Skip to content
GitLab
Explore
Sign in
Primary navigation
Search or go to…
Project
T
Toponym Geocoding
Manage
Activity
Members
Labels
Plan
Issues
0
Issue boards
Milestones
Code
Repository
Branches
Commits
Tags
Repository graph
Compare revisions
Deploy
Releases
Model registry
Monitor
Incidents
Analyze
Value stream analytics
Contributor analytics
Repository analytics
Model experiments
Help
Help
Support
GitLab documentation
Compare GitLab plans
Community forum
Contribute to GitLab
Provide feedback
Keyboard shortcuts
?
Snippets
Groups
Projects
Show more breadcrumbs
Jacques Fize
Toponym Geocoding
Commits
f16227b9
Commit
f16227b9
authored
4 years ago
by
Fize Jacques
Browse files
Options
Downloads
Patches
Plain Diff
it is now possible to load previous model and embedding ofr the training
parent
2abd0e7c
No related branches found
No related tags found
No related merge requests found
Changes
3
Hide whitespace changes
Inline
Side-by-side
Showing
3 changed files
helpers.py
+7
-1
7 additions, 1 deletion
helpers.py
parser_config/toponym_combination_embedding_v3.json
+3
-1
3 additions, 1 deletion
parser_config/toponym_combination_embedding_v3.json
train_geocoder_v2.py
+37
-52
37 additions, 52 deletions
train_geocoder_v2.py
with
47 additions
and
54 deletions
helpers.py
+
7
−
1
View file @
f16227b9
...
...
@@ -172,13 +172,19 @@ class Chronometer:
from
keras.callbacks
import
Callback
import
pandas
as
pd
import
time
import
os
class
EpochTimer
(
Callback
):
def
__init__
(
self
,
log_filename
):
self
.
epoch
=
0
self
.
timer
=
time
.
time
()
self
.
output
=
open
(
log_filename
,
'
w
'
)
if
os
.
path
.
exists
(
log_filename
):
self
.
output
=
open
(
log_filename
,
'
a
'
)
self
.
epoch
=
pd
.
read_csv
(
log_filename
).
Epoch
.
max
()
else
:
self
.
output
=
open
(
log_filename
,
'
w
'
)
self
.
output
.
write
(
"
{0},{1}
\n
"
.
format
(
"
Epoch
"
,
"
Execution Time
"
))
self
.
output
.
flush
()
...
...
This diff is collapsed.
Click to expand it.
parser_config/toponym_combination_embedding_v3.json
+
3
−
1
View file @
f16227b9
...
...
@@ -15,6 +15,8 @@
{
"short"
:
"-e"
,
"long"
:
"--epochs"
,
"type"
:
"int"
,
"default"
:
100
},
{
"short"
:
"-d"
,
"long"
:
"--dimension"
,
"type"
:
"int"
,
"default"
:
256
},
{
"short"
:
"-l"
,
"long"
:
"--lstm-layer"
,
"type"
:
"int"
,
"default"
:
2
,
"choices"
:
[
1
,
2
]
},
{
"long"
:
"--tokenization-method"
,
"type"
:
"str"
,
"default"
:
"char-level"
,
"choices"
:
[
"char-level"
,
"word-level"
,
"bert"
]
}
{
"long"
:
"--tokenization-method"
,
"type"
:
"str"
,
"default"
:
"char-level"
,
"choices"
:
[
"char-level"
,
"word-level"
,
"bert"
]
},
{
"long"
:
"--previous-state"
,
"type"
:
"str"
,
"help"
:
"If the model was trained before, give the path here"
}
]
}
\ No newline at end of file
This diff is collapsed.
Click to expand it.
train_geocoder_v2.py
+
37
−
52
View file @
f16227b9
...
...
@@ -61,6 +61,7 @@ DATASET_NAME = args.dataset_name
PREFIX_OUTPUT_FN
=
DATASET_NAME
PREFIX_OUTPUT_FN
+=
"
_{0}
"
.
format
(
NGRAM_SIZE
)
EMBEDDING_FN
=
"
outputs/{0}_embedding.npy
"
.
format
(
PREFIX_OUTPUT_FN
)
PREFIX_OUTPUT_FN
+=
"
_{0}
"
.
format
(
EPOCHS
)
if
args
.
adjacency
:
...
...
@@ -74,6 +75,7 @@ MODEL_OUTPUT_FN = "outputs/{0}.h5".format(PREFIX_OUTPUT_FN)
INDEX_FN
=
"
outputs/{0}_index
"
.
format
(
PREFIX_OUTPUT_FN
)
HISTORY_FN
=
"
outputs/{0}.csv
"
.
format
(
PREFIX_OUTPUT_FN
)
#############################################################################################
################################# LOAD DATA #################################################
#############################################################################################
...
...
@@ -121,9 +123,16 @@ logging.info("Done !")
################################# NGRAM EMBEDDINGS ##########################################
#############################################################################################
logging
.
info
(
"
Generating N-GRAM Embedding...
"
)
embedding_weights
=
index
.
get_embedding_layer
([
index
.
encode
(
p
)
for
p
in
np
.
concatenate
((
pairs_of_toponym
.
toponym
.
unique
(),
pairs_of_toponym
.
toponym_context
.
unique
()))],
dim
=
EMBEDDING_DIM
,
iter
=
WORDVEC_ITER
)
logging
.
info
(
"
Embedding generated !
"
)
if
os
.
path
.
exists
(
EMBEDDING_FN
):
logging
.
info
(
"
Load previous N-GRAM Embedding...
"
)
embedding_weights
=
np
.
load
(
EMBEDDING_FN
)
logging
.
info
(
"
Embedding loaded !
"
)
else
:
logging
.
info
(
"
Generating N-GRAM Embedding...
"
)
embedding_weights
=
index
.
get_embedding_layer
([
index
.
encode
(
p
)
for
p
in
np
.
concatenate
((
pairs_of_toponym
.
toponym
.
unique
(),
pairs_of_toponym
.
toponym_context
.
unique
()))],
dim
=
EMBEDDING_DIM
,
iter
=
WORDVEC_ITER
)
np
.
save
(
EMBEDDING_FN
,
embedding_weights
)
logging
.
info
(
"
Embedding generated !
"
)
#############################################################################################
################################# BUILD TRAIN/TEST DATASETS #################################
...
...
@@ -132,31 +141,6 @@ logging.info("Preparing Input and Output data...")
training_generator
=
DataGenerator
(
pairs_of_toponym
[
pairs_of_toponym
.
split
==
"
train
"
],
index
)
validation_generator
=
DataGenerator
(
pairs_of_toponym
[
pairs_of_toponym
.
split
==
"
test
"
],
index
)
# X_1_train,X_2_train=[],[]
# X_1_test,X_2_test=[],[]
# y_train,y_test = [],[]
# for couple in pairs_of_toponym["toponym toponym_context split longitude latitude".split()].itertuples():
# top,top_c,split_ = couple[1], couple[2], couple[3]
# coord = zero_one_encoding(couple[-2],couple[-1]) # 0 and 1 encoding
# enc_top, enc_top_c = index.encode(top),index.encode(top_c)
# if split_ == "train":
# X_1_train.append(enc_top)
# X_2_train.append(enc_top_c)
# y_train.append(list(coord))
# else:
# X_1_test.append(enc_top)
# X_2_test.append(enc_top_c)
# y_test.append(list(coord))
# # "NUMPYZE" inputs and output lists
# X_1_train = np.array(X_1_train)
# X_2_train = np.array(X_2_train)
# y_train = np.array(y_train)
# X_1_test = np.array(X_1_test)
# X_2_test = np.array(X_2_test)
# y_test = np.array(y_test)
logging
.
info
(
"
Data prepared !
"
)
...
...
@@ -178,31 +162,38 @@ embedding_layer = Embedding(num_words, EMBEDDING_DIM,input_length=index.max_len,
x1
=
embedding_layer
(
input_1
)
x2
=
embedding_layer
(
input_2
)
if
not
args
.
previous_state
:
# Each LSTM learn on a permutation of the input toponyms
if
args
.
lstm_layer
==
2
:
x1
=
Bidirectional
(
LSTM
(
100
))(
x1
)
x2
=
Bidirectional
(
LSTM
(
100
))(
x2
)
x
=
concatenate
([
x1
,
x2
])
else
:
lstm_unique_layer
=
Bidirectional
(
LSTM
(
100
))
x1
=
lstm_unique_layer
(
x1
)
x2
=
lstm_unique_layer
(
x2
)
x
=
concatenate
([
x1
,
x2
])
if
args
.
lstm_layer
==
2
:
x1
=
Bidirectional
(
LSTM
(
100
))(
x1
)
x2
=
Bidirectional
(
LSTM
(
100
))(
x2
)
x
=
concatenate
([
x1
,
x2
])
else
:
lstm_unique_layer
=
Bidirectional
(
LSTM
(
100
))
x1
=
lstm_unique_layer
(
x1
)
x2
=
lstm_unique_layer
(
x2
)
x
=
concatenate
([
x1
,
x2
])
x1
=
Dense
(
500
,
activation
=
"
relu
"
)(
x
)
x1
=
Dense
(
500
,
activation
=
"
relu
"
)(
x1
)
x1
=
Dense
(
500
,
activation
=
"
relu
"
)(
x
)
x1
=
Dense
(
500
,
activation
=
"
relu
"
)(
x1
)
x2
=
Dense
(
500
,
activation
=
"
relu
"
)(
x
)
x2
=
Dense
(
500
,
activation
=
"
relu
"
)(
x2
)
x2
=
Dense
(
500
,
activation
=
"
relu
"
)(
x
)
x2
=
Dense
(
500
,
activation
=
"
relu
"
)(
x2
)
output_lon
=
Dense
(
1
,
activation
=
"
sigmoid
"
,
name
=
"
Output_LON
"
)(
x1
)
output_lat
=
Dense
(
1
,
activation
=
"
sigmoid
"
,
name
=
"
Output_LAT
"
)(
x2
)
output_lon
=
Dense
(
1
,
activation
=
"
sigmoid
"
,
name
=
"
Output_LON
"
)(
x1
)
output_lat
=
Dense
(
1
,
activation
=
"
sigmoid
"
,
name
=
"
Output_LAT
"
)(
x2
)
output_coord
=
concatenate
([
output_lon
,
output_lat
],
name
=
"
output_coord
"
)
output_coord
=
concatenate
([
output_lon
,
output_lat
],
name
=
"
output_coord
"
)
model
=
Model
(
inputs
=
[
input_1
,
input_2
],
outputs
=
output_coord
)
#input_3
model
.
compile
(
loss
=
{
"
output_coord
"
:
haversine_tf_1circle
},
optimizer
=
'
adam
'
,
metrics
=
{
"
output_coord
"
:
accuracy_k
(
ACCURACY_TOLERANCE
)})
model
=
Model
(
inputs
=
[
input_1
,
input_2
],
outputs
=
output_coord
)
#input_3
model
.
compile
(
loss
=
{
"
output_coord
"
:
haversine_tf_1circle
},
optimizer
=
'
adam
'
,
metrics
=
{
"
output_coord
"
:
accuracy_k
(
ACCURACY_TOLERANCE
)})
else
:
if
not
os
.
path
.
exists
(
args
.
previous_state
):
print
(
"
Model previous state was not found !
"
)
sys
.
exit
(
1
)
print
(
"
Load Previous state of the model...
"
)
model
=
tf
.
keras
.
models
.
load_model
(
args
.
previous_state
,
custom_objects
=
{
"
haversine_tf_1circle
"
:
haversine_tf_1circle
,
"
compute_metric
"
:
accuracy_k
(
100
)})
print
(
"
Neural Network Architecture :
"
)
print
(
model
.
summary
())
#############################################################################################
...
...
@@ -220,12 +211,6 @@ history = model.fit(training_generator,verbose=True,
validation_data
=
validation_generator
,
callbacks
=
[
checkpoint
,
epoch_timer
],
epochs
=
EPOCHS
)
# history = model.fit(x=[X_1_train,X_2_train],
# y=y_train,
# verbose=True, batch_size=100,
# epochs=EPOCHS,
# validation_data=([X_1_test,X_2_test],y_test),#[y_lon_test,y_lat_test]),
# callbacks=[checkpoint,epoch_timer])
hist_df
=
pd
.
DataFrame
(
history
.
history
)
...
...
This diff is collapsed.
Click to expand it.
Preview
0%
Loading
Try again
or
attach a new file
.
Cancel
You are about to add
0
people
to the discussion. Proceed with caution.
Finish editing this message first!
Save comment
Cancel
Please
register
or
sign in
to comment