Multi input problem Keras. Expected to see 2 array(s), but instead got the following list of 1 arrays

Question

I have a model that takes two inputs of the same shape (batch_size,512,512,1), and predict two masks each of shape (batch_size,512,512,1).

dataset_input = tf.data.Dataset.zip((dataset_img_A, dataset_img_B))
dataset_output = tf.data.Dataset.zip((seg_A, seg_B))
dataset = tf.data.Dataset.zip((dataset_input, dataset_output))
dataset = dataset.repeat()
dataset = dataset.batch(batch_size, drop_remainder=True)

I'm creating a model like so:

image_inputs_A = layers.Input((512,512,1), batch_size=self.batch_size)
image_inputs_B = layers.Input((512,512,1), batch_size=self.batch_size)
output_A = some_layers(image_inputs_A)
output_B = some_layers(image_inputs_B)

model = models.Model([image_inputs_A, image_inputs_B],[output_A, output_B])

However I'm getting the following error

ValueError: Error when checking model input: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 2 array(s), but instead got the following list of 1 arrays: [<tf.Tensor 'IteratorGetNext:0' shape=(?, 2, ?, ?, ?) dtype=float32>]...

It seems that its concatenating the inputs to (batch_size,2,512,512,1), instead of listing them as a tuple of two tensors (batch_size,512,512,1 ). Is this the expected behaviour? How can I use multiple inputs without them concatenating?

EDIT: I have tried to use an layers.Input with shape (batch_size, 2, 512, 512, 1) and then pass through two lambda layers to split the tensor along the second axis, however.. I get the following error

ValueError: Error when checking input: expected input_1 to have 5 dimensions, but got array with shape (None, None, None, None)

EDIT 2: I've double checked the data im inputing into the model. INPUT: (512, 512, 1) <dtype: 'float32'> INPUT: (512, 512, 1) <dtype: 'float32'> OUTPUT: (512, 512, 1) <dtype: 'int64'> OUTPUT: (512, 512, 1) <dtype: 'int64'>

Answer 1

SOLVED: Turns out it was an issue with the data augmentation step where tensors where concatenated inputs. Lesson learnt