Kernel needs to restart

[ErnestTBass]

import glob

#wget http://vis-www.cs.umass.edu/lfw/lfw.tgz

#tar -xvzf lfw.tgz

face_images = glob.glob('lfw/**/*.jpg')


# In[2]:


import cv2
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split

from tensorflow.keras import Model, Input, regularizers
from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, UpSampling2D
from tensorflow.keras.callbacks import EarlyStopping
from keras.preprocessing import image

import PIL
from tqdm import tqdm
import warnings;
warnings.filterwarnings('ignore')


# In[3]:


all_images = []

for i in tqdm(face_images):
  img = image.load_img(i, target_size=(80,80,3))
  img = image.img_to_array(img)
  img = img/255.
  all_images.append(img)


# In[ ]:


all_images = np.array(all_images)

# split data into train and validation data
train_x, val_x = train_test_split(all_images, random_state=32, test_size=0.1)


# In[ ]:


# function to reduce image resolution while keeping the image size constant

def pixalate_image(image, scale_percent = 40):
  width = int(image.shape[1] * scale_percent / 100)
  height = int(image.shape[0] * scale_percent / 100)
  dim = (width, height)

  small_image = cv2.resize(image, dim, interpolation = cv2.INTER_AREA)
  
  # scale back to original size
  width = int(small_image.shape[1] * 100 / scale_percent)
  height = int(small_image.shape[0] * 100 / scale_percent)
  dim = (width, height)

  low_res_image = cv2.resize(small_image, dim, interpolation = cv2.INTER_AREA)

  return low_res_image


# In[ ]:


# get low resolution images for the training set
train_x_px = []

for i in range(train_x.shape[0]):
  temp = pixalate_image(train_x[i,:,:,:])
  train_x_px.append(temp)

train_x_px = np.array(train_x_px)


# get low resolution images for the validation set
val_x_px = []

for i in range(val_x.shape[0]):
  temp = pixalate_image(val_x[i,:,:,:])
  val_x_px.append(temp)

val_x_px = np.array(val_x_px)


# In[ ]:


# get low resolution images for the training set
train_x_px = []

for i in range(train_x.shape[0]):
  temp = pixalate_image(train_x[i,:,:,:])
  train_x_px.append(temp)

train_x_px = np.array(train_x_px)


# get low resolution images for the validation set
val_x_px = []

for i in range(val_x.shape[0]):
  temp = pixalate_image(val_x[i,:,:,:])
  val_x_px.append(temp)

val_x_px = np.array(val_x_px)


# In[ ]:


Input_img = Input(shape=(80, 80, 3))  
    
#encoding architecture
x1 = Conv2D(256, (3, 3), activation='relu', padding='same')(Input_img)
x2 = Conv2D(128, (3, 3), activation='relu', padding='same')(x1)
x2 = MaxPool2D( (2, 2))(x2)
encoded = Conv2D(64, (3, 3), activation='relu', padding='same')(x2)

# decoding architecture
x3 = Conv2D(64, (3, 3), activation='relu', padding='same')(encoded)
x3 = UpSampling2D((2, 2))(x3)
x2 = Conv2D(128, (3, 3), activation='relu', padding='same')(x3)
x1 = Conv2D(256, (3, 3), activation='relu', padding='same')(x2)
decoded = Conv2D(3, (3, 3), padding='same')(x1)

autoencoder = Model(Input_img, decoded)
autoencoder.compile(optimizer='adam', loss='mse')

The code shown above when run in a jupyter notebook stops with a kernel needs to restart
message a screenshot that I have attached. I am not sure why, it seems to run and and stall citing
certain tensorflow libraries libraries are missing when i run in the command line of jupyter notebook.
I installed tensorflow even though my laptop only has a Radeon processor. I know that current tensorflow defaults to the cpu version if the software does not detect an NVIDA GPU.

Again, why does this software not run to the end. It is not my coding it is from a
exercise shown here in this link:

https://www.analyticsvidhya.com/blog/202...esolution/

Any help appreciated. Thanks in advance.

Respectfully,

ErnestTBass