The kernel appears to have died. It will restart automatically

After making the dataset ready and using ‘tf.keras.models.Sequential’ to create a model, the kernel dies and message appears ‘The kernel appears to have died. It will restart automatically.’
Can someone help me with this.

Hi @Dhanashri_Parate, Welcome to TensorFlow Forum!

Could you provide details about your OS and version of Tensorflow you are using? If possible please provide the standalone code to reproduce the issue. Thank You.

OS details : 1) Edition: Windows 11 Pro 2) OS build: 22621.1848
Version of Tensorflow: 2.10.0

As soon as visualization of image is done the kernel dies

Code :

DNN

from keras.models import Sequential
from keras.layers import Activation, Dense
from keras import optimizers
from keras.layers import Flatten
import tensorflow as tf
from keras.layers import Convolution2D, Dropout, Dense, Flatten, BatchNormalization, MaxPooling2D, RandomFlip, RandomRotation, RandomZoom, Rescaling, Conv2D, AveragePooling2D, GlobalAveragePooling2D
from tensorflow.keras.optimizers import Adam

num_classes = 2

model = tf.keras.models.Sequential([

RandomFlip(“horizontal_and_vertical”, input_shape=(224, 224, 3)),
RandomRotation(0.3),
RandomZoom(0.2),
tf.keras.layers.RandomContrast(0.3),
tf.keras.layers.RandomBrightness(0.3),

Conv2D(16, 3, activation=‘relu’,input_shape=(224, 224, 3)),
AveragePooling2D(),
BatchNormalization(),
Conv2D(32, 3, activation=‘relu’),
MaxPooling2D(),
BatchNormalization(),
Dropout(0.1),
Conv2D(64, 3, activation=‘relu’),
BatchNormalization(),
Flatten(),
Dense(128, activation=‘relu’),
BatchNormalization(),
Dropout(0.1),
Dense(64, activation=‘relu’),
BatchNormalization(),
Dense(64, activation=‘relu’),
Dense(num_classes, name=“outputs”, activation = ‘sigmoid’)
])

Libraries to display graphs

import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle

Libraries for file operations

import os
import cv2
import numpy as np

img = cv2.imread(‘C:/DSC_0068.jpg’)
resized_image = cv2.resize(img, (224, 224))
resized_image = resized_image.astype(‘float32’)
resized_image = resized_image / 255.
X_train = np.array(resized_image)

Visualize resized training data

image = X_train
plt.imshow(image)

Hi @Dhanashri_Parate, I have Visualized the image in windows 11 with Tensorflow 2.10 in jupyter notebook and did not face any error. Thank You.

Hello @Kiran_Sai_Ramineni , can you please suggest if I could check anything else from my end to solve this issue.

Hi @Dhanashri_Parate, Could you please try to create a new environment and install cv2 using the below commands

  • conda create --name tf python=3.9
  • pip install --upgrade pip
  • pip install tensorflow==2.10
  • pip install opencv-python

and let us know if you are facing the error or not. Thank You