Grad-CAM in keras, ValueError: Graph disconnected: cannot obtain value for tensor Tensor "input_11_6:0", shape=(None, 150, 150, 3) - tensorflow

Question

How to perform Grad-CAM on pretrained custom model.
How to select last_conv_layer_name and classifier_layer_names?
What is its significances and how to select layers' names?
Should I consider Densenet121 sublayers or densenet as one functional layer?
How to perform Grad-CAM for this trained network?
These are the steps I tried,
#load model and custom metrics
dependencies = {'recall_m': recall_m, 'precision_m' : precision_m, 'f1_m' : f1_m }
model = keras.models.load_model("model_val_acc-73.33.h5", custom_objects = dependencies)
model.summary()
Model: "sequential_9"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
densenet121 (Functional) (None, 4, 4, 1024) 7037504
_________________________________________________________________
flatten (Flatten) (None, 16384) 0
_________________________________________________________________
dense_encoder (Dense) (None, 1024) 16778240
_________________________________________________________________
dropout_51 (Dropout) (None, 1024) 0
_________________________________________________________________
dense_2 (Dense) (None, 256) 262400
_________________________________________________________________
dropout_52 (Dropout) (None, 256) 0
_________________________________________________________________
dense_3 (Dense) (None, 128) 32896
_________________________________________________________________
dropout_53 (Dropout) (None, 128) 0
_________________________________________________________________
dense_4 (Dense) (None, 64) 8256
_________________________________________________________________
dropout_54 (Dropout) (None, 64) 0
_________________________________________________________________
dense_5 (Dense) (None, 32) 2080
_________________________________________________________________
dropout_55 (Dropout) (None, 32) 0
_________________________________________________________________
Final (Dense) (None, 2) 66
=================================================================
Total params: 24,121,442
Trainable params: 17,083,938
Non-trainable params: 7,037,504
This is the heat map function:-
###defining heat map
def make_gradcam_heatmap(img_array, model, last_conv_layer_name, classifier_layer_names):
# First, we create a model that maps the input image to the activations
# of the last conv layer
last_conv_layer = model.get_layer(last_conv_layer_name)
last_conv_layer_model = keras.Model(model.inputs, last_conv_layer.output)
# Second, we create a model that maps the activations of the last conv
# layer to the final class predictions
classifier_input = keras.Input(shape=last_conv_layer.output.shape[1:])
x = classifier_input
for layer_name in classifier_layer_names:
x = model.get_layer(layer_name)(x)
classifier_model = keras.Model(classifier_input, x)
# Then, we compute the gradient of the top predicted class for our input image
# with respect to the activations of the last conv layer
with tf.GradientTape() as tape:
# Compute activations of the last conv layer and make the tape watch it
last_conv_layer_output = last_conv_layer_model(img_array)
tape.watch(last_conv_layer_output)
# Compute class predictions
preds = classifier_model(last_conv_layer_output)
top_pred_index = tf.argmax(preds[0])
top_class_channel = preds[:, top_pred_index]
# This is the gradient of the top predicted class with regard to
# the output feature map of the last conv layer
grads = tape.gradient(top_class_channel, last_conv_layer_output)
# This is a vector where each entry is the mean intensity of the gradient
# over a specific feature map channel
pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
# We multiply each channel in the feature map array
# by "how important this channel is" with regard to the top predicted class
last_conv_layer_output = last_conv_layer_output.numpy()[0]
pooled_grads = pooled_grads.numpy()
for i in range(pooled_grads.shape[-1]):
last_conv_layer_output[:, :, i] *= pooled_grads[i]
# The channel-wise mean of the resulting feature map
# is our heatmap of class activation
heatmap = np.mean(last_conv_layer_output, axis=-1)
# For visualization purpose, we will also normalize the heatmap between 0 & 1
heatmap = np.maximum(heatmap, 0) / np.max(heatmap)
return heatmap
this is an image input
img_array = X_test[10] # 10th image sample
X_test[10].shape
#(150, 150, 3)
last_conv_layer_name = "densenet121"
classifier_layer_names = [ "dense_2", "dense_3", "dense_4", "dense_5", "Final" ]
# Generate class activation heatmap
heatmap = make_gradcam_heatmap(
img_array, model, last_conv_layer_name, classifier_layer_names
) ####===> (I'm getting error here, in this line)
So what is wrong with last_conv_layer_name and classifier_layer_names.
Can anyone please explain this?