diff --git a/src/main/java/de/monticore/lang/monticar/cnnarch/gluongenerator/CNNArch2GluonArchitectureSupportChecker.java b/src/main/java/de/monticore/lang/monticar/cnnarch/gluongenerator/CNNArch2GluonArchitectureSupportChecker.java
index a55ed53f6a514161ef6d3298594481d7b592a25d..e213c30b0dc0d166bdd204274c72f12995cc9111 100644
--- a/src/main/java/de/monticore/lang/monticar/cnnarch/gluongenerator/CNNArch2GluonArchitectureSupportChecker.java
+++ b/src/main/java/de/monticore/lang/monticar/cnnarch/gluongenerator/CNNArch2GluonArchitectureSupportChecker.java
@@ -15,8 +15,8 @@ public class CNNArch2GluonArchitectureSupportChecker extends ArchitectureSupport
return true;
}
- /*protected boolean checkMultipleOutputs(ArchitectureSymbol architecture) {
+ protected boolean checkMultipleOutputs(ArchitectureSymbol architecture) {
return true;
- }*/
+ }
}
diff --git a/src/main/resources/templates/gluon/CNNNet.ftl b/src/main/resources/templates/gluon/CNNNet.ftl
index d032cd4a4c31ae46f03a63e8da356f0386ebb178..0c75c37dded0dd72d7947bb155ae1d4dd8811beb 100644
--- a/src/main/resources/templates/gluon/CNNNet.ftl
+++ b/src/main/resources/templates/gluon/CNNNet.ftl
@@ -71,8 +71,15 @@ class NoNormalization(gluon.HybridBlock):
class Net(gluon.HybridBlock):
def __init__(self, data_mean=None, data_std=None, **kwargs):
super(Net, self).__init__(**kwargs)
+ self.last_layers = {}
with self.name_scope():
${tc.include(tc.architecture.streams[0], "ARCHITECTURE_DEFINITION")}
def hybrid_forward(self, F, ${tc.join(tc.architectureInputs, ", ")}):
-${tc.include(tc.architecture.streams[0], "FORWARD_FUNCTION")}
\ No newline at end of file
+ <#if tc.architectureOutputs?size gt 1>
+ outputs = []
+ </#if>
+${tc.include(tc.architecture.streams[0], "FORWARD_FUNCTION")}
+ <#if tc.architectureOutputs?size gt 1>
+ return tuple(outputs)
+ </#if>
\ No newline at end of file
diff --git a/src/main/resources/templates/gluon/CNNSupervisedTrainer.ftl b/src/main/resources/templates/gluon/CNNSupervisedTrainer.ftl
index 04aa5a657b1abfb14643a75233f328307345ac27..08a640b6c66ac05c598fa0bef4e19b093e99854a 100644
--- a/src/main/resources/templates/gluon/CNNSupervisedTrainer.ftl
+++ b/src/main/resources/templates/gluon/CNNSupervisedTrainer.ftl
@@ -69,15 +69,18 @@ class ${tc.fileNameWithoutEnding}:
trainer = mx.gluon.Trainer(self._net.collect_params(), optimizer, optimizer_params)
- if self._net.last_layer == 'softmax':
- loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss()
- elif self._net.last_layer == 'sigmoid':
- loss_function = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
- elif self._net.last_layer == 'linear':
- loss_function = mx.gluon.loss.L2Loss()
- else:
- loss_function = mx.gluon.loss.L2Loss()
- logging.warning("Invalid last_layer, defaulting to L2 loss")
+ loss_functions = {}
+
+ for output_name, last_layer in self._net.last_layers.items():
+ if last_layer == 'softmax':
+ loss_functions[output_name] = mx.gluon.loss.SoftmaxCrossEntropyLoss()
+ elif last_layer == 'sigmoid':
+ loss_functions[output_name] = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
+ elif last_layer == 'linear':
+ loss_functions[output_name] = mx.gluon.loss.L2Loss()
+ else:
+ loss_functions[output_name] = mx.gluon.loss.L2Loss()
+ logging.warning("Invalid last layer, defaulting to L2 loss")
speed_period = 50
tic = None
@@ -86,13 +89,16 @@ class ${tc.fileNameWithoutEnding}:
train_iter.reset()
for batch_i, batch in enumerate(train_iter):
<#list tc.architectureInputs as input_name>
- ${input_name} = batch.data[${input_name?index}].as_in_context(mx_context)
+ ${input_name}_data = batch.data[${input_name?index}].as_in_context(mx_context)
+ </#list>
+ <#list tc.architectureOutputs as output_name>
+ ${output_name}_label = batch.label[${output_name?index}].as_in_context(mx_context)
</#list>
- label = batch.label[0].as_in_context(mx_context)
with autograd.record():
- output = self._net(${tc.join(tc.architectureInputs, ",")})
- loss = loss_function(output, label)
+ ${tc.join(tc.architectureOutputs, ", ", "", "_output")} = self._net(${tc.join(tc.architectureInputs, ", ", "", "_data")})
+
+ loss = <#list tc.architectureOutputs as output_name>loss_functions['${output_name}'](${output_name}_output, ${output_name}_label)<#sep> + </#list>
loss.backward()
trainer.step(batch_size)
@@ -116,26 +122,40 @@ class ${tc.fileNameWithoutEnding}:
metric = mx.metric.create(eval_metric)
for batch_i, batch in enumerate(train_iter):
<#list tc.architectureInputs as input_name>
- ${input_name} = batch.data[${input_name?index}].as_in_context(mx_context)
+ ${input_name}_data = batch.data[${input_name?index}].as_in_context(mx_context)
</#list>
- label = batch.label[0].as_in_context(mx_context)
- output = self._net(${tc.join(tc.architectureInputs, ",")})
- predictions = mx.nd.argmax(output, axis=1)
- metric.update(preds=predictions, labels=label)
+ labels = [
+ <#list tc.architectureOutputs as output_name>batch.label[${output_name?index}].as_in_context(mx_context)<#sep>, </#list>
+ ]
+
+ ${tc.join(tc.architectureOutputs, ", ", "", "_output")} = self._net(${tc.join(tc.architectureInputs, ", ", "", "_data")})
+
+ predictions = [
+ <#list tc.architectureOutputs as output_name>mx.nd.argmax(${output_name}_output, axis=1)<#sep>, </#list>
+ ]
+
+ metric.update(preds=predictions, labels=labels)
train_metric_score = metric.get()[1]
test_iter.reset()
metric = mx.metric.create(eval_metric)
for batch_i, batch in enumerate(test_iter):
<#list tc.architectureInputs as input_name>
- ${input_name} = batch.data[${input_name?index}].as_in_context(mx_context)
+ ${input_name}_data = batch.data[${input_name?index}].as_in_context(mx_context)
</#list>
- label = batch.label[0].as_in_context(mx_context)
- output = self._net(${tc.join(tc.architectureInputs, ",")})
- predictions = mx.nd.argmax(output, axis=1)
- metric.update(preds=predictions, labels=label)
+ labels = [
+ <#list tc.architectureOutputs as output_name>batch.label[${output_name?index}].as_in_context(mx_context)<#sep>, </#list>
+ ]
+
+ ${tc.join(tc.architectureOutputs, ", ", "", "_output")} = self._net(${tc.join(tc.architectureInputs, ", ", "", "_data")})
+
+ predictions = [
+ <#list tc.architectureOutputs as output_name>mx.nd.argmax(${output_name}_output, axis=1)<#sep>, </#list>
+ ]
+
+ metric.update(preds=predictions, labels=labels)
test_metric_score = metric.get()[1]
logging.info("Epoch[%d] Train: %f, Test: %f" % (epoch, train_metric_score, test_metric_score))
diff --git a/src/main/resources/templates/gluon/elements/Output.ftl b/src/main/resources/templates/gluon/elements/Output.ftl
index 2d18ef6784e2808cf18de62a22b28ca970bb4b2b..0b48633eb5c6472419f9f8c83f3af18198a76834 100644
--- a/src/main/resources/templates/gluon/elements/Output.ftl
+++ b/src/main/resources/templates/gluon/elements/Output.ftl
@@ -2,13 +2,17 @@
<#assign mode = definition_mode.toString()>
<#if mode == "ARCHITECTURE_DEFINITION">
<#if element.softmaxOutput>
- self.last_layer = 'softmax'
+ self.last_layers['${element.name}'] = 'softmax'
<#elseif element.logisticRegressionOutput>
- self.last_layer = 'sigmoid'
+ self.last_layers['${element.name}'] = 'sigmoid'
<#elseif element.linearRegressionOutput>
- self.last_layer = 'linear'
+ self.last_layers['${element.name}'] = 'linear'
</#if>
</#if>
<#if mode == "FORWARD_FUNCTION">
+ <#if tc.architectureOutputs?size gt 1>
+ outputs.append(${input})
+ <#else>
return ${input}
+ </#if>
</#if>
diff --git a/src/test/java/de/monticore/lang/monticar/cnnarch/gluongenerator/GenerationTest.java b/src/test/java/de/monticore/lang/monticar/cnnarch/gluongenerator/GenerationTest.java
index 805d9b1a06a1ef4d9dae82f048b191f0feff786c..347041b1fa6f47b3bc65f8c204a7601654c8b854 100644
--- a/src/test/java/de/monticore/lang/monticar/cnnarch/gluongenerator/GenerationTest.java
+++ b/src/test/java/de/monticore/lang/monticar/cnnarch/gluongenerator/GenerationTest.java
@@ -127,7 +127,7 @@ public class GenerationTest extends AbstractSymtabTest {
Log.getFindings().clear();
String[] args = {"-m", "src/test/resources/invalid_tests", "-r", "MultipleOutputs"};
CNNArch2GluonCli.main(args);
- assertTrue(Log.getFindings().size() == 3);
+ assertTrue(Log.getFindings().size() == 2);
}
@Test
diff --git a/src/test/resources/target_code/CNNNet_Alexnet.py b/src/test/resources/target_code/CNNNet_Alexnet.py
index 3c142ca9d3946bf7f57b5f02c2c4806aaa839b78..9d6dc8901bcad040625712ca59eb4763f770e8b3 100644
--- a/src/test/resources/target_code/CNNNet_Alexnet.py
+++ b/src/test/resources/target_code/CNNNet_Alexnet.py
@@ -71,6 +71,7 @@ class NoNormalization(gluon.HybridBlock):
class Net(gluon.HybridBlock):
def __init__(self, data_mean=None, data_std=None, **kwargs):
super(Net, self).__init__(**kwargs)
+ self.last_layers = {}
with self.name_scope():
if data_mean:
assert(data_std)
@@ -194,8 +195,7 @@ class Net(gluon.HybridBlock):
self.fc8_ = gluon.nn.Dense(units=10, use_bias=True)
# fc8_, output shape: {[10,1,1]}
-
- self.last_layer = 'softmax'
+ self.last_layers['predictions'] = 'softmax'
def hybrid_forward(self, F, data):
@@ -261,3 +261,4 @@ class Net(gluon.HybridBlock):
dropout7_ = self.dropout7_(relu7_)
fc8_ = self.fc8_(dropout7_)
return fc8_
+
diff --git a/src/test/resources/target_code/CNNNet_CifarClassifierNetwork.py b/src/test/resources/target_code/CNNNet_CifarClassifierNetwork.py
index 3b1794a054c3c9c0f16e3d9b6579a97b8a7acdca..d4ecbf96c4d42abf842eceb984e6515fa59b22f6 100644
--- a/src/test/resources/target_code/CNNNet_CifarClassifierNetwork.py
+++ b/src/test/resources/target_code/CNNNet_CifarClassifierNetwork.py
@@ -71,6 +71,7 @@ class NoNormalization(gluon.HybridBlock):
class Net(gluon.HybridBlock):
def __init__(self, data_mean=None, data_std=None, **kwargs):
super(Net, self).__init__(**kwargs)
+ self.last_layers = {}
with self.name_scope():
if data_mean:
assert(data_std)
@@ -345,8 +346,7 @@ class Net(gluon.HybridBlock):
self.fc32_ = gluon.nn.Dense(units=10, use_bias=True)
# fc32_, output shape: {[10,1,1]}
-
- self.last_layer = 'softmax'
+ self.last_layers['softmax'] = 'softmax'
def hybrid_forward(self, F, data):
@@ -454,3 +454,4 @@ class Net(gluon.HybridBlock):
dropout31_ = self.dropout31_(fc31_)
fc32_ = self.fc32_(dropout31_)
return fc32_
+
diff --git a/src/test/resources/target_code/CNNNet_VGG16.py b/src/test/resources/target_code/CNNNet_VGG16.py
index 6e633348223207514edc342c621bc98ecb33d57e..5fc931c97a0c73ae4d39e22ef50f6e978afa7e46 100644
--- a/src/test/resources/target_code/CNNNet_VGG16.py
+++ b/src/test/resources/target_code/CNNNet_VGG16.py
@@ -71,6 +71,7 @@ class NoNormalization(gluon.HybridBlock):
class Net(gluon.HybridBlock):
def __init__(self, data_mean=None, data_std=None, **kwargs):
super(Net, self).__init__(**kwargs)
+ self.last_layers = {}
with self.name_scope():
if data_mean:
assert(data_std)
@@ -222,8 +223,7 @@ class Net(gluon.HybridBlock):
self.fc15_ = gluon.nn.Dense(units=1000, use_bias=True)
# fc15_, output shape: {[1000,1,1]}
-
- self.last_layer = 'softmax'
+ self.last_layers['predictions'] = 'softmax'
def hybrid_forward(self, F, data):
@@ -281,3 +281,4 @@ class Net(gluon.HybridBlock):
dropout15_ = self.dropout15_(relu15_)
fc15_ = self.fc15_(dropout15_)
return fc15_
+
diff --git a/src/test/resources/target_code/CNNSupervisedTrainer_Alexnet.py b/src/test/resources/target_code/CNNSupervisedTrainer_Alexnet.py
index 01e32cda7cd4a5e1451c24f33f21c606ebef314e..3355e6643cdf81636a24fb7a59cc1c305b0c2a8b 100644
--- a/src/test/resources/target_code/CNNSupervisedTrainer_Alexnet.py
+++ b/src/test/resources/target_code/CNNSupervisedTrainer_Alexnet.py
@@ -69,15 +69,18 @@ class CNNSupervisedTrainer_Alexnet:
trainer = mx.gluon.Trainer(self._net.collect_params(), optimizer, optimizer_params)
- if self._net.last_layer == 'softmax':
- loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss()
- elif self._net.last_layer == 'sigmoid':
- loss_function = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
- elif self._net.last_layer == 'linear':
- loss_function = mx.gluon.loss.L2Loss()
- else:
- loss_function = mx.gluon.loss.L2Loss()
- logging.warning("Invalid last_layer, defaulting to L2 loss")
+ loss_functions = {}
+
+ for output_name, last_layer in self._net.last_layers.items():
+ if last_layer == 'softmax':
+ loss_functions[output_name] = mx.gluon.loss.SoftmaxCrossEntropyLoss()
+ elif last_layer == 'sigmoid':
+ loss_functions[output_name] = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
+ elif last_layer == 'linear':
+ loss_functions[output_name] = mx.gluon.loss.L2Loss()
+ else:
+ loss_functions[output_name] = mx.gluon.loss.L2Loss()
+ logging.warning("Invalid last layer, defaulting to L2 loss")
speed_period = 50
tic = None
@@ -85,12 +88,13 @@ class CNNSupervisedTrainer_Alexnet:
for epoch in range(begin_epoch, begin_epoch + num_epoch):
train_iter.reset()
for batch_i, batch in enumerate(train_iter):
- data = batch.data[0].as_in_context(mx_context)
- label = batch.label[0].as_in_context(mx_context)
+ data_data = batch.data[0].as_in_context(mx_context)
+ predictions_label = batch.label[0].as_in_context(mx_context)
with autograd.record():
- output = self._net(data)
- loss = loss_function(output, label)
+ predictions_output = self._net(data_data)
+
+ loss = loss_functions['predictions'](predictions_output, predictions_label)
loss.backward()
trainer.step(batch_size)
@@ -113,23 +117,37 @@ class CNNSupervisedTrainer_Alexnet:
train_iter.reset()
metric = mx.metric.create(eval_metric)
for batch_i, batch in enumerate(train_iter):
- data = batch.data[0].as_in_context(mx_context)
- label = batch.label[0].as_in_context(mx_context)
+ data_data = batch.data[0].as_in_context(mx_context)
+
+ labels = [
+ batch.label[0].as_in_context(mx_context)
+ ]
+
+ predictions_output = self._net(data_data)
- output = self._net(data)
- predictions = mx.nd.argmax(output, axis=1)
- metric.update(preds=predictions, labels=label)
+ predictions = [
+ mx.nd.argmax(predictions_output, axis=1)
+ ]
+
+ metric.update(preds=predictions, labels=labels)
train_metric_score = metric.get()[1]
test_iter.reset()
metric = mx.metric.create(eval_metric)
for batch_i, batch in enumerate(test_iter):
- data = batch.data[0].as_in_context(mx_context)
- label = batch.label[0].as_in_context(mx_context)
+ data_data = batch.data[0].as_in_context(mx_context)
+
+ labels = [
+ batch.label[0].as_in_context(mx_context)
+ ]
+
+ predictions_output = self._net(data_data)
+
+ predictions = [
+ mx.nd.argmax(predictions_output, axis=1)
+ ]
- output = self._net(data)
- predictions = mx.nd.argmax(output, axis=1)
- metric.update(preds=predictions, labels=label)
+ metric.update(preds=predictions, labels=labels)
test_metric_score = metric.get()[1]
logging.info("Epoch[%d] Train: %f, Test: %f" % (epoch, train_metric_score, test_metric_score))
diff --git a/src/test/resources/target_code/CNNSupervisedTrainer_CifarClassifierNetwork.py b/src/test/resources/target_code/CNNSupervisedTrainer_CifarClassifierNetwork.py
index 05d4e708be9052ca376b40767fa99739a788da1a..3a4095116bb681e71b26950682a3b002e8b192a4 100644
--- a/src/test/resources/target_code/CNNSupervisedTrainer_CifarClassifierNetwork.py
+++ b/src/test/resources/target_code/CNNSupervisedTrainer_CifarClassifierNetwork.py
@@ -69,15 +69,18 @@ class CNNSupervisedTrainer_CifarClassifierNetwork:
trainer = mx.gluon.Trainer(self._net.collect_params(), optimizer, optimizer_params)
- if self._net.last_layer == 'softmax':
- loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss()
- elif self._net.last_layer == 'sigmoid':
- loss_function = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
- elif self._net.last_layer == 'linear':
- loss_function = mx.gluon.loss.L2Loss()
- else:
- loss_function = mx.gluon.loss.L2Loss()
- logging.warning("Invalid last_layer, defaulting to L2 loss")
+ loss_functions = {}
+
+ for output_name, last_layer in self._net.last_layers.items():
+ if last_layer == 'softmax':
+ loss_functions[output_name] = mx.gluon.loss.SoftmaxCrossEntropyLoss()
+ elif last_layer == 'sigmoid':
+ loss_functions[output_name] = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
+ elif last_layer == 'linear':
+ loss_functions[output_name] = mx.gluon.loss.L2Loss()
+ else:
+ loss_functions[output_name] = mx.gluon.loss.L2Loss()
+ logging.warning("Invalid last layer, defaulting to L2 loss")
speed_period = 50
tic = None
@@ -85,12 +88,13 @@ class CNNSupervisedTrainer_CifarClassifierNetwork:
for epoch in range(begin_epoch, begin_epoch + num_epoch):
train_iter.reset()
for batch_i, batch in enumerate(train_iter):
- data = batch.data[0].as_in_context(mx_context)
- label = batch.label[0].as_in_context(mx_context)
+ data_data = batch.data[0].as_in_context(mx_context)
+ softmax_label = batch.label[0].as_in_context(mx_context)
with autograd.record():
- output = self._net(data)
- loss = loss_function(output, label)
+ softmax_output = self._net(data_data)
+
+ loss = loss_functions['softmax'](softmax_output, softmax_label)
loss.backward()
trainer.step(batch_size)
@@ -113,23 +117,37 @@ class CNNSupervisedTrainer_CifarClassifierNetwork:
train_iter.reset()
metric = mx.metric.create(eval_metric)
for batch_i, batch in enumerate(train_iter):
- data = batch.data[0].as_in_context(mx_context)
- label = batch.label[0].as_in_context(mx_context)
+ data_data = batch.data[0].as_in_context(mx_context)
+
+ labels = [
+ batch.label[0].as_in_context(mx_context)
+ ]
+
+ softmax_output = self._net(data_data)
- output = self._net(data)
- predictions = mx.nd.argmax(output, axis=1)
- metric.update(preds=predictions, labels=label)
+ predictions = [
+ mx.nd.argmax(softmax_output, axis=1)
+ ]
+
+ metric.update(preds=predictions, labels=labels)
train_metric_score = metric.get()[1]
test_iter.reset()
metric = mx.metric.create(eval_metric)
for batch_i, batch in enumerate(test_iter):
- data = batch.data[0].as_in_context(mx_context)
- label = batch.label[0].as_in_context(mx_context)
+ data_data = batch.data[0].as_in_context(mx_context)
+
+ labels = [
+ batch.label[0].as_in_context(mx_context)
+ ]
+
+ softmax_output = self._net(data_data)
+
+ predictions = [
+ mx.nd.argmax(softmax_output, axis=1)
+ ]
- output = self._net(data)
- predictions = mx.nd.argmax(output, axis=1)
- metric.update(preds=predictions, labels=label)
+ metric.update(preds=predictions, labels=labels)
test_metric_score = metric.get()[1]
logging.info("Epoch[%d] Train: %f, Test: %f" % (epoch, train_metric_score, test_metric_score))
diff --git a/src/test/resources/target_code/CNNSupervisedTrainer_VGG16.py b/src/test/resources/target_code/CNNSupervisedTrainer_VGG16.py
index 90728f4bebc3f78f3915b44161e976d75fbe2d2f..f4c124e2dea236cea4c4f8202571d8ed0aae64cd 100644
--- a/src/test/resources/target_code/CNNSupervisedTrainer_VGG16.py
+++ b/src/test/resources/target_code/CNNSupervisedTrainer_VGG16.py
@@ -69,15 +69,18 @@ class CNNSupervisedTrainer_VGG16:
trainer = mx.gluon.Trainer(self._net.collect_params(), optimizer, optimizer_params)
- if self._net.last_layer == 'softmax':
- loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss()
- elif self._net.last_layer == 'sigmoid':
- loss_function = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
- elif self._net.last_layer == 'linear':
- loss_function = mx.gluon.loss.L2Loss()
- else:
- loss_function = mx.gluon.loss.L2Loss()
- logging.warning("Invalid last_layer, defaulting to L2 loss")
+ loss_functions = {}
+
+ for output_name, last_layer in self._net.last_layers.items():
+ if last_layer == 'softmax':
+ loss_functions[output_name] = mx.gluon.loss.SoftmaxCrossEntropyLoss()
+ elif last_layer == 'sigmoid':
+ loss_functions[output_name] = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
+ elif last_layer == 'linear':
+ loss_functions[output_name] = mx.gluon.loss.L2Loss()
+ else:
+ loss_functions[output_name] = mx.gluon.loss.L2Loss()
+ logging.warning("Invalid last layer, defaulting to L2 loss")
speed_period = 50
tic = None
@@ -85,12 +88,13 @@ class CNNSupervisedTrainer_VGG16:
for epoch in range(begin_epoch, begin_epoch + num_epoch):
train_iter.reset()
for batch_i, batch in enumerate(train_iter):
- data = batch.data[0].as_in_context(mx_context)
- label = batch.label[0].as_in_context(mx_context)
+ data_data = batch.data[0].as_in_context(mx_context)
+ predictions_label = batch.label[0].as_in_context(mx_context)
with autograd.record():
- output = self._net(data)
- loss = loss_function(output, label)
+ predictions_output = self._net(data_data)
+
+ loss = loss_functions['predictions'](predictions_output, predictions_label)
loss.backward()
trainer.step(batch_size)
@@ -113,23 +117,37 @@ class CNNSupervisedTrainer_VGG16:
train_iter.reset()
metric = mx.metric.create(eval_metric)
for batch_i, batch in enumerate(train_iter):
- data = batch.data[0].as_in_context(mx_context)
- label = batch.label[0].as_in_context(mx_context)
+ data_data = batch.data[0].as_in_context(mx_context)
+
+ labels = [
+ batch.label[0].as_in_context(mx_context)
+ ]
+
+ predictions_output = self._net(data_data)
- output = self._net(data)
- predictions = mx.nd.argmax(output, axis=1)
- metric.update(preds=predictions, labels=label)
+ predictions = [
+ mx.nd.argmax(predictions_output, axis=1)
+ ]
+
+ metric.update(preds=predictions, labels=labels)
train_metric_score = metric.get()[1]
test_iter.reset()
metric = mx.metric.create(eval_metric)
for batch_i, batch in enumerate(test_iter):
- data = batch.data[0].as_in_context(mx_context)
- label = batch.label[0].as_in_context(mx_context)
+ data_data = batch.data[0].as_in_context(mx_context)
+
+ labels = [
+ batch.label[0].as_in_context(mx_context)
+ ]
+
+ predictions_output = self._net(data_data)
+
+ predictions = [
+ mx.nd.argmax(predictions_output, axis=1)
+ ]
- output = self._net(data)
- predictions = mx.nd.argmax(output, axis=1)
- metric.update(preds=predictions, labels=label)
+ metric.update(preds=predictions, labels=labels)
test_metric_score = metric.get()[1]
logging.info("Epoch[%d] Train: %f, Test: %f" % (epoch, train_metric_score, test_metric_score))