Why is "step" argument necessary when predicting using data tensors? what does this error mean?

Question

I am trying to predict() the output for a single data point d, using my trained Keras model loaded from a file. But I get a ValueError If predicting from data tensors, you should specify the 'step' argument. What does that mean?

I tried setting step=1, but then I get a different error ValueError: Cannot feed value of shape () for Tensor u'input_1:0', which has shape '(?, 600)'.

Here is my code:

d = np.concatenate((hidden[p[i]], hidden[x[i]])).resize((1,600))
hidden[p[i]] = autoencoder.predict(d,steps=)

The model is expecting (?,600) as input. I have concatenated two numpy arrays of shape (300,) each to get (600,), which is resized to (1,600). This (1,600) is my input to predict().

Answer 1

In my case, the input to predict was None (because I had a bug in another part of the code).

Answer 2

-> Define value of steps argument,

d = np.concatenate((hidden[p[i]], 
hidden[x[i]])).resize((1,600))
hidden[p[i]] = autoencoder.predict(d,steps=1)