Tensorflow Keras Conv2D error with 2D numpy array input

Question

I would like to train a CNN using a 2D numpy array as input, but I am receiving this error: ValueError: Error when checking input: expected conv2d_input to have 4 dimensions, but got array with shape (21, 21).

My input is indeed a 21x21 numpy array of floats. The first layer of the network is defined as Conv2D(32, (3, 3), input_shape=(21, 21, 1)) to match the shape of the input array.

I have found some similar questions but none pertaining to a 2D input array, they mostly deal with images. According to the documentation, Conv2D is expecting an input of a 4D tensor containing (samples, channels, rows, cols), but I cannot find any documentation explaining the meaning of these values. Similar questions pertaining to image inputs suggest reshaping the input array using np.ndarray.reshape(), but when trying to do that I receive an input error.

How can I train a CNN on such an input array? Should input_shape be a different size tuple?

Balraj Ashwath · Accepted Answer · 2020-03-12 06:54:21Z

4

Your current numpy array has dimensions (21, 21). However, TensorFlow expects input tensors to have dimensions in the format (batch_size, height, width, channels) or BHWC implying that you need to convert your numpy input array to 4 dimensions (from the current 2 dimensions). One way to do so is as follows:

input = np.expand_dims(input, axis=0)
input = np.expand_dims(input, axis=-1)

Now, the numpy input array has dimensions: (1, 21, 21, 1) which can be passed to a TF Conv2D operation.

Hope this helps! :)

answered Mar 12, 2020 at 6:54

Balraj Ashwath

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1 Comment

Jules_96 Over a year ago

Yes thanks! I take a look to so many threads but this one works fine for me. My special case is that I create a synthetical image with the values 0 or 255. This starts from the np.zeros(width, height).

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