C3W4 low structural similarity index

[Deepti_Prasad]

Hello @MGriff

Issues with your notebook

1. in creating preprocessing function, where you def augment images(image, label),
   while you normalize the pixel values, you used true division // but you are suppose to /
   Next in the same grader cell you need to define function to take label as well using tf.one_hot, also remember instead of using num_classes use the value of num_classes

2. Create a function to generate the saliency map
   def do_salience(image, model, label, prefix):
   img_file=image (THIS CODE IS NOT REQUIRED)

3. in def do_salience
   Define the expected output array by one-hot encoding the label
   The length of the array is equal to the number of classes

@MGriff

label_one_hot = tf.one_hot(label, num_classes)

This code is incorrect, you need to use image call function where you add an additional dimension for the batch, i.e. img_new_dim.shape[0] to the label. Also using here num_classes is correct. Also this would be called expected_output rather than label_one_hot. Please replace the same in the loss recall function for expected_output.

4. generate the saliency maps for the 5 test images
   using label as label[0] is incorrect, you are only suppose to use 0 or 1 based on instruction Cats will have the label 0 while dogs will have the label 1 .

5. Configure the model for training,
   incorrect loss loss=‘sparse_categorical_crossentropy’,

“categorical” will be 2D one-hot encoded labels, “binary” will be 1D binary labels, “sparse” will be 1D integer labels, so guess which loss would be correct one? Extra hint in instructions: normalize the pixel values to [0, 1]

incorrect optimizer=tf.keras.optimizers.experimental.RMSprop, kindly use tf.keras.optimizers.RMSprop

Let me know once your issue is resolved.

Regards
DP