Code.py

import tensorflow as tf
from tensorflow import keras

from keras.api._v2.keras import callbacks
class myCallBack(tf.keras.callbacks.Callback):
  def on_epochs_end(self,epoch,logs={}):
    if(logs.get('Accuracy') >= 0.99): # here I've taken 99% accuracy for example you can take as your requirement
      print('\nReached 99% accuracy so cancelling the training!')
      self.model.stop_training = True

callbacks = myCallBack() # created instance of class myCallBack

mnist = tf.keras.datasets.mnist

(x_train,y_train),(x_test,y_test) = mnist.load_data()

x_train = x_train / 255.0
x_test = x_test / 255.0

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape = (28,28)),
    tf.keras.layers.Dense(512, activation = tf.nn.relu),
    tf.keras.layers.Dense(10,activation = tf.nn.softmax)
])

model.compile(optimizer='adam',
              loss = 'sparse_categorical_crossentropy',
              metrics = ['accuracy'])
model.fit(x_train,y_train,epochs = 10, callbacks=[callbacks])