AttributeError: module 'keras.api._v2.keras.utils' has no attribute 'Sequential'-CodePudding

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**

CodePudding user response：

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')