Trouble following example using custom model, using saliency() about tf-keras-vis

[ianbgroves]

Also, I'm deviating from the example by using

import PIL.Image
from matplotlib import pylab as P
def LoadImage(file_path):
  im = PIL.Image.open(file_path).convert('L')

  im = np.asarray(im)
  return im

def ShowImage(im, title='', ax=None):
  if ax is None:
    P.figure()
  P.axis('off')
  P.imshow(im)
  P.title(title)


# Load the image
import cv2
im_orig = LoadImage('/content/drive/MyDrive/9. ML project/August_ML_tests/Data/labeled_data/10_2/10.2_022.jpg')
im = cv2.resize(im_orig,(200,200))
im = np.reshape(im,(-1, 200,200, 1))

# Show the image
ShowImage(im_orig)

predictions = model.predict(im)
prediction_class = np.argmax(predictions[0])


print("Prediction class: " + str(prediction_class)) 

To load and preprocess my image.

[ianbgroves]

Hi all, in case anyone else has this issue. The follow addition solved my issue:

X = np.reshape(X,(-1, 200,200, 1))

from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.applications.vgg16 import preprocess_input

# Image titles
image_titles = ['10.2', '10.2', '10.2']

# Load images and Convert them to a Numpy array
img1 = load_img('/content/drive/MyDrive/9. ML project/August_ML_tests/Data/labeled_data/10_2/10.2_009.jpg', grayscale=True, target_size = (200,200))
img2 = load_img('/content/drive/MyDrive/9. ML project/August_ML_tests/Data/labeled_data/10_2/10.2_009.jpg', grayscale=True, target_size = (200,200))
img3 = load_img('/content/drive/MyDrive/9. ML project/August_ML_tests/Data/labeled_data/10_2/10.2_009.jpg', grayscale=True, target_size= (200,200))
images = np.asarray([np.array(img1), np.array(img2), np.array(img3)])

# Preparing input data for VGG16
X = preprocess_input(images)
X = np.reshape(X,(-1, 200,200, 1))

# Rendering
f, ax = plt.subplots(nrows=1, ncols=3, figsize=(12, 4))
for i, title in enumerate(image_titles):
    ax[i].set_title(title, fontsize=16)
    ax[i].imshow(images[i])
    ax[i].axis('off')
plt.tight_layout()
plt.show()