I have following inputs to be train on CNN.
x = np.array(Images)
y = [ [], [[76., 5., 9., 1., 0., 0.], [54., 4., 10., 51.]] ]
Since the ‘y’ input is a n-dimensions array of non-uniform sizes, I used RaggedTensor to represent ‘y’ input and fed it to the network.
y = tf.ragged.constant(y)
cnn_model.fit(x, y, epochs = 10, batch_size=32, validation_split=0.30)
I am receiving following error:
ValueError: validation_split is only supported for Tensors or NumPy arrays, found following types in the input: [<class ‘tensorflow.python.ops.ragged.ragged_tensor.RaggedTensor’>]
If I convert ‘y’ to numpy.ndarray and fit it to the model, I get following error,
cnn_model.fit(x, y.numpy(), epochs = 10, batch_size=32, validation_split=0.30)
ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type numpy.ndarray).
I would want to train this input ‘y’ of n-dimensional array to the model, kindly suggest which datatype representation would be suitable regarding this.