Commit 51c6e628 authored by Christian Fuß's avatar Christian Fuß

removed some unnecessary code

parent a5b546e2
Pipeline #156176 passed with stages
in 19 minutes and 6 seconds
......@@ -202,12 +202,6 @@ public class UnrollSymbol extends ArchitectureElementSymbol {
@Override
public List<ArchTypeSymbol> computeOutputTypes() {
try {
System.err.println("############################" + this.getIntValue(AllPredefinedLayers.BEAMSEARCH_MAX_LENGTH).get());
}catch (Exception e){
System.err.println("44444444444444444444444444");
e.printStackTrace();
}
if (getElements().isEmpty()){
if (getInputElement().isPresent()){
return getInputElement().get().getOutputTypes();
......
......@@ -66,7 +66,6 @@ public class AllCoCoTest extends AbstractCoCoTest {
checkValid("valid_tests", "Alexnet_alt2");
checkValid("valid_tests", "MultipleOutputs");
checkValid("valid_tests", "MultipleStreams");
checkValid("valid_tests", "Alexnet_alt_OneHotOutput");
}
@Test
......
architecture Alexnet_alt_OneHotOutput(img_height=224, img_width=224, 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=(11,11), channels=96, stride=(4,4), padding="no_loss") ->
Lrn(nsize=5, alpha=0.0001, beta=0.75) ->
Pooling(pool_type="max", kernel=(3,3), stride=(2,2), padding="no_loss") ->
Relu() ->
Split(n=2) ->
(
[0] ->
Convolution(kernel=(5,5), channels=128) ->
Lrn(nsize=5, alpha=0.0001, beta=0.75) ->
Pooling(pool_type="max", kernel=(3,3), stride=(2,2), padding="no_loss") ->
Relu()
|
[1] ->
Convolution(kernel=(5,5), channels=128) ->
Lrn(nsize=5, alpha=0.0001, beta=0.75) ->
Pooling(pool_type="max", kernel=(3,3), stride=(2,2), padding="no_loss") ->
Relu()
) ->
Concatenate() ->
Convolution(kernel=(3,3), channels=384) ->
Relu() ->
Split(n=2) ->
(
[0] ->
Convolution(kernel=(3,3), channels=192) ->
Relu() ->
Convolution(kernel=(3,3), channels=128) ->
Pooling(pool_type="max", kernel=(3,3), stride=(2,2), padding="no_loss") ->
Relu()
|
[1] ->
Convolution(kernel=(3,3), channels=192) ->
Relu() ->
Convolution(kernel=(3,3), channels=128) ->
Pooling(pool_type="max", kernel=(3,3), stride=(2,2), padding="no_loss") ->
Relu()
) ->
Concatenate() ->
FullyConnected(units=4096) ->
Relu() ->
Dropout() ->
FullyConnected(units=4096) ->
Relu() ->
Dropout() ->
FullyConnected(units=classes) ->
Softmax() ->
predictions;
}
\ No newline at end of file
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