Python Array Reshaping Issue to Array with Shape (None, 192)

Question

I have this error and I'm not sure how do I reshape where there's a dimension with None.

Exception: Error when checking : expected input_1 to have shape (None, 192) but got array with shape (192, 1)

How do I reshape an array to (None, 192)?

I've the array accuracy with shape (12, 16) and I did accuracy.reshape(-1) that gives (192,). However this is not (None, 192).

[Suspiciously similar](https://github.com/fchollet/keras/issues/3109) — Andras Deak -- Слава Україні, Oct 19 '16 at 23:11
So you need to tell us which module(s) you are running. Apparently this is not occurring in `numpy` itself. I'd expect the error to be, `expected (1,192) got ...`; `None` (`np.newaxis`) is used to create a a size `1` dimension, but you won't see it in a `shape` tuple or error message. But a module in early stages of development might still use it. — hpaulj, Oct 20 '16 at 01:34

score 0 · Answer 1 · edited May 23 '17 at 11:45

In keras/keras/engine/training.py

def standardize_input_data(data, names, shapes=None,
                           check_batch_dim=True,
                           exception_prefix=''):
     ...

    # check shapes compatibility
    if shapes:
        for i in range(len(names)):
        ...
        for j, (dim, ref_dim) in enumerate(zip(array.shape, shapes[i])):
            if not j and not check_batch_dim:
                # skip the first axis
                continue
            if ref_dim:
                if ref_dim != dim:
                    raise Exception('Error when checking ' + exception_prefix +
                                    ': expected ' + names[i] +
                                    ' to have shape ' + str(shapes[i]) +
                                    ' but got array with shape ' +
                                    str(array.shape))

Comparing that with the error

Error when checking : expected input_1 to have shape (None, 192) but got array with shape (192, 1)

So it is comparing (None, 192) with (192, 1), and skipping the 1st axis; that is comparing 192 and 1. If array has shape (n, 192) it probably would pass.

So basically, what ever is generating the (192,1) shape, as opposed to (1,192) or a broadcastable (192,) is causing the error.

I'm adding keras to the tags on the guess that this is the problem module.

Searching other keras tagged SO questions:

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Dimensions not matching in keras LSTM model

Getting shape dimension errors with a simple regression using Keras

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I don't know enough about keras to understand the answers, but there's more to it than simply reshaping your input array.