Trying to visualize ML with a grad-CAM heatmap but K.gradients returning zero values

for visualizing the results of the ML process i decided to use a CAM heatmap. I used the code provided here on my model yet the return of pooled_grads_value, conv_layer_output_value = iterate([x]) is all zeros for pooled_grads_value but the conv_layer_output_value has values in it.

this is how i used it:

import matplotlib.image as mpimg
from keras import backend as K
import matplotlib.pyplot as plt
import pandas as pd
import keras
import numpy as np
#%%
model_path = "pathto/model.h5"
img_path = "pathto/pic.tif"
output_path = "pathto/output.jpeg"
size = (1000,200)
#%%

model = keras.models.load_model(model_path)

img=mpimg.imread(img_path)
plt.imshow(img)
#%%

from keras.preprocessing import image
img = image.load_img(img_path, target_size=size)
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)

#%%
preds = model.predict(x)

argmax = np.argmax(preds[0])

output = model.output[:, argmax]
#%%
last_conv_layer = model.get_layer( "conv_6_l")
grads = K.gradients(output, last_conv_layer.output)[0]
pooled_grads = K.mean(grads, axis=(0, 1, 2))
iterate = K.function([model.input], [pooled_grads, last_conv_layer.output[0]])
#%%
pooled_grads_value,  conv_layer_output_value = iterate([x])
#%%
for i in range(64):
    conv_layer_output_value[:, :, i] *= pooled_grads_value[i]
#%%
heatmap = np.mean(conv_layer_output_value, axis=-1)
heatmap = np.maximum(heatmap, 0)
#heatmap /= np.max(heatmap)
#%%
import cv2
img = cv2.imread(img_path)
heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))
heatmap = np.uint8(255 * heatmap)
heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)
hif = .8
superimposed_img = heatmap * hif + img
cv2.imwrite("pathto/justheatmap.jpeg", heatmap * hif)
#%%
cv2.imwrite(output_path, superimposed_img)

my model looks like this:

model = Sequential()
model.add(Conv2D(8, (3, 3), input_shape=(1000, 200, 3), name = "conv_1"))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(16, (3, 3), name="conv_2"))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(32, (3, 3), name="conv_3"))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3),name="conv_4"))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(128, (3, 3),name="conv_5"))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3),name="conv_6_l"))
model.add(Activation('relu'))

model.add(GlobalAveragePooling2D())

model.add(Dropout(0.33))
model.add(Dense(5))
model.add(Activation('softmax'))

model.compile(loss='categorical_crossentropy',
          optimizer='Adam',
          metrics=['accuracy'])

Does anyone know what the source of my problem is?

I also tried a different aproach shown here using tensorflow.keras.GradientTape() function , yet this gives me an error module 'tensorflow' has no attribute 'GradientTape so I sticked to the first aproach.

I'm using keras V 2.3.1 and tensorflow V 2.1.0 in python.

I also uploaded a zip-file containing the .h5 file of my model and one picture for testing

Thanks in advance for everyone trying to help!

Hans