Regression Problem: How to solve the problem of highly decimal input features : <p>I have the following input data structure:</p>

<pre><code>   X1     |    X2     |    X3     | ... | Output (Label)
118.12341 | 118.12300 | 118.12001 | ... | [a value between 0 &amp; 1] e.g. 0.423645
</code></pre>

<p>Where I'm using <code>tensorflow</code> in order to solve the regression problem here of predicting the future value of the <code>Output</code> variable. For that i built a feed forward neural network with three hidden layers having <code>relu</code> activation functions and a final output layer with one node of <code>linear activation</code>. This network is trained with back-propagation using <code>adam</code> optimizer.</p>

<p>My problem is that after training the network for some thousands of epochs, I realized that this <strong>highly decimal</strong> values in both input features and the output, resulted in predictions near to the second decimal place only, for example:</p>

<pre><code>Real value = 0.456751 | Predicted value = 0.452364
</code></pre>

<p>However this is not accepted, where i need a precision to the forth decimal place (at least) to accept the value.</p>

<p><strong>Q:</strong> Is there any trustworthy technique to solve this problem properly for getting better results (maybe a transformation algorithm)?</p>

<p>Thanks in advance.</p>
