title: Tensorflow lite model is giving wrong output
body: <p>I am developing a deep learning model with regression predicts. I created a tflite model but its predictions are different from original model and they are fully wrong.. Here is my process:</p>
  
  <p>I trained my model with keras</p>
  
  <pre><code>model = Sequential()
  model.add(Dense(100, input_dim=x.shape[1], activation='relu')) # Hidden 1
  model.add(Dense(50, activation='relu')) # Hidden 2
  model.add(Dense(1)) # Output
  model.compile(loss='mean_squared_error', optimizer='adam')
  model.fit(x,y,verbose=0,epochs=500)
  </code></pre>
  
  <p>And saved my model as h5 file</p>
  
  <pre><code>model.save("keras_model.h5")
  </code></pre>
  
  <p>Then converted h5 file to tflile format by TocoConverter</p>
  
  <pre><code>converter = tf.contrib.lite.TocoConverter.from_keras_model_file("keras_model.h5")
  tflite_model = converter.convert()
  open("converted_model.tflite", "wb").write(tflite_model)
  </code></pre>
  
  <p>When i test both files with same input original keras model gives reasonable output, but converted model gives unreasonable output.</p>
  
  <pre><code># Load TFLite model and allocate tensors.
  interpreter = tf.contrib.lite.Interpreter(model_path="converted_model.tflite")
  interpreter.allocate_tensors()
  
  # Get input and output tensors.
  input_details = interpreter.get_input_details()
  output_details = interpreter.get_output_details()
  
  # Test model on random input data.
  input_shape = input_details[0]['shape']
  input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)
  interpreter.set_tensor(input_details[0]['index'], input_data)
  
  interpreter.invoke()
  output_data = interpreter.get_tensor(output_details[0]['index'])
  print(input_data)
  print(output_data)
  
  //Original model testing
  from keras.models import load_model
  model2 = load_model("keras_model.h5")
  pred = model2.predict(x)
  print(pred)
  </code></pre>
  
  <p>Output is like this:</p>
  
  <pre><code>[[10. 10. 10. 10. 10. 10.]]//input_data
  [[-1.4308803]]// tflite output (meaningless)
  [[335.0276]] // keras file output
  </code></pre>
  
  <p>Why this problem occur?</p>