Added an AlexNet wrapper using LRP layer definitions.#1
Added an AlexNet wrapper using LRP layer definitions.#1ruthcfong wants to merge 5 commits intoVigneshSrinivasan10:masterfrom
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…xpected with older code)
…port in convs); also add __init__.py files to allow for adding interprettensor to /data/ruthfong/tensorflow/:/data/ruthfong/tensorflow/models/slim/nets:/data/ruthfong/tensorflow/models:/data/ruthfong/tensorflow/cleverhans:/data/ruthfong/tensorflow/interprettensor::/users/ruthfong/sample_code/Caffe-ExcitationBP/python and then importing sub-folders as modules.
…sn't work (doberman.png), and helper file with imagenet classes
VigneshSrinivasan10
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VigneshSrinivasan10
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Hi,
Thanks for your contribution!
I have recently added for each layer - a possibility to initialize the weights.
So your convolution.py file, line 35: only groups=1 can stay.
| with tf.variable_scope(self.name): | ||
| self.weights = variables.weights(self.weights_shape) | ||
| self.biases = variables.biases(self.output_depth) | ||
| if self.weights is None: |
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Also, I am not exactly sure why you are doing this.
Though, the recent update for weight initialization should take care of this.
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Before, I wasn't able to set the weights to pre-trained weights after initialization, so I moved that logic to the point where we directly initialize the weights Variable. I'll take a look at the recent update though.
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I am not sure if having 'group' kernels helps in any way. The original AlexNet paper did it when they had problems with memory while computing.
BVLC/caffe#778
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modules/convolution.py was modified to support a) multiple kernel groups, per AlexNet's original architecture, and b) initialization of weights/biases with passed-in arrays (older code should still work).
models/alexnet.py allows pre-trained weights to be loaded.
(Weights can be downloaded from http://www.cs.toronto.edu/~guerzhoy/tf_alexnet/bvlc_alexnet.npy)
Note: This implementation is based on https://github.com/guerzh/tf_weights, which doesn't work perfectly / seems to do well on some images but not others,
as noted at https://github.com/guerzh/tf_weights/issues/.
examples/alexnet_demo.py
I've included demo code and two example images that demonstrate a correctly behaving (examples/poodle.png) and incorrectly behaving (examples/doberman.png) classification result.
__init__.pyandmodels/__init__.pyThis allows
/path/to/interprettensorto be exported to PYTHONPATH and modules and models to be called as done in examples/alexnet_demo.pyExample:
export PYTHONPATH="/data/ruthfong/tensorflow/interprettensor":$PYTHONPATH