title: TensorFlow .js Simple Model to Predict 2 Table multiple is not Showing Correct Output?
body: <p>i have made a tensorflow.js model to predict the output in multiple of two for example 16 should predict 32 like that and given the input data and label accordingly still output which printed is <strong>[[1],]</strong> after <strong>prediction.print()</strong></p>
  
  <p>Code : - </p>
  
  <pre><code>const data = tf.tensor([2,4,6,8,10,12,14,16,18,20,22,24,26,28,30])
  const label = tf.tensor([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15])
  
  const model = tf.sequential()
  model.add(tf.layers.dense({inputShape : [1], units : 32, activation : 'relu'}))
  model.add(tf.layers.dense({ units : 1, activation : 'softmax'}))   
  
  model.compile({
      optimizer:'sgd',
      loss:'meanSquaredError',
      metrics:['accuracy']
  })
  function onBatchEnd (batch,logs){
     // console.log(logs.acc)
  }
  model.fit(data,label,{
  epochs: 50,
  batchSize : 4,
  callbacks:{onBatchEnd}
  }).then(info =&gt;{
      console.log(info.history.acc);
      const prediction = model.predict(tf.tensor([16]))
      prediction.print() 
  })
  </code></pre>
  
  <p><strong>Output By Prediction.print() is [[1],]</strong></p>
  
  <p>and please explaint the meaning of inputshape here how to define which inputshape to give for example if i am having a tensor = [[1,2],[3,4],[5,6]]
  so what will be the inputshape for this and please give the link to study activation function such as relu and softmax and <strong>does output layer unit should be one (1)only if i give anything other than one it gives error ?(Explanation Not clear)</strong> </p>