title: Multi variable/feature Tensorflow
body: <p>i want to categorize the height of person using multilayer keras, if the height more than 170 should return 1 whereas less than 170 return 0</p>
  
  <pre><code>data_input = [[0,165], [0,166], [0,167], [0,172], [0,173]]
  data_output = [0,0,0,1,1]
  model = keras.Sequential([
    keras.layers.Flatten(input_shape=(1,)),
    keras.layers.Dense(2, activation=tf.nn.relu),
    keras.layers.Dense(2, activation=tf.nn.softmax)
  ])
  model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
  model.fit(data_input, data_output, epochs=5)
  predictions = model.predict([0,184])
  print(predictions)
  </code></pre>
  
  <p>but it gave me error "ValueError: Input arrays should have the same number of samples as target arrays. Found 2 input samples and 5 target samples."</p>