Problem with "target_category_loss" function about keras-grad-cam HOT 8 CLOSED

Currently only the tensorflow backend is supported, so the immediate thing would be to switch the keras backend to tensorflow.
I'll take a look later to try get it working with theano too.

from keras-grad-cam.

D'oh ... obviously! My bad: I switched to TensorFlow backend and now get the following error message:

File "grad-cam.py", line 72, in <module>
cam  = grad_cam(model, preprocessed_input, predicted_class, "block5_pool")
File "grad-cam.py", line 41, in grad_cam
grads = normalize(K.gradients(loss, conv_output)[0])
TypeError: Expected binary or unicode string, got <keras.layers.pooling.MaxPooling2D object at 0x119beaf10>

from keras-grad-cam.

Can you please check your tensorflow and keras versions?
That can be done by:
import tensorflow as tf
print tf.__version__
import keras
print keras.__version__

Perhaps tensorflow should be upgraded.

Also please make sure the code in grad-cam.py was not modified.

from keras-grad-cam.

Sure: tensorflow is at 0.11.0 and Keras at 1.1.1.

Haven't dabbled with the code either!

from keras-grad-cam.

@thiippal I think you can use the debug mode to check the code line by line, and then tell him the code in which line is not correct. I use theano instead of tensorflow, but I carefully read the code and it seems that there are no errors.

from keras-grad-cam.

I wasn't able to reproduce the error with these tensorflow and keras versions.
Are you sure that both the dimension ordering and backend are tensorflow, like this:
{
"image_dim_ordering": "tf",
"epsilon": 1e-07,
"floatx": "float32",
"backend": "tensorflow"
}
?

from keras-grad-cam.

I checked my Keras configuration file: the backend and image dimensions are both set for tensorflow.

Anyhow, I created a fresh virtual environment with Keras and TensorFlow and cloned the repository again, and now it works. I don't recall making any changes to the code, but it is likely that I might have tinkered with it.

My apologies for the unnecessary work on your behalf; I really appreciate your Keras implementations of all these new techniques.

from keras-grad-cam.

Thank you.
If you have thoughts on how to improve this or experimentations worth doing please suggest!

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