title: Keras softmax activation, category_crossentropy loss. But output is not 0, 1
body: <p>I trained CNN model for just one epoch with very little data. I use Keras 2.05.</p>
  
  <p>Here is the CNN model's (partial) last 2 layers, <code>number_outputs = 201</code>. Training data output is one hot encoded 201 output.</p>
  
  <pre><code>model.add(Dense(200, activation='relu', name='full_2'))
  model.add(Dense(40, activation='relu',  name='full_3'))
  model.add(Dense(number_outputs, activation='softmax'))
  
  model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])
  </code></pre>
  
  <p>The model is saved to a h5 file. Then, saved mode is loaded with same model as above. <code>batch_image</code> is an image file.</p>
  
  <pre><code>prediction = loaded_model.predict(batch_image, batch_size=1)
  </code></pre>
  
  <p>I get prediction like this:</p>
  
  <pre><code>ndarray: [[ 0.00498065  0.00497852  0.00498095  0.00496987  0.00497506  0.00496112
     0.00497585  0.00496474  0.00496769  0.0049708   0.00497027  0.00496049
     0.00496767  0.00498348  0.00497927  0.00497842  0.00497095  0.00496493
     0.00498282  0.00497441  0.00497477  0.00498019  0.00497417  0.00497654
     0.00498381  0.00497481  0.00497533  0.00497961  0.00498793  0.00496556
     0.0049665   0.00498809  0.00498689  0.00497886  0.00498933  0.00498056
  </code></pre>
  
  <p>Questions:</p>
  
  <ol>
  <li><p>Prediction array should be <code>1, 0</code>? Why do I get output like output activate as sigmoid, and loss is <code>binary_crossentropy</code>. What is wrong? I want to emphasize again, the model is not really trained well with data. It's almost just initialized with random weights.</p></li>
  <li><p>If I don't train the network well (not converge yet), such as just initializing weights with random number, should the prediction still be <code>1, 0</code>?</p></li>
  <li><p>If I want to get the probability of prediction, and then, I decide how to interpret it, how to get the probability prediction output after the CNN is trained?</p></li>
  </ol>