Johannes Filter jfilter

## tensorflow_finetune.py
"""
Example TensorFlow script for finetuning a VGG model on your own data.
Uses tf.contrib.data module which is in release v1.2
Based on PyTorch example from Justin Johnson
(https://gist.github.com/jcjohnson/6e41e8512c17eae5da50aebef3378a4c)

Required packages: tensorflow (v1.2)
Download the weights trained on ImageNet for VGG:
```
wget http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz

## ft_wiki_preproc.py
# See https://github.com/facebookresearch/fastText/blob/master/get-wikimedia.sh
#
# From https://github.com/facebookresearch/fastText/issues/161:
#
# We now have a script called 'get-wikimedia.sh', that you can use to download and
# process a recent wikipedia dump of any language. This script applies the preprocessing
# we used to create the published word vectors.
#
# The parameters we used to build the word vectors are the default skip-gram settings,
# except with a dimensionality of 300 as indicated on the top of the list of word

## multi-face.ipynb

      
        
          
            
              
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                yang-zhang
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              December 27, 2023 05:28
            
              
                Multi-task Deep Learning Experiment using fastai Pytorch
              
          
        
      
        
  
    
    

          
    
      
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	"""
	Example TensorFlow script for finetuning a VGG model on your own data.
	Uses tf.contrib.data module which is in release v1.2
	Based on PyTorch example from Justin Johnson
	(https://gist.github.com/jcjohnson/6e41e8512c17eae5da50aebef3378a4c)

	Required packages: tensorflow (v1.2)
	Download the weights trained on ImageNet for VGG:
	```
	wget http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz
	# See https://github.com/facebookresearch/fastText/blob/master/get-wikimedia.sh
	#
	# From https://github.com/facebookresearch/fastText/issues/161:
	#
	# We now have a script called 'get-wikimedia.sh', that you can use to download and
	# process a recent wikipedia dump of any language. This script applies the preprocessing
	# we used to create the published word vectors.
	#
	# The parameters we used to build the word vectors are the default skip-gram settings,
	# except with a dimensionality of 300 as indicated on the top of the list of word