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Since all networks' accuracy goes close to 100%, I would argue that all of the models are capable of learning this task. But the first two models are somewhat overfitting, since the validation accuracy doesn't get nearly as high. Granted the 2nd model uses dropout, but it seems that the dropout rate is not enough to bring these two accuracy metrics closer ...


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Here is a quick idea: first calculate the count of how many times each word occurs in these documents (I don't know whether to lowercase them or not, do interface and Interface mean different things?), and sort them in the descending order of occurrence. Most frequent words can be called "keywords" of your configurations (such as vlan), or maybe ...


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I was able to run the code without "any" modifications on Tensorflow 2.4.0, just had to replace the imports: import keras from keras.datasets import mnist ... -> import tensorflow.keras as keras from tensorflow.keras.datasets import mnist ... Output: Epoch 1/12 469/469 [==============================] - 4s 7ms/step - loss: 2.2914 - accuracy: 0....


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According to my experience, it is possible to reach 99%+ accuracy on MNIST within a few epochs using a simple CNN. MNIST is really an easy dataset. So, it's likely that you've broken something as you're modifying author's code.


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