Use of "sparse categorical cross entropy"

I am having problem understanding why sparse categorical cross entropy does not work for SVHN dataset.

import tensorflow as tf
from scipy.io import loadmat
import numpy as np

train = loadmat('data/train_32x32.mat')
test = loadmat('data/test_32x32.mat')

x_train = train['X']
y_train = train['y']
x_train = x_train.astype('float64')
y_train = y_train.astype('int64')

x_test = test['X']
x_test = x_test.astype('float64')
y_test = test['y']
y_test = y_test.astype('int64')

# reorder data
x_train = np.moveaxis(x_train, -1, 0)
x_test = np.moveaxis(x_test, -1, 0)
def colored_to_gray(x):
    '''
    input shape: n_sample, n_x, x_y, n_channel
    output shape: n_sample, n_x, x_y, 1
    this is a rudementary way of converting a colored image into gray image
    '''
    x = np.mean(x, axis=-1, keepdims=True)
    return x

def normalize_data(x):
    '''
    normalize data so that values are between 0 to 1
    '''
    x = x / 255.0
    return x

x_train = colored_to_gray(x_train)
x_test = colored_to_gray(x_test)

x_train = normalize_data(x_train)
x_test = normalize_data(x_test)
print("Shape of Training Data: {}".format(x_train.shape))
print("Shape of Training Labels: {}".format(y_train.shape))
print("Shape of Testing Data: {}".format(x_test.shape))
print("Shape of Testing Labels: {}".format(y_test.shape))

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Flatten, Dense

model = Sequential([
    Flatten(name='Flatten_Input', input_shape=x_train.shape[1:]),
    Dense(units=1024, activation='relu', name='Dense_1'),
    Dense(units=512, activation='relu', name='Dense_2'),
    Dense(units=256, activation='relu', name='Dense_3'),
    Dense(units=32, activation='relu', name='Dense_4'),
    Dense(units=10, activation='softmax', name='Output')
])

opt = tf.keras.optimizers.Adam(learning_rate=0.0001)

model.compile(optimizer=opt, loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
model.fit(x_train, y_train, epochs=2, batch_size=256)

with this model.fit calling, I was expecting it work on 10 classes and train. instead I got 'nan' as loss output and 0 for accuracy.

• Can someone explain what is happening here?
• is it related to the size of the input or to the type of the input?

Thanks,