title: Zero accuracy training a neural network in Keras
body: <p>I train a Neural Network of Regression Problem in Keras. 
  Why the output is only one Dimension, the accuracy in each Epoch always show acc: 0.0000e+00?</p>
  
  <h1>like this:</h1>
  
  <p>1000/199873 [..............................] - ETA: 5s - loss: 0.0057 - acc: 0.0000e+00</p>
  
  <p>2000/199873 [..............................] - ETA: 4s - loss: 0.0058 - acc: 0.0000e+00</p>
  
  <p>3000/199873 [..............................] - ETA: 3s - loss: 0.0057 - acc: 0.0000e+00</p>
  
  <p>4000/199873 [..............................] - ETA: 3s - loss: 0.0060 - acc:
  0.0000e+00
  ...</p>
  
  <p>198000/199873 [============================>.] - ETA: 0s - loss: 0.0055 - acc: 0.0000e+00</p>
  
  <p>199000/199873 [============================>.] - ETA: 0s - loss: 0.0055 - acc: 0.0000e+00</p>
  
  <p>199873/199873 [==============================] - 4s - loss: 0.0055 - acc: 0.0000e+00 - val_loss: 0.0180 - val_acc: 0.0000e+00</p>
  
  <h1>Epoch 50/50</h1>
  
  <p>But if the output are two Dimension or above, no problem for accuracy.</p>
  
  <p>My model as below:`</p>
  
  <pre><code>input_dim = 14
  batch_size = 1000
  nb_epoch = 50
  lrelu = LeakyReLU(alpha = 0.1)
  
  model = Sequential()
  model.add(Dense(126, input_dim=input_dim)) #Dense(output_dim(also hidden wight), input_dim = input_dim)
  model.add(lrelu) #Activation
  
  model.add(Dense(252))
  model.add(lrelu)
  model.add(Dense(1))
  model.add(Activation('linear'))
  
  model.compile(loss= 'mean_squared_error', optimizer='Adam', metrics=['accuracy'])
  model.summary()
  history = model.fit(X_train_1, y_train_1[:,0:1],
                      batch_size=batch_size,
                      nb_epoch=nb_epoch,
                      verbose=1,
                      validation_split=0.2)
  
  loss = history.history.get('loss')
  acc = history.history.get('acc')
  val_loss = history.history.get('val_loss')
  val_acc = history.history.get('val_acc')
  
  '''saving model'''
  from keras.models import load_model
  model.save('XXXXX')
  del model
  
  '''loading model'''
  model = load_model('XXXXX')
  
  '''prediction'''
  pred = model.predict(X_train_1, batch_size, verbose=1)
  ans = [np.argmax(r) for r in y_train_1[:,0:1]]
  </code></pre>