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aima-python/tests/test_deep_learning4e.py at master · erralb/aima-python · GitHub
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import
pytest
from
keras
.
datasets
import
imdb
from
deep_learning4e
import
*
from
learning4e
import
DataSet
,
grade_learner
,
err_ratio
random
.
seed
(
"aima-python"
)
iris_tests
=
[([
5.0
,
3.1
,
0.9
,
0.1
],
0
),
([
5.1
,
3.5
,
1.0
,
0.0
],
0
),
([
4.9
,
3.3
,
1.1
,
0.1
],
0
),
([
6.0
,
3.0
,
4.0
,
1.1
],
1
),
([
6.1
,
2.2
,
3.5
,
1.0
],
1
),
([
5.9
,
2.5
,
3.3
,
1.1
],
1
),
([
7.5
,
4.1
,
6.2
,
2.3
],
2
),
([
7.3
,
4.0
,
6.1
,
2.4
],
2
),
([
7.0
,
3.3
,
6.1
,
2.5
],
2
)]
def
test_neural_net
():
iris
=
DataSet
(
name
=
'iris'
)
classes
=
[
'setosa'
,
'versicolor'
,
'virginica'
]
iris
.
classes_to_numbers
(
classes
)
n_samples
,
n_features
=
len
(
iris
.
examples
),
iris
.
target
X
,
y
=
(
np
.
array
([
x
[:
n_features
]
for
x
in
iris
.
examples
]),
np
.
array
([
x
[
n_features
]
for
x
in
iris
.
examples
]))
nnl_gd
=
NeuralNetworkLearner
(
iris
, [
4
],
l_rate
=
0.15
,
epochs
=
100
,
optimizer
=
stochastic_gradient_descent
).
fit
(
X
,
y
)
assert
grade_learner
(
nnl_gd
,
iris_tests
)
>
0.7
assert
err_ratio
(
nnl_gd
,
iris
)
<
0.15
nnl_adam
=
NeuralNetworkLearner
(
iris
, [
4
],
l_rate
=
0.001
,
epochs
=
200
,
optimizer
=
adam
).
fit
(
X
,
y
)
assert
grade_learner
(
nnl_adam
,
iris_tests
)
>
0.7
assert
err_ratio
(
nnl_adam
,
iris
)
<
0.15
def
test_perceptron
():
iris
=
DataSet
(
name
=
'iris'
)
classes
=
[
'setosa'
,
'versicolor'
,
'virginica'
]
iris
.
classes_to_numbers
(
classes
)
n_samples
,
n_features
=
len
(
iris
.
examples
),
iris
.
target
X
,
y
=
(
np
.
array
([
x
[:
n_features
]
for
x
in
iris
.
examples
]),
np
.
array
([
x
[
n_features
]
for
x
in
iris
.
examples
]))
pl_gd
=
PerceptronLearner
(
iris
,
l_rate
=
0.01
,
epochs
=
100
,
optimizer
=
stochastic_gradient_descent
).
fit
(
X
,
y
)
assert
grade_learner
(
pl_gd
,
iris_tests
)
==
1
assert
err_ratio
(
pl_gd
,
iris
)
<
0.2
pl_adam
=
PerceptronLearner
(
iris
,
l_rate
=
0.01
,
epochs
=
100
,
optimizer
=
adam
).
fit
(
X
,
y
)
assert
grade_learner
(
pl_adam
,
iris_tests
)
==
1
assert
err_ratio
(
pl_adam
,
iris
)
<
0.2
def
test_rnn
():
data
=
imdb
.
load_data
(
num_words
=
5000
)
train
,
val
,
test
=
keras_dataset_loader
(
data
)
train
=
(
train
[
0
][:
1000
],
train
[
1
][:
1000
])
val
=
(
val
[
0
][:
200
],
val
[
1
][:
200
])
rnn
=
SimpleRNNLearner
(
train
,
val
)
score
=
rnn
.
evaluate
(
test
[
0
][:
200
],
test
[
1
][:
200
],
verbose
=
False
)
assert
score
[
1
]
>=
0.2
def
test_autoencoder
():
iris
=
DataSet
(
name
=
'iris'
)
classes
=
[
'setosa'
,
'versicolor'
,
'virginica'
]
iris
.
classes_to_numbers
(
classes
)
inputs
=
np
.
asarray
(
iris
.
examples
)
al
=
AutoencoderLearner
(
inputs
,
100
)
print
(
inputs
[
0
])
print
(
al
.
predict
(
inputs
[:
1
]))
if
__name__
==
"__main__"
:
pytest
.
main
()
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