168 - val_loss: 0.6311 - val_accuracy: 0.6398 - val_precision: 0.6366 - val_recall: 0.6516 - lr: 1.0000e-04
Epoch 65/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6457 - accuracy: 0.6226 - precision: 0.6243 - recall: 0.6158 - val_loss: 0.6303 - val_accuracy: 0.6408 - val_precision: 0.6368 - val_recall: 0.6551 - lr: 1.0000e-04
Epoch 66/1000
7680/7680 [==============================] - 176s 23ms/step - loss: 0.6449 - accuracy: 0.6239 - precision: 0.6257 - recall: 0.6169 - val_loss: 0.6293 - val_accuracy: 0.6425 - val_precision: 0.6408 - val_recall: 0.6488 - lr: 1.0000e-04
Epoch 67/1000
7680/7680 [==============================] - 175s 23ms/step - loss: 0.6448 - accuracy: 0.6247 - precision: 0.6266 - recall: 0.6173 - val_loss: 0.6294 - val_accuracy: 0.6430 - val_precision: 0.6389 - val_recall: 0.6579 - lr: 1.0000e-04
Epoch 68/1000
7680/7680 [==============================] - 178s 23ms/step - loss: 0.6444 - accuracy: 0.6250 - precision: 0.6269 - recall: 0.6176 - val_loss: 0.6288 - val_accuracy: 0.6429 - val_precision: 0.6392 - val_recall: 0.6560 - lr: 1.0000e-04
Epoch 69/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6438 - accuracy: 0.6261 - precision: 0.6273 - recall: 0.6215 - val_loss: 0.6282 - val_accuracy: 0.6440 - val_precision: 0.6426 - val_recall: 0.6486 - lr: 1.0000e-04
Epoch 70/1000
7680/7680 [==============================] - 184s 24ms/step - loss: 0.6429 - accuracy: 0.6262 - precision: 0.6281 - recall: 0.6185 - val_loss: 0.6274 - val_accuracy: 0.6448 - val_precision: 0.6402 - val_recall: 0.6611 - lr: 1.0000e-04
Epoch 71/1000
7680/7680 [==============================] - 183s 24ms/step - loss: 0.6432 - accuracy: 0.6265 - precision: 0.6285 - recall: 0.6187 - val_loss: 0.6258 - val_accuracy: 0.6466 - val_precision: 0.6467 - val_recall: 0.6463 - lr: 1.0000e-04
Epoch 72/1000
7680/7680 [==============================] - 182s 24ms/step - loss: 0.6424 - accuracy: 0.6268 - precision: 0.6291 - recall: 0.6177 - val_loss: 0.6256 - val_accuracy: 0.6466 - val_precision: 0.6490 - val_recall: 0.6383 - lr: 1.0000e-04
Epoch 73/1000
7680/7680 [==============================] - 199s 26ms/step - loss: 0.6415 - accuracy: 0.6286 - precision: 0.6302 - recall: 0.6223 - val_loss: 0.6252 - val_accuracy: 0.6474 - val_precision: 0.6448 - val_recall: 0.6564 - lr: 1.0000e-04
Epoch 74/1000
7680/7680 [==============================] - 204s 27ms/step - loss: 0.6414 - accuracy: 0.6291 - precision: 0.6306 - recall: 0.6236 - val_loss: 0.6248 - val_accuracy: 0.6473 - val_precision: 0.6432 - val_recall: 0.6616 - lr: 1.0000e-04
Epoch 75/1000
7680/7680 [==============================] - 207s 27ms/step - loss: 0.6404 - accuracy: 0.6289 - precision: 0.6307 - recall: 0.6218 - val_loss: 0.6244 - val_accuracy: 0.6481 - val_precision: 0.6471 - val_recall: 0.6516 - lr: 1.0000e-04
Epoch 76/1000
7680/7680 [==============================] - 205s 27ms/step - loss: 0.6404 - accuracy: 0.6298 - precision: 0.6320 - recall: 0.6215 - val_loss: 0.6225 - val_accuracy: 0.6499 - val_precision: 0.6483 - val_recall: 0.6553 - lr: 1.0000e-04
Epoch 77/1000
7680/7680 [==============================] - 199s 26ms/step - loss: 0.6399 - accuracy: 0.6303 - precision: 0.6325 - recall: 0.6221 - val_loss: 0.6221 - val_accuracy: 0.6502 - val_precision: 0.6480 - val_recall: 0.6575 - lr: 1.0000e-04
Epoch 78/1000
7680/7680 [==============================] - 200s 26ms/step - loss: 0.6393 - accuracy: 0.6312 - precision: 0.6335 - recall: 0.6227 - val_loss: 0.6218 - val_accuracy: 0.6509 - val_precision: 0.6491 - val_recall: 0.6569 - lr: 1.0000e-04
Epoch 79/1000
7680/7680 [==============================] - 209s 27ms/step - loss: 0.6392 - accuracy: 0.6311 - precision: 0.6329 - recall: 0.6242 - val_loss: 0.6214 - val_accuracy: 0.6512 - val_precision: 0.6488 - val_recall: 0.6592 - lr: 1.0000e-04
Epoch 80/1000
7680/7680 [==============================] - 209s 27ms/step - loss: 0.6384 - accuracy: 0.6324 - precision: 0.6344 - recall: 0.6246 - val_loss: 0.6208 - val_accuracy: 0.6512 - val_precision: 0.6487 - val_recall: 0.6599 - lr: 1.0000e-04
Epoch 81/1000
7680/7680 [==============================] - 212s 28ms/step - loss: 0.6377 - accuracy: 0.6326 - precision: 0.6346 - recall: 0.6252 - val_loss: 0.6200 - val_accuracy: 0.6521 - val_precision: 0.6486 - val_recall: 0.6639 - lr: 1.0000e-04
Epoch 82/1000
7680/7680 [==============================] - 214s 28ms/step - loss: 0.6374 - accuracy: 0.6339 - precision: 0.6359 - recall: 0.6265 - val_loss: 0.6193 - val_accuracy: 0.6535 - val_precision: 0.6532 - val_recall: 0.6545 - lr: 1.0000e-04
Epoch 83/1000
7680/7680 [==============================] - 212s 28ms/step - loss: 0.6367 - accuracy: 0.6344 - precision: 0.6366 - recall: 0.6262 - val_loss: 0.6179 - val_accuracy: 0.6541 - val_precision: 0.6549 - val_recall: 0.6516 - lr: 1.0000e-04
Epoch 84/1000
7680/7680 [==============================] - 213s 28ms/step - loss: 0.6362 - accuracy: 0.6347 - precision: 0.6375 - recall: 0.6245 - val_loss: 0.6176 - val_accuracy: 0.6549 - val_precision: 0.6539 - val_recall: 0.6582 - lr: 1.0000e-04
Epoch 85/1000
7680/7680 [==============================] - 208s 27ms/step - loss: 0.6359 - accuracy: 0.6349 - precision: 0.6375 - recall: 0.6258 - val_loss: 0.6191 - val_accuracy: 0.6538 - val_precision: 0.6483 - val_recall: 0.6723 - lr: 1.0000e-04
Epoch 86/1000
7680/7680 [==============================] - 211s 27ms/step - loss: 0.6359 - accuracy: 0.6346 - precision: 0.6370 - recall: 0.6255 - val_loss: 0.6163 - val_accuracy: 0.6565 - val_precision: 0.6562 - val_recall: 0.6574 - lr: 1.0000e-04
Epoch 87/1000
7680/7680 [==============================] - 193s 25ms/step - loss: 0.6355 - accuracy: 0.6353 - precision: 0.6372 - recall: 0.6282 - val_loss: 0.6170 - val_accuracy: 0.6557 - val_precision: 0.6524 - val_recall: 0.6663 - lr: 1.0000e-04
Epoch 88/1000
7680/7680 [==============================] - 194s 25ms/step - loss: 0.6347 - accuracy: 0.6357 - precision: 0.6377 - recall: 0.6283 - val_loss: 0.6159 - val_accuracy: 0.6571 - val_precision: 0.6584 - val_recall: 0.6530 - lr: 1.0000e-04
Epoch 89/1000
7680/7680 [==============================] - 192s 25ms/step - loss: 0.6344 - accuracy: 0.6369 - precision: 0.6392 - recall: 0.6286 - val_loss: 0.6150 - val_accuracy: 0.6585 - val_precision: 0.6568 - val_recall: 0.6637 - lr: 1.0000e-04
Epoch 90/1000
7680/7680 [==============================] - 196s 25ms/step - loss: 0.6340 - accuracy: 0.6373 - precision: 0.6397 - recall: 0.6289 - val_loss: 0.6142 - val_accuracy: 0.6585 - val_precision: 0.6591 - val_recall: 0.6567 - lr: 1.0000e-04
Epoch 91/1000
7680/7680 [==============================] - 192s 25ms/step - loss: 0.6338 - accuracy: 0.6373 - precision: 0.6399 - recall: 0.6283 - val_loss: 0.6140 - val_accuracy: 0.6592 - val_precision: 0.6559 - val_recall: 0.6698 - lr: 1.0000e-04
Epoch 92/1000
7680/7680 [==============================] - 191s 25ms/step - loss: 0.6329 - accuracy: 0.6387 - precision: 0.6409 - recall: 0.6306 - val_loss: 0.6125 - val_accuracy: 0.6607 - val_precision: 0.6644 - val_recall: 0.6494 - lr: 1.0000e-04
Epoch 93/1000
7680/7680 [==============================] - 189s 25ms/step - loss: 0.6325 - accuracy: 0.6387 - precision: 0.6408 - recall: 0.6310 - val_loss: 0.6122 - val_accuracy: 0.6612 - val_precision: 0.6577 - val_recall: 0.6722 - lr: 1.0000e-04
Epoch 94/1000
7680/7680 [==============================] - 193s 25ms/step - loss: 0.6318 - accuracy: 0.6392 - precision: 0.6414 - recall: 0.6318 - val_loss: 0.6112 - val_accuracy: 0.6623 - val_precision: 0.6606 - val_recall: 0.6674 - lr: 1.0000e-04
Epoch 95/1000
7680/7680 [==============================] - 192s 25ms/step - loss: 0.6317 - accuracy: 0.6397 - precision: 0.6423 - recall: 0.6306 - val_loss: 0.6116 - val_accuracy: 0.6616 - val_precision: 0.6637 - val_recall: 0.6550 - lr: 1.0000e-04
Epoch 96/1000
7680/7680 [==============================] - 196s 26ms/step - loss: 0.6310 - accuracy: 0.6406 - precision: 0.6438 - recall: 0.6293 - val_loss: 0.6112 - val_accuracy: 0.6620 - val_precision: 0.6629 - val_recall: 0.6594 - lr: 1.0000e-04
Epoch 97/1000
7680/7680 [==============================] - 195s 25ms/step - loss: 0.6311 - accuracy: 0.6406 - precision: 0.6428 - recall: 0.6328 - val_loss: 0.6106 - val_accuracy: 0.6623 - val_precision: 0.6623 - val_recall: 0.6623 - lr: 1.0000e-04
Epoch 98/1000
7680/7680 [==============================] - 193s 25ms/step - loss: 0.6305 - accuracy: 0.6409 - precision: 0.6435 - recall: 0.6318 - val_loss: 0.6103 - val_accuracy: 0.6634 - val_precision: 0.6663 - val_recall: 0.6545 - lr: 1.0000e-04
Epoch 99/1000
7680/7680 [==============================] - 190s 25ms/step - loss: 0.6300 - accuracy: 0.6415 - precision: 0.6440 - recall: 0.6329 - val_loss: 0.6102 - val_accuracy: 0.6629 - val_precision: 0.6598 - val_recall: 0.6725 - lr: 1.0000e-04
Epoch 100/1000
7680/7680 [==============================] - 185s 24ms/step - loss: 0.6297 - accuracy: 0.6422 - precision: 0.6449 - recall: 0.6331 - val_loss: 0.6097 - val_accuracy: 0.6629 - val_precision: 0.6617 - val_recall: 0.6667 - lr: 1.0000e-04
Epoch 101/1000
7680/7680 [==============================] - 180s 23ms/step - loss: 0.6297 - accuracy: 0.6422 - precision: 0.6448 - recall: 0.6334 - val_loss: 0.6085 - val_accuracy: 0.6651 - val_precision: 0.6627 - val_recall: 0.6722 - lr: 1.0000e-04
Epoch 102/1000
7680/7680 [==============================] - 203s 26ms/step - loss: 0.6294 - accuracy: 0.6416 - precision: 0.6444 - recall: 0.6321 - val_loss: 0.6079 - val_accuracy: 0.6655 - val_precision: 0.6668 - val_recall: 0.6617 - lr: 1.0000e-04
Epoch 103/1000
7680/7680 [==============================] - 214s 28ms/step - loss: 0.6287 - accuracy: 0.6426 - precision: 0.6454 - recall: 0.6332 - val_loss: 0.6060 - val_accuracy: 0.6669 - val_precision: 0.6669 - val_recall: 0.6670 - lr: 1.0000e-04
Epoch 104/1000
7680/7680 [==============================] - 212s 28ms/step - loss: 0.6282 - accuracy: 0.6438 - precision: 0.6463 - recall: 0.6355 - val_loss: 0.6068 - val_accuracy: 0.6662 - val_precision: 0.6632 - val_recall: 0.6756 - lr: 1.0000e-04
Epoch 105/1000
7680/7680 [==============================] - 203s 26ms/step - loss: 0.6279 - accuracy: 0.6437 - precision: 0.6462 - recall: 0.6354 - val_loss: 0.6059 - val_accuracy: 0.6678 - val_precision: 0.6693 - val_recall: 0.6636 - lr: 1.0000e-04
Epoch 106/1000
7680/7680 [==============================] - 201s 26ms/step - loss: 0.6274 - accuracy: 0.6445 - precision: 0.6470 - recall: 0.6360 - val_loss: 0.6043 - val_accuracy: 0.6688 - val_precision: 0.6693 - val_recall: 0.6673 - lr: 1.0000e-04
Epoch 107/1000
7680/7680 [==============================] - 200s 26ms/step - loss: 0.6269 - accuracy: 0.6456 - precision: 0.6481 - recall: 0.6371 - val_loss: 0.6045 - val_accuracy: 0.6690 - val_precision: 0.6677 - val_recall: 0.6727 - lr: 1.0000e-04
Epoch 108/1000
7680/7680 [==============================] - 199s 26ms/step - loss: 0.6267 - accuracy: 0.6447 - precision: 0.6476 - recall: 0.6352 - val_loss: 0.6042 - val_accuracy: 0.6692 - val_precision: 0.6637 - val_recall: 0.6858 - lr: 1.0000e-04
Epoch 109/1000
7680/7680 [==============================] - 198s 26ms/step - loss: 0.6263 - accuracy: 0.6461 - precision: 0.6486 - recall: 0.6376 - val_loss: 0.6033 - val_accuracy: 0.6706 - val_precision: 0.6703 - val_recall: 0.6714 - lr: 1.0000e-04
Epoch 110/1000
7680/7680 [==============================] - 197s 26ms/step - loss: 0.6256 - accuracy: 0.6473 - precision: 0.6502 - recall: 0.6376 - val_loss: 0.6042 - val_accuracy: 0.6695 - val_precision: 0.6740 - val_recall: 0.6564 - lr: 1.0000e-04
Epoch 111/1000
7680/7680 [==============================] - 198s 26ms/step - loss: 0.6254 - accuracy: 0.6468 - precision: 0.6500 - recall: 0.6359 - val_loss: 0.6013 - val_accuracy: 0.6719 - val_precision: 0.6745 - val_recall: 0.6644 - lr: 1.0000e-04
Epoch 112/1000
7680/7680 [==============================] - 199s 26ms/step - loss: 0.6247 - accuracy: 0.6476 - precision: 0.6500 - recall: 0.6398 - val_loss: 0.6018 - val_accuracy: 0.6715 - val_precision: 0.6716 - val_recall: 0.6710 - lr: 1.0000e-04
Epoch 113/1000
7680/7680 [==============================] - 197s 26ms/step - loss: 0.6246 - accuracy: 0.6479 - precision: 0.6506 - recall: 0.6387 - val_loss: 0.6010 - val_accuracy: 0.6720 - val_precision: 0.6724 - val_recall: 0.6707 - lr: 1.0000e-04
Epoch 114/1000
7680/7680 [==============================] - 180s 23ms/step - loss: 0.6242 - accuracy: 0.6486 - precision: 0.6512 - recall: 0.6401 - val_loss: 0.6023 - val_accuracy: 0.6716 - val_precision: 0.6731 - val_recall: 0.6672 - lr: 1.0000e-04
Epoch 115/1000
7680/7680 [==============================] - 219s 28ms/step - loss: 0.6239 - accuracy: 0.6484 - precision: 0.6507 - recall: 0.6405 - val_loss: 0.5999 - val_accuracy: 0.6736 - val_precision: 0.6742 - val_recall: 0.6718 - lr: 1.0000e-04
Epoch 116/1000
7680/7680 [==============================] - 221s 29ms/step - loss: 0.6234 - accuracy: 0.6488 - precision: 0.6512 - recall: 0.6408 - val_loss: 0.6024 - val_accuracy: 0.6723 - val_precision: 0.6694 - val_recall: 0.6808 - lr: 1.0000e-04
Epoch 117/1000
7680/7680 [==============================] - 192s 25ms/step - loss: 0.6231 - accuracy: 0.6499 - precision: 0.6524 - recall: 0.6414 - val_loss: 0.5994 - val_accuracy: 0.6744 - val_precision: 0.6734 - val_recall: 0.6775 - lr: 1.0000e-04
Epoch 118/1000
7680/7680 [==============================] - 170s 22ms/step - loss: 0.6229 - accuracy: 0.6494 - precision: 0.6515 - recall: 0.6426 - val_loss: 0.5990 - val_accuracy: 0.6750 - val_precision: 0.6792 - val_recall: 0.6632 - lr: 1.0000e-04
Epoch 119/1000
7680/7680 [==============================] - 186s 24ms/step - loss: 0.6222 - accuracy: 0.6505 - precision: 0.6530 - recall: 0.6425 - val_loss: 0.5972 - val_accuracy: 0.6762 - val_precision: 0.6784 - val_recall: 0.6703 - lr: 1.0000e-04
Epoch 120/1000
7680/7680 [==============================] - 194s 25ms/step - loss: 0.6221 - accuracy: 0.6507 - precision: 0.6528 - recall: 0.6439 - val_loss: 0.5986 - val_accuracy: 0.6749 - val_precision: 0.6706 - val_recall: 0.6876 - lr: 1.0000e-04
Epoch 121/1000
7680/7680 [==============================] - 189s 25ms/step - loss: 0.6213 - accuracy: 0.6523 - precision: 0.6551 - recall: 0.6432 - val_loss: 0.5971 - val_accuracy: 0.6771 - val_precision: 0.6772 - val_recall: 0.6765 - lr: 1.0000e-04
Epoch 122/1000
7680/7680 [==============================] - 187s 24ms/step - loss: 0.6212 - accuracy: 0.6520 - precision: 0.6553 - recall: 0.6413 - val_loss: 0.5974 - val_accuracy: 0.6766 - val_precision: 0.6752 - val_recall: 0.6806 - lr: 1.0000e-04
Epoch 123/1000
7680/7680 [==============================] - 196s 25ms/step - loss: 0.6202 - accuracy: 0.6523 - precision: 0.6550 - recall: 0.6438 - val_loss: 0.5956 - val_accuracy: 0.6781 - val_precision: 0.6790 - val_recall: 0.6758 - lr: 1.0000e-04
Epoch 124/1000
7680/7680 [==============================] - 202s 26ms/step - loss: 0.6212 - accuracy: 0.6520 - precision: 0.6544 - recall: 0.6442 - val_loss: 0.5952 - val_accuracy: 0.6782 - val_precision: 0.6775 - val_recall: 0.6803 - lr: 1.0000e-04
Epoch 125/1000
7680/7680 [==============================] - 196s 26ms/step - loss: 0.6200 - accuracy: 0.6534 - precision: 0.6558 - recall: 0.6456 - val_loss: 0.5947 - val_accuracy: 0.6784 - val_precision: 0.6789 - val_recall: 0.6772 - lr: 1.0000e-04
Epoch 126/1000
7680/7680 [==============================] - 201s 26ms/step - loss: 0.6195 - accuracy: 0.6525 - precision: 0.6553 - recall: 0.6435 - val_loss: 0.5951 - val_accuracy: 0.6794 - val_precision: 0.6790 - val_recall: 0.6806 - lr: 1.0000e-04
Epoch 127/1000
7680/7680 [==============================] - 189s 25ms/step - loss: 0.6194 - accuracy: 0.6537 - precision: 0.6559 - recall: 0.6465 - val_loss: 0.5931 - val_accuracy: 0.6800 - val_precision: 0.6811 - val_recall: 0.6771 - lr: 1.0000e-04
Epoch 128/1000
7680/7680 [==============================] - 188s 24ms/step - loss: 0.6193 - accuracy: 0.6538 - precision: 0.6562 - recall: 0.6462 - val_loss: 0.5942 - val_accuracy: 0.6802 - val_precision: 0.6792 - val_recall: 0.6830 - lr: 1.0000e-04
Epoch 129/1000
7680/7680 [==============================] - 187s 24ms/step - loss: 0.6189 - accuracy: 0.6543 - precision: 0.6569 - recall: 0.6460 - val_loss: 0.5926 - val_accuracy: 0.6812 - val_precision: 0.6829 - val_recall: 0.6766 - lr: 1.0000e-04
Epoch 130/1000
7680/7680 [==============================] - 185s 24ms/step - loss: 0.6184 - accuracy: 0.6554 - precision: 0.6583 - recall: 0.6463 - val_loss: 0.5935 - val_accuracy: 0.6806 - val_precision: 0.6807 - val_recall: 0.6804 - lr: 1.0000e-04
Epoch 131/1000
7680/7680 [==============================] - 168s 22ms/step - loss: 0.6186 - accuracy: 0.6551 - precision: 0.6573 - recall: 0.6481 - val_loss: 0.5934 - val_accuracy: 0.6806 - val_precision: 0.6814 - val_recall: 0.6785 - lr: 1.0000e-04
Epoch 132/1000
7680/7680 [==============================] - 166s 22ms/step - loss: 0.6176 - accuracy: 0.6550 - precision: 0.6570 - recall: 0.6486 - val_loss: 0.5920 - val_accuracy: 0.6815 - val_precision: 0.6836 - val_recall: 0.6755 - lr: 1.0000e-04
Epoch 133/1000
7680/7680 [==============================] - 168s 22ms/step - loss: 0.6173 - accuracy: 0.6553 - precision: 0.6578 - recall: 0.6476 - val_loss: 0.5918 - val_accuracy: 0.6822 - val_precision: 0.6812 - val_recall: 0.6849 - lr: 1.0000e-04
Epoch 134/1000
7680/7680 [==============================] - 164s 21ms/step - loss: 0.6165 - accuracy: 0.6566 - precision: 0.6592 - recall: 0.6485 - val_loss: 0.5903 - val_accuracy: 0.6835 - val_precision: 0.6818 - val_recall: 0.6880 - lr: 1.0000e-04
Epoch 135/1000
3753/7680 [=============>................] - ETA: 1:03 - loss: 0.6167 - accuracy: 0.6560 - precision: 0.6585 - recall: 0.6464

学习率是不是设置的太低了

The learning rate in a machine learning model is a hyperparameter that determines the step size at each iteration while moving towards a minimum of a loss function. Choosing the right learning rate is crucial as it can greatly affect the convergence of the model training and the final performance.

From the provided output of the training epochs, it seems that a learning rate (lr) of `1.0000e-04` (or 0.0001) is being used. Analyzing the output:

- The model is making progress, albeit slowly, as observed by the decreasing loss and improving accuracy, precision, and recall over the epochs.
- By epoch 135, the model has not yet shown signs of overfitting, as both training and validation accuracies are improving.

Based on these observations, the learning rate does not necessarily seem too low, as the model is improving and learning from the data. However, whether it could be optimized is a different question. Here are a few considerations:

1. If the model training is too slow: A learning rate scheduler or different optimization algorithms like Adam, which adapts the learning rate during training, could help improve training speed with a suitable initial learning rate.

2. If the improvements plateau early: It could be beneficial to try a slightly higher learning rate or employ a learning rate scheduler that starts higher and gradually decreases.

3. Learning rate too low: If the model improvements are minimal and training is very slow, a higher learning rate might be necessary.

4. Learning rate too high: On the other hand, if the model begins to show erratic behavior with large fluctuations in loss and accuracy, this could be a sign that the learning rate is too high.

In practice, it's common to experiment with various learning rates or use learning rate finding techniques to determine the best learning rate. It often involves starting with a small learning rate that is incrementally increased and observing the loss; when the loss starts to increase rapidly, the optimal learning rate is a bit lower than that point.

Remember that the learning rate is one of many hyperparameters, and the overall architecture, data preprocessing, activation functions, batch size, and other factors also contribute significantly to model performance. It's always a good idea to perform hyperparameter tuning systematically or use automated methods like grid search, random search, or Bayesian optimization methods.

请用中文

在机器学习模型中，学习率（learning rate）是一个超参数，它决定了在迭代过程中向损失函数最小值移动的步长大小。选择合适的学习率至关重要，因为它会直接影响到模型训练的收敛速度和最终性能。

从您提供的训练周期（epochs）输出来看，正在使用的学习率（lr）是 `1.0000e-04`（即0.0001）。分析输出：

- 模型正在取得进展，尽管进步缓慢，但可以通过损失逐渐减小以及准确度、精确度和召回率逐渐提高来观察到。
- 在第135周期，模型尚未表现出过拟合的迹象，因为训练和验证准确度都在提高。

根据这些观察，当前的学习率并不一定过低，因为模型正在改进并从数据中学习。然而，是否可以优化学习率则是另一个问题。以下是一些考虑因素：

1. 如果模型训练速度过慢：可以尝试使用学习率调度器，或尝试使用不同的优化算法（如Adam），后者会在训练过程中自适应调整学习率，有助于改进训练速度，并可以设置合理的初始学习率。

2. 如果性能提升很快就达到了平台期：这时候可以尝试使用稍微更高的学习率，或者采用一个初始值较高、随后逐渐降低的学习率调度器。

3. 学习率过低：如果模型改进很少并且训练速度非常慢，可能需要一个更高的学习率。

4. 学习率过高：另一方面，如果模型开始表现出忽上忽下的行为，损失和准确度波动很大，这可能是学习率设置过高的信号。

在实践中，常常需要尝试不同的学习率，或者使用学习率查找技术来确定最佳学习率。这通常涉及到从一个小的学习率开始，逐步增加学习率，并观察损失；当损失开始迅速上升时，最优学习率应该略低于那个点。

记住，学习率只是众多超参数中的一个，整体架构、数据预处理、激活函数、批处理大小等其他因素也对模型性能有着重要影响。往往需要系统地进行超参数调优，或者使用网格搜索（grid search）、随机搜索（random search）或贝叶斯优化方法等自动化方法。

我的这种速度在亿级的数据量中，算慢的吗

数据量达到亿级别时，你的模型训练速度确实会被认为是较慢的。根据你的日志显示，每个epoch需要几分钟的训练时间，对于大型数据集而言，完成所有epoch可能需要相当长的时间。

对于如此大规模的数据集，一些常见的处理方式可能包括：

1. **分布式训练**：通过多个GPU并行处理数据来加速训练。框架如TensorFlow和PyTorch都提供了分布式训练的支持。

2. **使用更高效的数据加载和预处理**：例如，确保数据预处理不成为瓶颈，使用多线程或者异步加载数据。

3. **模型简化**：选择使用更简单的模型或减少模型复杂性，这可以使训练速度加快。

4. **学习率调整**：实施学习率调度器，如warmup随后逐步减少学习率或使用自适应学习率算法。

5. **数据降维**：如果输入特征非常多，使用PCA或其他降维方法可能加快训练速度。

6. **增大批处理大小（batch size）**：如果你的硬件资源允许的话，增加批处理大小可以更充分地利用GPU资源，加快训练过程。

7. **数据子集**：在较小的数据子集上先进行模型原型设计和迭代，找到比较好的模型和超参数之后再扩展到更大的数据集。

8. **转移学习**：如果可能，使用已经在类似任务上预训练过的模型，这样通常只需调整后几层即可。

对于大规模数据的训练，通常需要一个平衡速度和准确性的综合策略。可能还需要一些特定的硬件优化，比如使用高速的SSD硬盘来存储数据集，以减少从磁盘加载数据的时间。

在实际应用中，这些策略可能需要根据具体的硬件配置、数据集特点和模型要求等因素来调整。最重要的还是要理解数据和模型的特点，从而找到合适的解决方案。

按照这个速度，你觉得跑完之后的理想准确率是多少

从您的训练日志看，到了第135个epoch时，模型的验证准确率接近68%。可以预计，随着训练的继续，在不出现严重过拟合的情况下，模型准确率可能会继续小幅提升。

但要确定一个“理想的准确率”，必须考虑以下因素：

1. **基准性能（Baseline Performance）**：如果存在预先定义的基线模型或业界标准，这将有助于设定一个目标准确率。例如，如果当前任务是一个标准的图像识别问题，并且已知最佳模型能够达到90%的准确率，那么您的模型准确率应尽量接近这个水平。

2. **问题难度**：有些问题本质上就比其他问题更难解决，因此即使高级模型也可能无法达到非常高的准确率。

3. **数据质量**：数据的噪声水平、标注的准确性等因素，都会影响到模型能达到的最高准确率。

4. **类别平衡**：如果您在处理一个分类问题，其中某些类别的样本数量远多于其他类别，那么这可能会偏向于在那些具有更多样本的类别上获得更好的表现。

理想的准确率应当是基于任务的具体背景和上述因素来确定的。如果您目前的模型已经超出了业界标准，或者达到了类似问题上的先进水平，那么您的模型准确率就可以认为是理想的。反之，如果它低于期望，那么可能还需要进一步调整模型、增强数据处理流程或考虑使用不同的模型架构。

最后，准确度并不是衡量所有类型模型好坏的唯一指标，其他指标如精确度、召回率、F1分数、AUC-ROC曲线等可能会提供额外的性能信息，特别是在类别不平衡的情况下。因此，在设置目标准确率时，请综合考虑不同的性能指标。

您为什么不使用训练集的准确率，而是关注验证集

在机器学习中，我们通常会关注验证集（validation set）上的性能而不仅仅是训练集（training set）的性能，原因如下：

1. **泛化能力**: 训练集上的准确率主要反映了模型对训练数据的拟合程度，而不一定代表模型对未知数据的泛化能力。使用验证集（通常是训练过程中看不到的数据）能更好地评估模型在处理新数据时的性能。

2. **过拟合**: 如果一个模型完美地记住了训练集数据而没有学到泛化的能力，它在训练集上的准确率可能会非常高。但这样的模型在新数据上的表现可能很差，因为它过度拟合了训练数据中的噪声和特定的模式。

3. **模型选择和调优**: 在模型训练过程中，我们经常需要进行模型选择（model selection）和超参数调优（hyperparameter tuning）。使用验证集可以帮助我们选择性能最佳的模型和设置，而不是只选择在训练集上表现良好的模型和设置。

4. **评估度量的可靠性**: 基于独立于训练数据的验证集所得到的评估指标（如准确率、损失函数等），通常被认为更可靠，因为它们反映了模型对未见过的数据的实际表现。

最终，我们的目标是开发一个在真实世界应用中表现良好的模型，而验证集提供了一种在模型还没接触测试集或真实数据前，评估其可能性能的方法。然而，验证集也不是终极的性能衡量标准。在完成模型的选择和调优后，我们还会在一个从未用于训练和验证的测试集（test set）上评估最终模型的性能，以获得对模型真实泛化能力的最终评估。

为什么模型会在一个平台波动四五轮之后开始继续下降

在训练深度学习模型时，可能会观察到损失或者准确率在某个值附近波动，然后再次开始改善或者下降的现象。这个现象可能由多种因素引起：

1. **局部最小/鞍点**：优化过程可能暂时卡在局部最小值或鞍点附近。鞍点是指在某一方向是最大值，在另一方向是最小值的点。在这些点附近，梯度可能非常微小，导致优化器需要一些epoch才能“逃脱”出来。

2. **学习率**：如果使用的是固定学习率，模型可能在接近最小值的时候步长太大，从而来回“跳过”最优点。如果使用自适应学习率，优化器可能在学习率减小时找到了更好的下降路径，从而继续改善性能。

3. **批量大小**：较大的批量会提供更稳定的梯度估计，但可能需要更长的时间才能适应loss surface的变化。较小的批量会导致梯度估计的方差增大，可能有助于跳出局部最小值，但同时也可能导致训练不稳定。

4. **数据特性**：训练集中可能包含一些异常的或噪声较大的数据，这些数据可能会在模型学习时引起波动。

5. **权重初始化和正则化**：不同的初始化和正则化策略可能会影响模型的学习轨迹，有时可能导致在优化过程中出现平台期。

6. **动量或其他加速器**：如果使用动量或Adam等优化算法，更新的历史可能会影响当前的梯度方向和大小，导致波动。

7. **异质性数据**：训练数据可能不是均匀分布的，某些数据批次可能比其他的难学习，导致性能暂时停滞或波动。

通常，经历一段时间的波动后继续下降或改善是一个正常现象，表示模型仍在学习和适应数据。如果波动持续存在，可能需要调整训练策略，比如更改学习率、优化器设置或尝试其他正则化技术。同时，这也是为什么训练期间监视验证集性能很重要的原因，它可以帮助我们了解模型是否正在过拟合，或者是否需要调整训练策略以改善模型的泛化能力。

工业界训练十亿数据，在单个2060显卡上，一般要几天

要估算在单个NVIDIA RTX 2060显卡上训练十亿数据所需的时间，需要考虑多个因素，如模型复杂度、数据的特征维度、训练算法的效率、数据预处理和加载的时间、批次大小（batch size）、每个epoch的迭代次数，以及是否应用了高效的优化技术等等。

以下是一些可能影响训练时间的主要因素：

1. **模型复杂度**：模型的层数和参数数量直接影响前向传播和反向传播的计算负载。

2. **数据维度**：输入数据的维度越高，需要的计算量也越大。

3. **批量大小**：批量大小会受到GPU内存的限制。批量大小越大，GPU的并行计算能力越能得到利用，但过大的批量可能受限于显存大小。

4. **数据传输**：数据需要从磁盘加载到内存，再从内存传输到GPU。如果这个过程没有得到很好的优化，会成为训练的瓶颈。

5. **优化算法效率**：不同的优化算法（如SGD、Adam等）将影响收敛速度。

6. **并行和分布式训练**：在一个GPU上训练十亿条数据通常不切实际。在实际应用中，工业界会通过多GPU或分布式训练来加速这个过程。

7. **epoch数量**：需要运行多少个训练周期，以及数据是否需要多次遍历（如使用迭代次数小于数据量的随机梯度下降方法）。

估算单个RTX 2060显卡训练10亿数据的时间非常困难，因为这取决于上述提到的众多因素，而且也因机器学习任务的不同（分类、回归、强化学习等）而异。

如果您的任务确实涉及到大规模数据集，并打算在单个GPU上进行训练，建议采用以下措施来尽可能高效地利用硬件资源：

- **模型简化**：选择更小或者更优化的网络。
- **数据降维**：通过特征选择或降维技术减小输入的大小。
- **有效的数据加载**：使用足够快的数据预处理和加载流程，例如使用多线程数据加载等。
- **学习率调度**：使用学习率调度策略来加速训练过程及收敛。
- **模型剪枝**：去除不那么重要的权重来减少模型大小，以减少计算负担。

即便如此，若要在单个RTX 2060上训练十亿数据点的模型，我们仍然可以预测这将是一个持续多天甚至数周的长期过程。如果可能，寻找提高硬件资源（例如升级到更强大的GPU或使用多GPU系统）或减少数据规模的方法会是更好的选择。

经历了7轮波动，准确率终于升到了66%，这是否说明我的学习率设置的是恰当的，不需要调高：

从你的描述中，准确率在经过一段时间的波动后上升到了66%，这意味着模型仍然在学习，并且最终找到了一个比之前更好的点来减少损失。这种行为可能表明，在目前的超参数设定（包括学习率）下，模型确实还有改进的空间。

是否需要调整学习率取决于多个因素：

1. **训练速度**：如果你认为训练的速度可以接受，并且准确率仍在稳步上升，那么当前的学习率可能是适当的。

2. **收敛状态**：如果准确率提升很缓慢，或者出现了明显的停滞（如多个epochs中都没有任何改善），那么增加学习率可能有助于加快学习过程。

3. **过拟合情况**：如果你观察到训练准确率明显高于验证准确率，并且差异随着时间越来越大，那么这可能是过拟合的信号。在这种情况下，增加学习率未必是最佳选择，反而可能需要增加正则化或采取早停（early stopping）等措施。

4. **波动情况**：一些波动是正常的，但如果波动非常剧烈，这可能表明学习率过高。如果波动相对较小并且模型最终能够从波动中恢复并继续改进，那么当前的学习率可能是可行的。

而且，记住学习率不是一个孤立的参数，它和其他超参数（如批量大小、正则化系数等）一起决定了模型训练的行为。有时候，改变其中一个超参数，可能需要重新调整其他超参数才能得到最佳结果。

如果你有充分的时间和资源，尝试使用一个学习率范围进行训练并监视模型的表现，或者采用学习率调度策略，如逐步减小学习率或者使用预热（warm-up）策略，都是不错的方法来找到最佳的学习率设置。这些策略可以帮助你更系统地调整学习率并评估影响。

Epoch 1/1000
7680/7680 [==============================] - 182s 23ms/step - loss: 0.7901 - accuracy: 0.5237 - precision: 0.5238 - recall: 0.5217 - val_loss: 0.7325 - val_accuracy: 0.5495 - val_precision: 0.5408 - val_recall: 0.6552 - lr: 1.0000e-04
Epoch 2/1000
7680/7680 [==============================] - 171s 22ms/step - loss: 0.7174 - accuracy: 0.5440 - precision: 0.5445 - recall: 0.5387 - val_loss: 0.7015 - val_accuracy: 0.5575 - val_precision: 0.5527 - val_recall: 0.6037 - lr: 1.0000e-04
Epoch 3/1000
7680/7680 [==============================] - 172s 22ms/step - loss: 0.6977 - accuracy: 0.5515 - precision: 0.5520 - recall: 0.5467 - val_loss: 0.6902 - val_accuracy: 0.5622 - val_precision: 0.5592 - val_recall: 0.5875 - lr: 1.0000e-04
Epoch 4/1000
7680/7680 [==============================] - 173s 23ms/step - loss: 0.6904 - accuracy: 0.5560 - precision: 0.5564 - recall: 0.5517 - val_loss: 0.6856 - val_accuracy: 0.5659 - val_precision: 0.5636 - val_recall: 0.5842 - lr: 1.0000e-04
Epoch 5/1000
7680/7680 [==============================] - 174s 23ms/step - loss: 0.6873 - accuracy: 0.5591 - precision: 0.5595 - recall: 0.5556 - val_loss: 0.6831 - val_accuracy: 0.5684 - val_precision: 0.5650 - val_recall: 0.5947 - lr: 1.0000e-04
Epoch 6/1000
7680/7680 [==============================] - 173s 22ms/step - loss: 0.6853 - accuracy: 0.5614 - precision: 0.5617 - recall: 0.5588 - val_loss: 0.6814 - val_accuracy: 0.5712 - val_precision: 0.5678 - val_recall: 0.5969 - lr: 1.0000e-04
Epoch 7/1000
7680/7680 [==============================] - 173s 23ms/step - loss: 0.6841 - accuracy: 0.5634 - precision: 0.5639 - recall: 0.5592 - val_loss: 0.6798 - val_accuracy: 0.5734 - val_precision: 0.5699 - val_recall: 0.5982 - lr: 1.0000e-04
Epoch 8/1000
7680/7680 [==============================] - 172s 22ms/step - loss: 0.6828 - accuracy: 0.5658 - precision: 0.5662 - recall: 0.5626 - val_loss: 0.6786 - val_accuracy: 0.5755 - val_precision: 0.5718 - val_recall: 0.6011 - lr: 1.0000e-04
Epoch 9/1000
7680/7680 [==============================] - 172s 22ms/step - loss: 0.6822 - accuracy: 0.5675 - precision: 0.5676 - recall: 0.5663 - val_loss: 0.6776 - val_accuracy: 0.5774 - val_precision: 0.5718 - val_recall: 0.6166 - lr: 1.0000e-04
Epoch 10/1000
7680/7680 [==============================] - 172s 22ms/step - loss: 0.6811 - accuracy: 0.5694 - precision: 0.5695 - recall: 0.5687 - val_loss: 0.6766 - val_accuracy: 0.5795 - val_precision: 0.5734 - val_recall: 0.6213 - lr: 1.0000e-04
Epoch 11/1000
7680/7680 [==============================] - 171s 22ms/step - loss: 0.6803 - accuracy: 0.5713 - precision: 0.5715 - recall: 0.5699 - val_loss: 0.6757 - val_accuracy: 0.5807 - val_precision: 0.5768 - val_recall: 0.6060 - lr: 1.0000e-04
Epoch 12/1000
7680/7680 [==============================] - 170s 22ms/step - loss: 0.6794 - accuracy: 0.5733 - precision: 0.5738 - recall: 0.5701 - val_loss: 0.6745 - val_accuracy: 0.5834 - val_precision: 0.5812 - val_recall: 0.5968 - lr: 1.0000e-04
Epoch 13/1000
7680/7680 [==============================] - 170s 22ms/step - loss: 0.6789 - accuracy: 0.5734 - precision: 0.5736 - recall: 0.5719 - val_loss: 0.6744 - val_accuracy: 0.5827 - val_precision: 0.5775 - val_recall: 0.6164 - lr: 1.0000e-04
Epoch 14/1000
7680/7680 [==============================] - 171s 22ms/step - loss: 0.6778 - accuracy: 0.5757 - precision: 0.5760 - recall: 0.5739 - val_loss: 0.6729 - val_accuracy: 0.5852 - val_precision: 0.5826 - val_recall: 0.6010 - lr: 1.0000e-04
Epoch 15/1000
7680/7680 [==============================] - 170s 22ms/step - loss: 0.6771 - accuracy: 0.5777 - precision: 0.5785 - recall: 0.5727 - val_loss: 0.6721 - val_accuracy: 0.5868 - val_precision: 0.5815 - val_recall: 0.6195 - lr: 1.0000e-04
Epoch 16/1000
7680/7680 [==============================] - 172s 22ms/step - loss: 0.6765 - accuracy: 0.5783 - precision: 0.5784 - recall: 0.5774 - val_loss: 0.6713 - val_accuracy: 0.5882 - val_precision: 0.5818 - val_recall: 0.6272 - lr: 1.0000e-04
Epoch 17/1000
7680/7680 [==============================] - 171s 22ms/step - loss: 0.6758 - accuracy: 0.5793 - precision: 0.5796 - recall: 0.5775 - val_loss: 0.6694 - val_accuracy: 0.5924 - val_precision: 0.5906 - val_recall: 0.6023 - lr: 1.0000e-04
Epoch 18/1000
7680/7680 [==============================] - 172s 22ms/step - loss: 0.6752 - accuracy: 0.5803 - precision: 0.5806 - recall: 0.5782 - val_loss: 0.6696 - val_accuracy: 0.5912 - val_precision: 0.5859 - val_recall: 0.6219 - lr: 1.0000e-04
Epoch 19/1000
7680/7680 [==============================] - 173s 23ms/step - loss: 0.6743 - accuracy: 0.5827 - precision: 0.5827 - recall: 0.5829 - val_loss: 0.6678 - val_accuracy: 0.5946 - val_precision: 0.5912 - val_recall: 0.6134 - lr: 1.0000e-04
Epoch 20/1000
7680/7680 [==============================] - 173s 22ms/step - loss: 0.6737 - accuracy: 0.5845 - precision: 0.5850 - recall: 0.5815 - val_loss: 0.6669 - val_accuracy: 0.5946 - val_precision: 0.5911 - val_recall: 0.6138 - lr: 1.0000e-04
Epoch 21/1000
7680/7680 [==============================] - 173s 22ms/step - loss: 0.6727 - accuracy: 0.5850 - precision: 0.5851 - recall: 0.5846 - val_loss: 0.6661 - val_accuracy: 0.5961 - val_precision: 0.5924 - val_recall: 0.6161 - lr: 1.0000e-04
Epoch 22/1000
7680/7680 [==============================] - 173s 23ms/step - loss: 0.6722 - accuracy: 0.5861 - precision: 0.5864 - recall: 0.5842 - val_loss: 0.6659 - val_accuracy: 0.5967 - val_precision: 0.5931 - val_recall: 0.6158 - lr: 1.0000e-04
Epoch 23/1000
7680/7680 [==============================] - 167s 22ms/step - loss: 0.6712 - accuracy: 0.5881 - precision: 0.5885 - recall: 0.5853 - val_loss: 0.6646 - val_accuracy: 0.5983 - val_precision: 0.5942 - val_recall: 0.6199 - lr: 1.0000e-04
Epoch 24/1000
7680/7680 [==============================] - 165s 21ms/step - loss: 0.6711 - accuracy: 0.5880 - precision: 0.5883 - recall: 0.5859 - val_loss: 0.6638 - val_accuracy: 0.5999 - val_precision: 0.5944 - val_recall: 0.6290 - lr: 1.0000e-04
Epoch 25/1000
7680/7680 [==============================] - 165s 22ms/step - loss: 0.6701 - accuracy: 0.5896 - precision: 0.5898 - recall: 0.5889 - val_loss: 0.6625 - val_accuracy: 0.6017 - val_precision: 0.5991 - val_recall: 0.6144 - lr: 1.0000e-04
Epoch 26/1000
7680/7680 [==============================] - 166s 22ms/step - loss: 0.6695 - accuracy: 0.5914 - precision: 0.5915 - recall: 0.5911 - val_loss: 0.6617 - val_accuracy: 0.6036 - val_precision: 0.6004 - val_recall: 0.6197 - lr: 1.0000e-04
Epoch 27/1000
7680/7680 [==============================] - 165s 22ms/step - loss: 0.6689 - accuracy: 0.5914 - precision: 0.5918 - recall: 0.5894 - val_loss: 0.6611 - val_accuracy: 0.6032 - val_precision: 0.6010 - val_recall: 0.6141 - lr: 1.0000e-04
Epoch 28/1000
7680/7680 [==============================] - 166s 22ms/step - loss: 0.6678 - accuracy: 0.5929 - precision: 0.5930 - recall: 0.5919 - val_loss: 0.6605 - val_accuracy: 0.6047 - val_precision: 0.5987 - val_recall: 0.6347 - lr: 1.0000e-04
Epoch 29/1000
7680/7680 [==============================] - 165s 22ms/step - loss: 0.6674 - accuracy: 0.5925 - precision: 0.5924 - recall: 0.5927 - val_loss: 0.6606 - val_accuracy: 0.6046 - val_precision: 0.5998 - val_recall: 0.6286 - lr: 1.0000e-04
Epoch 30/1000
7680/7680 [==============================] - 166s 22ms/step - loss: 0.6668 - accuracy: 0.5949 - precision: 0.5952 - recall: 0.5935 - val_loss: 0.6591 - val_accuracy: 0.6060 - val_precision: 0.6036 - val_recall: 0.6178 - lr: 1.0000e-04
Epoch 31/1000
7680/7680 [==============================] - 167s 22ms/step - loss: 0.6658 - accuracy: 0.5957 - precision: 0.5959 - recall: 0.5943 - val_loss: 0.6574 - val_accuracy: 0.6079 - val_precision: 0.6040 - val_recall: 0.6267 - lr: 1.0000e-04
Epoch 32/1000
7680/7680 [==============================] - 165s 22ms/step - loss: 0.6652 - accuracy: 0.5966 - precision: 0.5967 - recall: 0.5958 - val_loss: 0.6562 - val_accuracy: 0.6092 - val_precision: 0.6076 - val_recall: 0.6163 - lr: 1.0000e-04
Epoch 33/1000
7680/7680 [==============================] - 166s 22ms/step - loss: 0.6646 - accuracy: 0.5980 - precision: 0.5982 - recall: 0.5971 - val_loss: 0.6554 - val_accuracy: 0.6109 - val_precision: 0.6085 - val_recall: 0.6220 - lr: 1.0000e-04
Epoch 34/1000
7680/7680 [==============================] - 171s 22ms/step - loss: 0.6640 - accuracy: 0.5989 - precision: 0.5992 - recall: 0.5974 - val_loss: 0.6556 - val_accuracy: 0.6102 - val_precision: 0.6069 - val_recall: 0.6257 - lr: 1.0000e-04
Epoch 35/1000
7680/7680 [==============================] - 180s 23ms/step - loss: 0.6633 - accuracy: 0.6002 - precision: 0.6006 - recall: 0.5979 - val_loss: 0.6541 - val_accuracy: 0.6120 - val_precision: 0.6115 - val_recall: 0.6143 - lr: 1.0000e-04
Epoch 36/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6629 - accuracy: 0.6002 - precision: 0.6007 - recall: 0.5978 - val_loss: 0.6537 - val_accuracy: 0.6131 - val_precision: 0.6117 - val_recall: 0.6192 - lr: 1.0000e-04
Epoch 37/1000
7680/7680 [==============================] - 175s 23ms/step - loss: 0.6621 - accuracy: 0.6010 - precision: 0.6011 - recall: 0.6003 - val_loss: 0.6523 - val_accuracy: 0.6151 - val_precision: 0.6150 - val_recall: 0.6156 - lr: 1.0000e-04
Epoch 38/1000
7680/7680 [==============================] - 175s 23ms/step - loss: 0.6613 - accuracy: 0.6030 - precision: 0.6038 - recall: 0.5989 - val_loss: 0.6520 - val_accuracy: 0.6155 - val_precision: 0.6131 - val_recall: 0.6258 - lr: 1.0000e-04
Epoch 39/1000
7680/7680 [==============================] - 179s 23ms/step - loss: 0.6608 - accuracy: 0.6030 - precision: 0.6036 - recall: 0.5999 - val_loss: 0.6508 - val_accuracy: 0.6161 - val_precision: 0.6149 - val_recall: 0.6209 - lr: 1.0000e-04
Epoch 40/1000
7680/7680 [==============================] - 176s 23ms/step - loss: 0.6600 - accuracy: 0.6041 - precision: 0.6052 - recall: 0.5992 - val_loss: 0.6503 - val_accuracy: 0.6165 - val_precision: 0.6140 - val_recall: 0.6272 - lr: 1.0000e-04
Epoch 41/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6594 - accuracy: 0.6053 - precision: 0.6068 - recall: 0.5987 - val_loss: 0.6489 - val_accuracy: 0.6188 - val_precision: 0.6212 - val_recall: 0.6088 - lr: 1.0000e-04
Epoch 42/1000
7680/7680 [==============================] - 178s 23ms/step - loss: 0.6589 - accuracy: 0.6061 - precision: 0.6080 - recall: 0.5974 - val_loss: 0.6487 - val_accuracy: 0.6194 - val_precision: 0.6213 - val_recall: 0.6115 - lr: 1.0000e-04
Epoch 43/1000
7680/7680 [==============================] - 178s 23ms/step - loss: 0.6581 - accuracy: 0.6079 - precision: 0.6088 - recall: 0.6037 - val_loss: 0.6470 - val_accuracy: 0.6210 - val_precision: 0.6221 - val_recall: 0.6167 - lr: 1.0000e-04
Epoch 44/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6576 - accuracy: 0.6075 - precision: 0.6091 - recall: 0.6003 - val_loss: 0.6468 - val_accuracy: 0.6214 - val_precision: 0.6181 - val_recall: 0.6352 - lr: 1.0000e-04
Epoch 45/1000
7680/7680 [==============================] - 178s 23ms/step - loss: 0.6572 - accuracy: 0.6083 - precision: 0.6092 - recall: 0.6042 - val_loss: 0.6458 - val_accuracy: 0.6230 - val_precision: 0.6212 - val_recall: 0.6304 - lr: 1.0000e-04
Epoch 46/1000
7680/7680 [==============================] - 178s 23ms/step - loss: 0.6565 - accuracy: 0.6092 - precision: 0.6102 - recall: 0.6046 - val_loss: 0.6451 - val_accuracy: 0.6237 - val_precision: 0.6258 - val_recall: 0.6155 - lr: 1.0000e-04
Epoch 47/1000
7680/7680 [==============================] - 178s 23ms/step - loss: 0.6556 - accuracy: 0.6106 - precision: 0.6113 - recall: 0.6071 - val_loss: 0.6445 - val_accuracy: 0.6247 - val_precision: 0.6257 - val_recall: 0.6206 - lr: 1.0000e-04
Epoch 48/1000
7680/7680 [==============================] - 176s 23ms/step - loss: 0.6557 - accuracy: 0.6101 - precision: 0.6115 - recall: 0.6038 - val_loss: 0.6442 - val_accuracy: 0.6244 - val_precision: 0.6250 - val_recall: 0.6218 - lr: 1.0000e-04
Epoch 49/1000
7680/7680 [==============================] - 178s 23ms/step - loss: 0.6549 - accuracy: 0.6112 - precision: 0.6128 - recall: 0.6042 - val_loss: 0.6425 - val_accuracy: 0.6267 - val_precision: 0.6247 - val_recall: 0.6346 - lr: 1.0000e-04
Epoch 50/1000
7680/7680 [==============================] - 175s 23ms/step - loss: 0.6540 - accuracy: 0.6130 - precision: 0.6145 - recall: 0.6064 - val_loss: 0.6418 - val_accuracy: 0.6279 - val_precision: 0.6255 - val_recall: 0.6374 - lr: 1.0000e-04
Epoch 51/1000
7680/7680 [==============================] - 175s 23ms/step - loss: 0.6535 - accuracy: 0.6143 - precision: 0.6152 - recall: 0.6101 - val_loss: 0.6409 - val_accuracy: 0.6285 - val_precision: 0.6314 - val_recall: 0.6176 - lr: 1.0000e-04
Epoch 52/1000
7680/7680 [==============================] - 175s 23ms/step - loss: 0.6528 - accuracy: 0.6141 - precision: 0.6155 - recall: 0.6081 - val_loss: 0.6402 - val_accuracy: 0.6297 - val_precision: 0.6272 - val_recall: 0.6394 - lr: 1.0000e-04
Epoch 53/1000
7680/7680 [==============================] - 176s 23ms/step - loss: 0.6521 - accuracy: 0.6149 - precision: 0.6164 - recall: 0.6084 - val_loss: 0.6394 - val_accuracy: 0.6302 - val_precision: 0.6338 - val_recall: 0.6169 - lr: 1.0000e-04
Epoch 54/1000
7680/7680 [==============================] - 176s 23ms/step - loss: 0.6518 - accuracy: 0.6161 - precision: 0.6173 - recall: 0.6108 - val_loss: 0.6391 - val_accuracy: 0.6309 - val_precision: 0.6270 - val_recall: 0.6461 - lr: 1.0000e-04
Epoch 55/1000
7680/7680 [==============================] - 178s 23ms/step - loss: 0.6510 - accuracy: 0.6164 - precision: 0.6177 - recall: 0.6106 - val_loss: 0.6379 - val_accuracy: 0.6324 - val_precision: 0.6289 - val_recall: 0.6458 - lr: 1.0000e-04
Epoch 56/1000
7680/7680 [==============================] - 180s 23ms/step - loss: 0.6508 - accuracy: 0.6166 - precision: 0.6182 - recall: 0.6096 - val_loss: 0.6367 - val_accuracy: 0.6341 - val_precision: 0.6336 - val_recall: 0.6363 - lr: 1.0000e-04
Epoch 57/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6499 - accuracy: 0.6185 - precision: 0.6201 - recall: 0.6119 - val_loss: 0.6368 - val_accuracy: 0.6330 - val_precision: 0.6307 - val_recall: 0.6418 - lr: 1.0000e-04
Epoch 58/1000
7680/7680 [==============================] - 179s 23ms/step - loss: 0.6495 - accuracy: 0.6182 - precision: 0.6200 - recall: 0.6105 - val_loss: 0.6361 - val_accuracy: 0.6351 - val_precision: 0.6353 - val_recall: 0.6343 - lr: 1.0000e-04
Epoch 59/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6494 - accuracy: 0.6188 - precision: 0.6206 - recall: 0.6112 - val_loss: 0.6355 - val_accuracy: 0.6356 - val_precision: 0.6355 - val_recall: 0.6358 - lr: 1.0000e-04
Epoch 60/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6486 - accuracy: 0.6192 - precision: 0.6203 - recall: 0.6143 - val_loss: 0.6351 - val_accuracy: 0.6366 - val_precision: 0.6376 - val_recall: 0.6332 - lr: 1.0000e-04
Epoch 61/1000
7680/7680 [==============================] - 176s 23ms/step - loss: 0.6478 - accuracy: 0.6205 - precision: 0.6222 - recall: 0.6136 - val_loss: 0.6329 - val_accuracy: 0.6380 - val_precision: 0.6357 - val_recall: 0.6463 - lr: 1.0000e-04
Epoch 62/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6470 - accuracy: 0.6221 - precision: 0.6236 - recall: 0.6158 - val_loss: 0.6322 - val_accuracy: 0.6385 - val_precision: 0.6351 - val_recall: 0.6514 - lr: 1.0000e-04
Epoch 63/1000
7680/7680 [==============================] - 176s 23ms/step - loss: 0.6466 - accuracy: 0.6227 - precision: 0.6244 - recall: 0.6158 - val_loss: 0.6320 - val_accuracy: 0.6390 - val_precision: 0.6389 - val_recall: 0.6391 - lr: 1.0000e-04
Epoch 64/1000
7680/7680 [==============================] - 179s 23ms/step - loss: 0.6465 - accuracy: 0.6226 - precision: 0.6240 - recall: 0.6168 - val_loss: 0.6311 - val_accuracy: 0.6398 - val_precision: 0.6366 - val_recall: 0.6516 - lr: 1.0000e-04
Epoch 65/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6457 - accuracy: 0.6226 - precision: 0.6243 - recall: 0.6158 - val_loss: 0.6303 - val_accuracy: 0.6408 - val_precision: 0.6368 - val_recall: 0.6551 - lr: 1.0000e-04
Epoch 66/1000
7680/7680 [==============================] - 176s 23ms/step - loss: 0.6449 - accuracy: 0.6239 - precision: 0.6257 - recall: 0.6169 - val_loss: 0.6293 - val_accuracy: 0.6425 - val_precision: 0.6408 - val_recall: 0.6488 - lr: 1.0000e-04
Epoch 67/1000
7680/7680 [==============================] - 175s 23ms/step - loss: 0.6448 - accuracy: 0.6247 - precision: 0.6266 - recall: 0.6173 - val_loss: 0.6294 - val_accuracy: 0.6430 - val_precision: 0.6389 - val_recall: 0.6579 - lr: 1.0000e-04
Epoch 68/1000
7680/7680 [==============================] - 178s 23ms/step - loss: 0.6444 - accuracy: 0.6250 - precision: 0.6269 - recall: 0.6176 - val_loss: 0.6288 - val_accuracy: 0.6429 - val_precision: 0.6392 - val_recall: 0.6560 - lr: 1.0000e-04
Epoch 69/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6438 - accuracy: 0.6261 - precision: 0.6273 - recall: 0.6215 - val_loss: 0.6282 - val_accuracy: 0.6440 - val_precision: 0.6426 - val_recall: 0.6486 - lr: 1.0000e-04
Epoch 70/1000
7680/7680 [==============================] - 184s 24ms/step - loss: 0.6429 - accuracy: 0.6262 - precision: 0.6281 - recall: 0.6185 - val_loss: 0.6274 - val_accuracy: 0.6448 - val_precision: 0.6402 - val_recall: 0.6611 - lr: 1.0000e-04
Epoch 71/1000
7680/7680 [==============================] - 183s 24ms/step - loss: 0.6432 - accuracy: 0.6265 - precision: 0.6285 - recall: 0.6187 - val_loss: 0.6258 - val_accuracy: 0.6466 - val_precision: 0.6467 - val_recall: 0.6463 - lr: 1.0000e-04
Epoch 72/1000
7680/7680 [==============================] - 182s 24ms/step - loss: 0.6424 - accuracy: 0.6268 - precision: 0.6291 - recall: 0.6177 - val_loss: 0.6256 - val_accuracy: 0.6466 - val_precision: 0.6490 - val_recall: 0.6383 - lr: 1.0000e-04
Epoch 73/1000
7680/7680 [==============================] - 199s 26ms/step - loss: 0.6415 - accuracy: 0.6286 - precision: 0.6302 - recall: 0.6223 - val_loss: 0.6252 - val_accuracy: 0.6474 - val_precision: 0.6448 - val_recall: 0.6564 - lr: 1.0000e-04
Epoch 74/1000
7680/7680 [==============================] - 204s 27ms/step - loss: 0.6414 - accuracy: 0.6291 - precision: 0.6306 - recall: 0.6236 - val_loss: 0.6248 - val_accuracy: 0.6473 - val_precision: 0.6432 - val_recall: 0.6616 - lr: 1.0000e-04
Epoch 75/1000
7680/7680 [==============================] - 207s 27ms/step - loss: 0.6404 - accuracy: 0.6289 - precision: 0.6307 - recall: 0.6218 - val_loss: 0.6244 - val_accuracy: 0.6481 - val_precision: 0.6471 - val_recall: 0.6516 - lr: 1.0000e-04
Epoch 76/1000
7680/7680 [==============================] - 205s 27ms/step - loss: 0.6404 - accuracy: 0.6298 - precision: 0.6320 - recall: 0.6215 - val_loss: 0.6225 - val_accuracy: 0.6499 - val_precision: 0.6483 - val_recall: 0.6553 - lr: 1.0000e-04
Epoch 77/1000
7680/7680 [==============================] - 199s 26ms/step - loss: 0.6399 - accuracy: 0.6303 - precision: 0.6325 - recall: 0.6221 - val_loss: 0.6221 - val_accuracy: 0.6502 - val_precision: 0.6480 - val_recall: 0.6575 - lr: 1.0000e-04
Epoch 78/1000
7680/7680 [==============================] - 200s 26ms/step - loss: 0.6393 - accuracy: 0.6312 - precision: 0.6335 - recall: 0.6227 - val_loss: 0.6218 - val_accuracy: 0.6509 - val_precision: 0.6491 - val_recall: 0.6569 - lr: 1.0000e-04
Epoch 79/1000
7680/7680 [==============================] - 209s 27ms/step - loss: 0.6392 - accuracy: 0.6311 - precision: 0.6329 - recall: 0.6242 - val_loss: 0.6214 - val_accuracy: 0.6512 - val_precision: 0.6488 - val_recall: 0.6592 - lr: 1.0000e-04
Epoch 80/1000
7680/7680 [==============================] - 209s 27ms/step - loss: 0.6384 - accuracy: 0.6324 - precision: 0.6344 - recall: 0.6246 - val_loss: 0.6208 - val_accuracy: 0.6512 - val_precision: 0.6487 - val_recall: 0.6599 - lr: 1.0000e-04
Epoch 81/1000
7680/7680 [==============================] - 212s 28ms/step - loss: 0.6377 - accuracy: 0.6326 - precision: 0.6346 - recall: 0.6252 - val_loss: 0.6200 - val_accuracy: 0.6521 - val_precision: 0.6486 - val_recall: 0.6639 - lr: 1.0000e-04
Epoch 82/1000
7680/7680 [==============================] - 214s 28ms/step - loss: 0.6374 - accuracy: 0.6339 - precision: 0.6359 - recall: 0.6265 - val_loss: 0.6193 - val_accuracy: 0.6535 - val_precision: 0.6532 - val_recall: 0.6545 - lr: 1.0000e-04
Epoch 83/1000
7680/7680 [==============================] - 212s 28ms/step - loss: 0.6367 - accuracy: 0.6344 - precision: 0.6366 - recall: 0.6262 - val_loss: 0.6179 - val_accuracy: 0.6541 - val_precision: 0.6549 - val_recall: 0.6516 - lr: 1.0000e-04
Epoch 84/1000
7680/7680 [==============================] - 213s 28ms/step - loss: 0.6362 - accuracy: 0.6347 - precision: 0.6375 - recall: 0.6245 - val_loss: 0.6176 - val_accuracy: 0.6549 - val_precision: 0.6539 - val_recall: 0.6582 - lr: 1.0000e-04
Epoch 85/1000
7680/7680 [==============================] - 208s 27ms/step - loss: 0.6359 - accuracy: 0.6349 - precision: 0.6375 - recall: 0.6258 - val_loss: 0.6191 - val_accuracy: 0.6538 - val_precision: 0.6483 - val_recall: 0.6723 - lr: 1.0000e-04
Epoch 86/1000
7680/7680 [==============================] - 211s 27ms/step - loss: 0.6359 - accuracy: 0.6346 - precision: 0.6370 - recall: 0.6255 - val_loss: 0.6163 - val_accuracy: 0.6565 - val_precision: 0.6562 - val_recall: 0.6574 - lr: 1.0000e-04
Epoch 87/1000
7680/7680 [==============================] - 193s 25ms/step - loss: 0.6355 - accuracy: 0.6353 - precision: 0.6372 - recall: 0.6282 - val_loss: 0.6170 - val_accuracy: 0.6557 - val_precision: 0.6524 - val_recall: 0.6663 - lr: 1.0000e-04
Epoch 88/1000
7680/7680 [==============================] - 194s 25ms/step - loss: 0.6347 - accuracy: 0.6357 - precision: 0.6377 - recall: 0.6283 - val_loss: 0.6159 - val_accuracy: 0.6571 - val_precision: 0.6584 - val_recall: 0.6530 - lr: 1.0000e-04
Epoch 89/1000
7680/7680 [==============================] - 192s 25ms/step - loss: 0.6344 - accuracy: 0.6369 - precision: 0.6392 - recall: 0.6286 - val_loss: 0.6150 - val_accuracy: 0.6585 - val_precision: 0.6568 - val_recall: 0.6637 - lr: 1.0000e-04
Epoch 90/1000
7680/7680 [==============================] - 196s 25ms/step - loss: 0.6340 - accuracy: 0.6373 - precision: 0.6397 - recall: 0.6289 - val_loss: 0.6142 - val_accuracy: 0.6585 - val_precision: 0.6591 - val_recall: 0.6567 - lr: 1.0000e-04
Epoch 91/1000
7680/7680 [==============================] - 192s 25ms/step - loss: 0.6338 - accuracy: 0.6373 - precision: 0.6399 - recall: 0.6283 - val_loss: 0.6140 - val_accuracy: 0.6592 - val_precision: 0.6559 - val_recall: 0.6698 - lr: 1.0000e-04
Epoch 92/1000
7680/7680 [==============================] - 191s 25ms/step - loss: 0.6329 - accuracy: 0.6387 - precision: 0.6409 - recall: 0.6306 - val_loss: 0.6125 - val_accuracy: 0.6607 - val_precision: 0.6644 - val_recall: 0.6494 - lr: 1.0000e-04
Epoch 93/1000
7680/7680 [==============================] - 189s 25ms/step - loss: 0.6325 - accuracy: 0.6387 - precision: 0.6408 - recall: 0.6310 - val_loss: 0.6122 - val_accuracy: 0.6612 - val_precision: 0.6577 - val_recall: 0.6722 - lr: 1.0000e-04
Epoch 94/1000
7680/7680 [==============================] - 193s 25ms/step - loss: 0.6318 - accuracy: 0.6392 - precision: 0.6414 - recall: 0.6318 - val_loss: 0.6112 - val_accuracy: 0.6623 - val_precision: 0.6606 - val_recall: 0.6674 - lr: 1.0000e-04
Epoch 95/1000
7680/7680 [==============================] - 192s 25ms/step - loss: 0.6317 - accuracy: 0.6397 - precision: 0.6423 - recall: 0.6306 - val_loss: 0.6116 - val_accuracy: 0.6616 - val_precision: 0.6637 - val_recall: 0.6550 - lr: 1.0000e-04
Epoch 96/1000
7680/7680 [==============================] - 196s 26ms/step - loss: 0.6310 - accuracy: 0.6406 - precision: 0.6438 - recall: 0.6293 - val_loss: 0.6112 - val_accuracy: 0.6620 - val_precision: 0.6629 - val_recall: 0.6594 - lr: 1.0000e-04
Epoch 97/1000
7680/7680 [==============================] - 195s 25ms/step - loss: 0.6311 - accuracy: 0.6406 - precision: 0.6428 - recall: 0.6328 - val_loss: 0.6106 - val_accuracy: 0.6623 - val_precision: 0.6623 - val_recall: 0.6623 - lr: 1.0000e-04
Epoch 98/1000
7680/7680 [==============================] - 193s 25ms/step - loss: 0.6305 - accuracy: 0.6409 - precision: 0.6435 - recall: 0.6318 - val_loss: 0.6103 - val_accuracy: 0.6634 - val_precision: 0.6663 - val_recall: 0.6545 - lr: 1.0000e-04
Epoch 99/1000
7680/7680 [==============================] - 190s 25ms/step - loss: 0.6300 - accuracy: 0.6415 - precision: 0.6440 - recall: 0.6329 - val_loss: 0.6102 - val_accuracy: 0.6629 - val_precision: 0.6598 - val_recall: 0.6725 - lr: 1.0000e-04
Epoch 100/1000
7680/7680 [==============================] - 185s 24ms/step - loss: 0.6297 - accuracy: 0.6422 - precision: 0.6449 - recall: 0.6331 - val_loss: 0.6097 - val_accuracy: 0.6629 - val_precision: 0.6617 - val_recall: 0.6667 - lr: 1.0000e-04
Epoch 101/1000
7680/7680 [==============================] - 180s 23ms/step - loss: 0.6297 - accuracy: 0.6422 - precision: 0.6448 - recall: 0.6334 - val_loss: 0.6085 - val_accuracy: 0.6651 - val_precision: 0.6627 - val_recall: 0.6722 - lr: 1.0000e-04
Epoch 102/1000
7680/7680 [==============================] - 203s 26ms/step - loss: 0.6294 - accuracy: 0.6416 - precision: 0.6444 - recall: 0.6321 - val_loss: 0.6079 - val_accuracy: 0.6655 - val_precision: 0.6668 - val_recall: 0.6617 - lr: 1.0000e-04
Epoch 103/1000
7680/7680 [==============================] - 214s 28ms/step - loss: 0.6287 - accuracy: 0.6426 - precision: 0.6454 - recall: 0.6332 - val_loss: 0.6060 - val_accuracy: 0.6669 - val_precision: 0.6669 - val_recall: 0.6670 - lr: 1.0000e-04
Epoch 104/1000
7680/7680 [==============================] - 212s 28ms/step - loss: 0.6282 - accuracy: 0.6438 - precision: 0.6463 - recall: 0.6355 - val_loss: 0.6068 - val_accuracy: 0.6662 - val_precision: 0.6632 - val_recall: 0.6756 - lr: 1.0000e-04
Epoch 105/1000
7680/7680 [==============================] - 203s 26ms/step - loss: 0.6279 - accuracy: 0.6437 - precision: 0.6462 - recall: 0.6354 - val_loss: 0.6059 - val_accuracy: 0.6678 - val_precision: 0.6693 - val_recall: 0.6636 - lr: 1.0000e-04
Epoch 106/1000
7680/7680 [==============================] - 201s 26ms/step - loss: 0.6274 - accuracy: 0.6445 - precision: 0.6470 - recall: 0.6360 - val_loss: 0.6043 - val_accuracy: 0.6688 - val_precision: 0.6693 - val_recall: 0.6673 - lr: 1.0000e-04
Epoch 107/1000
7680/7680 [==============================] - 200s 26ms/step - loss: 0.6269 - accuracy: 0.6456 - precision: 0.6481 - recall: 0.6371 - val_loss: 0.6045 - val_accuracy: 0.6690 - val_precision: 0.6677 - val_recall: 0.6727 - lr: 1.0000e-04
Epoch 108/1000
7680/7680 [==============================] - 199s 26ms/step - loss: 0.6267 - accuracy: 0.6447 - precision: 0.6476 - recall: 0.6352 - val_loss: 0.6042 - val_accuracy: 0.6692 - val_precision: 0.6637 - val_recall: 0.6858 - lr: 1.0000e-04
Epoch 109/1000
7680/7680 [==============================] - 198s 26ms/step - loss: 0.6263 - accuracy: 0.6461 - precision: 0.6486 - recall: 0.6376 - val_loss: 0.6033 - val_accuracy: 0.6706 - val_precision: 0.6703 - val_recall: 0.6714 - lr: 1.0000e-04
Epoch 110/1000
7680/7680 [==============================] - 197s 26ms/step - loss: 0.6256 - accuracy: 0.6473 - precision: 0.6502 - recall: 0.6376 - val_loss: 0.6042 - val_accuracy: 0.6695 - val_precision: 0.6740 - val_recall: 0.6564 - lr: 1.0000e-04
Epoch 111/1000
7680/7680 [==============================] - 198s 26ms/step - loss: 0.6254 - accuracy: 0.6468 - precision: 0.6500 - recall: 0.6359 - val_loss: 0.6013 - val_accuracy: 0.6719 - val_precision: 0.6745 - val_recall: 0.6644 - lr: 1.0000e-04
Epoch 112/1000
7680/7680 [==============================] - 199s 26ms/step - loss: 0.6247 - accuracy: 0.6476 - precision: 0.6500 - recall: 0.6398 - val_loss: 0.6018 - val_accuracy: 0.6715 - val_precision: 0.6716 - val_recall: 0.6710 - lr: 1.0000e-04
Epoch 113/1000
7680/7680 [==============================] - 197s 26ms/step - loss: 0.6246 - accuracy: 0.6479 - precision: 0.6506 - recall: 0.6387 - val_loss: 0.6010 - val_accuracy: 0.6720 - val_precision: 0.6724 - val_recall: 0.6707 - lr: 1.0000e-04
Epoch 114/1000
7680/7680 [==============================] - 180s 23ms/step - loss: 0.6242 - accuracy: 0.6486 - precision: 0.6512 - recall: 0.6401 - val_loss: 0.6023 - val_accuracy: 0.6716 - val_precision: 0.6731 - val_recall: 0.6672 - lr: 1.0000e-04
Epoch 115/1000
7680/7680 [==============================] - 219s 28ms/step - loss: 0.6239 - accuracy: 0.6484 - precision: 0.6507 - recall: 0.6405 - val_loss: 0.5999 - val_accuracy: 0.6736 - val_precision: 0.6742 - val_recall: 0.6718 - lr: 1.0000e-04
Epoch 116/1000
7680/7680 [==============================] - 221s 29ms/step - loss: 0.6234 - accuracy: 0.6488 - precision: 0.6512 - recall: 0.6408 - val_loss: 0.6024 - val_accuracy: 0.6723 - val_precision: 0.6694 - val_recall: 0.6808 - lr: 1.0000e-04
Epoch 117/1000
7680/7680 [==============================] - 192s 25ms/step - loss: 0.6231 - accuracy: 0.6499 - precision: 0.6524 - recall: 0.6414 - val_loss: 0.5994 - val_accuracy: 0.6744 - val_precision: 0.6734 - val_recall: 0.6775 - lr: 1.0000e-04
Epoch 118/1000
7680/7680 [==============================] - 170s 22ms/step - loss: 0.6229 - accuracy: 0.6494 - precision: 0.6515 - recall: 0.6426 - val_loss: 0.5990 - val_accuracy: 0.6750 - val_precision: 0.6792 - val_recall: 0.6632 - lr: 1.0000e-04
Epoch 119/1000
7680/7680 [==============================] - 186s 24ms/step - loss: 0.6222 - accuracy: 0.6505 - precision: 0.6530 - recall: 0.6425 - val_loss: 0.5972 - val_accuracy: 0.6762 - val_precision: 0.6784 - val_recall: 0.6703 - lr: 1.0000e-04
Epoch 120/1000
7680/7680 [==============================] - 194s 25ms/step - loss: 0.6221 - accuracy: 0.6507 - precision: 0.6528 - recall: 0.6439 - val_loss: 0.5986 - val_accuracy: 0.6749 - val_precision: 0.6706 - val_recall: 0.6876 - lr: 1.0000e-04
Epoch 121/1000
7680/7680 [==============================] - 189s 25ms/step - loss: 0.6213 - accuracy: 0.6523 - precision: 0.6551 - recall: 0.6432 - val_loss: 0.5971 - val_accuracy: 0.6771 - val_precision: 0.6772 - val_recall: 0.6765 - lr: 1.0000e-04
Epoch 122/1000
7680/7680 [==============================] - 187s 24ms/step - loss: 0.6212 - accuracy: 0.6520 - precision: 0.6553 - recall: 0.6413 - val_loss: 0.5974 - val_accuracy: 0.6766 - val_precision: 0.6752 - val_recall: 0.6806 - lr: 1.0000e-04
Epoch 123/1000
7680/7680 [==============================] - 196s 25ms/step - loss: 0.6202 - accuracy: 0.6523 - precision: 0.6550 - recall: 0.6438 - val_loss: 0.5956 - val_accuracy: 0.6781 - val_precision: 0.6790 - val_recall: 0.6758 - lr: 1.0000e-04
Epoch 124/1000
7680/7680 [==============================] - 202s 26ms/step - loss: 0.6212 - accuracy: 0.6520 - precision: 0.6544 - recall: 0.6442 - val_loss: 0.5952 - val_accuracy: 0.6782 - val_precision: 0.6775 - val_recall: 0.6803 - lr: 1.0000e-04
Epoch 125/1000
7680/7680 [==============================] - 196s 26ms/step - loss: 0.6200 - accuracy: 0.6534 - precision: 0.6558 - recall: 0.6456 - val_loss: 0.5947 - val_accuracy: 0.6784 - val_precision: 0.6789 - val_recall: 0.6772 - lr: 1.0000e-04
Epoch 126/1000
7680/7680 [==============================] - 201s 26ms/step - loss: 0.6195 - accuracy: 0.6525 - precision: 0.6553 - recall: 0.6435 - val_loss: 0.5951 - val_accuracy: 0.6794 - val_precision: 0.6790 - val_recall: 0.6806 - lr: 1.0000e-04
Epoch 127/1000
7680/7680 [==============================] - 189s 25ms/step - loss: 0.6194 - accuracy: 0.6537 - precision: 0.6559 - recall: 0.6465 - val_loss: 0.5931 - val_accuracy: 0.6800 - val_precision: 0.6811 - val_recall: 0.6771 - lr: 1.0000e-04
Epoch 128/1000
7680/7680 [==============================] - 188s 24ms/step - loss: 0.6193 - accuracy: 0.6538 - precision: 0.6562 - recall: 0.6462 - val_loss: 0.5942 - val_accuracy: 0.6802 - val_precision: 0.6792 - val_recall: 0.6830 - lr: 1.0000e-04
Epoch 129/1000
7680/7680 [==============================] - 187s 24ms/step - loss: 0.6189 - accuracy: 0.6543 - precision: 0.6569 - recall: 0.6460 - val_loss: 0.5926 - val_accuracy: 0.6812 - val_precision: 0.6829 - val_recall: 0.6766 - lr: 1.0000e-04
Epoch 130/1000
7680/7680 [==============================] - 185s 24ms/step - loss: 0.6184 - accuracy: 0.6554 - precision: 0.6583 - recall: 0.6463 - val_loss: 0.5935 - val_accuracy: 0.6806 - val_precision: 0.6807 - val_recall: 0.6804 - lr: 1.0000e-04
Epoch 131/1000
7680/7680 [==============================] - 168s 22ms/step - loss: 0.6186 - accuracy: 0.6551 - precision: 0.6573 - recall: 0.6481 - val_loss: 0.5934 - val_accuracy: 0.6806 - val_precision: 0.6814 - val_recall: 0.6785 - lr: 1.0000e-04
Epoch 132/1000
7680/7680 [==============================] - 166s 22ms/step - loss: 0.6176 - accuracy: 0.6550 - precision: 0.6570 - recall: 0.6486 - val_loss: 0.5920 - val_accuracy: 0.6815 - val_precision: 0.6836 - val_recall: 0.6755 - lr: 1.0000e-04
Epoch 133/1000
7680/7680 [==============================] - 168s 22ms/step - loss: 0.6173 - accuracy: 0.6553 - precision: 0.6578 - recall: 0.6476 - val_loss: 0.5918 - val_accuracy: 0.6822 - val_precision: 0.6812 - val_recall: 0.6849 - lr: 1.0000e-04
Epoch 134/1000
7680/7680 [==============================] - 164s 21ms/step - loss: 0.6165 - accuracy: 0.6566 - precision: 0.6592 - recall: 0.6485 - val_loss: 0.5903 - val_accuracy: 0.6835 - val_precision: 0.6818 - val_recall: 0.6880 - lr: 1.0000e-04
Epoch 135/1000
7680/7680 [==============================] - 173s 23ms/step - loss: 0.6172 - accuracy: 0.6562 - precision: 0.6593 - recall: 0.6463 - val_loss: 0.5896 - val_accuracy: 0.6841 - val_precision: 0.6874 - val_recall: 0.6752 - lr: 1.0000e-04
Epoch 136/1000
7680/7680 [==============================] - 172s 22ms/step - loss: 0.6167 - accuracy: 0.6561 - precision: 0.6587 - recall: 0.6479 - val_loss: 0.5908 - val_accuracy: 0.6823 - val_precision: 0.6815 - val_recall: 0.6846 - lr: 1.0000e-04
Epoch 137/1000
7680/7680 [==============================] - 167s 22ms/step - loss: 0.6158 - accuracy: 0.6578 - precision: 0.6600 - recall: 0.6511 - val_loss: 0.5901 - val_accuracy: 0.6834 - val_precision: 0.6805 - val_recall: 0.6913 - lr: 1.0000e-04
Epoch 138/1000
7680/7680 [==============================] - 165s 21ms/step - loss: 0.6150 - accuracy: 0.6585 - precision: 0.6601 - recall: 0.6537 - val_loss: 0.5887 - val_accuracy: 0.6850 - val_precision: 0.6860 - val_recall: 0.6824 - lr: 1.0000e-04
Epoch 139/1000
7680/7680 [==============================] - 165s 21ms/step - loss: 0.6150 - accuracy: 0.6592 - precision: 0.6616 - recall: 0.6517 - val_loss: 0.5871 - val_accuracy: 0.6859 - val_precision: 0.6851 - val_recall: 0.6881 - lr: 1.0000e-04
Epoch 140/1000
7680/7680 [==============================] - 171s 22ms/step - loss: 0.6151 - accuracy: 0.6579 - precision: 0.6602 - recall: 0.6507 - val_loss: 0.5879 - val_accuracy: 0.6862 - val_precision: 0.6877 - val_recall: 0.6823 - lr: 1.0000e-04
Epoch 141/1000
7680/7680 [==============================] - 165s 22ms/step - loss: 0.6151 - accuracy: 0.6575 - precision: 0.6602 - recall: 0.6489 - val_loss: 0.5878 - val_accuracy: 0.6861 - val_precision: 0.6831 - val_recall: 0.6944 - lr: 1.0000e-04
Epoch 142/1000
7680/7680 [==============================] - 168s 22ms/step - loss: 0.6136 - accuracy: 0.6597 - precision: 0.6621 - recall: 0.6522 - val_loss: 0.5880 - val_accuracy: 0.6865 - val_precision: 0.6851 - val_recall: 0.6905 - lr: 1.0000e-04
Epoch 143/1000
7680/7680 [==============================] - 167s 22ms/step - loss: 0.6139 - accuracy: 0.6590 - precision: 0.6612 - recall: 0.6523 - val_loss: 0.5855 - val_accuracy: 0.6880 - val_precision: 0.6903 - val_recall: 0.6819 - lr: 1.0000e-04
Epoch 144/1000
7680/7680 [==============================] - 169s 22ms/step - loss: 0.6136 - accuracy: 0.6598 - precision: 0.6621 - recall: 0.6525 - val_loss: 0.5848 - val_accuracy: 0.6885 - val_precision: 0.6861 - val_recall: 0.6949 - lr: 1.0000e-04
Epoch 145/1000
7680/7680 [==============================] - 165s 21ms/step - loss: 0.6131 - accuracy: 0.6607 - precision: 0.6630 - recall: 0.6535 - val_loss: 0.5849 - val_accuracy: 0.6887 - val_precision: 0.6861 - val_recall: 0.6957 - lr: 1.0000e-04
Epoch 146/1000
5844/7680 [=====================>........] - ETA: 30s - loss: 0.6127 - accuracy: 0.6611 - precision: 0.6628 - recall: 0.6564

根据您提供的训练日志，模型的验证准确率确实在逐渐提高，即使在某些时期经历波动。学习率是否合适，不能只根据准确率的提升判断，需要考虑多个因素。您目前的学习率可能允许了模型在参数空间中稳定地探索，并最终找到了损失函数更低的区域。

这里有几个关键点可以考虑：

- 如果模型准确率仍然在稳健地提升，并没有出现明显迹象表明进步停滞不前，那么当前的学习率可能是适当的。

- 进行了140个epoch后，准确率从52%提升到了大约68%，这意味着模型正在学习并且正在走向更好的性能。如此稳定的提升通常说明学习率设定是合适的。

- 虽然经历了几轮波动，但最终准确率仍然能够提升，这也可能是因为模型越过了某些局部最小点，或者是因为梯度下降中的随机性而偶然发现了更好的学习路径。

- 从波动之后重回准确率提升的轨道，说明模型有能力从波动中恢复，并找到梯度优化的方向，这也指示当前的学习率还可以继续使用。

总的来说，只要训练指标（如损失和准确率）继续向好的方向发展，并且没有出现过拟合的迹象，就暂时没有必要调整学习率。不过，如果改进变得缓慢或者停滞，可能需要考虑调整学习率或者使用学习率调度策略，如学习率衰减或周期性调整等。此外，在实际的模型训练中，通常会使用诸如验证集上准确率提前停止增长时降低学习率的策略，来动态调整学习率。