title: Multi-class classification find probability of all classes
body: <p>I took an example from the keras.</p>
  
  <p><a href="https://github.com/keras-team/keras/blob/master/examples/pretrained_word_embeddings.py" rel="nofollow noreferrer">https://github.com/keras-team/keras/blob/master/examples/pretrained_word_embeddings.py</a></p>
  
  <pre><code>sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')
  embedded_sequences = embedding_layer(sequence_input)
  x = Conv1D(128, 5, activation='relu')(embedded_sequences)
  x = MaxPooling1D(5)(x)
  x = Conv1D(128, 5, activation='relu')(x)
  x = MaxPooling1D(5)(x)
  x = Conv1D(128, 5, activation='relu')(x)
  x = GlobalMaxPooling1D()(x)
  x = Dense(128, activation='relu')(x)
  preds = Dense(len(labels_index), activation='softmax')(x)
  
  model = Model(sequence_input, preds)
  model.compile(loss='categorical_crossentropy',
                optimizer='rmsprop',
                metrics=['acc'])
  </code></pre>
  
  <p>The model predicted classes probabilities which are lesser than 0. I know softmax will sum it to 1. The only one output i can see in probability using <code>np.argmax(pre)</code>
  I want the other classes probabilities at least readable. </p>
  
  <pre><code>Prediction output:
      [2.8300792e-06 4.5637703e-03 7.2316222e-02 6.7710824e-02 5.2243233e-01
       3.7763064e-04 1.2326813e-02 2.9277834e-01 4.1662962e-03 1.0876421e-05
       2.3830748e-06 1.3590348e-04 2.3074823e-02 3.3520879e-05 4.0551484e-05
       1.9896568e-06 1.0994432e-05 4.7518920e-06 2.3408763e-06 6.7659844e-06]
  </code></pre>
  
  <p>All of them are yielding the lesser than 0 as probability. When I use np.argmax I got <code>4</code> . <strong>How do I get probability results above 0? Instead softmax which activation should i use to get more positive probabilities?</strong></p>