AssertionError: Argument a must have NDArray type, but got

Gluon
1

Hi All,

I trust you’re staying safe, I am acquainting myself with the Gluon API and still trying to comprehend what the errors I encounter really mean. I keep getting the listed error and googling it I stumbled upon this post, however, following the listed suggestions did not eradicate the error. Its apparent I’m over looking at something here. If you’re wondering about the data I am reading the it from an HDF5 file.

Thanks in advance!!, and I would appreciate your help greatly on this
Here is the details of the error:
AssertionError: Argument a must have NDArray type, but got [[ 30.55 66.25 1009.15 63.52]
[ 13.21 41.2 1016.63 74.1 ]
[ 26.99 72.99 1008. 76.1 ]
…
[ 18.59 41.1 1001.93 58.16]
[ 14.49 41.16 1000.5 82.17]
[ 26.56 65.59 1012.6 64.25]]

And the code responsible for the error:

def sgd(params, lr, batch_size):
    print("entered sgd: ")
    for p in params:
	    p[:] -=  lr * p.grad / batch_size

#@save
from d2l import mxnet as d2l
import mxnet as mx
from mxnet import np, npx

def linreg(X, w, b):
    return np.dot(X, w) + b

def squared_loss(y_hat, y):
    return (y_hat - y.reshape(y_hat.shape))**2/2

#@save
def train_ch11(trainer_fn, lr, batch_size, data_iter, num_epochs=2):
    # Initialization
    print("Entering train_ch11")
    #feature_dim = data.shape[1]
    w = np.random.normal(scale=.01, size=(4, 1))
    b = np.zeros(1)
    w.attach_grad()
    b.attach_grad()
    lr = 0.01
    net = linreg 
    loss = squared_loss

    print("setting up net and loss functions")
    n, timer = 0, d2l.Timer()

    for _ in range(num_epochs):
	    ctx =  mx.gpu() if mx.context.num_gpus() else mx.cpu()
	    timer.start()
	    for X, y in data_iter:
		    Xdata, ydata = X.as_in_context(ctx), y.as_in_context(ctx)
		    X, y = np.float64(Xdata), np.float64(ydata)
		    with mx.autograd.record():
			    inter = net(*[X], w, b) #X producing ArgumentError
			    l = loss(inter, y)
		    l.backward()
		    sdg([w, b], lr, batch_size)
    #train_l = loss(net(features, w, b), labels)
    timer.stop()
    print("finish training model")
    print(f'performance in Gigaflops: block {2 / timer.times[3]:.3f}')
2

At what line are you getting this error?
Also the meaning of this error is : you are passing a Sequence or 2D list instead of NDArray.
Please verify if you are converting the input to NDArray before invoking any mxnet operator.