title: Tensorflow LSTM RNN output activation function
body: <p>I have an input image with grey scale values ranging from let's say <code>25000 - 35000</code>. I'm doing a binary pixel-wise classification, so the output ground truth is a matrix of either <code>0's</code> or <code>1's</code>. </p>
  
  <p>Does anyone know what the default output activation function is? My question is, is it a ReLu? I want it to be a SoftMax function. In which case, each prediction value would be between <code>0</code> and <code>1</code> (obviously close to my ground truth data).</p>
  
  <p>I'm using example code from <a href="https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/recurrent_network.py" rel="noreferrer">here</a> that I have adjusted to make work for my data. </p>
  
  <p>I have a working network that is training, but the minibatch loss is at about 425 right now and the accuracy at 0.0, and for the LSTM MNIST example code (linked) the minibatch loss was about 0.1 and the accruacy about 1.0. My hope is that if I can change the activation function to use the SoftMax function, I can improve results.</p>