Adapted Templates Convolution, FullyConnected and Pooling. Added LeNet.cnna model

src/main/resources/templates/caffe2/elements/Convolution.ftl

 <#assign input = element.inputs[0]>
-<#if element.padding??>
+<#if element.padding??>  <#-- Check wheather padding null is. -->
 <#assign input = element.name>
-        ${element.name} = mx.symbol.pad(data=${element.inputs[0]},  #TODO: pending to adapt
-            mode='constant',
-            pad_width=(${tc.join(element.padding, ",")}),
-            constant_value=0)
+        #TODO: check how to adapt CNNArchLang argument pad_width=${element.padding}
 </#if>
-        ${element.name} = brew.conv(model, ${input}, '${element.name}', dim_in=1, dim_out=20, kernel=5)
+        ${element.name} = brew.conv(model, ${input}, '${element.name}', dim_in=1, dim_out=${element.channels?c}, kernel=${element.kernel}, stride=${element.stride})
+        #TODO: check how to adapt CNNArchLang argument no_bias=${element.noBias?string("True","False")}
+        #TODO: check how to adapt CNNArchLang argument pad_width=${element.padding}
 <#include "OutputShape.ftl">

src/main/resources/templates/caffe2/elements/FullyConnected.ftl

 <#assign flatten = element.element.inputTypes[0].height != 1 || element.element.inputTypes[0].width != 1>
 <#assign input = element.inputs[0]>
 <#if flatten>
-        ${element.name} = mx.symbol.flatten(data=${input}) #TODO: Pending to adapt
+        ${element.name} = mx.symbol.flatten(data=${input}) #TODO: check how to adapt CNNArchLang flatten
 <#assign input = element.name>
 </#if>
-        ${element.name} = brew.fc(model, ${input}, '${element.name}', dim_in=50 * 4 * 4, dim_out=500)
+        ${element.name} = brew.fc(model, ${input}, '${element.name}', dim_in=50 * 4 * 4, dim_out=${element.units?c})
+
+        #TODO: check how to adapt CNNArchLang argument no_bias=${element.noBias?string("True","False")}

src/main/resources/templates/caffe2/elements/MaxPooling.ftl

-<#assign input = element.inputs[0]>
-<#if element.padding??>
-<#assign input = element.name>
-        ${element.name} = mx.symbol.pad(data=${element.inputs[0]},  #TODO: Pending to adapt o eliminate
-            mode='constant',
-            pad_width=(${tc.join(element.padding, ",")}),
-            constant_value=0)
-</#if>
-        ${element.name} = brew.max_pool(model, ${input}, '${element.name}', kernel=2, stride=2)
-
-
-
-

src/main/resources/templates/caffe2/elements/Pooling.ftl

 <#assign input = element.inputs[0]>
 <#if element.padding??>
 <#assign input = element.name>
-        ${element.name} = mx.symbol.pad(data=${element.inputs[0]},
-            mode='constant',
-            pad_width=(${tc.join(element.padding, ",")}),
-            constant_value=0)
+        #TODO: check how to adapt CNNArchLang argument pad_width=${element.padding}
 </#if>
-        ${element.name} = mx.symbol.Pooling(data=${input},
-            kernel=(${tc.join(element.kernel, ",")}),
-            pool_type=${element.poolType},
-            stride=(${tc.join(element.stride, ",")}),
-            name="${element.name}")
+
+<#if element.poolType == "max">
+        ${element.name} = brew.max_pool(model, ${input}, '${element.name}', kernel=${element.kernel}, stride=${element.stride})
+<#elseif element.poolType == "avg">
+        ${element.name} = brew.average_pool(model, ${input}, '${element.name}', kernel=${element.kernel}, stride=${element.stride})
+</#if>
+
+
 <#include "OutputShape.ftl">
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src/test/resources/valid_tests/LeNet.cnna

-architecture LeNet(img_height=28, img_width=28, img_channels=3, classes=10){
+architecture LeNet(img_height=32, img_width=32, img_channels=3, classes=10){
     def input Z(0:255)^{img_channels, img_height, img_width} image
     def output Q(0:1)^{classes} predictions
 
     image ->
-    Convolution(kernel=(5,5), channels=20, stride=(1,1), padding="no_loss") ->
-    MaxPooling(kernel=(3,3), stride=(2,2), padding="no_loss") ->
-    FullyConnected(units=64, no_bias=true) ->
-    Tanh() ->
-    FullyConnected(units=classes, no_bias=true) ->
+    Convolution(kernel=(5,5), channels=20) ->
+    Pooling(pool_type="max", kernel=(2,2), stride=(2,2)) ->
+    Convolution(kernel=(5,5), channels=50) ->
+    Pooling(pool_type="max", kernel=(2,2), stride=(2,2)) ->
+    FullyConnected(units=500) ->
+    Relu() ->
+    FullyConnected(units=classes) ->
     Softmax() ->
     predictions
 }
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