Answers.Body: <p>What happened with your model is that it suffered from a <a href="https://ar5iv.labs.arxiv.org/html/2206.04041#:%7E:text=Abstract,the%20respective%20class%20feature%20means" rel="nofollow noreferrer">Neural Network collapse</a>. This means that your network didn't learn to generalize with the data or that the local minimum found in the gradient descent was to output an output that almost doesn't change. This often happens when the neural network didn't learn the features of the data and the best pattern it found was to give an almost constant mean value that maximizes the accuracy. Let's say that your distribution on the training data is 60% for class 1 and 40% for class 2, then it will try to predict all data as class 1, in order to maximize the accuracy. Things that you may try for solving this issue could be, increasing the layers of your model, adding more inductive biases that align with your data, or changing hyperparameters. You may want to see the output probabilities of your model to understand my point. Another thing about your data is that your loss doesn't seem to decrease, but changes randomly. I plotted your log prints and it looks like this, which for a Neural Network, is not a good loss curve.
  <a href="https://i.sstatic.net/mnBdm.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/mnBdm.png" alt="enter image description here" /></a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-10-15T15:21:12.573Z
Answers.LastActivityDate: 2023-10-15T15:21:12.573Z
Answers.UserDisplayName: Cesar Ruiz
Body: <p><strong>Question summary</strong></p>
  <ol>
  <li>What informations can get from this epoch_accuracy graph?</li>
  <li>Is it possible training accuracy never changed like after 10 epoch in graph while training?</li>
  </ol>
  <p><strong>Body</strong></p>
  <p>I do some experiments with neural networks, just for hobby.
  but, I get some strange epoch-accuracy graphs.</p>
  <p><a href="https://i.sstatic.net/PSeVA.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/PSeVA.png" alt="enter image description here" /></a></p>
  <p>This graph result came from my models, and I have some questions about this graph.
  In epochs 10, and training accuracy never changed. I see text result when training with <code>models.fit()</code> in <code>tensorflow.keras</code>. Before I learned, Training(not validation) accuracy should be changed while training. Because Model updating parameters, and parameters continuous changed while training. so, This is my first time seeing a graph like this.</p>
  <p>I use flatten layer for input layer, and only use MLP for hidden + output layer. so, I really wondering Is it possible training accuracy never changed while training?</p>
  <p><strong>Edited(update loss, accuracy log)</strong>
  Sorry,I try to update loss graph, but I deleted my model.(because It's just hobby, and I found some stranged status.)</p>
  <p>So, I make new model to represent that situations.
  <a href="https://i.sstatic.net/ronTX.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/ronTX.png" alt="enter image description here" /></a></p>
  <p>After 20 epochs, then accuracy never changed. I show the train log. Please note that loss and val_loss are changed very little, accuracy and val_accuracy never changed after 20 epochs.</p>
  <pre><code>Epoch 18/10000
  80/88 [==========================&gt;...] - ETA: 0s - loss: 1.5232 - accuracy: 0.4410
  Epoch 18: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.5273 - accuracy: 0.4410 - val_loss: 1.5631 - val_accuracy: 0.4818
  Epoch 19/10000
  80/88 [==========================&gt;...] - ETA: 0s - loss: 1.6061 - accuracy: 0.4176
  Epoch 19: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6053 - accuracy: 0.4186 - val_loss: 1.5737 - val_accuracy: 0.4320
  Epoch 20/10000
  81/88 [==========================&gt;...] - ETA: 0s - loss: 1.6062 - accuracy: 0.4109
  Epoch 20: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6055 - accuracy: 0.4115 - val_loss: 1.5734 - val_accuracy: 0.4320
  Epoch 21/10000
  82/88 [==========================&gt;...] - ETA: 0s - loss: 1.6069 - accuracy: 0.4097
  Epoch 21: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6047 - accuracy: 0.4115 - val_loss: 1.5757 - val_accuracy: 0.4320
  Epoch 22/10000
  80/88 [==========================&gt;...] - ETA: 0s - loss: 1.6040 - accuracy: 0.4125
  Epoch 22: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6044 - accuracy: 0.4115 - val_loss: 1.5735 - val_accuracy: 0.4320
  Epoch 23/10000
  80/88 [==========================&gt;...] - ETA: 0s - loss: 1.6041 - accuracy: 0.4121
  Epoch 23: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6050 - accuracy: 0.4115 - val_loss: 1.5747 - val_accuracy: 0.4320
  Epoch 24/10000
  80/88 [==========================&gt;...] - ETA: 0s - loss: 1.6065 - accuracy: 0.4105
  Epoch 24: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6046 - accuracy: 0.4115 - val_loss: 1.5779 - val_accuracy: 0.4320
  Epoch 25/10000
  80/88 [==========================&gt;...] - ETA: 0s - loss: 1.5998 - accuracy: 0.4168
  Epoch 25: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6054 - accuracy: 0.4115 - val_loss: 1.5769 - val_accuracy: 0.4320
  Epoch 26/10000
  79/88 [=========================&gt;....] - ETA: 0s - loss: 1.6081 - accuracy: 0.4086
  Epoch 26: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6057 - accuracy: 0.4115 - val_loss: 1.5739 - val_accuracy: 0.4320
  Epoch 27/10000
  79/88 [=========================&gt;....] - ETA: 0s - loss: 1.6015 - accuracy: 0.4146
  Epoch 27: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6047 - accuracy: 0.4115 - val_loss: 1.5723 - val_accuracy: 0.4320
  Epoch 28/10000
  80/88 [==========================&gt;...] - ETA: 0s - loss: 1.6021 - accuracy: 0.4125
  Epoch 28: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6053 - accuracy: 0.4115 - val_loss: 1.5726 - val_accuracy: 0.4320
  Epoch 29/10000
  79/88 [=========================&gt;....] - ETA: 0s - loss: 1.6051 - accuracy: 0.4110
  Epoch 29: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6046 - accuracy: 0.4115 - val_loss: 1.5741 - val_accuracy: 0.4320
  Epoch 30/10000
  82/88 [==========================&gt;...] - ETA: 0s - loss: 1.6050 - accuracy: 0.4116
  Epoch 30: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6055 - accuracy: 0.4115 - val_loss: 1.5764 - val_accuracy: 0.4320
  Epoch 31/10000
  81/88 [==========================&gt;...] - ETA: 0s - loss: 1.6045 - accuracy: 0.4113
  Epoch 31: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6048 - accuracy: 0.4115 - val_loss: 1.5747 - val_accuracy: 0.4320
  Epoch 32/10000
  79/88 [=========================&gt;....] - ETA: 0s - loss: 1.6057 - accuracy: 0.4082
  Epoch 32: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6055 - accuracy: 0.4115 - val_loss: 1.5730 - val_accuracy: 0.4320
  Epoch 33/10000
  80/88 [==========================&gt;...] - ETA: 0s - loss: 1.5995 - accuracy: 0.4148
  Epoch 33: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6047 - accuracy: 0.4115 - val_loss: 1.5745 - val_accuracy: 0.4320
  Epoch 34/10000
  80/88 [==========================&gt;...] - ETA: 0s - loss: 1.6082 - accuracy: 0.4094
  Epoch 34: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6047 - accuracy: 0.4115 - val_loss: 1.5736 - val_accuracy: 0.4320
  Epoch 35/10000
  80/88 [==========================&gt;...] - ETA: 0s - loss: 1.6038 - accuracy: 0.4105
  Epoch 35: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6045 - accuracy: 0.4115 - val_loss: 1.5740 - val_accuracy: 0.4320
  Epoch 36/10000
  80/88 [==========================&gt;...] - ETA: 0s - loss: 1.6071 - accuracy: 0.4090
  Epoch 36: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6052 - accuracy: 0.4115 - val_loss: 1.5733 - val_accuracy: 0.4320
  Epoch 37/10000
  82/88 [==========================&gt;...] - ETA: 0s - loss: 1.6022 - accuracy: 0.4131
  Epoch 37: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6045 - accuracy: 0.4115 - val_loss: 1.5737 - val_accuracy: 0.4320
  Epoch 38/10000
  80/88 [==========================&gt;...] - ETA: 0s - loss: 1.6033 - accuracy: 0.4121
  Epoch 38: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6052 - accuracy: 0.4115 - val_loss: 1.5755 - val_accuracy: 0.4320
  Epoch 39/10000
  80/88 [==========================&gt;...] - ETA: 0s - loss: 1.6027 - accuracy: 0.4152
  Epoch 39: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6051 - accuracy: 0.4115 - val_loss: 1.5745 - val_accuracy: 0.4320
  Epoch 40/10000
  80/88 [==========================&gt;...] - ETA: 0s - loss: 1.6124 - accuracy: 0.4039
  Epoch 40: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6048 - accuracy: 0.4115 - val_loss: 1.5741 - val_accuracy: 0.4320
  Epoch 41/10000
  80/88 [==========================&gt;...] - ETA: 0s - loss: 1.6011 - accuracy: 0.4145
  Epoch 41: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6052 - accuracy: 0.4115 - val_loss: 1.5734 - val_accuracy: 0.4320
  Epoch 42/10000
  81/88 [==========================&gt;...] - ETA: 0s - loss: 1.6060 - accuracy: 0.4117
  Epoch 42: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6054 - accuracy: 0.4115 - val_loss: 1.5746 - val_accuracy: 0.4320
  Epoch 43/10000
  80/88 [==========================&gt;...] - ETA: 0s - loss: 1.6046 - accuracy: 0.4105
  Epoch 43: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6048 - accuracy: 0.4115 - val_loss: 1.5743 - val_accuracy: 0.4320
  Epoch 44/10000
  81/88 [==========================&gt;...] - ETA: 0s - loss: 1.6020 - accuracy: 0.4136
  Epoch 44: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6044 - accuracy: 0.4115 - val_loss: 1.5732 - val_accuracy: 0.4320
  Epoch 45/10000
  81/88 [==========================&gt;...] - ETA: 0s - loss: 1.6118 - accuracy: 0.4062
  Epoch 45: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6054 - accuracy: 0.4115 - val_loss: 1.5757 - val_accuracy: 0.4320
  Epoch 46/10000
  80/88 [==========================&gt;...] - ETA: 0s - loss: 1.6063 - accuracy: 0.4090
  Epoch 46: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6050 - accuracy: 0.4115 - val_loss: 1.5745 - val_accuracy: 0.4320
  Epoch 47/10000
  80/88 [==========================&gt;...] - ETA: 0s - loss: 1.6035 - accuracy: 0.4121
  Epoch 47: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6052 - accuracy: 0.4115 - val_loss: 1.5760 - val_accuracy: 0.4320
  Epoch 48/10000
  79/88 [=========================&gt;....] - ETA: 0s - loss: 1.6094 - accuracy: 0.4098
  Epoch 48: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6050 - accuracy: 0.4115 - val_loss: 1.5738 - val_accuracy: 0.4320
  Epoch 49/10000
  79/88 [=========================&gt;....] - ETA: 0s - loss: 1.6120 - accuracy: 0.4051
  Epoch 49: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6049 - accuracy: 0.4115 - val_loss: 1.5737 - val_accuracy: 0.4320
  Epoch 50/10000
  79/88 [=========================&gt;....] - ETA: 0s - loss: 1.6088 - accuracy: 0.4094
  Epoch 50: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6046 - accuracy: 0.4115 - val_loss: 1.5761 - val_accuracy: 0.4320
  Epoch 51/10000
  79/88 [=========================&gt;....] - ETA: 0s - loss: 1.6042 - accuracy: 0.4126
  Epoch 51: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6050 - accuracy: 0.4115 - val_loss: 1.5762 - val_accuracy: 0.4320
  Epoch 52/10000
  79/88 [=========================&gt;....] - ETA: 0s - loss: 1.6049 - accuracy: 0.4114
  Epoch 52: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6051 - accuracy: 0.4115 - val_loss: 1.5737 - val_accuracy: 0.4320
  Epoch 53/10000
  79/88 [=========================&gt;....] - ETA: 0s - loss: 1.5987 - accuracy: 0.4157
  Epoch 53: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6045 - accuracy: 0.4115 - val_loss: 1.5753 - val_accuracy: 0.4320
  Epoch 54/10000
  80/88 [==========================&gt;...] - ETA: 0s - loss: 1.6033 - accuracy: 0.4133
  Epoch 54: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6054 - accuracy: 0.4115 - val_loss: 1.5737 - val_accuracy: 0.4320
  Epoch 55/10000
  84/88 [===========================&gt;..] - ETA: 0s - loss: 1.5977 - accuracy: 0.4185
  Epoch 55: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6043 - accuracy: 0.4115 - val_loss: 1.5780 - val_accuracy: 0.4320
  Epoch 56/10000
  80/88 [==========================&gt;...] - ETA: 0s - loss: 1.6007 - accuracy: 0.4160
  Epoch 56: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6059 - accuracy: 0.4115 - val_loss: 1.5768 - val_accuracy: 0.4320
  Epoch 57/10000
  79/88 [=========================&gt;....] - ETA: 0s - loss: 1.6001 - accuracy: 0.4153
  Epoch 57: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6051 - accuracy: 0.4115 - val_loss: 1.5758 - val_accuracy: 0.4320
  Epoch 58/10000
  80/88 [==========================&gt;...] - ETA: 0s - loss: 1.6060 - accuracy: 0.4113
  Epoch 58: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6050 - accuracy: 0.4115 - val_loss: 1.5732 - val_accuracy: 0.4320
  Epoch 59/10000
  80/88 [==========================&gt;...] - ETA: 0s - loss: 1.6009 - accuracy: 0.4148
  Epoch 59: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6047 - accuracy: 0.4115 - val_loss: 1.5732 - val_accuracy: 0.4320
  Epoch 60/10000
  79/88 [=========================&gt;....] - ETA: 0s - loss: 1.6017 - accuracy: 0.4153
  Epoch 60: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6048 - accuracy: 0.4115 - val_loss: 1.5737 - val_accuracy: 0.4320
  Epoch 61/10000
  83/88 [===========================&gt;..] - ETA: 0s - loss: 1.6038 - accuracy: 0.4134
  Epoch 61: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6045 - accuracy: 0.4115 - val_loss: 1.5766 - val_accuracy: 0.4320
  Epoch 62/10000
  81/88 [==========================&gt;...] - ETA: 0s - loss: 1.6066 - accuracy: 0.4093
  Epoch 62: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6047 - accuracy: 0.4115 - val_loss: 1.5738 - val_accuracy: 0.4320
  Epoch 62: early stopping
  </code></pre>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-10-13T20:32:19.313Z
Comments.Text: It's not possible to answer this question without also considering loss curves
Comments.UserDisplayName: Karl
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-10-15T08:08:25.270Z
Comments.Text: @Karl Sorry, I added the train log. I knew this model can't learning well. But why this model's accuracy and val_accuracy looks like fixed value? Thank you.
Comments.UserDisplayName: Yang
Tags: <classification><multilayer-perceptrons><accuracy>
Title: Is it possible training accuracy never changed while training?
UserDisplayName: Yang
ThreadText: # Is it possible training accuracy never changed while training?
  
  (Asked by: username_0 on 2023-10-12)
  
  **Question summary**
  
  1. What informations can get from this epoch\_accuracy graph?
  2. Is it possible training accuracy never changed like after 10 epoch in graph while training?
  
  **Body**
  
  I do some experiments with neural networks, just for hobby.
  but, I get some strange epoch-accuracy graphs.
  
  [![enter image description here](https://i.sstatic.net/PSeVA.png)](https://i.sstatic.net/PSeVA.png)
  
  This graph result came from my models, and I have some questions about this graph.
  In epochs 10, and training accuracy never changed. I see text result when training with `models.fit()` in `tensorflow.keras`. Before I learned, Training(not validation) accuracy should be changed while training. Because Model updating parameters, and parameters continuous changed while training. so, This is my first time seeing a graph like this.
  
  I use flatten layer for input layer, and only use MLP for hidden + output layer. so, I really wondering Is it possible training accuracy never changed while training?
  
  **Edited(update loss, accuracy log)**
  Sorry,I try to update loss graph, but I deleted my model.(because It's just hobby, and I found some stranged status.)
  
  So, I make new model to represent that situations.
  [![enter image description here](https://i.sstatic.net/ronTX.png)](https://i.sstatic.net/ronTX.png)
  
  After 20 epochs, then accuracy never changed. I show the train log. Please note that loss and val\_loss are changed very little, accuracy and val\_accuracy never changed after 20 epochs.
  
  ```
  Epoch 18/10000
  80/88 [==========================>...] - ETA: 0s - loss: 1.5232 - accuracy: 0.4410
  Epoch 18: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.5273 - accuracy: 0.4410 - val_loss: 1.5631 - val_accuracy: 0.4818
  Epoch 19/10000
  80/88 [==========================>...] - ETA: 0s - loss: 1.6061 - accuracy: 0.4176
  Epoch 19: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6053 - accuracy: 0.4186 - val_loss: 1.5737 - val_accuracy: 0.4320
  Epoch 20/10000
  81/88 [==========================>...] - ETA: 0s - loss: 1.6062 - accuracy: 0.4109
  Epoch 20: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6055 - accuracy: 0.4115 - val_loss: 1.5734 - val_accuracy: 0.4320
  Epoch 21/10000
  82/88 [==========================>...] - ETA: 0s - loss: 1.6069 - accuracy: 0.4097
  Epoch 21: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6047 - accuracy: 0.4115 - val_loss: 1.5757 - val_accuracy: 0.4320
  Epoch 22/10000
  80/88 [==========================>...] - ETA: 0s - loss: 1.6040 - accuracy: 0.4125
  Epoch 22: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6044 - accuracy: 0.4115 - val_loss: 1.5735 - val_accuracy: 0.4320
  Epoch 23/10000
  80/88 [==========================>...] - ETA: 0s - loss: 1.6041 - accuracy: 0.4121
  Epoch 23: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6050 - accuracy: 0.4115 - val_loss: 1.5747 - val_accuracy: 0.4320
  Epoch 24/10000
  80/88 [==========================>...] - ETA: 0s - loss: 1.6065 - accuracy: 0.4105
  Epoch 24: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6046 - accuracy: 0.4115 - val_loss: 1.5779 - val_accuracy: 0.4320
  Epoch 25/10000
  80/88 [==========================>...] - ETA: 0s - loss: 1.5998 - accuracy: 0.4168
  Epoch 25: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6054 - accuracy: 0.4115 - val_loss: 1.5769 - val_accuracy: 0.4320
  Epoch 26/10000
  79/88 [=========================>....] - ETA: 0s - loss: 1.6081 - accuracy: 0.4086
  Epoch 26: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6057 - accuracy: 0.4115 - val_loss: 1.5739 - val_accuracy: 0.4320
  Epoch 27/10000
  79/88 [=========================>....] - ETA: 0s - loss: 1.6015 - accuracy: 0.4146
  Epoch 27: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6047 - accuracy: 0.4115 - val_loss: 1.5723 - val_accuracy: 0.4320
  Epoch 28/10000
  80/88 [==========================>...] - ETA: 0s - loss: 1.6021 - accuracy: 0.4125
  Epoch 28: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6053 - accuracy: 0.4115 - val_loss: 1.5726 - val_accuracy: 0.4320
  Epoch 29/10000
  79/88 [=========================>....] - ETA: 0s - loss: 1.6051 - accuracy: 0.4110
  Epoch 29: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6046 - accuracy: 0.4115 - val_loss: 1.5741 - val_accuracy: 0.4320
  Epoch 30/10000
  82/88 [==========================>...] - ETA: 0s - loss: 1.6050 - accuracy: 0.4116
  Epoch 30: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6055 - accuracy: 0.4115 - val_loss: 1.5764 - val_accuracy: 0.4320
  Epoch 31/10000
  81/88 [==========================>...] - ETA: 0s - loss: 1.6045 - accuracy: 0.4113
  Epoch 31: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6048 - accuracy: 0.4115 - val_loss: 1.5747 - val_accuracy: 0.4320
  Epoch 32/10000
  79/88 [=========================>....] - ETA: 0s - loss: 1.6057 - accuracy: 0.4082
  Epoch 32: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6055 - accuracy: 0.4115 - val_loss: 1.5730 - val_accuracy: 0.4320
  Epoch 33/10000
  80/88 [==========================>...] - ETA: 0s - loss: 1.5995 - accuracy: 0.4148
  Epoch 33: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6047 - accuracy: 0.4115 - val_loss: 1.5745 - val_accuracy: 0.4320
  Epoch 34/10000
  80/88 [==========================>...] - ETA: 0s - loss: 1.6082 - accuracy: 0.4094
  Epoch 34: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6047 - accuracy: 0.4115 - val_loss: 1.5736 - val_accuracy: 0.4320
  Epoch 35/10000
  80/88 [==========================>...] - ETA: 0s - loss: 1.6038 - accuracy: 0.4105
  Epoch 35: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6045 - accuracy: 0.4115 - val_loss: 1.5740 - val_accuracy: 0.4320
  Epoch 36/10000
  80/88 [==========================>...] - ETA: 0s - loss: 1.6071 - accuracy: 0.4090
  Epoch 36: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6052 - accuracy: 0.4115 - val_loss: 1.5733 - val_accuracy: 0.4320
  Epoch 37/10000
  82/88 [==========================>...] - ETA: 0s - loss: 1.6022 - accuracy: 0.4131
  Epoch 37: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6045 - accuracy: 0.4115 - val_loss: 1.5737 - val_accuracy: 0.4320
  Epoch 38/10000
  80/88 [==========================>...] - ETA: 0s - loss: 1.6033 - accuracy: 0.4121
  Epoch 38: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6052 - accuracy: 0.4115 - val_loss: 1.5755 - val_accuracy: 0.4320
  Epoch 39/10000
  80/88 [==========================>...] - ETA: 0s - loss: 1.6027 - accuracy: 0.4152
  Epoch 39: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6051 - accuracy: 0.4115 - val_loss: 1.5745 - val_accuracy: 0.4320
  Epoch 40/10000
  80/88 [==========================>...] - ETA: 0s - loss: 1.6124 - accuracy: 0.4039
  Epoch 40: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6048 - accuracy: 0.4115 - val_loss: 1.5741 - val_accuracy: 0.4320
  Epoch 41/10000
  80/88 [==========================>...] - ETA: 0s - loss: 1.6011 - accuracy: 0.4145
  Epoch 41: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6052 - accuracy: 0.4115 - val_loss: 1.5734 - val_accuracy: 0.4320
  Epoch 42/10000
  81/88 [==========================>...] - ETA: 0s - loss: 1.6060 - accuracy: 0.4117
  Epoch 42: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6054 - accuracy: 0.4115 - val_loss: 1.5746 - val_accuracy: 0.4320
  Epoch 43/10000
  80/88 [==========================>...] - ETA: 0s - loss: 1.6046 - accuracy: 0.4105
  Epoch 43: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6048 - accuracy: 0.4115 - val_loss: 1.5743 - val_accuracy: 0.4320
  Epoch 44/10000
  81/88 [==========================>...] - ETA: 0s - loss: 1.6020 - accuracy: 0.4136
  Epoch 44: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6044 - accuracy: 0.4115 - val_loss: 1.5732 - val_accuracy: 0.4320
  Epoch 45/10000
  81/88 [==========================>...] - ETA: 0s - loss: 1.6118 - accuracy: 0.4062
  Epoch 45: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6054 - accuracy: 0.4115 - val_loss: 1.5757 - val_accuracy: 0.4320
  Epoch 46/10000
  80/88 [==========================>...] - ETA: 0s - loss: 1.6063 - accuracy: 0.4090
  Epoch 46: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6050 - accuracy: 0.4115 - val_loss: 1.5745 - val_accuracy: 0.4320
  Epoch 47/10000
  80/88 [==========================>...] - ETA: 0s - loss: 1.6035 - accuracy: 0.4121
  Epoch 47: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6052 - accuracy: 0.4115 - val_loss: 1.5760 - val_accuracy: 0.4320
  Epoch 48/10000
  79/88 [=========================>....] - ETA: 0s - loss: 1.6094 - accuracy: 0.4098
  Epoch 48: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6050 - accuracy: 0.4115 - val_loss: 1.5738 - val_accuracy: 0.4320
  Epoch 49/10000
  79/88 [=========================>....] - ETA: 0s - loss: 1.6120 - accuracy: 0.4051
  Epoch 49: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6049 - accuracy: 0.4115 - val_loss: 1.5737 - val_accuracy: 0.4320
  Epoch 50/10000
  79/88 [=========================>....] - ETA: 0s - loss: 1.6088 - accuracy: 0.4094
  Epoch 50: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6046 - accuracy: 0.4115 - val_loss: 1.5761 - val_accuracy: 0.4320
  Epoch 51/10000
  79/88 [=========================>....] - ETA: 0s - loss: 1.6042 - accuracy: 0.4126
  Epoch 51: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6050 - accuracy: 0.4115 - val_loss: 1.5762 - val_accuracy: 0.4320
  Epoch 52/10000
  79/88 [=========================>....] - ETA: 0s - loss: 1.6049 - accuracy: 0.4114
  Epoch 52: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6051 - accuracy: 0.4115 - val_loss: 1.5737 - val_accuracy: 0.4320
  Epoch 53/10000
  79/88 [=========================>....] - ETA: 0s - loss: 1.5987 - accuracy: 0.4157
  Epoch 53: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6045 - accuracy: 0.4115 - val_loss: 1.5753 - val_accuracy: 0.4320
  Epoch 54/10000
  80/88 [==========================>...] - ETA: 0s - loss: 1.6033 - accuracy: 0.4133
  Epoch 54: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6054 - accuracy: 0.4115 - val_loss: 1.5737 - val_accuracy: 0.4320
  Epoch 55/10000
  84/88 [===========================>..] - ETA: 0s - loss: 1.5977 - accuracy: 0.4185
  Epoch 55: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6043 - accuracy: 0.4115 - val_loss: 1.5780 - val_accuracy: 0.4320
  Epoch 56/10000
  80/88 [==========================>...] - ETA: 0s - loss: 1.6007 - accuracy: 0.4160
  Epoch 56: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6059 - accuracy: 0.4115 - val_loss: 1.5768 - val_accuracy: 0.4320
  Epoch 57/10000
  79/88 [=========================>....] - ETA: 0s - loss: 1.6001 - accuracy: 0.4153
  Epoch 57: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6051 - accuracy: 0.4115 - val_loss: 1.5758 - val_accuracy: 0.4320
  Epoch 58/10000
  80/88 [==========================>...] - ETA: 0s - loss: 1.6060 - accuracy: 0.4113
  Epoch 58: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6050 - accuracy: 0.4115 - val_loss: 1.5732 - val_accuracy: 0.4320
  Epoch 59/10000
  80/88 [==========================>...] - ETA: 0s - loss: 1.6009 - accuracy: 0.4148
  Epoch 59: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6047 - accuracy: 0.4115 - val_loss: 1.5732 - val_accuracy: 0.4320
  Epoch 60/10000
  79/88 [=========================>....] - ETA: 0s - loss: 1.6017 - accuracy: 0.4153
  Epoch 60: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6048 - accuracy: 0.4115 - val_loss: 1.5737 - val_accuracy: 0.4320
  Epoch 61/10000
  83/88 [===========================>..] - ETA: 0s - loss: 1.6038 - accuracy: 0.4134
  Epoch 61: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6045 - accuracy: 0.4115 - val_loss: 1.5766 - val_accuracy: 0.4320
  Epoch 62/10000
  81/88 [==========================>...] - ETA: 0s - loss: 1.6066 - accuracy: 0.4093
  Epoch 62: val_accuracy did not improve from 0.54146
  88/88 [==============================] - 0s 5ms/step - loss: 1.6047 - accuracy: 0.4115 - val_loss: 1.5738 - val_accuracy: 0.4320
  Epoch 62: early stopping
  
  ```
  
  **Tags:** classification, multilayer-perceptrons, accuracy
  
  > **Comments:**
  > **username_1**: It's not possible to answer this question without also considering loss curves  
  > **username_0**: @username_1 Sorry, I added the train log. I knew this model can't learning well. But why this model's accuracy and val_accuracy looks like fixed value? Thank you.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2023-10-15. Score: 2 | Accepted answer
  
  What happened with your model is that it suffered from a [Neural Network collapse](https://ar5iv.labs.arxiv.org/html/2206.04041#:%7E:text=Abstract,the%20respective%20class%20feature%20means). This means that your network didn't learn to generalize with the data or that the local minimum found in the gradient descent was to output an output that almost doesn't change. This often happens when the neural network didn't learn the features of the data and the best pattern it found was to give an almost constant mean value that maximizes the accuracy. Let's say that your distribution on the training data is 60% for class 1 and 40% for class 2, then it will try to predict all data as class 1, in order to maximize the accuracy. Things that you may try for solving this issue could be, increasing the layers of your model, adding more inductive biases that align with your data, or changing hyperparameters. You may want to see the output probabilities of your model to understand my point. Another thing about your data is that your loss doesn't seem to decrease, but changes randomly. I plotted your log prints and it looks like this, which for a Neural Network, is not a good loss curve.
  [![enter image description here](https://i.sstatic.net/mnBdm.png)](https://i.sstatic.net/mnBdm.png)
  
  
  ---
  
  