title: Why is "step" argument necessary when predicting using data tensors? what does this error mean?
body: <p>I am trying to <code>predict()</code> the output for a single data point <code>d</code>, using my trained Keras model loaded from a file. But I get a <code>ValueError If predicting from data tensors, you should specify the 'step' argument.</code> What does that mean? </p>
  
  <p>I tried setting <code>step=1</code>, but then I get a different error <code>ValueError: Cannot feed value of shape () for Tensor u'input_1:0', which has shape '(?, 600)'</code>. </p>
  
  <p>Here is my code:</p>
  
  <pre><code>d = np.concatenate((hidden[p[i]], hidden[x[i]])).resize((1,600))
  hidden[p[i]] = autoencoder.predict(d,steps=)
  </code></pre>
  
  <p>The model is expecting <code>(?,600)</code> as input. I have concatenated two <code>numpy</code> arrays of <code>shape (300,)</code> each to get <code>(600,)</code>, which is resized to <code>(1,600)</code>. This <code>(1,600)</code> is my input to <code>predict()</code>.</p>