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tensorflow/tensorflow/python/keras/testing_utils.py at master · codeplaysoftware/tensorflow · GitHub
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Utilities for unit-testing Keras."""
from
__future__
import
absolute_import
from
__future__
import
division
from
__future__
import
print_function
import
numpy
as
np
from
tensorflow
.
python
import
keras
from
tensorflow
.
python
.
framework
import
tensor_shape
from
tensorflow
.
python
.
training
.
rmsprop
import
RMSPropOptimizer
from
tensorflow
.
python
.
util
import
tf_inspect
def
get_test_data
(
train_samples
,
test_samples
,
input_shape
,
num_classes
):
"""Generates test data to train a model on.
Arguments:
train_samples: Integer, how many training samples to generate.
test_samples: Integer, how many test samples to generate.
input_shape: Tuple of integers, shape of the inputs.
num_classes: Integer, number of classes for the data and targets.
Returns:
A tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
"""
num_sample
=
train_samples
+
test_samples
templates
=
2
*
num_classes
*
np
.
random
.
random
((
num_classes
,)
+
input_shape
)
y
=
np
.
random
.
randint
(
0
,
num_classes
,
size
=
(
num_sample
,))
x
=
np
.
zeros
((
num_sample
,)
+
input_shape
,
dtype
=
np
.
float32
)
for
i
in
range
(
num_sample
):
x
[
i
]
=
templates
[
y
[
i
]]
+
np
.
random
.
normal
(
loc
=
0
,
scale
=
1.
,
size
=
input_shape
)
return
((
x
[:
train_samples
],
y
[:
train_samples
]),
(
x
[
train_samples
:],
y
[
train_samples
:]))
def
layer_test
(
layer_cls
,
kwargs
=
None
,
input_shape
=
None
,
input_dtype
=
None
,
input_data
=
None
,
expected_output
=
None
,
expected_output_dtype
=
None
):
"""Test routine for a layer with a single input and single output.
Arguments:
layer_cls: Layer class object.
kwargs: Optional dictionary of keyword arguments for instantiating the
layer.
input_shape: Input shape tuple.
input_dtype: Data type of the input data.
input_data: Numpy array of input data.
expected_output: Shape tuple for the expected shape of the output.
expected_output_dtype: Data type expected for the output.
Returns:
The output data (Numpy array) returned by the layer, for additional
checks to be done by the calling code.
"""
if
input_data
is
None
:
assert
input_shape
if
not
input_dtype
:
input_dtype
=
'float32'
input_data_shape
=
list
(
input_shape
)
for
i
,
e
in
enumerate
(
input_data_shape
):
if
e
is
None
:
input_data_shape
[
i
]
=
np
.
random
.
randint
(
1
,
4
)
input_data
=
10
*
np
.
random
.
random
(
input_data_shape
)
if
input_dtype
[:
5
]
==
'float'
:
input_data
-=
0.5
input_data
=
input_data
.
astype
(
input_dtype
)
elif
input_shape
is
None
:
input_shape
=
input_data
.
shape
if
input_dtype
is
None
:
input_dtype
=
input_data
.
dtype
if
expected_output_dtype
is
None
:
expected_output_dtype
=
input_dtype
# instantiation
kwargs
=
kwargs
or
{}
layer
=
layer_cls
(
**
kwargs
)
# test get_weights , set_weights at layer level
weights
=
layer
.
get_weights
()
layer
.
set_weights
(
weights
)
# test and instantiation from weights
if
'weights'
in
tf_inspect
.
getargspec
(
layer_cls
.
__init__
):
kwargs
[
'weights'
]
=
weights
layer
=
layer_cls
(
**
kwargs
)
# test in functional API
x
=
keras
.
layers
.
Input
(
shape
=
input_shape
[
1
:],
dtype
=
input_dtype
)
y
=
layer
(
x
)
if
keras
.
backend
.
dtype
(
y
)
!=
expected_output_dtype
:
raise
AssertionError
(
'When testing layer %s, for input %s, found output '
'dtype=%s but expected to find %s.
\n
Full kwargs: %s'
%
(
layer_cls
.
__name__
,
x
,
keras
.
backend
.
dtype
(
y
),
expected_output_dtype
,
kwargs
))
# check shape inference
model
=
keras
.
models
.
Model
(
x
,
y
)
expected_output_shape
=
tuple
(
layer
.
compute_output_shape
(
tensor_shape
.
TensorShape
(
input_shape
)).
as_list
())
actual_output
=
model
.
predict
(
input_data
)
actual_output_shape
=
actual_output
.
shape
for
expected_dim
,
actual_dim
in
zip
(
expected_output_shape
,
actual_output_shape
):
if
expected_dim
is
not
None
:
if
expected_dim
!=
actual_dim
:
raise
AssertionError
(
'When testing layer %s, for input %s, found output_shape='
'%s but expected to find %s.
\n
Full kwargs: %s'
%
(
layer_cls
.
__name__
,
x
,
actual_output_shape
,
expected_output_shape
,
kwargs
))
if
expected_output
is
not
None
:
np
.
testing
.
assert_allclose
(
actual_output
,
expected_output
,
rtol
=
1e-3
)
# test serialization, weight setting at model level
model_config
=
model
.
get_config
()
recovered_model
=
keras
.
models
.
Model
.
from_config
(
model_config
)
if
model
.
weights
:
weights
=
model
.
get_weights
()
recovered_model
.
set_weights
(
weights
)
output
=
recovered_model
.
predict
(
input_data
)
np
.
testing
.
assert_allclose
(
output
,
actual_output
,
rtol
=
1e-3
)
# test training mode (e.g. useful for dropout tests)
model
.
compile
(
RMSPropOptimizer
(
0.01
),
'mse'
)
model
.
train_on_batch
(
input_data
,
actual_output
)
# test as first layer in Sequential API
layer_config
=
layer
.
get_config
()
layer_config
[
'batch_input_shape'
]
=
input_shape
layer
=
layer
.
__class__
.
from_config
(
layer_config
)
model
=
keras
.
models
.
Sequential
()
model
.
add
(
layer
)
actual_output
=
model
.
predict
(
input_data
)
actual_output_shape
=
actual_output
.
shape
for
expected_dim
,
actual_dim
in
zip
(
expected_output_shape
,
actual_output_shape
):
if
expected_dim
is
not
None
:
if
expected_dim
!=
actual_dim
:
raise
AssertionError
(
'When testing layer %s, for input %s, found output_shape='
'%s but expected to find %s.
\n
Full kwargs: %s'
%
(
layer_cls
.
__name__
,
x
,
actual_output_shape
,
expected_output_shape
,
kwargs
))
if
expected_output
is
not
None
:
np
.
testing
.
assert_allclose
(
actual_output
,
expected_output
,
rtol
=
1e-3
)
# test serialization, weight setting at model level
model_config
=
model
.
get_config
()
recovered_model
=
keras
.
models
.
Sequential
.
from_config
(
model_config
)
if
model
.
weights
:
weights
=
model
.
get_weights
()
recovered_model
.
set_weights
(
weights
)
output
=
recovered_model
.
predict
(
input_data
)
np
.
testing
.
assert_allclose
(
output
,
actual_output
,
rtol
=
1e-3
)
# for further checks in the caller function
return
actual_output
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