clearing up folders

Dpnet/.gitignore

+target/

Dpnet/generate_for_dpnet.sh

+#!/usr/bin/env bash
+echo "Generating files.."
+java -jar embedded-montiarc-emadl-generator-0.2.4-SNAPSHOT-jar-with-dependencies.jar -m src/models -r Dpnet -o target --backend=MXNET

EMADLCNNModels/src/models/Dpnet.emadl → Dpnet/src/models/Dpnet.emadl

@@ -4,7 +4,7 @@ component Dpnet{
 
     implementation CNN {
 
-        def conv(kernel, channels, hasPool=true, convStride=(1,1)){
+       def conv(kernel, channels, hasPool=true, convStride=(1,1)){
             Convolution(kernel=kernel, channels=channels, stride=convStride) ->
             Relu() ->
             Pooling(pool_type="max", kernel=(3,3), stride=(2,2), ?=hasPool)
@@ -21,9 +21,13 @@ component Dpnet{
         conv(kernel=(3,3), channels=384, hasPool=false) ->
         conv(kernel=(3,3), channels=384, hasPool=false) ->
         conv(kernel=(3,3), channels=256) ->
-        fc() -> 
-        fc() -> 
-        FullyConnected(units=2, no_bias=true) ->
+        fc() ->
+        fc() ->
+        FullyConnected(units=256) ->
+        Relu() ->
+        Dropout() ->
+	Relu() ->
+        FullyConnected(units=14, no_bias=true) ->
         predictions
 
     }

EMADL2TORCS/.gitignore

+target/

EMADLCNNModels/.gitignore

-generated/
-out/
-.idea/
-.git
-*.iml

EMADLCNNModels/generate_for_dpnet.sh

-#!/usr/bin/env bash
-echo "Generating files.."
-java -jar embedded-montiarc-emadl-generator-0.2.1-SNAPSHOT-jar-with-dependencies.jar -m src/models -r Dpnet -o generated

EMADLCNNModels/generate_for_safetynet.sh

-#!/usr/bin/env bash
-echo "Generating files.."
-java -jar embedded-montiarc-emadl-generator-0.2.1-SNAPSHOT-jar-with-dependencies.jar -m src/models -r Safetynet -o generated

EMADLCNNModels/src/models/Safetynet.cnnt

-configuration Safetynet{
-     num_epoch : 100
-     batch_size : 64
-     context:cpu
-     normalize: true
-     optimizer : sgd{
-	learning_rate: 0.01
-         // reduce the learning rate starting from 0.01 every 8000 iterations by a factor of 0.9 (decrease by 10%)
-	 learning_rate_decay: 0.9
-         step_size: 8000
-         weight_decay : 0.0005
-     }
-
-}

EMADLCNNModels/src/models/Safetynet.emadl

-component Safetynet{
-    ports in Z(0:255)^{3, 210, 280} data,
-         out Q(0:1)^{2,1,1} predictions;
-
-    implementation CNN {
-
-        def conv(kernel, channels, hasPool=true, convStride=(1,1)){
-            Convolution(kernel=kernel, channels=channels, stride=convStride) ->
-            Relu() ->
-            Pooling(pool_type="max", kernel=(3,3), stride=(2,2), ?=hasPool)
-        }
-        def fc(){
-            FullyConnected(units=4096) ->
-            Relu() ->
-            Dropout()
-        }
-
-        image ->
-        conv(kernel=(11,11), channels=96, convStride=(4,4)) ->
-        conv(kernel=(5,5), channels=256, convStride=(4,4)) ->
-        conv(kernel=(3,3), channels=384, hasPool=false) ->
-        conv(kernel=(3,3), channels=384, hasPool=false) ->
-        conv(kernel=(3,3), channels=256) ->
-        fc() ->
-        fc() ->
-        FullyConnected(units=2) ->
-	Softmax() ->
-        predictions
-
-    }
-}

GeneratedDpnetTrainingCode/.gitignore

-model/
-__pycache__/
-venv/
-.idea/
-.git
-*.iml
-*.pyc
-*.log

GeneratedDpnetTrainingCode/CNNTrainer_Dpnet.py

-import logging
-import mxnet as mx
-import CNNCreator_dpnet
-
-if __name__ == "__main__":
-    logging.basicConfig(level=logging.DEBUG)
-    logger = logging.getLogger()
-    handler = logging.FileHandler("train.log","a", encoding=None, delay="true")
-    logger.addHandler(handler)
-
-    dpnet = CNNCreator_dpnet.CNNCreator_dpnet()
-    dpnet.train(
-        batch_size = 64,
-        num_epoch = 100,
-        context = 'cpu',
-        normalize = True,
-        optimizer = 'sgd',
-        optimizer_params = {
-            'learning_rate': 0.01,
-            'learning_rate_decay': 0.9,
-            'step_size': 8000}
-
-
-
-
-    )
-