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README.md

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+# Greedy Layerwise CNN
+
+This is a peliminary research code.
+
+Code for experiments on greedy supervised layerwise CNNs
+
+## Imagenet
+Imagenet experiments for 1-hidden layer use the standalone imagenet_single_layer.py
+
+Imagenet experiments for k=2+ can be run with imagenet.py
+
+Note k in the paper corresponds to nlin in the code
+
+
+To obtain the results for Imagenet
+
+k=3 
+```
+python IMAGENER_DIR -j THREADS  imagenet.py --ncnn 8 --nlin 2 
+
+```
+
+k=2 
+
+```
+python IMAGENER_DIR -j THREADS imagenet.py --ncnn 8 --nlin 1 
+
+```
+
+k=1 model
+```
+python IMAGENER_DIR -j THREADS  imagenet_single_layer.py --ncnn 8
+
+```
+### VGG-11
+
+The VGG-11 model was trained with a new refactored and more modular codebase different from the codebase used for the above models and is thus run from the standalone directory 
+refactored_imagenet/
+
+To train the VGG-11 with k=3
+
+```
+python imagenet_greedy.py IMAGENER_DIR -j THREADS --arch vgg11_bn --half --dynamic-loss-scale
+
+```
+to train the baseline:
+
+```
+python imagenet.py IMAGENER_DIR -j THREADS --arch vgg11_bn --half --dynamic-loss-scale
+
+```
+
+### Linear Separability
+Linear separability experiments are in linear_separability folder. A notebook is included that produces the plots. to run different settings
+
+
+This will create and train a model, using K non-linearity, F features and the model is stored in checkpoint.
+
+```
+python cifar.py --ncnn 5 --nlin K --feature_size F
+```
+
+This will use the model "filename", to train probes on top of these at layer "j"
+```
+python train_lr.py filename j
+
+```
+
+### CIFAR experiments
+CIFAR experiments can be reproduced using cifar.py
+
+The CIFAR-10 models can be trained:
+
+k=3 (~91.7) 
+```
+python cifar.py --ncnn 4 --nlin 2 --feature_size 128 --down [1] --bn 1
+
+```
+
+k=2 (~90.4)
+
+```
+python cifar.py --ncnn 4 --nlin 1 --feature_size 128 --down [1] --bn 1
+
+```
+
+k=1 (~88.3) 
+```
+python cifar.py --ncnn 5 --nlin 0 --feature_size 256 
+
+```
+