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monticore
EmbeddedMontiArc
generators
EMADL2CPP
Commits
eb892398
Commit
eb892398
authored
Apr 08, 2019
by
Nicola Gatto
Browse files
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Browse Files
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Plain Diff
Adapt tests to new version
parent
7c707a98
Changes
8
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Showing
8 changed files
with
209 additions
and
195 deletions
+209
-195
pom.xml
pom.xml
+1
-1
src/test/java/de/monticore/lang/monticar/emadl/GenerationTest.java
...java/de/monticore/lang/monticar/emadl/GenerationTest.java
+2
-0
src/test/java/de/monticore/lang/monticar/emadl/IntegrationTest.java
...ava/de/monticore/lang/monticar/emadl/IntegrationTest.java
+0
-3
src/test/resources/target_code/gluon/CNNCreator_mnist_mnistClassifier_net.py
...target_code/gluon/CNNCreator_mnist_mnistClassifier_net.py
+0
-189
src/test/resources/target_code/gluon/CNNCreator_mnist_mnistClassifier_net.pyc
...arget_code/gluon/CNNCreator_mnist_mnistClassifier_net.pyc
+0
-0
src/test/resources/target_code/gluon/CNNDataLoader_mnist_mnistClassifier_net.py
...get_code/gluon/CNNDataLoader_mnist_mnistClassifier_net.py
+57
-0
src/test/resources/target_code/gluon/CNNTrainer_mnist_mnistClassifier_net.py
...target_code/gluon/CNNTrainer_mnist_mnistClassifier_net.py
+8
-2
src/test/resources/target_code/gluon/supervised_trainer.py
src/test/resources/target_code/gluon/supervised_trainer.py
+141
-0
No files found.
pom.xml
View file @
eb892398
...
...
@@ -19,7 +19,7 @@
<CNNTrain.version>
0.2.6
</CNNTrain.version>
<cnnarch-mxnet-generator.version>
0.2.14-SNAPSHOT
</cnnarch-mxnet-generator.version>
<cnnarch-caffe2-generator.version>
0.2.11-SNAPSHOT
</cnnarch-caffe2-generator.version>
<cnnarch-gluon-generator.version>
0.1.
5
</cnnarch-gluon-generator.version>
<cnnarch-gluon-generator.version>
0.1.
6
</cnnarch-gluon-generator.version>
<embedded-montiarc-math-opt-generator>
0.1.4
</embedded-montiarc-math-opt-generator>
<!-- .. Libraries .................................................. -->
...
...
src/test/java/de/monticore/lang/monticar/emadl/GenerationTest.java
View file @
eb892398
...
...
@@ -177,6 +177,8 @@ public class GenerationTest extends AbstractSymtabTest {
"mnist_mnistClassifier.h"
,
"CNNCreator_mnist_mnistClassifier_net.py"
,
"CNNPredictor_mnist_mnistClassifier_net.h"
,
"CNNDataLoader_mnist_mnistClassifier_net.py"
,
"supervised_trainer.py"
,
"mnist_mnistClassifier_net.h"
,
"HelperA.h"
,
"CNNTranslator.h"
,
...
...
src/test/java/de/monticore/lang/monticar/emadl/IntegrationTest.java
View file @
eb892398
...
...
@@ -160,7 +160,4 @@ public abstract class IntegrationTest extends AbstractSymtabTest {
deleteHashFile
();
}
}
src/test/resources/target_code/gluon/CNNCreator_mnist_mnistClassifier_net.py
View file @
eb892398
import
mxnet
as
mx
import
logging
import
os
import
errno
import
shutil
import
h5py
import
sys
import
numpy
as
np
import
time
from
mxnet
import
gluon
,
autograd
,
nd
from
CNNNet_mnist_mnistClassifier_net
import
Net
@
mx
.
init
.
register
class
MyConstant
(
mx
.
init
.
Initializer
):
def
__init__
(
self
,
value
):
super
(
MyConstant
,
self
).
__init__
(
value
=
value
)
self
.
value
=
value
def
_init_weight
(
self
,
_
,
arr
):
arr
[:]
=
mx
.
nd
.
array
(
self
.
value
)
class
CNNCreator_mnist_mnistClassifier_net
:
_data_dir_
=
"data/mnist.LeNetNetwork/"
_model_dir_
=
"model/mnist.LeNetNetwork/"
_model_prefix_
=
"model"
_input_names_
=
[
'image'
]
_input_shapes_
=
[(
1
,
28
,
28
)]
_output_names_
=
[
'predictions_label'
]
def
__init__
(
self
):
self
.
weight_initializer
=
mx
.
init
.
Normal
()
...
...
@@ -60,176 +41,6 @@ class CNNCreator_mnist_mnistClassifier_net:
return
lastEpoch
def
load_data
(
self
,
batch_size
):
train_h5
,
test_h5
=
self
.
load_h5_files
()
data_mean
=
train_h5
[
self
.
_input_names_
[
0
]][:].
mean
(
axis
=
0
)
data_std
=
train_h5
[
self
.
_input_names_
[
0
]][:].
std
(
axis
=
0
)
+
1e-5
train_iter
=
mx
.
io
.
NDArrayIter
(
train_h5
[
self
.
_input_names_
[
0
]],
train_h5
[
self
.
_output_names_
[
0
]],
batch_size
=
batch_size
,
data_name
=
self
.
_input_names_
[
0
],
label_name
=
self
.
_output_names_
[
0
])
test_iter
=
None
if
test_h5
!=
None
:
test_iter
=
mx
.
io
.
NDArrayIter
(
test_h5
[
self
.
_input_names_
[
0
]],
test_h5
[
self
.
_output_names_
[
0
]],
batch_size
=
batch_size
,
data_name
=
self
.
_input_names_
[
0
],
label_name
=
self
.
_output_names_
[
0
])
return
train_iter
,
test_iter
,
data_mean
,
data_std
def
load_h5_files
(
self
):
train_h5
=
None
test_h5
=
None
train_path
=
self
.
_data_dir_
+
"train.h5"
test_path
=
self
.
_data_dir_
+
"test.h5"
if
os
.
path
.
isfile
(
train_path
):
train_h5
=
h5py
.
File
(
train_path
,
'r'
)
if
not
(
self
.
_input_names_
[
0
]
in
train_h5
and
self
.
_output_names_
[
0
]
in
train_h5
):
logging
.
error
(
"The HDF5 file '"
+
os
.
path
.
abspath
(
train_path
)
+
"' has to contain the datasets: "
+
"'"
+
self
.
_input_names_
[
0
]
+
"', '"
+
self
.
_output_names_
[
0
]
+
"'"
)
sys
.
exit
(
1
)
test_iter
=
None
if
os
.
path
.
isfile
(
test_path
):
test_h5
=
h5py
.
File
(
test_path
,
'r'
)
if
not
(
self
.
_input_names_
[
0
]
in
test_h5
and
self
.
_output_names_
[
0
]
in
test_h5
):
logging
.
error
(
"The HDF5 file '"
+
os
.
path
.
abspath
(
test_path
)
+
"' has to contain the datasets: "
+
"'"
+
self
.
_input_names_
[
0
]
+
"', '"
+
self
.
_output_names_
[
0
]
+
"'"
)
sys
.
exit
(
1
)
else
:
logging
.
warning
(
"Couldn't load test set. File '"
+
os
.
path
.
abspath
(
test_path
)
+
"' does not exist."
)
return
train_h5
,
test_h5
else
:
logging
.
error
(
"Data loading failure. File '"
+
os
.
path
.
abspath
(
train_path
)
+
"' does not exist."
)
sys
.
exit
(
1
)
def
train
(
self
,
batch_size
=
64
,
num_epoch
=
10
,
eval_metric
=
'acc'
,
optimizer
=
'adam'
,
optimizer_params
=
((
'learning_rate'
,
0.001
),),
load_checkpoint
=
True
,
context
=
'gpu'
,
checkpoint_period
=
5
,
normalize
=
True
):
if
context
==
'gpu'
:
mx_context
=
mx
.
gpu
()
elif
context
==
'cpu'
:
mx_context
=
mx
.
cpu
()
else
:
logging
.
error
(
"Context argument is '"
+
context
+
"'. Only 'cpu' and 'gpu are valid arguments'."
)
if
'weight_decay'
in
optimizer_params
:
optimizer_params
[
'wd'
]
=
optimizer_params
[
'weight_decay'
]
del
optimizer_params
[
'weight_decay'
]
if
'learning_rate_decay'
in
optimizer_params
:
min_learning_rate
=
1e-08
if
'learning_rate_minimum'
in
optimizer_params
:
min_learning_rate
=
optimizer_params
[
'learning_rate_minimum'
]
del
optimizer_params
[
'learning_rate_minimum'
]
optimizer_params
[
'lr_scheduler'
]
=
mx
.
lr_scheduler
.
FactorScheduler
(
optimizer_params
[
'step_size'
],
factor
=
optimizer_params
[
'learning_rate_decay'
],
stop_factor_lr
=
min_learning_rate
)
del
optimizer_params
[
'step_size'
]
del
optimizer_params
[
'learning_rate_decay'
]
train_iter
,
test_iter
,
data_mean
,
data_std
=
self
.
load_data
(
batch_size
)
if
self
.
net
==
None
:
if
normalize
:
self
.
construct
(
context
=
mx_context
,
data_mean
=
nd
.
array
(
data_mean
),
data_std
=
nd
.
array
(
data_std
))
else
:
self
.
construct
(
context
=
mx_context
)
begin_epoch
=
0
if
load_checkpoint
:
begin_epoch
=
self
.
load
(
mx_context
)
else
:
if
os
.
path
.
isdir
(
self
.
_model_dir_
):
shutil
.
rmtree
(
self
.
_model_dir_
)
try
:
os
.
makedirs
(
self
.
_model_dir_
)
except
OSError
:
if
not
os
.
path
.
isdir
(
self
.
_model_dir_
):
raise
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
:
# TODO: Change default?
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
logging
.
warning
(
"Invalid last_layer, defaulting to L2 loss"
)
speed_period
=
50
tic
=
None
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
)
with
autograd
.
record
():
output
=
self
.
net
(
data
)
loss
=
loss_function
(
output
,
label
)
loss
.
backward
()
trainer
.
step
(
batch_size
)
if
tic
is
None
:
tic
=
time
.
time
()
else
:
if
batch_i
%
speed_period
==
0
:
try
:
speed
=
speed_period
*
batch_size
/
(
time
.
time
()
-
tic
)
except
ZeroDivisionError
:
speed
=
float
(
"inf"
)
logging
.
info
(
"Epoch[%d] Batch[%d] Speed: %.2f samples/sec"
%
(
epoch
,
batch_i
,
speed
))
tic
=
time
.
time
()
tic
=
None
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
)
output
=
self
.
net
(
data
)
predictions
=
mx
.
nd
.
argmax
(
output
,
axis
=
1
)
metric
.
update
(
preds
=
predictions
,
labels
=
label
)
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
)
output
=
self
.
net
(
data
)
predictions
=
mx
.
nd
.
argmax
(
output
,
axis
=
1
)
metric
.
update
(
preds
=
predictions
,
labels
=
label
)
test_metric_score
=
metric
.
get
()[
1
]
logging
.
info
(
"Epoch[%d] Train: %f, Test: %f"
%
(
epoch
,
train_metric_score
,
test_metric_score
))
if
(
epoch
-
begin_epoch
)
%
checkpoint_period
==
0
:
self
.
net
.
save_parameters
(
self
.
_model_dir_
+
self
.
_model_prefix_
+
'-'
+
str
(
epoch
).
zfill
(
4
)
+
'.params'
)
self
.
net
.
save_parameters
(
self
.
_model_dir_
+
self
.
_model_prefix_
+
'-'
+
str
(
num_epoch
+
begin_epoch
).
zfill
(
4
)
+
'.params'
)
self
.
net
.
export
(
self
.
_model_dir_
+
self
.
_model_prefix_
+
'_newest'
,
epoch
=
0
)
def
construct
(
self
,
context
,
data_mean
=
None
,
data_std
=
None
):
self
.
net
=
Net
(
data_mean
=
data_mean
,
data_std
=
data_std
)
self
.
net
.
collect_params
().
initialize
(
self
.
weight_initializer
,
ctx
=
context
)
...
...
src/test/resources/target_code/gluon/CNNCreator_mnist_mnistClassifier_net.pyc
deleted
100644 → 0
View file @
7c707a98
File deleted
src/test/resources/target_code/gluon/CNNDataLoader_mnist_mnistClassifier_net.py
0 → 100644
View file @
eb892398
import
os
import
h5py
import
mxnet
as
mx
import
logging
import
sys
class
mnist_mnistClassifier_netDataLoader
:
_input_names_
=
[
'image'
]
_output_names_
=
[
'predictions_label'
]
def
__init__
(
self
):
self
.
_data_dir
=
"data/mnist.LeNetNetwork/"
def
load_data
(
self
,
batch_size
):
train_h5
,
test_h5
=
self
.
load_h5_files
()
data_mean
=
train_h5
[
self
.
_input_names_
[
0
]][:].
mean
(
axis
=
0
)
data_std
=
train_h5
[
self
.
_input_names_
[
0
]][:].
std
(
axis
=
0
)
+
1e-5
train_iter
=
mx
.
io
.
NDArrayIter
(
train_h5
[
self
.
_input_names_
[
0
]],
train_h5
[
self
.
_output_names_
[
0
]],
batch_size
=
batch_size
,
data_name
=
self
.
_input_names_
[
0
],
label_name
=
self
.
_output_names_
[
0
])
test_iter
=
None
if
test_h5
!=
None
:
test_iter
=
mx
.
io
.
NDArrayIter
(
test_h5
[
self
.
_input_names_
[
0
]],
test_h5
[
self
.
_output_names_
[
0
]],
batch_size
=
batch_size
,
data_name
=
self
.
_input_names_
[
0
],
label_name
=
self
.
_output_names_
[
0
])
return
train_iter
,
test_iter
,
data_mean
,
data_std
def
load_h5_files
(
self
):
train_h5
=
None
test_h5
=
None
train_path
=
self
.
_data_dir
+
"train.h5"
test_path
=
self
.
_data_dir
+
"test.h5"
if
os
.
path
.
isfile
(
train_path
):
train_h5
=
h5py
.
File
(
train_path
,
'r'
)
if
not
(
self
.
_input_names_
[
0
]
in
train_h5
and
self
.
_output_names_
[
0
]
in
train_h5
):
logging
.
error
(
"The HDF5 file '"
+
os
.
path
.
abspath
(
train_path
)
+
"' has to contain the datasets: "
+
"'"
+
self
.
_input_names_
[
0
]
+
"', '"
+
self
.
_output_names_
[
0
]
+
"'"
)
sys
.
exit
(
1
)
test_iter
=
None
if
os
.
path
.
isfile
(
test_path
):
test_h5
=
h5py
.
File
(
test_path
,
'r'
)
if
not
(
self
.
_input_names_
[
0
]
in
test_h5
and
self
.
_output_names_
[
0
]
in
test_h5
):
logging
.
error
(
"The HDF5 file '"
+
os
.
path
.
abspath
(
test_path
)
+
"' has to contain the datasets: "
+
"'"
+
self
.
_input_names_
[
0
]
+
"', '"
+
self
.
_output_names_
[
0
]
+
"'"
)
sys
.
exit
(
1
)
else
:
logging
.
warning
(
"Couldn't load test set. File '"
+
os
.
path
.
abspath
(
test_path
)
+
"' does not exist."
)
return
train_h5
,
test_h5
else
:
logging
.
error
(
"Data loading failure. File '"
+
os
.
path
.
abspath
(
train_path
)
+
"' does not exist."
)
sys
.
exit
(
1
)
\ No newline at end of file
src/test/resources/target_code/gluon/CNNTrainer_mnist_mnistClassifier_net.py
View file @
eb892398
import
logging
import
mxnet
as
mx
import
supervised_trainer
import
CNNCreator_mnist_mnistClassifier_net
import
CNNDataLoader_mnist_mnistClassifier_net
if
__name__
==
"__main__"
:
logging
.
basicConfig
(
level
=
logging
.
DEBUG
)
...
...
@@ -8,8 +10,12 @@ if __name__ == "__main__":
handler
=
logging
.
FileHandler
(
"train.log"
,
"w"
,
encoding
=
None
,
delay
=
"true"
)
logger
.
addHandler
(
handler
)
mnist_mnistClassifier_net
=
CNNCreator_mnist_mnistClassifier_net
.
CNNCreator_mnist_mnistClassifier_net
()
mnist_mnistClassifier_net
.
train
(
mnist_mnistClassifier_net_creator
=
CNNCreator_mnist_mnistClassifier_net
.
CNNCreator_mnist_mnistClassifier_net
()
mnist_mnistClassifier_net_loader
=
CNNDataLoader_mnist_mnistClassifier_net
.
mnist_mnistClassifier_netDataLoader
()
mnist_mnistClassifier_net_trainer
=
supervised_trainer
.
CNNSupervisedTrainer
(
mnist_mnistClassifier_net_loader
,
mnist_mnistClassifier_net_creator
)
mnist_mnistClassifier_net_trainer
.
train
(
batch_size
=
64
,
num_epoch
=
11
,
context
=
'gpu'
,
...
...
src/test/resources/target_code/gluon/supervised_trainer.py
0 → 100644
View file @
eb892398
import
mxnet
as
mx
import
logging
import
numpy
as
np
import
time
import
os
import
shutil
from
mxnet
import
gluon
,
autograd
,
nd
class
CNNSupervisedTrainer
(
object
):
def
__init__
(
self
,
data_loader
,
net_constructor
,
net
=
None
):
self
.
_data_loader
=
data_loader
self
.
_net_creator
=
net_constructor
self
.
_net
=
net
def
train
(
self
,
batch_size
=
64
,
num_epoch
=
10
,
eval_metric
=
'acc'
,
optimizer
=
'adam'
,
optimizer_params
=
((
'learning_rate'
,
0.001
),),
load_checkpoint
=
True
,
context
=
'gpu'
,
checkpoint_period
=
5
,
normalize
=
True
):
if
context
==
'gpu'
:
mx_context
=
mx
.
gpu
()
elif
context
==
'cpu'
:
mx_context
=
mx
.
cpu
()
else
:
logging
.
error
(
"Context argument is '"
+
context
+
"'. Only 'cpu' and 'gpu are valid arguments'."
)
if
'weight_decay'
in
optimizer_params
:
optimizer_params
[
'wd'
]
=
optimizer_params
[
'weight_decay'
]
del
optimizer_params
[
'weight_decay'
]
if
'learning_rate_decay'
in
optimizer_params
:
min_learning_rate
=
1e-08
if
'learning_rate_minimum'
in
optimizer_params
:
min_learning_rate
=
optimizer_params
[
'learning_rate_minimum'
]
del
optimizer_params
[
'learning_rate_minimum'
]
optimizer_params
[
'lr_scheduler'
]
=
mx
.
lr_scheduler
.
FactorScheduler
(
optimizer_params
[
'step_size'
],
factor
=
optimizer_params
[
'learning_rate_decay'
],
stop_factor_lr
=
min_learning_rate
)
del
optimizer_params
[
'step_size'
]
del
optimizer_params
[
'learning_rate_decay'
]
train_iter
,
test_iter
,
data_mean
,
data_std
=
self
.
_data_loader
.
load_data
(
batch_size
)
if
self
.
_net
is
None
:
if
normalize
:
self
.
_net_creator
.
construct
(
context
=
mx_context
,
data_mean
=
nd
.
array
(
data_mean
),
data_std
=
nd
.
array
(
data_std
))
else
:
self
.
_net_creator
.
construct
(
context
=
mx_context
)
begin_epoch
=
0
if
load_checkpoint
:
begin_epoch
=
self
.
_net_creator
.
load
(
mx_context
)
else
:
if
os
.
path
.
isdir
(
self
.
_net_creator
.
_model_dir_
):
shutil
.
rmtree
(
self
.
_net_creator
.
_model_dir_
)
self
.
_net
=
self
.
_net_creator
.
net
try
:
os
.
makedirs
(
self
.
_net_creator
.
_model_dir_
)
except
OSError
:
if
not
os
.
path
.
isdir
(
self
.
_net_creator
.
_model_dir_
):
raise
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
:
# TODO: Change default?
loss_function
=
mx
.
gluon
.
loss
.
L2Loss
()
logging
.
warning
(
"Invalid last_layer, defaulting to L2 loss"
)
speed_period
=
50
tic
=
None
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
)
with
autograd
.
record
():
output
=
self
.
_net
(
data
)
loss
=
loss_function
(
output
,
label
)
loss
.
backward
()
trainer
.
step
(
batch_size
)
if
tic
is
None
:
tic
=
time
.
time
()
else
:
if
batch_i
%
speed_period
==
0
:
try
:
speed
=
speed_period
*
batch_size
/
(
time
.
time
()
-
tic
)
except
ZeroDivisionError
:
speed
=
float
(
"inf"
)
logging
.
info
(
"Epoch[%d] Batch[%d] Speed: %.2f samples/sec"
%
(
epoch
,
batch_i
,
speed
))
tic
=
time
.
time
()
tic
=
None
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
)
output
=
self
.
_net
(
data
)
predictions
=
mx
.
nd
.
argmax
(
output
,
axis
=
1
)
metric
.
update
(
preds
=
predictions
,
labels
=
label
)
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
)
output
=
self
.
_net
(
data
)
predictions
=
mx
.
nd
.
argmax
(
output
,
axis
=
1
)
metric
.
update
(
preds
=
predictions
,
labels
=
label
)
test_metric_score
=
metric
.
get
()[
1
]
logging
.
info
(
"Epoch[%d] Train: %f, Test: %f"
%
(
epoch
,
train_metric_score
,
test_metric_score
))
if
(
epoch
-
begin_epoch
)
%
checkpoint_period
==
0
:
self
.
_net
.
save_parameters
(
self
.
parameter_path
()
+
'-'
+
str
(
epoch
).
zfill
(
4
)
+
'.params'
)
self
.
_net
.
save_parameters
(
self
.
parameter_path
()
+
'-'
+
str
(
num_epoch
+
begin_epoch
).
zfill
(
4
)
+
'.params'
)
self
.
_net
.
export
(
self
.
parameter_path
()
+
'_newest'
,
epoch
=
0
)
def
parameter_path
(
self
):
return
self
.
_net_creator
.
_model_dir_
+
self
.
_net_creator
.
_model_prefix_
\ No newline at end of file
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