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deep-learning-workshop
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Authored by
Carlos-Francisco Méndez-Cruz
2019-05-08 14:56:58 -0500
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8df904d52af3a2a372380728dd9a659c8148799d
8df904d5
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ab0e7326
Deep Learning Workshop
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data-sets/get-hga-training-test-py27-v1.py
data-sets/get-hga-training-test-py27-v1.py
View file @
8df904d
...
...
@@ -136,8 +136,15 @@ if __name__ == "__main__":
# Model definition
model
=
Sequential
()
model
.
add
(
Conv1D
(
filters
=
32
,
kernel_size
=
12
,
input_shape
=
(
train_features
.
shape
[
1
],
4
)))
# Original considering only 4 nucleotides:
# model.add(Conv1D(filters=32, kernel_size=12,
# input_shape=(train_features.shape[1], 4)))
# We change for 5 nucleotides, as we added X:
# Do we have to change kernel_size to 15, becaause of the 5 nucleotides?
model
.
add
(
Conv1D
(
filters
=
32
,
kernel_size
=
15
,
input_shape
=
(
train_features
.
shape
[
1
],
5
)))
# Original: model.add(MaxPooling1D(pool_size=4))
# Do we have to change to 5, because of the 5 nucleotides?
model
.
add
(
MaxPooling1D
(
pool_size
=
4
))
model
.
add
(
Flatten
())
model
.
add
(
Dense
(
16
,
activation
=
'relu'
))
...
...
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