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bob
bob.pad.voice
Commits
690b5fcd
Commit
690b5fcd
authored
Oct 10, 2017
by
Pavel KORSHUNOV
Browse files
corrected eval algo
parent
c7404221
Pipeline
#13085
passed with stages
in 9 minutes and 37 seconds
Changes
1
Pipelines
1
Hide whitespace changes
Inline
Side-by-side
bob/pad/voice/algorithm/LSTMEval.py
View file @
690b5fcd
...
...
@@ -38,18 +38,19 @@ class LSTMEval(Algorithm):
self
.
data_std
=
None
# import ipdb
# ipdb.set_trace()
features_length
=
input_shape
[
1
]
if
normalization_file
and
os
.
path
.
exists
(
normalization_file
):
npzfile
=
numpy
.
load
(
normalization_file
)
self
.
data_mean
=
npzfile
[
'data_mean'
]
self
.
data_std
=
npzfile
[
'data_std'
]
if
not
self
.
data_std
.
shape
:
# if std was saved as scalar
self
.
data_std
=
numpy
.
ones
(
input_shape
)
if
self
.
data_mean
.
shape
[
0
]
>
input_shape
[
0
]:
self
.
data_mean
=
self
.
data_mean
[:
input_shape
[
0
]]
self
.
data_mean
=
numpy
.
reshape
(
self
.
data_mean
,
input_shape
)
if
self
.
data_std
.
shape
[
0
]
>
input_shape
[
0
]:
self
.
data_std
=
self
.
data_std
[:
input_shape
[
0
]]
self
.
data_std
=
numpy
.
reshape
(
self
.
data_std
,
input_shape
)
self
.
data_std
=
numpy
.
ones
(
features_length
)
#
if self.data_mean.shape[0] > input_shape[0]:
#
self.data_mean = self.data_mean[:input_shape[0]]
#
self.data_mean = numpy.reshape(self.data_mean, input_shape)
#
if self.data_std.shape[0] > input_shape[0]:
#
self.data_std = self.data_std[:input_shape[0]]
#
self.data_std = numpy.reshape(self.data_std, input_shape)
else
:
self
.
data_mean
=
0
self
.
data_std
=
1
...
...
@@ -74,7 +75,7 @@ class LSTMEval(Algorithm):
# Creating an LSTM network
graph
=
lstm
(
inputs
,
lstm_cell_size
,
num_time_steps
=
num_time_steps
,
batch_size
=
batch_size
,
output_activation_size
=
num_classes
,
scope
=
'lstm'
,
output_activation_size
=
num_classes
,
scope
=
'lstm'
,
name
=
'sync_cell'
,
weights_initializer
=
initializer
,
activation
=
tf
.
nn
.
sigmoid
,
reuse
=
reuse
)
# fully connect the LSTM output to the classes
...
...
@@ -126,6 +127,10 @@ class LSTMEval(Algorithm):
if
not
self
.
data_reader
:
self
.
data_reader
=
DiskAudio
([
0
],
[
0
],
[
1
]
+
self
.
input_shape
)
# normalize the feature using pre-loaded normalization parameters
if
self
.
data_std
is
not
None
and
self
.
data_std
.
all
()
>
0
:
feature
=
numpy
.
divide
(
feature
-
self
.
data_mean
,
self
.
data_std
)
# split the feature in the sliding window frames
frames
,
_
=
self
.
data_reader
.
split_features_in_windows
(
features
=
feature
,
label
=
1
,
win_size
=
self
.
num_time_steps
,
...
...
@@ -138,9 +143,6 @@ class LSTMEval(Algorithm):
for
i
in
range
(
frames
.
shape
[
0
]):
frame
=
frames
[
i
]
frame
=
numpy
.
reshape
(
frame
,
[
1
]
+
self
.
input_shape
)
# normalize the frame using pre-loaded normalization parameters
if
self
.
data_std
is
not
None
and
self
.
data_std
.
all
()
>
0
:
frame
=
numpy
.
divide
(
frame
-
self
.
data_mean
,
self
.
data_std
)
#logger.info(" .... projecting frame of shape {0} onto DNN model".format(frame.shape))
if
self
.
session
is
not
None
:
...
...
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