--- a
+++ b/dsb2018_topcoders/selim/train_all.sh
@@ -0,0 +1,112 @@
+#!/usr/bin/env bash
+
+##################### Resnet152 FPN  with Sigmoid activation ##############################
+
+python train.py \
+--gpu "0"  \
+--fold "0,1,2,3" \
+--num_workers 8  \
+--network resnet152_2 \
+--freeze_till_layer input_1  \
+--loss double_head_loss \
+--optimizer adam  \
+--learning_rate 0.0001  \
+--decay 0.0001  \
+--batch_size 16  \
+--crop_size 224 \
+--steps_per_epoch 500 \
+--epochs 2 \
+--preprocessing_function caffe 
+
+python train.py \
+--gpu "0"  \
+--fold "0,1,2,3" \
+--num_workers 8  \
+--network resnet152_2 \
+--freeze_till_layer input_1  \
+--loss double_head_loss \
+--optimizer adam  \
+--learning_rate 0.0001  \
+--decay 0.0001  \
+--batch_size 16  \
+--crop_size 224 \
+--steps_per_epoch 500 \
+--epochs 70 \
+--preprocessing_function caffe \
+--weights "nn_models/best_resnet152_2_fold{}.h5"
+
+
+##################### Densenet169 FPN with Softmax activation ##############################
+
+python train.py \
+--gpu "0"  \
+--fold "0,1,2,3" \
+--num_workers 8  \
+--network densenet169_softmax \
+--freeze_till_layer input_1  \
+--loss categorical_dice \
+--optimizer adam  \
+--use_softmax \
+--learning_rate 0.0001  \
+--decay 0.0001  \
+--batch_size 16  \
+--crop_size 256 \
+--steps_per_epoch 500 \
+--epochs 2 \
+--preprocessing_function torch
+
+python train.py \
+--gpu "0"  \
+--fold "0,1,2,3" \
+--num_workers 8  \
+--network densenet169_softmax \
+--freeze_till_layer input_1  \
+--loss categorical_dice \
+--optimizer adam  \
+--use_softmax \
+--learning_rate 0.0001  \
+--decay 0.0001  \
+--batch_size 16  \
+--crop_size 256 \
+--steps_per_epoch 500 \
+--epochs 70 \
+--preprocessing_function torch \
+--weights "nn_models/best_densenet169_softmax_fold{}.h5"
+
+##################### Resnet101 FPN  Full masks with Sigmoid activation ##############################
+
+python train.py \
+--gpu "0"  \
+--fold "0,1,2,3" \
+--num_workers 8  \
+--network resnet101_2 \
+--freeze_till_layer input_1  \
+--loss double_head_loss \
+--optimizer adam  \
+--learning_rate 0.0001  \
+--decay 0.0001  \
+--batch_size 16  \
+--crop_size 224 \
+--steps_per_epoch 500 \
+--epochs 2 \
+--use_full_masks \
+--preprocessing_function caffe 
+
+python train.py \
+--gpu "0"  \
+--fold "0,1,2,3" \
+--num_workers 8  \
+--network resnet101_2 \
+--freeze_till_layer input_1  \
+--loss double_head_loss \
+--optimizer adam  \
+--learning_rate 0.0001  \
+--decay 0.0001  \
+--batch_size 16  \
+--crop_size 256 \
+--steps_per_epoch 500 \
+--epochs 70 \
+--use_full_masks \
+--preprocessing_function caffe \
+--weights "nn_models/best_resnet101_2_fold{}.h5"
+