Changed Range syntax and added ResNet152 architecture. (#24)

* Changed Range syntax and added ResNet152 architecture.

* Add files via upload

* Add files via upload

* Update README.md

* Update README.md

* Update README.md

README.md

@@ -128,21 +128,17 @@ The body of a new method is constructed from other layers including other user-d
 The compiler will throw an error if recursion occurs. 
 The following is a example of multiple method declarations.
 ```
-    def conv(filter, channels, stride=1, act=true){
+    def conv(kernel, channels, stride=1, act=true){
         Convolution(kernel=(filter,filter), channels=channels, stride=(stride,stride)) ->
         BatchNorm() ->
         Relu(?=act)
     }
-    def skip(channels, stride){
-        Convolution(kernel=(1,1), channels=channels, stride=(stride,stride)) ->
-        BatchNorm()
-    }
     def resLayer(channels, stride=1){
         (
-            conv(filter=3, channels=channels, stride=stride) ->
-            conv(filter=3, channels=channels, act=false)
+            conv(kernel=3, channels=channels, stride=stride) ->
+            conv(kernel=3, channels=channels, act=false)
         |
-            skip(channels=channels, stride=stride, ?=(stride!=1))
+            conv(kernel=1, channels=channels, stride=stride, act=false, ?=(stride!=1))
         ) ->
         Add() ->
         Relu()
@@ -151,30 +147,18 @@ The following is a example of multiple method declarations.
 The method `resLayer` in this example corresponds to a building block of a Residual Network. 
 The `?` argument is a special argument which is explained in the next section.
 
-## Special Arguments
-There exists special structural arguments which can be used in each method. 
-These are `->`, `|` and `?`. `->` and `|` can only be positive integers and `?` can only be a boolean. 
-The argument `?` does nothing if it is true and removes the layer completely if it is false. 
-The other two arguments create an iteration of the method. 
-We will show their effect with examples. 
+## Structural Arguments
+Structural arguments are special arguments which can be set for each layer and which do not correspond to a layer parameter. The three structural arguments are "?", "->" and "|". The conditional argument "?" is a boolean. It does nothing if it is true and it removes the layer completely if it is false. This argument is only useful for layer construction. The other two structural arguments are non-negative integers which repeat the layer *x* number of times where *x* is equal to their value. The layer operator between each repetition has the same symbol as the argument.
+
 Assuming `a` is a method without required arguments, 
 then `a(-> = 3)->` is equal to `a()->a()->a()->`, 
 `a(| = 3)->` is equal to `(a() | a() | a())->` and 
 `a(-> = 3, | = 2)->` is equal to `(a()->a()->a() | a()->a()->a())->`. 
 
 ## Argument Sequences
-It is also possible to iterate a method through the use of argument sequences. 
-The following are valid sequences: `[2->5->3]`, `[true|false|false]`, `[2->1|4->4->6]`, `[ |2->3]`, `1->..->5` and `3|..|-2`. 
-All values in these examples could also be replaced by variable names or expressions. 
-The first three are standard sequences and the last two are ranges. 
-A range can be translated to a standard sequence. 
-The range `3|..|-2` is equal to `[3|2|1|0|-1|-2]` and `1->..->5` is equal to `[1->2->3->4->5]`. 
-
-If a argument is set to a sequence, the method will be repeated for each value in the sequence and the connection between the layers will be the same as it is between the values of the sequence. 
-An argument which has a single value is neutral to the repetition which means that it will be repeated an arbitrary number of times without interfering with the repetition. 
-If a method contains multiple argument sequences, CNNArch will try to combine the sequences. 
-The language will throw an error at compile time if this fails. 
-Assuming the method `m(a, b, c)` exists, the line `m(a=[5->3], b=[3|4|2], c=2)->` is equal to:
+Argument sequences can be used instead of regular arguments to declare that a layer should be repeated with the values of the given sequence. The operator between these so stacked layers is also given by the sequence. Other arguments that only have a single value are neutral to the repetition which means that the single value will be repeated an arbitrary number of times without having influence on the number of repetitions.
+
+The following are valid sequences: `[1->2->3->4]`, `[true | false]`, `{[1 | 3->2]`, `[ |2->3]` and `[1->..->4]`. All values in these examples could also be replaced by variable names or arithmetic or logical expressions. The last sequence is defined as a range and equal to the first one. A range in CNNArch is closed which means the start and end value are both in the sequence. Moreover, a range has always a step size of one. Thus, the range `[0|..|-4]` would be empty. The data flow operators can be used both in the same argument sequence in which case a single parallelization block is created. A parallel group in this block can be empty, which is why `[ |2->3]` is a valid sequence. If a method contains multiple argument sequences, the language will try to combine them by expanding the smaller one and will throw an error at model creation if this fails. Let `m` be a layer with parameters `a`, `b` and `c`, then the expression `m(a=[3->2],b=1)` is equal to `m(a=3,b=1)->m(a=2,b=1)`. Furthermore, the line `m(a=[5->3],b=[3|4|2],c=2)->` is equal to:
 ```
 (
     m(a=5, b=3, c=2) ->
@@ -187,7 +171,7 @@ Assuming the method `m(a, b, c)` exists, the line `m(a=[5->3], b=[3|4|2], c=2)->
     m(a=3, b=2, c=2)
 ) ->
 ```
-And `m(a=[|5|3->4], b=[1|1|2], c=2)` is equal to: 
+And `m(a=[|5|3->4], b=[|1|2], c=2)` is equal to: 
 ```
 (
 
@@ -198,7 +182,7 @@ And `m(a=[|5|3->4], b=[1|1|2], c=2)` is equal to:
     m(a=4, b=2, c=2)
 ) ->
 ```
-However, `m(a=[5->3], b=[2|4->6], c=2)->` and `m(a=[5->3], b=[2->4->6], c=2)->` would throw an error because it is not possible to expand *a* such that it is the same size as *b*.
+However, `m(a=[5->3], b=[2|4->6], c=2)->` and `m(a=[5->3], b=[2->4->6], c=2)->` would fail because it is not possible to expand *a* such that it is the same size as *b*.
 
 
 

src/main/grammars/de/monticore/lang/monticar/CNNArch.mc4

@@ -64,11 +64,11 @@ grammar CNNArch extends de.monticore.lang.math.Math {
 
     ArchSerialSequence = serialValues:(ArchSimpleExpression || "->")*;
 
-    ArchValueRange implements ArchValueSequence = start:ArchSimpleExpression
+    ArchValueRange implements ArchValueSequence = "[" start:ArchSimpleExpression
                                                   (serial:"->" | parallel:"|")
                                                   ".."
                                                   (serial2:"->" | parallel2:"|")
-                                                  end:ArchSimpleExpression;
+                                                  end:ArchSimpleExpression "]";
 
 
     ArchSimpleExpression = (arithmeticExpression:MathArithmeticExpression

src/main/java/de/monticore/lang/monticar/cnnarch/_symboltable/ArchSimpleExpressionSymbol.java

@@ -145,22 +145,7 @@ public class ArchSimpleExpressionSymbol extends ArchExpressionSymbol {
             return computeValue((MathNameExpressionSymbol) getMathExpression().get());
         }
         else if (getMathExpression().get() instanceof TupleExpressionSymbol){
-            Map<String, String> replacementMap = new HashMap<>();
-            List<Object> valueList = new ArrayList<>();
-            TupleExpressionSymbol tuple = (TupleExpressionSymbol) getMathExpression().get();
-            for (MathExpressionSymbol mathExp : tuple.getExpressions()){
-                if (mathExp instanceof MathNameExpressionSymbol){
-                    valueList.add(computeValue((MathNameExpressionSymbol) mathExp));
-                }
-                else {
-                    ArchSimpleExpressionSymbol temp = ArchSimpleExpressionSymbol.of(mathExp);
-                    temp.setEnclosingScope(getEnclosingScope().getAsMutableScope());
-                    temp.resolveOrError();
-                    valueList.add(temp.getValue().get());
-                    getEnclosingScope().getAsMutableScope().remove(temp);
-                }
-            }
-            return valueList;
+            return computeValue((TupleExpressionSymbol) getMathExpression().get());
         }
         else {
             Map<String, String> replacementMap = new HashMap<>();
@@ -179,8 +164,25 @@ public class ArchSimpleExpressionSymbol extends ArchExpressionSymbol {
         }
     }
 
+    private List<Object> computeValue(TupleExpressionSymbol tupleExpression){
+        List<Object> valueList = new ArrayList<>();
+        for (MathExpressionSymbol mathExp : tupleExpression.getExpressions()){
+            if (mathExp instanceof MathNameExpressionSymbol){
+                valueList.add(computeValue((MathNameExpressionSymbol) mathExp));
+            }
+            else {
+                ArchSimpleExpressionSymbol temp = ArchSimpleExpressionSymbol.of(mathExp);
+                temp.setEnclosingScope(getEnclosingScope().getAsMutableScope());
+                temp.resolveOrError();
+                valueList.add(temp.getValue().get());
+                getEnclosingScope().getAsMutableScope().remove(temp);
+            }
+        }
+        return valueList;
+    }
+
     private Object computeValue(MathNameExpressionSymbol mathExpression){
-        String name = ((MathNameExpressionSymbol) mathExpression).getNameToAccess();
+        String name = mathExpression.getNameToAccess();
         VariableSymbol variable = (VariableSymbol) getEnclosingScope().resolve(name, VariableSymbol.KIND).get();
         variable.getExpression().resolveOrError();
 

src/test/java/de/monticore/lang/monticar/cnnarch/AbstractSymtabTest.java

@@ -27,9 +27,11 @@ import de.monticore.lang.monticar.cnnarch._symboltable.CNNArchCompilationUnitSym
 import de.monticore.lang.monticar.cnnarch._symboltable.CNNArchLanguage;
 import de.monticore.symboltable.GlobalScope;
 import de.monticore.symboltable.Scope;
+import de.se_rwth.commons.logging.Log;
 
 import java.nio.file.Paths;
 
+import static de.monticore.lang.monticar.cnnarch.ParserTest.ENABLE_FAIL_QUICK;
 import static org.junit.Assert.assertNotNull;
 
 public class AbstractSymtabTest {

src/test/java/de/monticore/lang/monticar/cnnarch/SymtabTest.java

@@ -23,14 +23,24 @@ package de.monticore.lang.monticar.cnnarch;
 import de.monticore.lang.monticar.cnnarch._parser.CNNArchParser;
 import de.monticore.lang.monticar.cnnarch._symboltable.CNNArchCompilationUnitSymbol;
 import de.monticore.symboltable.Scope;
+import de.se_rwth.commons.logging.Log;
+import org.junit.Before;
 import org.junit.Ignore;
 import org.junit.Test;
 
+import static de.monticore.lang.monticar.cnnarch.ParserTest.ENABLE_FAIL_QUICK;
 import static org.junit.Assert.assertNotNull;
 import static org.junit.Assert.assertTrue;
 
 public class SymtabTest extends AbstractSymtabTest {
 
+    @Before
+    public void setUp() {
+        // ensure an empty log
+        Log.getFindings().clear();
+        Log.enableFailQuick(ENABLE_FAIL_QUICK);
+    }
+
     @Test
     public void testParsing() throws Exception {
         CNNArchParser parser = new CNNArchParser();

src/test/java/de/monticore/lang/monticar/cnnarch/cocos/AllCoCoTest.java

@@ -23,10 +23,13 @@ package de.monticore.lang.monticar.cnnarch.cocos;
 import de.monticore.lang.monticar.cnnarch._cocos.*;
 import de.monticore.lang.monticar.cnnarch.helper.ErrorCodes;
 import de.se_rwth.commons.logging.Log;
+import org.junit.Before;
 import org.junit.Test;
 
 import java.io.IOException;
 
+import static de.monticore.lang.monticar.cnnarch.ParserTest.ENABLE_FAIL_QUICK;
+
 public class AllCoCoTest extends AbstractCoCoTest {
     String baseDir="src/test/resources";
 
@@ -34,9 +37,17 @@ public class AllCoCoTest extends AbstractCoCoTest {
         Log.enableFailQuick(false);
     }
 
+    @Before
+    public void setUp() {
+        // ensure an empty log
+        Log.getFindings().clear();
+        Log.enableFailQuick(ENABLE_FAIL_QUICK);
+    }
+
     @Test
     public void testValidCoCos() throws IOException {
 
+        checkValid("architectures", "ResNet152");
         checkValid("architectures", "Alexnet");
         checkValid("architectures", "ResNeXt50");
         checkValid("architectures", "ResNet34");

src/test/resources/architectures/Alexnet.cnna

 architecture Alexnet(img_height=224, img_width=224, img_channels=3, classes=10){
     def input Z(0:255)^{img_channels, img_height, img_width} data
-    def output Q(0:1)^{classes} softmax
+    def output Q(0:1)^{classes} predictions
 
     def split1(i){
         [i] ->
@@ -39,5 +39,5 @@ architecture Alexnet(img_height=224, img_width=224, img_channels=3, classes=10){
     fc(->=2) ->
     FullyConnected(units=10) ->
     Softmax() ->
-    softmax
+    predictions
 }
\ No newline at end of file

src/test/resources/architectures/ResNeXt50.cnna

 architecture ResNeXt50(img_height=224, img_width=224, img_channels=3, classes=1000){
     def input Z(0:255)^{img_channels, img_height, img_width} data
-    def output Q(0:1)^{classes} softmax
+    def output Q(0:1)^{classes} predictions
 
-    def conv(filter, channels, stride=1, act=true){
-        Convolution(kernel=filter, channels=channels, stride=(stride,stride)) ->
+    def conv(kernel, channels, stride=1, act=true){
+        Convolution(kernel=kernel, channels=channels, stride=(stride,stride)) ->
         BatchNorm() ->
         Relu(?=act)
     }
     def resGroup(innerChannels, outChannels, stride=1){
-        conv(filter=(1,1), channels=innerChannels) ->
-        conv(filter=(3,3), channels=innerChannels, stride=stride) ->
-        conv(filter=(1,1), channels=outChannels, act=false)
+        conv(kernel=(1,1), channels=innerChannels) ->
+        conv(kernel=(3,3), channels=innerChannels, stride=stride) ->
+        conv(kernel=(1,1), channels=outChannels, act=false)
     }
     def skip(outChannels, stride){
         Convolution(kernel=(1,1), channels=outChannels, stride=(stride,stride)) ->
         BatchNorm()
     }
-    def resLayer(innerChannels, outChannels, stride=1, changedChannels=false){
+    def resLayer(innerChannels, outChannels, stride=1, addSkipConv=false){
         (
             resGroup(innerChannels=innerChannels,
                      outChannels=outChannels,
@@ -24,24 +24,25 @@ architecture ResNeXt50(img_height=224, img_width=224, img_channels=3, classes=10
                      | = 32) ->
             Add()
         |
-            skip(outChannels=outChannels, stride=stride, ? = stride!=1 || changedChannels)
+            skip(outChannels=outChannels, stride=stride, ? = addSkipConv)
         ) ->
         Add() ->
         Relu()
     }
 
     data ->
-    conv(filter=(7,7), channels=64, stride=2) ->
+    conv(kernel=(7,7), channels=64, stride=2) ->
     Pooling(pool_type="max", kernel=(3,3), stride=(2,2)) ->
-    resLayer(innerChannels=4, outChannels=256, changedChannels=true, -> = 3) ->
-    resLayer(innerChannels=8, outChannels=512, stride=2) ->
+    resLayer(innerChannels=4, outChannels=256, addSkipConv=true) ->
+    resLayer(innerChannels=4, outChannels=256, -> = 2) ->
+    resLayer(innerChannels=8, outChannels=512, stride=2, addSkipConv=true) ->
     resLayer(innerChannels=8, outChannels=512, -> = 3) ->
-    resLayer(innerChannels=16, outChannels=1024, stride=2) ->
+    resLayer(innerChannels=16, outChannels=1024, stride=2, addSkipConv=true) ->
     resLayer(innerChannels=16, outChannels=1024, -> = 5) ->
-    resLayer(innerChannels=32, outChannels=2048, stride=2) ->
+    resLayer(innerChannels=32, outChannels=2048, stride=2, addSkipConv=true) ->
     resLayer(innerChannels=32, outChannels=2048, -> = 2) ->
     GlobalPooling(pool_type="avg") ->
     FullyConnected(units=classes) ->
     Softmax() ->
-    softmax
+    predictions
 }
\ No newline at end of file

src/test/resources/architectures/ResNet152.cnna

+architecture ResNet152(img_height=224, img_width=224, img_channels=3, classes=1000){
+    def input Z(0:255)^{img_channels, img_height, img_width} data
+    def output Q(0:1)^{classes} predictions
+
+    def conv(kernel, channels, stride=1, act=true){
+        Convolution(kernel=(kernel,kernel), channels=channels, stride=(stride,stride)) ->
+        BatchNorm() ->
+        Relu(?=act)
+    }
+    def skip(channels, stride){
+        Convolution(kernel=(1,1), channels=channels, stride=(stride,stride)) ->
+        BatchNorm()
+    }
+    def resLayer(channels, stride=1, addSkipConv=false){
+        (
+            conv(kernel=1, channels=channels, stride=stride) ->
+            conv(kernel=3, channels=channels) ->
+            conv(kernel=1, channels=4*channels, act=false)
+        |
+            skip(channels=4*channels, stride=stride, ? = addSkipConv)
+        ) ->
+        Add() ->
+        Relu()
+    }
+
+    data ->
+    conv(kernel=7, channels=64, stride=2) ->
+    Pooling(pool_type="max", kernel=(3,3), stride=(2,2)) ->
+    resLayer(channels=64, addSkipConv=true) ->
+    resLayer(channels=64, ->=2) ->
+    resLayer(channels=128, stride=2, addSkipConv=true) ->
+    resLayer(channels=128, ->=7) ->
+    resLayer(channels=256, stride=2, addSkipConv=true) ->
+    resLayer(channels=256, ->=35) ->
+    resLayer(channels=512, stride=2, addSkipConv=true) ->
+    resLayer(channels=512, ->=2) ->
+    GlobalPooling(pool_type="avg") ->
+    FullyConnected(units=classes) ->
+    Softmax() ->
+    predictions
+}
\ No newline at end of file

src/test/resources/architectures/ResNet34.cnna

 architecture ResNet34(img_height=224, img_width=224, img_channels=3, classes=1000){
     def input Z(0:255)^{img_channels, img_height, img_width} data
-    def output Q(0:1)^{classes} softmax
+    def output Q(0:1)^{classes} predictions
 
-    def conv(filter, channels, stride=1, act=true){
-        Convolution(kernel=(filter,filter), channels=channels, stride=(stride,stride)) ->
+    def conv(kernel, channels, stride=1, act=true){
+        Convolution(kernel=(kernel,kernel), channels=channels, stride=(stride,stride)) ->
         BatchNorm() ->
         Relu(?=act)
     }
@@ -13,8 +13,8 @@ architecture ResNet34(img_height=224, img_width=224, img_channels=3, classes=100
     }
     def resLayer(channels, stride=1){
         (
-            conv(filter=3, channels=channels, stride=stride) ->
-            conv(filter=3, channels=channels, act=false)
+            conv(kernel=3, channels=channels, stride=stride) ->
+            conv(kernel=3, channels=channels, act=false)
         |
             skip(channels=channels, stride=stride, ? = (stride != 1))
         ) ->
@@ -23,7 +23,7 @@ architecture ResNet34(img_height=224, img_width=224, img_channels=3, classes=100
     }
 
     data ->
-    conv(filter=7, channels=64, stride=2) ->
+    conv(kernel=7, channels=64, stride=2) ->
     Pooling(pool_type="max", kernel=(3,3), stride=(2,2)) ->
     resLayer(channels=64, ->=3) ->
     resLayer(channels=128, stride=2) ->
@@ -35,5 +35,5 @@ architecture ResNet34(img_height=224, img_width=224, img_channels=3, classes=100
     GlobalPooling(pool_type="avg") ->
     FullyConnected(units=classes) ->
     Softmax() ->
-    softmax
+    predictions
 }

src/test/resources/architectures/SequentialAlexnet.cnna

 architecture SequentialAlexnet(img_height=224, img_width=224, img_channels=3, classes=10){
     def input Z(0:255)^{img_channels, img_height, img_width} data
-    def output Q(0:1)^{classes} softmax
+    def output Q(0:1)^{classes} predictions
 
-    def conv(filter, channels, hasPool=true, convStride=(1,1)){
-        Convolution(kernel=filter, channels=channels, stride=convStride) ->
+    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)
     }
@@ -14,16 +14,16 @@ architecture SequentialAlexnet(img_height=224, img_width=224, img_channels=3, cl
     }
 
     data ->
-    conv(filter=(11,11), channels=96, convStride=(4,4)) ->
+    conv(kernel=(11,11), channels=96, convStride=(4,4)) ->
     Lrn(nsize=5, alpha=0.0001, beta=0.75) ->
-    conv(filter=(5,5), channels=256, convStride=(4,4)) ->
+    conv(kernel=(5,5), channels=256, convStride=(4,4)) ->
     Lrn(nsize=5, alpha=0.0001, beta=0.75) ->
-    conv(filter=(3,3), channels=384, hasPool=false) ->
-    conv(filter=(3,3), channels=384, hasPool=false) ->
-    conv(filter=(3,3), channels=256) ->
+    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=classes) ->
     Softmax() ->
-    softmax
+    predictions
 }

src/test/resources/architectures/ThreeInputCNN_M14.cnna

@@ -5,28 +5,28 @@ architecture ThreeInputCNN_M14(img_height=200, img_width=300, img_channels=3, cl
      *Year: 2017*/
 
     def input Z(0:255)^{img_channels, img_height, img_width} data[3]
-    def output Q(0:1)^{classes} softmax
+    def output Q(0:1)^{classes} predictions
 
-    def conv(filter, channels){
-        Convolution(kernel=filter, channels=channels) ->
+    def conv(kernel, channels){
+        Convolution(kernel=kernel, channels=channels) ->
         Relu()
     }
 
     def inputGroup(index){
         [index] ->
-        conv(filter=(3,3), channels=32, ->=3) ->
+        conv(kernel=(3,3), channels=32, ->=3) ->
         Pooling(pool_type="max", kernel=(2,2), stride=(2,2))
     }
 
     data ->
-    inputGroup(index=0|..|2) ->
+    inputGroup(index=[0|..|2]) ->
     Concatenate() ->
-    conv(filter=(3,3), channels=64) ->
+    conv(kernel=(3,3), channels=64) ->
     Pooling(pool_type="max", kernel=(2,2), stride=(2,2)) ->
 
     FullyConnected(units=32) ->
     Relu() ->
     FullyConnected(units=classes) ->
     Softmax() ->
-    softmax
+    predictions
 }
\ No newline at end of file

src/test/resources/architectures/VGG16.cnna

 architecture VGG16(img_height=224, img_width=224, img_channels=3, classes=1000){
     def input Z(0:255)^{img_channels, img_height, img_width} data
-    def output Q(0:1)^{classes} softmax
+    def output Q(0:1)^{classes} predictions
 
-    def conv(filter, channels){
-        Convolution(kernel=(filter,filter), channels=channels) ->
+    def conv(kernel, channels){
+        Convolution(kernel=(kernel,kernel), channels=channels) ->
         Relu()
     }
     def fc(){
@@ -13,19 +13,19 @@ architecture VGG16(img_height=224, img_width=224, img_channels=3, classes=1000){
     }
 
     data ->
-    conv(filter=3, channels=64, ->=2) ->
+    conv(kernel=3, channels=64, ->=2) ->
     Pooling(pool_type="max", kernel=(2,2), stride=(2,2)) ->
-    conv(filter=3, channels=128, ->=2) ->
+    conv(kernel=3, channels=128, ->=2) ->
     Pooling(pool_type="max", kernel=(2,2), stride=(2,2)) ->
-    conv(filter=3, channels=256, ->=3) ->
+    conv(kernel=3, channels=256, ->=3) ->
     Pooling(pool_type="max", kernel=(2,2), stride=(2,2)) ->
-    conv(filter=3, channels=512, ->=3) ->
+    conv(kernel=3, channels=512, ->=3) ->
     Pooling(pool_type="max", kernel=(2,2), stride=(2,2)) ->
-    conv(filter=3, channels=512, ->=3) ->
+    conv(kernel=3, channels=512, ->=3) ->
     Pooling(pool_type="max", kernel=(2,2), stride=(2,2)) ->
     fc() ->
     fc() ->
     FullyConnected(units=classes) ->
     Softmax() ->
-    softmax
+    predictions
 }

src/test/resources/invalid_tests/ArgumentConstraintTest1.cnna

@@ -2,8 +2,8 @@ architecture ArgumentConstraintTest1(img_height=224, img_width=224, img_channels
     def input Z(0:255)^{img_channels, img_height, img_width} image
     def output Q(0:1)^{classes} predictions
 
-    def conv(filter, channels, stride=1, act=true){
-        Convolution(kernel=(filter,filter), channels=channels, stride=(stride,stride)) ->
+    def conv(kernel, channels, stride=1, act=true){
+        Convolution(kernel=(kernel,kernel), channels=channels, stride=(stride,stride)) ->
         BatchNorm() ->
         Relu(?=act)
     }
@@ -13,8 +13,8 @@ architecture ArgumentConstraintTest1(img_height=224, img_width=224, img_channels
     }
     def resLayer(channels, stride=1){
         (
-            conv(filter=3, channels=channels, stride=stride) ->
-            conv(filter=3, channels=channels, stride=stride, act=false)
+            conv(kernel=3, channels=channels, stride=stride) ->
+            conv(kernel=3, channels=channels, stride=stride, act=false)
         |
             skip(channels=channels, stride=stride, ?=(stride!=1))
         ) ->
@@ -23,7 +23,7 @@ architecture ArgumentConstraintTest1(img_height=224, img_width=224, img_channels
     }
 
     image ->
-    conv(filter=7, channels=64, stride=2) ->
+    conv(kernel=7, channels=64, stride=2) ->
     Pooling(pool_type="max", kernel=(3,3), stride=(2,2)) ->
     resLayer(channels=64, ->=3) ->
     resLayer(channels=128, stride=2) ->

src/test/resources/invalid_tests/ArgumentConstraintTest2.cnna

@@ -2,8 +2,8 @@ architecture ArgumentConstraintTest2(img_height=224, img_width=224, img_channels
     def input Z(0:255)^{img_channels, img_height, img_width} image
     def output Q(0:1)^{classes} predictions
 
-    def conv(filter, channels, stride=1, act=true){
-        Convolution(kernel=(filter,filter), channels=channels, stride=(stride,stride)) ->
+    def conv(kernel, channels, stride=1, act=true){
+        Convolution(kernel=(kernel,kernel), channels=channels, stride=(stride,stride)) ->
         BatchNorm() ->
         Relu(?=act)
     }
@@ -13,8 +13,8 @@ architecture ArgumentConstraintTest2(img_height=224, img_width=224, img_channels
     }
     def resLayer(channels, stride=1){
         (
-            conv(filter=3, channels=channels, stride=stride) ->
-            conv(filter=3, channels=channels, stride=stride, act=false)
+            conv(kernel=3, channels=channels, stride=stride) ->
+            conv(kernel=3, channels=channels, stride=stride, act=false)
         |
             skip(channels=channels, stride=stride, ?=(stride!=1))
         ) ->
@@ -23,7 +23,7 @@ architecture ArgumentConstraintTest2(img_height=224, img_width=224, img_channels
     }
 
     image ->
-    conv(filter=7, channels=64, stride=2) ->
+    conv(kernel=7, channels=64, stride=2) ->
     Pooling(pool_type="max", kernel=(3,3), stride=(2,2)) ->
     resLayer(channels=64, ->=3) ->
     resLayer(channels=128, stride=2) ->

src/test/resources/invalid_tests/ArgumentConstraintTest3.cnna

@@ -2,8 +2,8 @@ architecture ArgumentConstraintTest3(img_height=224, img_width=224, img_channels
     def input Z(0:255)^{img_channels, img_height, img_width} image
     def output Q(0:1)^{classes} predictions
 
-    def conv(filter, channels, stride=1, act=true){
-        Convolution(kernel=(filter,filter), channels=channels, stride=(stride,stride)) ->
+    def conv(kernel, channels, stride=1, act=true){
+        Convolution(kernel=(kernel,kernel), channels=channels, stride=(stride,stride)) ->
         BatchNorm() ->
         Relu(?=act)
     }
@@ -13,8 +13,8 @@ architecture ArgumentConstraintTest3(img_height=224, img_width=224, img_channels
     }
     def resLayer(channels, stride=1){
         (
-            conv(filter=3, channels=channels, stride=stride) ->
-            conv(filter=3, channels=channels, stride=stride, act=false)
+            conv(kernel=3, channels=channels, stride=stride) ->
+            conv(kernel=3, channels=channels, stride=stride, act=false)
         |
             skip(channels=channels, stride=stride, ?=(stride!=1))
         ) ->
@@ -23,7 +23,7 @@ architecture ArgumentConstraintTest3(img_height=224, img_width=224, img_channels
     }
 
     image ->
-    conv(filter=7, channels=64, stride=2) ->
+    conv(kernel=7, channels=64, stride=2) ->
     Pooling(pool_type="max", kernel=(3,3), stride=(2,2), padding="valid") ->
     resLayer(channels=64, ->=3) ->
     resLayer(channels=128, stride=2) ->

src/test/resources/invalid_tests/ArgumentConstraintTest4.cnna

@@ -2,8 +2,8 @@ architecture ArgumentConstraintTest4(img_height=224, img_width=224, img_channels
     def input Z(0:255)^{img_channels, img_height, img_width} image
     def output Q(0:1)^{classes} predictions
 
-    def conv(filter, channels, stride=1, act=true){
-        Convolution(kernel=(filter,filter), channels=channels, stride=(stride,stride)) ->
+    def conv(kernel, channels, stride=1, act=true){
+        Convolution(kernel=(kernel,kernel), channels=channels, stride=(stride,stride)) ->
         BatchNorm() ->