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Fabian Mersch
SimpleHTR
Commits
7e0da952
Commit
7e0da952
authored
6 years ago
by
Chazzz
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Added batch normalization and updated model.zip
parent
35ddc487
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model/model.zip
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model/model.zip
src/Model.py
+11
-5
11 additions, 5 deletions
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11 additions
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5 deletions
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src/Model.py
+
11
−
5
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7e0da952
...
@@ -27,6 +27,9 @@ class Model:
...
@@ -27,6 +27,9 @@ class Model:
self
.
mustRestore
=
mustRestore
self
.
mustRestore
=
mustRestore
self
.
snapID
=
0
self
.
snapID
=
0
# Whether to use normalization over a batch or a population
self
.
is_train
=
tf
.
placeholder
(
tf
.
bool
,
name
=
"
is_train
"
);
# input image batch
# input image batch
self
.
inputImgs
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
(
None
,
Model
.
imgSize
[
0
],
Model
.
imgSize
[
1
]))
self
.
inputImgs
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
(
None
,
Model
.
imgSize
[
0
],
Model
.
imgSize
[
1
]))
...
@@ -38,6 +41,8 @@ class Model:
...
@@ -38,6 +41,8 @@ class Model:
# setup optimizer to train NN
# setup optimizer to train NN
self
.
batchesTrained
=
0
self
.
batchesTrained
=
0
self
.
learningRate
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
[])
self
.
learningRate
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
[])
self
.
update_ops
=
tf
.
get_collection
(
tf
.
GraphKeys
.
UPDATE_OPS
)
with
tf
.
control_dependencies
(
self
.
update_ops
):
self
.
optimizer
=
tf
.
train
.
RMSPropOptimizer
(
self
.
learningRate
).
minimize
(
self
.
loss
)
self
.
optimizer
=
tf
.
train
.
RMSPropOptimizer
(
self
.
learningRate
).
minimize
(
self
.
loss
)
# initialize TF
# initialize TF
...
@@ -59,7 +64,8 @@ class Model:
...
@@ -59,7 +64,8 @@ class Model:
for
i
in
range
(
numLayers
):
for
i
in
range
(
numLayers
):
kernel
=
tf
.
Variable
(
tf
.
truncated_normal
([
kernelVals
[
i
],
kernelVals
[
i
],
featureVals
[
i
],
featureVals
[
i
+
1
]],
stddev
=
0.1
))
kernel
=
tf
.
Variable
(
tf
.
truncated_normal
([
kernelVals
[
i
],
kernelVals
[
i
],
featureVals
[
i
],
featureVals
[
i
+
1
]],
stddev
=
0.1
))
conv
=
tf
.
nn
.
conv2d
(
pool
,
kernel
,
padding
=
'
SAME
'
,
strides
=
(
1
,
1
,
1
,
1
))
conv
=
tf
.
nn
.
conv2d
(
pool
,
kernel
,
padding
=
'
SAME
'
,
strides
=
(
1
,
1
,
1
,
1
))
relu
=
tf
.
nn
.
relu
(
conv
)
conv_norm
=
tf
.
layers
.
batch_normalization
(
conv
,
training
=
self
.
is_train
)
relu
=
tf
.
nn
.
relu
(
conv_norm
)
pool
=
tf
.
nn
.
max_pool
(
relu
,
(
1
,
poolVals
[
i
][
0
],
poolVals
[
i
][
1
],
1
),
(
1
,
strideVals
[
i
][
0
],
strideVals
[
i
][
1
],
1
),
'
VALID
'
)
pool
=
tf
.
nn
.
max_pool
(
relu
,
(
1
,
poolVals
[
i
][
0
],
poolVals
[
i
][
1
],
1
),
(
1
,
strideVals
[
i
][
0
],
strideVals
[
i
][
1
],
1
),
'
VALID
'
)
self
.
cnnOut4d
=
pool
self
.
cnnOut4d
=
pool
...
@@ -205,7 +211,7 @@ class Model:
...
@@ -205,7 +211,7 @@ class Model:
sparse
=
self
.
toSparse
(
batch
.
gtTexts
)
sparse
=
self
.
toSparse
(
batch
.
gtTexts
)
rate
=
0.01
if
self
.
batchesTrained
<
10
else
(
0.001
if
self
.
batchesTrained
<
10000
else
0.0001
)
# decay learning rate
rate
=
0.01
if
self
.
batchesTrained
<
10
else
(
0.001
if
self
.
batchesTrained
<
10000
else
0.0001
)
# decay learning rate
evalList
=
[
self
.
optimizer
,
self
.
loss
]
evalList
=
[
self
.
optimizer
,
self
.
loss
]
feedDict
=
{
self
.
inputImgs
:
batch
.
imgs
,
self
.
gtTexts
:
sparse
,
self
.
seqLen
:
[
Model
.
maxTextLen
]
*
numBatchElements
,
self
.
learningRate
:
rate
}
feedDict
=
{
self
.
inputImgs
:
batch
.
imgs
,
self
.
gtTexts
:
sparse
,
self
.
seqLen
:
[
Model
.
maxTextLen
]
*
numBatchElements
,
self
.
learningRate
:
rate
,
self
.
is_train
:
True
}
(
_
,
lossVal
)
=
self
.
sess
.
run
(
evalList
,
feedDict
)
(
_
,
lossVal
)
=
self
.
sess
.
run
(
evalList
,
feedDict
)
self
.
batchesTrained
+=
1
self
.
batchesTrained
+=
1
return
lossVal
return
lossVal
...
@@ -217,7 +223,7 @@ class Model:
...
@@ -217,7 +223,7 @@ class Model:
# decode, optionally save RNN output
# decode, optionally save RNN output
numBatchElements
=
len
(
batch
.
imgs
)
numBatchElements
=
len
(
batch
.
imgs
)
evalList
=
[
self
.
decoder
]
+
([
self
.
ctcIn3dTBC
]
if
calcProbability
else
[])
evalList
=
[
self
.
decoder
]
+
([
self
.
ctcIn3dTBC
]
if
calcProbability
else
[])
feedDict
=
{
self
.
inputImgs
:
batch
.
imgs
,
self
.
seqLen
:
[
Model
.
maxTextLen
]
*
numBatchElements
}
feedDict
=
{
self
.
inputImgs
:
batch
.
imgs
,
self
.
seqLen
:
[
Model
.
maxTextLen
]
*
numBatchElements
,
self
.
is_train
:
False
}
evalRes
=
self
.
sess
.
run
([
self
.
decoder
,
self
.
ctcIn3dTBC
],
feedDict
)
evalRes
=
self
.
sess
.
run
([
self
.
decoder
,
self
.
ctcIn3dTBC
],
feedDict
)
decoded
=
evalRes
[
0
]
decoded
=
evalRes
[
0
]
texts
=
self
.
decoderOutputToText
(
decoded
,
numBatchElements
)
texts
=
self
.
decoderOutputToText
(
decoded
,
numBatchElements
)
...
@@ -228,7 +234,7 @@ class Model:
...
@@ -228,7 +234,7 @@ class Model:
sparse
=
self
.
toSparse
(
batch
.
gtTexts
)
if
probabilityOfGT
else
self
.
toSparse
(
texts
)
sparse
=
self
.
toSparse
(
batch
.
gtTexts
)
if
probabilityOfGT
else
self
.
toSparse
(
texts
)
ctcInput
=
evalRes
[
1
]
ctcInput
=
evalRes
[
1
]
evalList
=
self
.
lossPerElement
evalList
=
self
.
lossPerElement
feedDict
=
{
self
.
savedCtcInput
:
ctcInput
,
self
.
gtTexts
:
sparse
,
self
.
seqLen
:
[
Model
.
maxTextLen
]
*
numBatchElements
}
feedDict
=
{
self
.
savedCtcInput
:
ctcInput
,
self
.
gtTexts
:
sparse
,
self
.
seqLen
:
[
Model
.
maxTextLen
]
*
numBatchElements
,
self
.
is_train
:
False
}
lossVals
=
self
.
sess
.
run
(
evalList
,
feedDict
)
lossVals
=
self
.
sess
.
run
(
evalList
,
feedDict
)
probs
=
np
.
exp
(
-
lossVals
)
probs
=
np
.
exp
(
-
lossVals
)
return
(
texts
,
probs
)
return
(
texts
,
probs
)
...
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