Invalid value encountered in true_divide x /= x.std ()

Course Q&A TensorFlow Developer Professional Convolutional Neural Networks in TensorFlow
1

i encoutred this exception : RuntimeWarning: invalid value encountered in true_divide
x /= x.std ()

when i use the argument :

np.seterr(divide=‘ignore’, invalid=‘ignore’)

the plt dosent show anything, my code:

import tensorflow as tf
from keras_preprocessing.image import load_img, img_to_array
import numpy as np
import matplotlib.pyplot as plt
import os, random
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.preprocessing.image import ImageDataGenerator



model = tf.keras.models.load_model(mypath)

outputs = [layer.output for layer in model.layers[1:]]
vis_model = tf.keras.models.Model(inputs=model.input, outputs=outputs)

data_dir =r"G:\dogs_cats\training"
cats_dir = os.path.join(data_dir, "cat")
dogs_dir = os.path.join(data_dir, "dog")

train_cat_fnames = os.listdir( cats_dir )
train_dog_fnames = os.listdir( dogs_dir )

cat_img_files = [os.path.join(cats_dir, f) for f in train_cat_fnames]
dog_img_files = [os.path.join(dogs_dir, f) for f in train_dog_fnames]
img_path = random.choice(cat_img_files + dog_img_files)



img = load_img(img_path, target_size=(150, 150))  # this is a PIL image
x   = img_to_array(img)                           # Numpy array with shape (150, 150, 3)
x   = x.reshape((1,) + x.shape)                   # Numpy array with shape (1, 150, 150, 3)

# Scale by 1/255
x /= 255.0

os.system('cls')


map = vis_model.predict(x)


layer_names = [layer.name for layer in model.layers]

for layer_name, feature_map in zip(layer_names, map):
   if len(feature_map.shape) == 4:
   
     
       n_features = feature_map.shape[-1]  # number of features in the feature map
       size       = feature_map.shape[ 1]  # feature map shape (1, size, size, n_features)
       
       # Tile the images in this matrix
       display_grid = np.zeros((size, size * n_features))
       
       #-------------------------------------------------
       # Postprocess the feature to be visually palatable
       #-------------------------------------------------
       for i in range(n_features):
           x  = feature_map[0, :, :, i]
           x -= x.mean()
           x /= x.std ()
           x *=  64
           x += 128
           x  = np.clip(x, 0, 255).astype('uint8')
           display_grid[:, i * size : (i + 1) * size] = x # Tile each filter into a horizontal grid

       #-----------------
       # Display the grid
       #-----------------
       scale = 20. / n_features
       plt.figure( figsize=(scale * n_features, scale) )
       plt.title ( layer_name )
       plt.grid  ( False )
       plt.imshow( display_grid, aspect='auto', cmap='viridis' )

also doing this:

 if x.any() != 0:
      x /= x.std ()

will end the process with no errors but will show no greed

2

I think somewhere 0 is being passed in that equation…i need to check with my fellow mentors…give me some time
Thanks

3

Can you share the notebook you are using as .pdf and .ipynb file
Thanks