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Commit 637fc715 authored by Christian Fuß's avatar Christian Fuß
Browse files

fixed naming Nets in CNNNET.ftl

parent fba1a685
Pipeline #175291 failed with stages
in 18 seconds
......@@ -109,9 +109,12 @@ public class CNNArch2GluonTemplateController extends CNNArchTemplateController {
include(unrollElement.getBody().getElements().get(0).getResolvedThis().get(), writer, netDefinitionMode);
}
for(int i = 0; i < (int)unrollElement.getIntValue(AllPredefinedLayers.BEAMSEARCH_MAX_LENGTH).get(); i++) {
//System.err.println("TIME: " + unrollElement.getIntValue(AllPredefinedLayers.BEAMSEARCH_T_NAME).get());
int timestep = 0;//unrollElement.getIntValue(AllPredefinedLayers.BEAMSEARCH_T_NAME).get();
System.err.println("i: " + i);
while (timestep < unrollElement.getIntValue(AllPredefinedLayers.BEAMSEARCH_MAX_LENGTH).get()) {
System.err.println("i: " + timestep);
for (ArchitectureElementSymbol element : unrollElement.getBody().getElements()) {
previousElement = getCurrentElement();
......@@ -126,6 +129,7 @@ public class CNNArch2GluonTemplateController extends CNNArchTemplateController {
}
}
}
timestep++;
}
......
......@@ -96,23 +96,7 @@ ${tc.include(stream, "FORWARD_FUNCTION")}
<#list tc.architecture.unrolls as unroll>
<#if unroll.isTrainable()>
class Net_${unroll?index}(gluon.HybridBlock):
def __init__(self, data_mean=None, data_std=None, **kwargs):
super(Net_${unroll?index}, self).__init__(**kwargs)
self.last_layers = {}
with self.name_scope():
${tc.include(unroll, "ARCHITECTURE_DEFINITION")}
def hybrid_forward(self, F, ${tc.join(tc.getUnrollInputNames(unroll), ", ")}):
${tc.include(unroll, "FORWARD_FUNCTION")}
return ${tc.join(tc.getUnrollOutputNames(unroll), ", ")}
</#if>
</#list>
<#list tc.architecture.unrolls as unroll>
<#if unroll.isTrainable()>
class Net_${unroll?index}(gluon.HybridBlock):
class Net_${tc.architecture.streams?size + unroll?index}(gluon.HybridBlock):
def __init__(self, data_mean=None, data_std=None, **kwargs):
super(Net_${unroll?index}, self).__init__(**kwargs)
self.last_layers = {}
......
architecture RNNencdec(max_length=5, vocabulary_size=30000, hidden_size=1000){
def input Q(0:1)^{vocabulary_size} source
def output Q(0:1)^{vocabulary_size} target
def output Q(0:1)^{vocabulary_size} target[3]
timed <t> BeamSearchStart(max_length=5) {
source ->
source -> target[0];
timed <t=2> BeamSearchStart(max_length=5) {
target[t-1] ->
FullyConnected(units=vocabulary_size) ->
Softmax() ->
target
target[t]
};
}
\ No newline at end of file
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