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basicnet.cpp
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Sat, Jun 8, 18:30
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Mon, Jun 10, 18:30 (2 d)
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R9868 DeepHealth_UC13_seizure_detection
basicnet.cpp
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#include <eddl/apis/eddl.h>
#include <eddl/apis/eddlT.h>
#include <eddl/tensor/tensor.h>
#include <string>
#include "basicnet.h"
#define NB_CHNS 4
#define L2_K 0.1
#define L2_B 0.1
#define L2_A 0.0
#define DROPOUT_RATE 0.5
BasicNet
::
BasicNet
()
{
using
namespace
eddl
;
layer
in_
=
Input
({
1
,
NB_CHNS
,
1280
});
layer
l
=
in_
;
l
=
L2
(
GlorotUniform
(
Conv
(
l
,
16
,
{
3
,
5
},
{
1
,
1
},
"same"
)),
L2_K
);
l
=
BatchNormalization
(
l
,
0.99
,
0.001
);
l
=
Activation
(
l
,
"relu"
);
l
=
MaxPool
(
l
,
{
1
,
2
},
{
1
,
2
},
"same"
);
l
=
L2
(
GlorotUniform
(
Conv
(
l
,
32
,
{
3
,
3
},
{
1
,
1
},
"same"
)),
L2_K
);
l
=
BatchNormalization
(
l
,
0.99
,
0.001
);
l
=
Activation
(
l
,
"relu"
);
l
=
MaxPool
(
l
,
{
1
,
2
},
{
1
,
2
},
"same"
);
l
=
L2
(
GlorotUniform
(
Conv
(
l
,
32
,
{
3
,
3
},
{
2
,
2
},
"same"
)),
L2_K
);
l
=
BatchNormalization
(
l
,
0.99
,
0.001
);
l
=
Activation
(
l
,
"relu"
);
l
=
Dropout
(
l
,
DROPOUT_RATE
);
l
=
Flatten
(
l
);
// l = Dense(l, 64, kernel_initialiser='glorot_uniform', bias_initialiser='zeros', kernel_regularizer=L2(l2_k), bias_regularizer=L2(l2_b));
l
=
L2
(
GlorotUniform
(
Dense
(
l
,
64
)),
L2_K
);
l
=
Activation
(
l
,
"relu"
);
l
=
Dropout
(
l
,
DROPOUT_RATE
);
// l = Dense(l, 1, kernel_initialiser='glorot_uniform', bias_initialiser='zeros');
l
=
GlorotUniform
(
Dense
(
l
,
2
));
l
=
Activation
(
l
,
"softmax"
);
layer
out_
=
l
;
net
=
Model
({
in_
},
{
out_
});
build
(
net
,
sgd
(
0.01
,
0.9
,
0.0
,
true
),
{
"cross_entropy"
},
{
"categorical_accuracy"
},
CS_CPU
(
4
,
"full_mem"
));
summary
(
net
);
}
void
BasicNet
::
fit
(
Tensor
*
x_train
,
Tensor
*
y_train
,
Tensor
*
x_val
,
Tensor
*
y_val
,
int
batch_size
,
int
epochs
,
float
learning_rate
)
{
eddl
::
setlr
(
net
,
{
learning_rate
});
for
(
int
e
=
0
;
e
<
epochs
;
e
++
)
{
eddl
::
fit
(
net
,
{
x_train
},
{
y_train
},
batch_size
,
1
);
eddl
::
evaluate
(
net
,
{
x_val
},
{
y_val
});
}
}
void
BasicNet
::
evaluate
(
Tensor
*
x
,
Tensor
*
y
)
{
eddl
::
evaluate
(
net
,
{
x
},
{
y
});
}
void
BasicNet
::
save
(
std
::
string
file_name
)
{
eddl
::
save
(
net
,
file_name
);
}
void
BasicNet
::
load
(
std
::
string
file_name
)
{
eddl
::
load
(
net
,
file_name
);
}
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