TensorFlow: how to create training and testing image datasets

Question

I've been looking forever on the internet trying to create, train and test my own tensorflow model. But I have unsuccessfully done that. From investigating my code, I think it's how I create my dataset of images. Most online tutorials would just import a prepared dataset, but my dataset is specifically for use-case diagrams and holds each element within those diagrams. My aim is to train a tensorflow model to predict each element in a diagram and hopefully the errors too. Here's the code:

def createDataSet(labelList, label, filePath, width, height):
    dataList = []
    for img in os.listdir(filePath):
        filename = str(img)
        if filename[len(filename) - 3:len(filename)] != "npy":
            pic = cv.imread(os.path.join(filePath, img))
            pic = cv.cvtColor(pic, cv.COLOR_BGR2RGB)
            pic = cv.resize(pic, (width, height))
            dataList.append(pic)
            labelList.append(label)
return dataList, labelList
# appending the pics to the training data list
training_dataset, train_labels = createDataSet(train_labels, train_label, path, width, height)
test_dataset, test_labels = createDataSet(test_labels, test_label, path2, width, height)
#converting the list to numpy array and saving it to a file using #numpy.save
np.save(os.path.join(path,train_label),np.array(training_dataset))
np.save(os.path.join(path2,test_label),np.array(test_dataset))
#loading the saved file once again
train_images = np.array(training_dataset)
test_images = np.array(test_dataset)

As of now, the function creates a list which will be saved as a numpy array and that numpy array will be used for my model. But it causes errors like UNIMPLEMENTED: Cast string to float is not supported. I'm sure that I'm creating the train, test data and the labels for both incorrectly