title: can the accuracy measure val_acc be trusted?
body: <p>Building a sequence</p>
  
  <pre><code>simple_seq= [x for x in list(range(1000)) if x % 3 == 0]
  </code></pre>
  
  <p>after reshape and split </p>
  
  <pre><code>x_train, x_test shape = (159, 5, 1)
  y_train, y_test shape = (159, 2)
  </code></pre>
  
  <p>Model</p>
  
  <pre><code>model = Sequential(name='acc_test')
  model.add(Conv1D(
    kernel_size = 2,
    filters= 128,
    strides= 1,
    use_bias= True,
    activation= 'relu',
    padding='same', 
    input_shape=(x_train.shape[1], x_train.shape[2])))
  
  model.add(AveragePooling1D(pool_size =(2), strides= [1]))
  model.add(Flatten())
  model.add(Dense(2))
  
  optimizer = Adam(lr=0.001)
  model.compile( optimizer= optimizer, loss= 'mse',  metrics=['accuracy'])
  </code></pre>
  
  <p>Train</p>
  
  <pre><code>hist = model.fit(
            x=x_train, 
            y = y_train, 
            epochs=100, 
            validation_split=0.2)
  </code></pre>
  
  <p>The Result:</p>
  
  <pre><code>Epoch 100/100
  127/127 [==============================] - 0s 133us/sample - loss: 0.0096 - acc: 1.0000 - val_loss: 0.6305 - val_acc: 1.0000
  </code></pre>
  
  <p>But if using this model to predict: </p>
  
  <pre><code>x_test[-1:] = array([[[9981],
          [9984],
          [9987],
          [9990],
          [9993]]])
  
  model.predict(x_test[-1:])
  result is: array([[10141.571, 10277.236]], dtype=float32)
  </code></pre>
  
  <p>How can the vall_acc be 1 if the result is so far from the truth, result was </p>
  
  <pre><code>
  step    1          2
  true [9996,      9999     ]
  pred [10141.571, 10277.236] 
  </code></pre>