text: Title: Strange results for gradient tape : Getting positive gradients for negative response
  Body: ### TensorFlow version
  
  2.11.0
  
  ### Custom code
  
  Yes
  
  ### OS platform and distribution
  
  Windows
  
  ### Python version
  
  3.7.16
  
  Hello,
  
  I'm working with some gradient based interpretability method ([based on the GradCam code from Keras ](https://keras.io/examples/vision/grad_cam/) ) , and I'm running into a result that seems inconsistent with what would expect from backpropagation.  
  
  I am working with a pertrained VGG16 on imagenet, and I am interested in find the most relevent filters for a given class.
  
  I start by forward propagating an image through the network, and then from the relevant bin, I find the gradients to the layer in question (just like they do in the Keras tutorial).
  
  Then, from the pooled gradients (`pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))`), I find the top-K highest/most pertinent filters. 
  
  From this experiment, I run into 2 strange results. 
  
  1.  For almost any image I pass through (even completely different classes), the network almost always seems to be placing the most importance to the same 1 Filter.
  
  2.  And this result I understand even less; many times, the gradients point "strongly" to a filter, even though the filter's output is 0/negative (before relu).  From the backpropagation equation, a negative response should result in a Null gradient, right ?
  
  $$ \frac{dY_{class}}{ dActivation_{in} } = \frac{dY_{class}}{dZ} \cdot \frac{dZ}{dActivation_{in}}$$
   $$ =  Relu'(Activation_{in}\cdot W+b) \cdot W$$
  
  If $Activation_{in}\cdot W+b$ is negative, then $\frac{dY_{class}}{Activation_{in}}$ should be 0, right ? 
  
  
  I provided 3 images.  
  
  All 3 images point consistently to Filter155 (For observation 1).
  
  And for Img3.JPEG, I find the Top5 most relevant filters: Filter336 has a strong gradient, and yet a completely null output. 
  
  Is there a problem with my code, the gradient computations or just my understanding?
  
  Thanks for your help.
  
  Liam
  ![img1](https://github.com/user-attachments/assets/e78c1a73-6599-408f-9ba3-e2589cf7e155)
  ![img2](https://github.com/user-attachments/assets/1fab6726-8ee3-49f8-b668-c19f2a0a0242)
  ![img3](https://github.com/user-attachments/assets/118ca170-7528-4d95-aa1b-d719d6bc724a)
  
  
  
  
  
  
  
  ### Standalone code to reproduce the issue
  
  ```shell
  import matplotlib.pyplot as plt
  import tensorflow as tf
  from tensorflow.keras.applications.vgg16 import decode_predictions
  from tensorflow.keras.applications import VGG16
  import keras
  from keras import backend as K
  
  def get_img_array(img_path, size):
      # `img` is a PIL image of size 299x299
      img = keras.utils.load_img(img_path, target_size=size)
      # `array` is a float32 Numpy array of shape (299, 299, 3)
      array = keras.utils.img_to_array(img)
      # We add a dimension to transform our array into a "batch"
      # of size (1, 299, 299, 3)
      array = np.expand_dims(array, axis=0)
      return array
  
  img = "img3.JPEG"
  img = keras.applications.vgg16.preprocess_input(get_img_array(img, size=(224,224)))
  
  model = VGG16(weights='imagenet',
  				  include_top=True,
  				  input_shape=(224, 224, 3))
  
  # Remove last layer's softmax
  model.layers[-1].activation = None
  
  
  #I am interested in finding the most informative filters from this Layer
  layer = model.get_layer("block5_conv3")
  
  grad_model = keras.models.Model(
      model.inputs,  [layer.output, model.output]
  )
  
  
  pred_idx = None
  with tf.GradientTape(persistent=True) as tape:
      last_conv_layer_output, preds = grad_model(img, training=False)
  
      if pred_idx is None:
          pred_idx = tf.argmax(preds[0])
      print(tf.argmax(preds[0]))
      print(decode_predictions(preds.numpy()))
      class_channel = preds[:, pred_idx]
  
  
  grads = tape.gradient(class_channel,  last_conv_layer_output) #
  pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
  topFilters = tf.math.top_k(pooled_grads, k=5).indices.numpy()
  
  print("Top Filters : ", topFilters)
  print("Filter responses: " , tf.math.reduce_euclidean_norm(last_conv_layer_output, axis=(0,1,2)).numpy()[topFilters])
  
  plt.imshow(last_conv_layer_output[0,:,:,336])
  plt.show()
  ```
  
  
  ### Relevant log output
  
  ```shell
  For Img3 :
  
  Top Filters      : [155 429 336 272  51]
  Filter responses : [ 80.908226 208.93723    0.       232.99017  746.0348  ]
  ```
  