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docker-python/tests/test_tensorflow.py at require-hashes · Kaggle/docker-python · GitHub
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import
unittest
import
os
.
path
import
numpy
as
np
import
tensorflow
as
tf
from
common
import
gpu_test
class
TestTensorflow
(
unittest
.
TestCase
):
def
test_addition
(
self
):
result
=
tf
.
add
([
1
,
2
], [
3
,
4
])
self
.
assertEqual
([
2
],
result
.
shape
)
def
test_conv2d
(
self
):
input
=
tf
.
random
.
normal
([
1
,
2
,
2
,
1
])
filter
=
tf
.
random
.
normal
([
1
,
1
,
1
,
1
])
result
=
tf
.
nn
.
conv2d
(
input
,
filter
,
strides
=
[
1
,
1
,
1
,
1
],
padding
=
'SAME'
)
self
.
assertEqual
(
4
,
len
(
result
.
shape
))
def
test_tf_keras
(
self
):
x_train
=
np
.
random
.
random
((
100
,
28
,
28
))
y_train
=
np
.
random
.
randint
(
10
,
size
=
(
100
,
1
))
x_test
=
np
.
random
.
random
((
20
,
28
,
28
))
y_test
=
np
.
random
.
randint
(
10
,
size
=
(
20
,
1
))
model
=
tf
.
keras
.
models
.
Sequential
([
tf
.
keras
.
layers
.
Flatten
(
input_shape
=
(
28
,
28
)),
tf
.
keras
.
layers
.
Dense
(
128
,
activation
=
'relu'
),
tf
.
keras
.
layers
.
Dropout
(
0.2
),
tf
.
keras
.
layers
.
Dense
(
10
,
activation
=
'softmax'
)
])
model
.
compile
(
optimizer
=
'adam'
,
loss
=
'sparse_categorical_crossentropy'
,
metrics
=
[
'accuracy'
])
model
.
fit
(
x_train
,
y_train
,
epochs
=
1
)
result
=
model
.
evaluate
(
x_test
,
y_test
)
self
.
assertEqual
(
2
,
len
(
result
))
# exercices pydot path.
tf
.
keras
.
utils
.
plot_model
(
model
,
to_file
=
"tf_plot_model.png"
)
self
.
assertTrue
(
os
.
path
.
isfile
(
"tf_plot_model.png"
))
def
test_lstm
(
self
):
x_train
=
np
.
random
.
random
((
100
,
28
,
28
))
y_train
=
np
.
random
.
randint
(
10
,
size
=
(
100
,
1
))
x_test
=
np
.
random
.
random
((
20
,
28
,
28
))
y_test
=
np
.
random
.
randint
(
10
,
size
=
(
20
,
1
))
model
=
tf
.
keras
.
Sequential
([
tf
.
keras
.
layers
.
Bidirectional
(
tf
.
keras
.
layers
.
LSTM
(
64
,
input_shape
=
(
28
,
28
))),
tf
.
keras
.
layers
.
Dense
(
64
,
activation
=
'relu'
),
tf
.
keras
.
layers
.
Dense
(
1
,
activation
=
'sigmoid'
)
])
model
.
compile
(
loss
=
'binary_crossentropy'
,
optimizer
=
'adam'
,
metrics
=
[
'accuracy'
])
model
.
fit
(
x_train
,
y_train
,
epochs
=
1
)
result
=
model
.
evaluate
(
x_test
,
y_test
)
self
.
assertEqual
(
2
,
len
(
result
))
@
gpu_test
def
test_gpu
(
self
):
with
tf
.
device
(
'/gpu:0'
):
m1
=
tf
.
constant
([
2.0
,
3.0
],
shape
=
[
1
,
2
],
name
=
'a'
)
m2
=
tf
.
constant
([
3.0
,
4.0
],
shape
=
[
2
,
1
],
name
=
'b'
)
result
=
tf
.
matmul
(
m1
,
m2
)
self
.
assertEqual
([
1
,
1
],
result
.
shape
)
@
gpu_test
def
test_is_built_with_cuda
(
self
):
self
.
assertTrue
(
tf
.
test
.
is_built_with_cuda
())
@
gpu_test
def
test_is_gpu_available
(
self
):
self
.
assertTrue
(
tf
.
test
.
is_gpu_available
(
cuda_only
=
True
))
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