Keras predict getting incorrect shape?

Question

I'm new to Keras and am trying to test out a model I've just trained.

I'm using Tensorflow backend and Python 3.

However, the shape my input has and the shape Keras says it has in an error are completely different. Here's my code:

testnote = np.zeros((3,))
testnote[0] = 70
testnote[1] = 70
print(testnote.shape)
pred = model.predict(testnote)
print(pred)

My consistent output is "(3,)" for the shape of testnote and then an error for my predict line: "ValueError: Error when checking input: expected dense_1_input to have shape (3,) but got array with shape (1,)"

How is it that Keras reads testnote as having shape (1,) when I've just confirmed that the shape is (3,)? Is it using some sort of different standard for what "shape" means? I've tried reshaping and adding brackets and a bunch of other things, but I don't really know what the problem is.

For additional context, the model takes in an array with 3 scalar input (representing pitch, velocity, and instrument class) and outputs an array with 1025 scalar outputs. I am carefully not using the word "dimension" since I think this is where I'm getting confused, and technically both are only 1 dimension. I'm sure there are many problems with my model which I will have to fix following this. However, I'd like to just get this prediction function working so I can understand what my output looks like.

Thanks in advance for any help.

Could you include a minimal complete example that demonstrates the problem? — NPE

sdcbr sdcbr · Accepted Answer · 2018-08-08T16:09:02

A Keras Model implicitly expects that your data (passed as a np array) has a dimension for the batch size. Currently, your model is interpreting testnote as being 3 examples of shape 1. Try adding the batch dimension to 'testnote' as follows:

testnote = testnote.reshape(1,-1)

This will reshape testnote to shape (1, 3), so that you explicitly define the batch size to be 1.

Keras predict getting incorrect shape?

1 Answers