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Lucas Miranda
deepOF
Commits
c25f8d92
Commit
c25f8d92
authored
Apr 22, 2021
by
lucas_miranda
Browse files
Changed epochs default for model training
parent
c87ee026
Changes
2
Hide whitespace changes
Inline
Side-by-side
deepof/models.py
View file @
c25f8d92
...
...
@@ -341,7 +341,7 @@ class SEQ_2_SEQ_GMVAE:
"bidirectional_merge"
:
"concat"
,
"clipvalue"
:
1.0
,
"dense_activation"
:
"relu"
,
"dense_layers_per_branch"
:
3
,
"dense_layers_per_branch"
:
1
,
"dropout_rate"
:
0.05
,
"learning_rate"
:
1e-3
,
"units_conv"
:
64
,
...
...
@@ -399,7 +399,7 @@ class SEQ_2_SEQ_GMVAE:
use_bias
=
True
,
)
Model
_E
4
=
[
seq
_E
=
[
Dense
(
self
.
DENSE_2
,
activation
=
self
.
dense_activation
,
...
...
@@ -409,13 +409,16 @@ class SEQ_2_SEQ_GMVAE:
)
for
_
in
range
(
self
.
dense_layers_per_branch
)
]
Model_E4
=
[]
for
l
in
seq_E
:
Model_E4
.
append
(
l
)
Model_E4
.
append
(
BatchNormalization
())
# Decoder layers
Model_B1
=
BatchNormalization
()
Model_B2
=
BatchNormalization
()
Model_B3
=
BatchNormalization
()
Model_B4
=
BatchNormalization
()
Model_D1
=
[
seq_D
=
[
Dense
(
self
.
DENSE_2
,
activation
=
self
.
dense_activation
,
...
...
@@ -424,6 +427,11 @@ class SEQ_2_SEQ_GMVAE:
)
for
_
in
range
(
self
.
dense_layers_per_branch
)
]
Model_D1
=
[]
for
l
in
seq_D
:
Model_D1
.
append
(
l
)
Model_D1
.
append
(
BatchNormalization
())
Model_D2
=
Dense
(
self
.
DENSE_1
,
activation
=
self
.
dense_activation
,
...
...
@@ -511,7 +519,6 @@ class SEQ_2_SEQ_GMVAE:
Model_B1
,
Model_B2
,
Model_B3
,
Model_B4
,
Model_D1
,
Model_D2
,
Model_D3
,
...
...
@@ -540,7 +547,6 @@ class SEQ_2_SEQ_GMVAE:
Model_B1
,
Model_B2
,
Model_B3
,
Model_B4
,
Model_D1
,
Model_D2
,
Model_D3
,
...
...
@@ -565,7 +571,6 @@ class SEQ_2_SEQ_GMVAE:
encoder
=
BatchNormalization
()(
encoder
)
encoder
=
Dropout
(
self
.
DROPOUT_RATE
)(
encoder
)
encoder
=
Sequential
(
Model_E4
)(
encoder
)
# encoder = BatchNormalization()(encoder)
# encoding_shuffle = deepof.model_utils.MCDropout(self.DROPOUT_RATE)(encoder)
z_cat
=
Dense
(
...
...
@@ -626,7 +631,9 @@ class SEQ_2_SEQ_GMVAE:
tfd
.
Independent
(
tfd
.
Normal
(
loc
=
gauss
[
1
][...,
:
self
.
ENCODING
,
k
],
scale
=
softplus
(
gauss
[
1
][...,
self
.
ENCODING
:,
k
])
+
1e-5
,
scale
=
1e-3
+
softplus
(
gauss
[
1
][...,
self
.
ENCODING
:,
k
])
+
1e-5
,
),
reinterpreted_batch_ndims
=
1
,
)
...
...
@@ -674,22 +681,28 @@ class SEQ_2_SEQ_GMVAE:
# Define and instantiate generator
g
=
Input
(
shape
=
self
.
ENCODING
)
generator
=
Sequential
(
Model_D1
)(
g
)
generator
=
Model_B1
(
generator
)
generator
=
Model_D2
(
generator
)
generator
=
Model_B
2
(
generator
)
generator
=
Model_B
1
(
generator
)
generator
=
Model_D3
(
generator
)
generator
=
Model_D4
(
generator
)
generator
=
Model_B
3
(
generator
)
generator
=
Model_B
2
(
generator
)
generator
=
Model_D5
(
generator
)
generator
=
Model_B4
(
generator
)
generator
=
Dense
(
tfpl
.
IndependentNormal
.
params_size
(
input_shape
[
2
:]))(
generator
generator
=
Model_B3
(
generator
)
x_decoded_mean
=
Dense
(
tfpl
.
IndependentNormal
.
params_size
(
input_shape
[
2
:])
//
2
)(
generator
)
x_decoded_var
=
tf
.
keras
.
activations
.
softplus
(
Dense
(
tfpl
.
IndependentNormal
.
params_size
(
input_shape
[
2
:])
//
2
)(
generator
)
)
x_decoded_var
=
tf
.
keras
.
layers
.
Lambda
(
lambda
x
:
1e-3
+
x
)(
x_decoded_var
)
x_decoded
=
tf
.
keras
.
layers
.
concatenate
(
[
x_decoded_mean
,
x_decoded_var
],
axis
=-
1
)
x_decoded_mean
=
tfpl
.
IndependentNormal
(
event_shape
=
input_shape
[
2
:],
convert_to_tensor_fn
=
tfp
.
distributions
.
Distribution
.
mean
,
name
=
"vae_reconstruction"
,
)(
generator
)
)(
x_decoded
)
# define individual branches as models
encoder
=
Model
(
x
,
z
,
name
=
"SEQ_2_SEQ_VEncoder"
)
...
...
@@ -720,14 +733,25 @@ class SEQ_2_SEQ_GMVAE:
predictor
=
BatchNormalization
()(
predictor
)
predictor
=
Model_P3
(
predictor
)
predictor
=
BatchNormalization
()(
predictor
)
predictor
=
Dense
(
tfpl
.
IndependentNormal
.
params_size
(
input_shape
[
2
:]))(
predictor
x_predicted_mean
=
Dense
(
tfpl
.
IndependentNormal
.
params_size
(
input_shape
[
2
:])
//
2
)(
predictor
)
x_predicted_var
=
tf
.
keras
.
activations
.
softplus
(
Dense
(
tfpl
.
IndependentNormal
.
params_size
(
input_shape
[
2
:])
//
2
)(
predictor
)
)
x_predicted_var
=
tf
.
keras
.
layers
.
Lambda
(
lambda
x
:
1e-3
+
x
)(
x_predicted_var
)
x_decoded
=
tf
.
keras
.
layers
.
concatenate
(
[
x_predicted_mean
,
x_predicted_var
],
axis
=-
1
)
x_predicted_mean
=
tfpl
.
IndependentNormal
(
event_shape
=
input_shape
[
2
:],
convert_to_tensor_fn
=
tfp
.
distributions
.
Distribution
.
mean
,
name
=
"vae_prediction"
,
)(
predictor
)
)(
x_decoded
)
model_outs
.
append
(
x_predicted_mean
)
model_losses
.
append
(
log_loss
)
...
...
deepof/train_utils.py
View file @
c25f8d92
...
...
@@ -633,16 +633,16 @@ def tune_search(
Xvals
,
yvals
=
X_val
[:
-
1
],
[
X_val
[:
-
1
],
X_val
[
1
:]]
if
phenotype_prediction
>
0.0
:
ys
+=
[
y_train
[
-
Xs
.
shape
[
0
]:,
0
]]
yvals
+=
[
y_val
[
-
Xvals
.
shape
[
0
]:,
0
]]
ys
+=
[
y_train
[
-
Xs
.
shape
[
0
]
:,
0
]]
yvals
+=
[
y_val
[
-
Xvals
.
shape
[
0
]
:,
0
]]
# Remove the used column (phenotype) from both y arrays
y_train
=
y_train
[:,
1
:]
y_val
=
y_val
[:,
1
:]
if
rule_based_prediction
>
0.0
:
ys
+=
[
y_train
[
-
Xs
.
shape
[
0
]:]]
yvals
+=
[
y_val
[
-
Xvals
.
shape
[
0
]:]]
ys
+=
[
y_train
[
-
Xs
.
shape
[
0
]
:]]
yvals
+=
[
y_val
[
-
Xvals
.
shape
[
0
]
:]]
tuner
.
search
(
Xs
,
...
...
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