I need a numpy vector with the shape (N,1) and not (N,) for multiple inputs, __data_generator XY problem i need my X

I need to have X[0].shape be (N, 120, 160, 3) which works but i also need X[1].shape = (N, 1, 1) but i only get (N,)

I have tried reshaping it but i didn't manage to make it work.

I am working on a data_generator. I am trying to load inputs from a camera on a car into XY to be able to train a model on it (goal make an self driving car). I am struggling with it.

Here is the part that makes my head hurt.

image_data = {
    "image": np.zeros((120, 160, 3), np.float32),
    "speed": 3.4,
    "throttle": 0.4,
    "steering": 0.14,
}
inputs=["image", "speed"]
outputs=["steering", "throttle"]
batch_size = 64

X = []
Y = []
for j in range(len(inputs)):
    L = []
    for i in range(batch_size):
        data = np.array(list(image_data.items()))
        L.append(data[j][1])
    X.append(np.array(L))

Here is the fix I found, i wasn't shearching for the correct solution.

def __data_generation(self): 
 X = [[] for _ in range(len(self.inputs))]
        Y = [[] for _ in range(len(self.outputs))]

        rdm_paths = np.random.choice(self.paths, size=self.batch_size)
        for path in rdm_paths:
            try:
                image_data = io.load_image_data(path)
                self.augm(image_data)
                for i, inp in enumerate(self.inputs):
                    data = np.array(image_data[inp])
                    if len(data.shape) < 2:
                        data = np.expand_dims(data, axis=0)
                    X[i].append(data)

                for i, out in enumerate(self.outputs):
                    data = np.array(image_data[out])
                    if len(data.shape) < 2:
                        data = np.expand_dims(data, axis=0)
                    Y[i].append(data)
            except Exception:
                logging.debug(f"Error processing {path}")

        X = [np.array(x) for x in X]
        Y = [np.array(y) for y in Y]

        return X, Y ```