Open Meywether opened 4 years ago
@Meywether This might be due to heatmap.min being equal to heatmap.max. You might want to remove the last softmax layer of the Resnet to prevent this from happening.
@Meywether This might be due to heatmap.min being equal to heatmap.max. You might want to remove the last softmax layer of the Resnet to prevent this from happening.
@RaphaelMeudec is right here. Even I had the same issue. I resolved it by removing the last layer from my pre-trained model.
Hello @RaphaelMeudec and @nishantagarwal , thanks for your response.
model = tf.keras.applications.resnet_v2.ResNet50V2(weights="imagenet", include_top=False)
- is not working
So I created a second model by using:
model2 = Model(model.input, model.layers[-2].output)
model2.summary() shows me that the props layer is gone, but still getting the same error
I thought that model2 = Model(model.input, model.layers[-1].output)
should be enough but in my case the Dense probs layer was still in my model.
May I ask you to provide me a code snippet ?
Best regards
Meywether
I have the same problem as @Meywether , with a custom keras model with multi output ( segmentation and classification).
Dear Sicara, thanks for this great easy-to-use library. I do have to mention a bug in your script if I am using one of the ResnetV2 Models: Starting with a small modified version of your provided example, tf-explain throws following error by using:
The "V1" Versions are working properly. -> Tested with all three variants!
The script throws following error:
Thanks in advance! Best regards Meywether
Attached: My version of your provided example: