AttributeError: module 'keras.api._v2.keras.utils' has no attribute 'Sequential' i have just started Neural network so help would be appriciated

Question

import cv2
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from keras import Sequential
from tensorflow import keras
import os

mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = tf.keras.utils.normalize(x_train, axis=1)
x_test = tf.keras.utils.normalize(x_test, axis=1)


model = tf.keras.utils.Sequential()
model.add(tf.keras.layers.Flatten(input_shape=(28, 28)))
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dense(10, activation='softmax'))

model.compile(optimizer='adam', loss='spare_categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=3)
model.save('handwritten.model')

Traceback (most recent call last):
  File "C:\Users\DELL\PycharmProjects\NeuralNetworks\main.py", line 15, in <module>
    model = tf.keras.utils.Sequential()
AttributeError: module 'keras.api._v2.keras.utils' has no attribute 'Sequential'
Process finished with exit code 1**

Answer 1

You should be using tf.keras.Sequential() or tf.keras.models.Sequential(). Also, you need to define a valid loss function. Here is a working example:

import cv2
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from keras import Sequential
from tensorflow import keras
import os

mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = tf.keras.utils.normalize(x_train, axis=1)
x_test = tf.keras.utils.normalize(x_test, axis=1)


model = tf.keras.Sequential()
model.add(tf.keras.layers.Flatten(input_shape=(28, 28)))
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dense(10, activation='softmax'))

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=3)
model.save('handwritten.model')