Running into an issue with training my image segmentation model

Hey all, first time poster, and have not been active due to my projects, I am very new to deep learning and ML, I have trained some semantic segmentation models in the past, but not a whole lot. I am just asking for some help with an error that I keep getting, it is a no gradient provided for any variable error, I am doing some semantic segmentation of brain data, its taking brain data and categorizing (onehot) into 4 specific categories, this data was filtered for the specific categories needed. Below is my main code, I believe it is due to the one hot encoding method from keras but if anyone has ever dealt with something similar, some tips would be really helpful.

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Thanks all, and have a great day, here is the code:

imgs_list_train = glob.glob(“D:/BMEN/Data/Test/BrainSet2/*.npy”)
masks_list_train = glob.glob(“D:/BMEN/Data/Test/BrainSet2/*.npy”)

batch_size = 32
gen_train = DataEncoder(imgs_list_train, masks_list_train, batch_size = batch_size)

model_name = “C:/Users/Keilan Pieper/Desktop/BMEN/ImageSegmentation/Models/BrainStructure”
early_stop = tf.keras.callbacks.EarlyStopping(monitor= ‘val_loss’, patience = 10)

model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate = 1e-4))

history = model.fit(gen_train, epochs=1, verbose = 1,
callbacks= [early_stop, monitor, lr_schedule])

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Data encoder class

class DataEncoder(keras.utils.Sequence):
‘Generates data for Keras’
def __init__(self, imgs_list, masks_list, patch_size=(128, 128), batch_size=32, shuffle=True):

self.imgs_list = imgs_list
self.masks_list = masks_list
self.patch_size = patch_size
self.batch_size = batch_size
self.nsamples = len(imgs_list)
self.shuffle = True
self.on_epoch_end()

def __len__(self):
‘Denotes the number of batches per epoch’
return len(self.imgs_list) // self.batch_size

def __getitem__(self, index):
‘Generate one batch of data’
# Generate indexes of the batch
batch_indexes = self.indexes[index * self.batch_size:(index + 1) * self.batch_size]

# Generate data
X, Y = self.__data_generation(batch_indexes)

return X, Y

def on_epoch_end(self):
‘Updates indexes after each epoch’
self.indexes = np.arange(self.nsamples)
if self.shuffle == True:
np.random.shuffle(self.indexes)

def __data_generation(self, batch_indexes):
‘Generates data containing batch_size samples’
# Initialization
X = np.empty((self.batch_size, self.patch_size[0], self.patch_size[1], 1))
Y = np.empty((self.batch_size, self.patch_size[0], self.patch_size[1], 1))

for (jj, ii) in enumerate(batch_indexes):
aux_img = np.load(self.imgs_list[ii])
aux_mask = np.load(self.masks_list[ii])

# Implement data augmentation function
aux_img_patch, aux_mask_patch = self.__extract_patch(aux_img, aux_mask)

X[jj, :, :, 0] = aux_img_patch
tf.one_hot(Y, 4)
Y[jj, :, :, 0] = aux_mask_patch

return X, Y

def __extract_patch(self, img, mask):
crop_idx = [None] * 2
crop_idx[0] = np.random.randint(0, img.shape[0] – self.patch_size[0])
crop_idx[1] = np.random.randint(0, img.shape[1] – self.patch_size[1])
img_cropped = img[crop_idx[0]:crop_idx[0] + self.patch_size[0],
crop_idx[1]:crop_idx[1] + self.patch_size[1]]
mask_cropped = mask[crop_idx[0]:crop_idx[0] + self.patch_size[0],
crop_idx[1]:crop_idx[1] + self.patch_size[1]]

return img_cropped, mask_cropped