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monticore
EmbeddedMontiArc
generators
EMADL2CPP
Commits
948dfb62
Commit
948dfb62
authored
Apr 03, 2020
by
Julian Dierkes
Browse files
adjusted tests
parent
90023d67
Pipeline
#264315
failed with stages
Changes
19
Pipelines
1
Hide whitespace changes
Inline
Side-by-side
src/main/java/de/monticore/lang/monticar/emadl/generator/EMADLGenerator.java
View file @
948dfb62
...
...
@@ -16,8 +16,12 @@ import de.monticore.lang.monticar.cnnarch.generator.CNNTrainGenerator;
import
de.monticore.lang.monticar.cnnarch.generator.DataPathConfigParser
;
import
de.monticore.lang.monticar.cnnarch.gluongenerator.CNNTrain2Gluon
;
import
de.monticore.lang.monticar.cnnarch.gluongenerator.annotations.ArchitectureAdapter
;
import
de.monticore.lang.monticar.cnnarch.gluongenerator.preprocessing.PreprocessingComponentParameterAdapter
;
import
de.monticore.lang.monticar.cnnarch.gluongenerator.preprocessing.PreprocessingPortChecker
;
import
de.monticore.lang.monticar.cnntrain._cocos.CNNTrainCocos
;
import
de.monticore.lang.monticar.cnntrain._symboltable.ConfigurationSymbol
;
import
de.monticore.lang.monticar.cnntrain._symboltable.LearningMethod
;
import
de.monticore.lang.monticar.cnntrain._symboltable.PreprocessingComponentSymbol
;
import
de.monticore.lang.monticar.emadl._cocos.DataPathCocos
;
import
de.monticore.lang.monticar.emadl._cocos.EMADLCocos
;
import
de.monticore.lang.monticar.emadl.tagging.dltag.DataPathSymbol
;
...
...
@@ -30,6 +34,7 @@ import de.monticore.lang.monticar.generator.pythonwrapper.GeneratorPythonWrapper
import
de.monticore.lang.monticar.generator.cpp.converter.TypeConverter
;
import
de.monticore.lang.monticar.generator.pythonwrapper.GeneratorPythonWrapperFactory
;
import
de.monticore.lang.monticar.generator.pythonwrapper.GeneratorPythonWrapperStandaloneApi
;
import
de.monticore.lang.monticar.generator.pythonwrapper.symbolservices.data.ComponentPortInformation
;
import
de.monticore.lang.tagging._symboltable.TagSymbol
;
import
de.monticore.lang.tagging._symboltable.TaggingResolver
;
import
de.monticore.symboltable.Scope
;
...
...
@@ -621,7 +626,6 @@ public class EMADLGenerator {
}
discriminator
.
get
().
setComponentName
(
fullDiscriminatorName
);
configuration
.
setDiscriminatorNetwork
(
new
ArchitectureAdapter
(
fullDiscriminatorName
,
discriminator
.
get
()));
//CNNTrainCocos.checkCriticCocos(configuration);
}
// Resolve QNetwork if present
...
...
@@ -643,11 +647,16 @@ public class EMADLGenerator {
}
qnetwork
.
get
().
setComponentName
(
fullQNetworkName
);
configuration
.
setQNetwork
(
new
ArchitectureAdapter
(
fullQNetworkName
,
qnetwork
.
get
()));
//CNNTrainCocos.checkCriticCocos(configuration);
}
if
(
configuration
.
getLearningMethod
()
==
LearningMethod
.
GAN
)
CNNTrainCocos
.
checkGANCocos
(
configuration
);
if
(
configuration
.
hasPreprocessor
())
{
String
fullPreprocessorName
=
configuration
.
getPreprocessingName
().
get
();
PreprocessingComponentSymbol
preprocessingSymbol
=
configuration
.
getPreprocessingComponent
().
get
();
List
<
String
>
fullNameOfComponent
=
preprocessingSymbol
.
getPreprocessingComponentName
();
String
fullPreprocessorName
=
String
.
join
(
"."
,
fullNameOfComponent
);
int
indexOfFirstNameCharacter
=
fullPreprocessorName
.
lastIndexOf
(
'.'
)
+
1
;
fullPreprocessorName
=
fullPreprocessorName
.
substring
(
0
,
indexOfFirstNameCharacter
)
+
fullPreprocessorName
.
substring
(
indexOfFirstNameCharacter
,
indexOfFirstNameCharacter
+
1
).
toUpperCase
()
...
...
@@ -671,7 +680,11 @@ public class EMADLGenerator {
}
String
targetPath
=
getGenerationTargetPath
();
pythonWrapper
.
generateAndTryBuilding
(
processor_instance
,
targetPath
+
"/pythonWrapper"
,
targetPath
);
ComponentPortInformation
componentPortInformation
;
componentPortInformation
=
pythonWrapper
.
generateAndTryBuilding
(
processor_instance
,
targetPath
+
"/pythonWrapper"
,
targetPath
);
PreprocessingComponentParameterAdapter
componentParameter
=
new
PreprocessingComponentParameterAdapter
(
componentPortInformation
);
PreprocessingPortChecker
.
check
(
componentParameter
);
preprocessingSymbol
.
setPreprocessingComponentParameter
(
componentParameter
);
}
cnnTrainGenerator
.
setInstanceName
(
componentInstance
.
getFullName
().
replaceAll
(
"\\."
,
"_"
));
...
...
src/main/java/de/monticore/lang/monticar/emadl/generator/reinforcementlearning/RewardFunctionCppGenerator.java
View file @
948dfb62
...
...
@@ -22,7 +22,7 @@ public class RewardFunctionCppGenerator implements RewardFunctionSourceGenerator
.<
EMAComponentInstanceSymbol
>
resolve
(
rootModel
,
EMAComponentInstanceSymbol
.
KIND
);
if
(!
instanceSymbol
.
isPresent
())
{
Log
.
error
(
"Generation of
reward function
is not possible: Cannot resolve component instance "
Log
.
error
(
"Generation of
component
is not possible: Cannot resolve component instance "
+
rootModel
);
}
...
...
@@ -40,7 +40,7 @@ public class RewardFunctionCppGenerator implements RewardFunctionSourceGenerator
try
{
generator
.
generate
(
componentInstanceSymbol
,
taggingResolver
);
}
catch
(
IOException
e
)
{
Log
.
error
(
"Generation of
rewa
rd function is not possible: "
+
e
.
getMessage
());
Log
.
error
(
"Generation of rd function is not possible: "
+
e
.
getMessage
());
}
}
...
...
src/test/resources/target_code/gluon/CNNCreator_mnist_mnistClassifier_net.py
View file @
948dfb62
...
...
@@ -58,3 +58,17 @@ class CNNCreator_mnist_mnistClassifier_net:
for
i
,
network
in
self
.
networks
.
items
():
network
.
export
(
self
.
_model_dir_
+
self
.
_model_prefix_
+
"_"
+
str
(
i
),
epoch
=
0
)
def
getInputs
(
self
):
inputs
=
{}
input_dimensions
=
(
1
,
28
,
28
,)
input_domains
=
(
int
,
0.0
,
255.0
,)
inputs
[
"image_"
]
=
input_domains
+
(
input_dimensions
,)
return
inputs
def
getOutputs
(
self
):
outputs
=
{}
output_dimensions
=
(
10
,
1
,
1
,)
output_domains
=
(
float
,
0.0
,
1.0
,)
outputs
[
"predictions_"
]
=
output_domains
+
(
output_dimensions
,)
return
outputs
src/test/resources/target_code/gluon/CNNDataLoader_mnist_mnistClassifier_net.py
View file @
948dfb62
...
...
@@ -4,7 +4,6 @@ import mxnet as mx
import
logging
import
sys
import
numpy
as
np
import
cv2
import
importlib
from
mxnet
import
nd
...
...
@@ -78,6 +77,7 @@ class CNNDataLoader_mnist_mnistClassifier_net:
train_label
=
{}
data_mean
=
{}
data_std
=
{}
train_images
=
{}
shape_output
=
self
.
preprocess_data
(
instance
,
inp
,
0
,
train_h5
)
train_len
=
len
(
train_h5
[
self
.
_input_names_
[
0
]])
...
...
@@ -140,6 +140,7 @@ class CNNDataLoader_mnist_mnistClassifier_net:
for
output_name
in
self
.
_output_names_
:
test_label
[
output_name
][
i
]
=
getattr
(
shape_output
,
output_name
+
"_out"
)
test_images
=
{}
if
'images'
in
test_h5
:
test_images
=
test_h5
[
'images'
]
...
...
@@ -151,103 +152,7 @@ class CNNDataLoader_mnist_mnistClassifier_net:
def
preprocess_data
(
self
,
instance_wrapper
,
input_wrapper
,
index
,
data_h5
):
for
input_name
in
self
.
_input_names_
:
data
=
data_h5
[
input_name
][
0
]
attr
=
getattr
(
input_wrapper
,
input_name
)
if
(
type
(
data
))
==
np
.
ndarray
:
data
=
np
.
asfortranarray
(
data
).
astype
(
attr
.
dtype
)
else
:
data
=
type
(
attr
)(
data
)
setattr
(
input_wrapper
,
input_name
,
data
)
for
output_name
in
self
.
_output_names_
:
data
=
data_h5
[
output_name
][
0
]
attr
=
getattr
(
input_wrapper
,
output_name
)
if
(
type
(
data
))
==
np
.
ndarray
:
data
=
np
.
asfortranarray
(
data
).
astype
(
attr
.
dtype
)
else
:
data
=
type
(
attr
)(
data
)
setattr
(
input_wrapper
,
output_name
,
data
)
return
instance_wrapper
.
execute
(
input_wrapper
)
def
load_preprocessed_data
(
self
,
batch_size
,
preproc_lib
):
train_h5
,
test_h5
=
self
.
load_h5_files
()
wrapper
=
importlib
.
import_module
(
preproc_lib
)
instance
=
getattr
(
wrapper
,
preproc_lib
)()
instance
.
init
()
lib_head
,
_sep
,
tail
=
preproc_lib
.
rpartition
(
'_'
)
inp
=
getattr
(
wrapper
,
lib_head
+
"_input"
)()
train_data
=
{}
train_label
=
{}
data_mean
=
{}
data_std
=
{}
shape_output
=
self
.
preprocess_data
(
instance
,
inp
,
0
,
train_h5
)
train_len
=
len
(
train_h5
[
self
.
_input_names_
[
0
]])
for
input_name
in
self
.
_input_names_
:
if
type
(
getattr
(
shape_output
,
input_name
+
"_out"
))
==
np
.
ndarray
:
cur_shape
=
(
train_len
,)
+
getattr
(
shape_output
,
input_name
+
"_out"
).
shape
else
:
cur_shape
=
(
train_len
,
1
)
train_data
[
input_name
]
=
mx
.
nd
.
zeros
(
cur_shape
)
for
output_name
in
self
.
_output_names_
:
if
type
(
getattr
(
shape_output
,
output_name
+
"_out"
))
==
nd
.
array
:
cur_shape
=
(
train_len
,)
+
getattr
(
shape_output
,
output_name
+
"_out"
).
shape
else
:
cur_shape
=
(
train_len
,
1
)
train_label
[
output_name
]
=
mx
.
nd
.
zeros
(
cur_shape
)
for
i
in
range
(
train_len
):
output
=
self
.
preprocess_data
(
instance
,
inp
,
i
,
train_h5
)
for
input_name
in
self
.
_input_names_
:
train_data
[
input_name
][
i
]
=
getattr
(
output
,
input_name
+
"_out"
)
for
output_name
in
self
.
_output_names_
:
train_label
[
output_name
][
i
]
=
getattr
(
shape_output
,
output_name
+
"_out"
)
for
input_name
in
self
.
_input_names_
:
data_mean
[
input_name
+
'_'
]
=
nd
.
array
(
train_data
[
input_name
][:].
mean
(
axis
=
0
))
data_std
[
input_name
+
'_'
]
=
nd
.
array
(
train_data
[
input_name
][:].
asnumpy
().
std
(
axis
=
0
)
+
1e-5
)
train_iter
=
mx
.
io
.
NDArrayIter
(
data
=
train_data
,
label
=
train_label
,
batch_size
=
batch_size
)
test_data
=
{}
test_label
=
{}
shape_output
=
self
.
preprocess_data
(
instance
,
inp
,
0
,
test_h5
)
test_len
=
len
(
test_h5
[
self
.
_input_names_
[
0
]])
for
input_name
in
self
.
_input_names_
:
if
type
(
getattr
(
shape_output
,
input_name
+
"_out"
))
==
np
.
ndarray
:
cur_shape
=
(
test_len
,)
+
getattr
(
shape_output
,
input_name
+
"_out"
).
shape
else
:
cur_shape
=
(
test_len
,
1
)
test_data
[
input_name
]
=
mx
.
nd
.
zeros
(
cur_shape
)
for
output_name
in
self
.
_output_names_
:
if
type
(
getattr
(
shape_output
,
output_name
+
"_out"
))
==
nd
.
array
:
cur_shape
=
(
test_len
,)
+
getattr
(
shape_output
,
output_name
+
"_out"
).
shape
else
:
cur_shape
=
(
test_len
,
1
)
test_label
[
output_name
]
=
mx
.
nd
.
zeros
(
cur_shape
)
for
i
in
range
(
test_len
):
output
=
self
.
preprocess_data
(
instance
,
inp
,
i
,
test_h5
)
for
input_name
in
self
.
_input_names_
:
test_data
[
input_name
][
i
]
=
getattr
(
output
,
input_name
+
"_out"
)
for
output_name
in
self
.
_output_names_
:
test_label
[
output_name
][
i
]
=
getattr
(
shape_output
,
output_name
+
"_out"
)
test_iter
=
mx
.
io
.
NDArrayIter
(
data
=
test_data
,
label
=
test_label
,
batch_size
=
batch_size
)
return
train_iter
,
test_iter
,
data_mean
,
data_std
def
preprocess_data
(
self
,
instance_wrapper
,
input_wrapper
,
index
,
data_h5
):
for
input_name
in
self
.
_input_names_
:
data
=
data_h5
[
input_name
][
0
]
data
=
data_h5
[
input_name
][
index
]
attr
=
getattr
(
input_wrapper
,
input_name
)
if
(
type
(
data
))
==
np
.
ndarray
:
data
=
np
.
asfortranarray
(
data
).
astype
(
attr
.
dtype
)
...
...
@@ -255,7 +160,7 @@ class CNNDataLoader_mnist_mnistClassifier_net:
data
=
type
(
attr
)(
data
)
setattr
(
input_wrapper
,
input_name
,
data
)
for
output_name
in
self
.
_output_names_
:
data
=
data_h5
[
output_name
][
0
]
data
=
data_h5
[
output_name
][
index
]
attr
=
getattr
(
input_wrapper
,
output_name
)
if
(
type
(
data
))
==
np
.
ndarray
:
data
=
np
.
asfortranarray
(
data
).
astype
(
attr
.
dtype
)
...
...
src/test/resources/target_code/gluon/CNNNet_mnist_mnistClassifier_net.py
View file @
948dfb62
...
...
@@ -148,16 +148,3 @@ class Net_0(gluon.HybridBlock):
return
predictions_
def
getInputs
(
self
):
inputs
=
{}
input_dimensions
=
(
1
,
28
,
28
)
input_domains
=
(
int
,
0.0
,
255.0
)
inputs
[
"image_"
]
=
input_domains
+
(
input_dimensions
,)
return
inputs
def
getOutputs
(
self
):
outputs
=
{}
output_dimensions
=
(
10
,
1
,
1
)
output_domains
=
(
float
,
0.0
,
1.0
)
outputs
[
"predictions_"
]
=
output_domains
+
(
output_dimensions
,)
return
outputs
src/test/resources/target_code/gluon/CNNTrainer_mnist_mnistClassifier_net.py
View file @
948dfb62
...
...
@@ -21,6 +21,7 @@ if __name__ == "__main__":
batch_size
=
64
,
num_epoch
=
11
,
context
=
'gpu'
,
preprocessing
=
False
,
eval_metric
=
'accuracy'
,
eval_metric_params
=
{
},
...
...
src/test/resources/target_code/gluon/reinforcementModel/cartpole/CNNCreator_cartpole_master_dqn.py
View file @
948dfb62
...
...
@@ -58,3 +58,17 @@ class CNNCreator_cartpole_master_dqn:
for
i
,
network
in
self
.
networks
.
items
():
network
.
export
(
self
.
_model_dir_
+
self
.
_model_prefix_
+
"_"
+
str
(
i
),
epoch
=
0
)
def
getInputs
(
self
):
inputs
=
{}
input_dimensions
=
(
4
,)
input_domains
=
(
float
,
0
,
1
,)
inputs
[
"state_"
]
=
input_domains
+
(
input_dimensions
,)
return
inputs
def
getOutputs
(
self
):
outputs
=
{}
output_dimensions
=
(
2
,
1
,
1
,)
output_domains
=
(
float
,
float
(
'-inf'
),
float
(
'inf'
),)
outputs
[
"qvalues_"
]
=
output_domains
+
(
output_dimensions
,)
return
outputs
src/test/resources/target_code/gluon/reinforcementModel/cartpole/CNNNet_cartpole_master_dqn.py
View file @
948dfb62
...
...
@@ -123,17 +123,3 @@ class Net_0(gluon.HybridBlock):
return
qvalues_
def
getInputs
(
self
):
inputs
=
{}
input_dimensions
=
(
4
)
input_domains
=
(
float
,
float
(
'-inf'
),
float
(
'inf'
))
input_domains
=
(
float
,
0
,
1
)
inputs
[
"state_"
]
=
input_domains
+
(
input_dimensions
,)
return
inputs
def
getOutputs
(
self
):
outputs
=
{}
output_dimensions
=
(
2
,
1
,
1
)
output_domains
=
(
float
,
float
(
'-inf'
),
float
(
'inf'
))
outputs
[
"qvalues_"
]
=
output_domains
+
(
output_dimensions
,)
return
outputs
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/CNNCreator_mountaincar_master_actor.py
View file @
948dfb62
...
...
@@ -58,3 +58,17 @@ class CNNCreator_mountaincar_master_actor:
for
i
,
network
in
self
.
networks
.
items
():
network
.
export
(
self
.
_model_dir_
+
self
.
_model_prefix_
+
"_"
+
str
(
i
),
epoch
=
0
)
def
getInputs
(
self
):
inputs
=
{}
input_dimensions
=
(
2
,)
input_domains
=
(
float
,
0
,
1
,)
inputs
[
"state_"
]
=
input_domains
+
(
input_dimensions
,)
return
inputs
def
getOutputs
(
self
):
outputs
=
{}
output_dimensions
=
(
1
,
1
,
1
,)
output_domains
=
(
float
,
-
1.0
,
1.0
,)
outputs
[
"action_"
]
=
output_domains
+
(
output_dimensions
,)
return
outputs
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/CNNNet_mountaincar_master_actor.py
View file @
948dfb62
...
...
@@ -125,16 +125,3 @@ class Net_0(gluon.HybridBlock):
return
action_
def
getInputs
(
self
):
inputs
=
{}
input_dimensions
=
(
2
)
input_domains
=
(
float
,
0
,
1
)
inputs
[
"state_"
]
=
input_domains
+
(
input_dimensions
,)
return
inputs
def
getOutputs
(
self
):
outputs
=
{}
output_dimensions
=
(
1
,
1
,
1
)
output_domains
=
(
float
,
-
1.0
,
1.0
)
outputs
[
"action_"
]
=
output_domains
+
(
output_dimensions
,)
return
outputs
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/reinforcement_learning/CNNCreator_mountaincar_agent_mountaincarCritic.py
View file @
948dfb62
...
...
@@ -58,3 +58,20 @@ class CNNCreator_mountaincar_agent_mountaincarCritic:
for
i
,
network
in
self
.
networks
.
items
():
network
.
export
(
self
.
_model_dir_
+
self
.
_model_prefix_
+
"_"
+
str
(
i
),
epoch
=
0
)
def
getInputs
(
self
):
inputs
=
{}
input_dimensions
=
(
2
,)
input_domains
=
(
float
,
0
,
1
,)
inputs
[
"state_"
]
=
input_domains
+
(
input_dimensions
,)
input_dimensions
=
(
1
,)
input_domains
=
(
float
,
-
1.0
,
1.0
,)
inputs
[
"action_"
]
=
input_domains
+
(
input_dimensions
,)
return
inputs
def
getOutputs
(
self
):
outputs
=
{}
output_dimensions
=
(
1
,
1
,
1
,)
output_domains
=
(
float
,
float
(
'-inf'
),
float
(
'inf'
),)
outputs
[
"qvalues_"
]
=
output_domains
+
(
output_dimensions
,)
return
outputs
src/test/resources/target_code/gluon/reinforcementModel/mountaincar/reinforcement_learning/CNNNet_mountaincar_agent_mountaincarCritic.py
View file @
948dfb62
...
...
@@ -136,20 +136,3 @@ class Net_0(gluon.HybridBlock):
return
qvalues_
def
getInputs
(
self
):
inputs
=
{}
input_dimensions
=
(
2
)
input_domains
=
(
float
,
float
(
'-inf'
),
float
(
'inf'
))
input_domains
=
(
float
,
0
,
1
)
inputs
[
"state_"
]
=
input_domains
+
(
input_dimensions
,)
input_dimensions
=
(
1
)
input_domains
=
(
float
,
-
1.0
,
1.0
)
inputs
[
"action_"
]
=
input_domains
+
(
input_dimensions
,)
return
inputs
def
getOutputs
(
self
):
outputs
=
{}
output_dimensions
=
(
1
,
1
,
1
)
output_domains
=
(
float
,
float
(
'-inf'
),
float
(
'inf'
))
outputs
[
"qvalues_"
]
=
output_domains
+
(
output_dimensions
,)
return
outputs
src/test/resources/target_code/gluon/reinforcementModel/torcs/CNNCreator_torcs_agent_torcsAgent_dqn.py
View file @
948dfb62
...
...
@@ -58,3 +58,17 @@ class CNNCreator_torcs_agent_torcsAgent_dqn:
for
i
,
network
in
self
.
networks
.
items
():
network
.
export
(
self
.
_model_dir_
+
self
.
_model_prefix_
+
"_"
+
str
(
i
),
epoch
=
0
)
def
getInputs
(
self
):
inputs
=
{}
input_dimensions
=
(
5
,)
input_domains
=
(
float
,
0
,
1
,)
inputs
[
"state_"
]
=
input_domains
+
(
input_dimensions
,)
return
inputs
def
getOutputs
(
self
):
outputs
=
{}
output_dimensions
=
(
30
,
1
,
1
,)
output_domains
=
(
float
,
float
(
'-inf'
),
float
(
'inf'
),)
outputs
[
"qvalues_"
]
=
output_domains
+
(
output_dimensions
,)
return
outputs
src/test/resources/target_code/gluon/reinforcementModel/torcs/CNNNet_torcs_agent_torcsAgent_dqn.py
View file @
948dfb62
...
...
@@ -123,16 +123,3 @@ class Net_0(gluon.HybridBlock):
return
qvalues_
def
getInputs
(
self
):
inputs
=
{}
input_dimensions
=
(
5
)
input_domains
=
(
float
,
0
,
1
)
inputs
[
"state_"
]
=
input_domains
+
(
input_dimensions
,)
return
inputs
def
getOutputs
(
self
):
outputs
=
{}
output_dimensions
=
(
30
,
1
,
1
)
output_domains
=
(
float
,
float
(
'-inf'
),
float
(
'inf'
))
outputs
[
"qvalues_"
]
=
output_domains
+
(
output_dimensions
,)
return
outputs
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/CNNCreator_torcs_agent_torcsAgent_actor.py
View file @
948dfb62
...
...
@@ -58,3 +58,17 @@ class CNNCreator_torcs_agent_torcsAgent_actor:
for
i
,
network
in
self
.
networks
.
items
():
network
.
export
(
self
.
_model_dir_
+
self
.
_model_prefix_
+
"_"
+
str
(
i
),
epoch
=
0
)
def
getInputs
(
self
):
inputs
=
{}
input_dimensions
=
(
29
,)
input_domains
=
(
float
,
0
,
1
,)
inputs
[
"state_"
]
=
input_domains
+
(
input_dimensions
,)
return
inputs
def
getOutputs
(
self
):
outputs
=
{}
output_dimensions
=
(
3
,
1
,
1
,)
output_domains
=
(
float
,
-
1.0
,
1.0
,)
outputs
[
"commands_"
]
=
output_domains
+
(
output_dimensions
,)
return
outputs
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/CNNNet_torcs_agent_torcsAgent_actor.py
View file @
948dfb62
...
...
@@ -125,16 +125,3 @@ class Net_0(gluon.HybridBlock):
return
commands_
def
getInputs
(
self
):
inputs
=
{}
input_dimensions
=
(
29
)
input_domains
=
(
float
,
0
,
1
)
inputs
[
"state_"
]
=
input_domains
+
(
input_dimensions
,)
return
inputs
def
getOutputs
(
self
):
outputs
=
{}
output_dimensions
=
(
3
,
1
,
1
)
output_domains
=
(
float
,
-
1.0
,
1.0
)
outputs
[
"commands_"
]
=
output_domains
+
(
output_dimensions
,)
return
outputs
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/reinforcement_learning/CNNCreator_torcs_agent_network_torcsCritic.py
View file @
948dfb62
...
...
@@ -58,3 +58,20 @@ class CNNCreator_torcs_agent_network_torcsCritic:
for
i
,
network
in
self
.
networks
.
items
():
network
.
export
(
self
.
_model_dir_
+
self
.
_model_prefix_
+
"_"
+
str
(
i
),
epoch
=
0
)
def
getInputs
(
self
):
inputs
=
{}
input_dimensions
=
(
29
,)
input_domains
=
(
float
,
0
,
1
,)
inputs
[
"state_"
]
=
input_domains
+
(
input_dimensions
,)
input_dimensions
=
(
3
,)
input_domains
=
(
float
,
-
1.0
,
1.0
,)
inputs
[
"action_"
]
=
input_domains
+
(
input_dimensions
,)
return
inputs
def
getOutputs
(
self
):
outputs
=
{}
output_dimensions
=
(
1
,
1
,
1
,)
output_domains
=
(
float
,
float
(
'-inf'
),
float
(
'inf'
),)
outputs
[
"qvalues_"
]
=
output_domains
+
(
output_dimensions
,)
return
outputs
src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/reinforcement_learning/CNNNet_torcs_agent_network_torcsCritic.py
View file @
948dfb62
...
...
@@ -132,19 +132,3 @@ class Net_0(gluon.HybridBlock):
return
qvalues_
def
getInputs
(
self
):
inputs
=
{}
input_dimensions
=
(
29
)
input_domains
=
(
float
,
0
,
1
)
inputs
[
"state_"
]
=
input_domains
+
(
input_dimensions
,)
input_dimensions
=
(
3
)
input_domains
=
(
float
,
-
1.0
,
1.0
)
inputs
[
"action_"
]
=
input_domains
+
(
input_dimensions
,)
return
inputs
def
getOutputs
(
self
):
outputs
=
{}
output_dimensions
=
(
1
,
1
,
1
)
output_domains
=
(
float
,
float
(
'-inf'
),
float
(
'inf'
))
outputs
[
"qvalues_"
]
=
output_domains
+
(
output_dimensions
,)
return
outputs
train.log
View file @
948dfb62
Epoch[0] Train-accuracy=0.100000
Epoch[0] Time cost=0.0
49
Epoch[0] Validation-accuracy=0.
2
00000
Epoch[1] Train-accuracy=0.
1
00000
Epoch[1] Time cost=0.0
5
1
Epoch[0] Time cost=0.0
76
Epoch[0] Validation-accuracy=0.
5
00000
Epoch[1] Train-accuracy=0.
5
00000
Epoch[1] Time cost=0.0
6
1
Epoch[1] Validation-accuracy=0.300000
Epoch[2] Train-accuracy=0.600000
Epoch[2] Time cost=0.0
4
9
Epoch[2] Validation-accuracy=0.
2
00000
Epoch[3] Train-accuracy=0.
7
00000
Epoch[3] Time cost=0.0
45
Epoch[3] Validation-accuracy=0.
1
00000
Epoch[4] Train-accuracy=0.
5
00000
Epoch[4] Time cost=0.0
69
Epoch[2] Time cost=0.09
6
Epoch[2] Validation-accuracy=0.
3
00000
Epoch[3] Train-accuracy=0.
5
00000
Epoch[3] Time cost=0.0
76
Epoch[3] Validation-accuracy=0.
2
00000
Epoch[4] Train-accuracy=0.
6
00000
Epoch[4] Time cost=0.0
48
Saved checkpoint to "model/instanceTestCifar.CifarNetwork/model-0005.params"
Epoch[4] Validation-accuracy=0.200000
Epoch[5] Train-accuracy=0.
5
00000
Epoch[5] Time cost=0.0
55
Epoch[5] Train-accuracy=0.
4
00000
Epoch[5] Time cost=0.0
70
Epoch[5] Validation-accuracy=0.200000
Epoch[6] Train-accuracy=0.
5
00000
Epoch[6] Time cost=0.06
8
Epoch[6] Validation-accuracy=0.
2
00000
Epoch[7] Train-accuracy=0.
4
00000
Epoch[7] Time cost=0.0
7
6
Epoch[7] Validation-accuracy=0.
2
00000
Epoch[6] Train-accuracy=0.
4
00000
Epoch[6] Time cost=0.06
2
Epoch[6] Validation-accuracy=0.
1
00000
Epoch[7] Train-accuracy=0.
5
00000
Epoch[7] Time cost=0.0
4
6
Epoch[7] Validation-accuracy=0.
1
00000
Epoch[8] Train-accuracy=0.400000
Epoch[8] Time cost=0.0
5
1
Epoch[8] Validation-accuracy=0.
2
00000
Epoch[8] Time cost=0.0
6
1
Epoch[8] Validation-accuracy=0.
1
00000
Epoch[9] Train-accuracy=0.400000
Epoch[9] Time cost=0