如何测试 Keras 自定义层的正确性?
How to test the correctness of a Keras custom layer?
在创建了一个带有训练权重的Keras自定义层之后,如何测试代码的正确性? Keras的手册里好像没有描述。
例如,要测试函数的预期行为,可以编写单元测试。我们如何为 Keras 自定义层执行此操作?
您仍然可以通过获取给定输入的自定义层的输出并根据手动计算的输出对其进行验证来执行单元测试之类的操作,
假设您的自定义图层 Custom
将 (None, 3, 200) 作为输入形状并且 returns (None, 3)
from keras.layers import Input
from keras.models import Model
inp = Input(shape=(3, 200))
out = Custom()(inp)
model = Model(inp, out)
output = model.predict(your_input)
您可以使用已知输入 your_input
的预期输出来验证层输出 output
。
layer_test
在 keras 实用程序中。
https://github.com/keras-team/keras/blob/master/keras/utils/test_utils.py
他们提供以下代码,测试形状、实际结果、序列化和训练:
def layer_test(layer_cls, kwargs={}, input_shape=None, input_dtype=None,
input_data=None, expected_output=None,
expected_output_dtype=None, fixed_batch_size=False):
"""Test routine for a layer with a single input tensor
and single output tensor.
"""
# generate input data
if input_data is None:
assert input_shape
if not input_dtype:
input_dtype = K.floatx()
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))
input_data = input_data.astype(input_dtype)
else:
if 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
layer = layer_cls(**kwargs)
# test get_weights , set_weights at layer level
weights = layer.get_weights()
layer.set_weights(weights)
expected_output_shape = layer.compute_output_shape(input_shape)
# test in functional API
if fixed_batch_size:
x = Input(batch_shape=input_shape, dtype=input_dtype)
else:
x = Input(shape=input_shape[1:], dtype=input_dtype)
y = layer(x)
assert K.dtype(y) == expected_output_dtype
# check with the functional API
model = Model(x, y)
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:
assert expected_dim == actual_dim
if expected_output is not None:
assert_allclose(actual_output, expected_output, rtol=1e-3)
# test serialization, weight setting at model level
model_config = model.get_config()
recovered_model = model.__class__.from_config(model_config)
if model.weights:
weights = model.get_weights()
recovered_model.set_weights(weights)
_output = recovered_model.predict(input_data)
assert_allclose(_output, actual_output, rtol=1e-3)
# test training mode (e.g. useful when the layer has a
# different behavior at training and testing time).
if has_arg(layer.call, 'training'):
model.compile('rmsprop', 'mse')
model.train_on_batch(input_data, actual_output)
# test instantiation from layer config
layer_config = layer.get_config()
layer_config['batch_input_shape'] = input_shape
layer = layer.__class__.from_config(layer_config)
# for further checks in the caller function
return actual_output
在创建了一个带有训练权重的Keras自定义层之后,如何测试代码的正确性? Keras的手册里好像没有描述。
例如,要测试函数的预期行为,可以编写单元测试。我们如何为 Keras 自定义层执行此操作?
您仍然可以通过获取给定输入的自定义层的输出并根据手动计算的输出对其进行验证来执行单元测试之类的操作,
假设您的自定义图层 Custom
将 (None, 3, 200) 作为输入形状并且 returns (None, 3)
from keras.layers import Input
from keras.models import Model
inp = Input(shape=(3, 200))
out = Custom()(inp)
model = Model(inp, out)
output = model.predict(your_input)
您可以使用已知输入 your_input
的预期输出来验证层输出 output
。
layer_test
在 keras 实用程序中。
https://github.com/keras-team/keras/blob/master/keras/utils/test_utils.py
他们提供以下代码,测试形状、实际结果、序列化和训练:
def layer_test(layer_cls, kwargs={}, input_shape=None, input_dtype=None,
input_data=None, expected_output=None,
expected_output_dtype=None, fixed_batch_size=False):
"""Test routine for a layer with a single input tensor
and single output tensor.
"""
# generate input data
if input_data is None:
assert input_shape
if not input_dtype:
input_dtype = K.floatx()
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))
input_data = input_data.astype(input_dtype)
else:
if 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
layer = layer_cls(**kwargs)
# test get_weights , set_weights at layer level
weights = layer.get_weights()
layer.set_weights(weights)
expected_output_shape = layer.compute_output_shape(input_shape)
# test in functional API
if fixed_batch_size:
x = Input(batch_shape=input_shape, dtype=input_dtype)
else:
x = Input(shape=input_shape[1:], dtype=input_dtype)
y = layer(x)
assert K.dtype(y) == expected_output_dtype
# check with the functional API
model = Model(x, y)
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:
assert expected_dim == actual_dim
if expected_output is not None:
assert_allclose(actual_output, expected_output, rtol=1e-3)
# test serialization, weight setting at model level
model_config = model.get_config()
recovered_model = model.__class__.from_config(model_config)
if model.weights:
weights = model.get_weights()
recovered_model.set_weights(weights)
_output = recovered_model.predict(input_data)
assert_allclose(_output, actual_output, rtol=1e-3)
# test training mode (e.g. useful when the layer has a
# different behavior at training and testing time).
if has_arg(layer.call, 'training'):
model.compile('rmsprop', 'mse')
model.train_on_batch(input_data, actual_output)
# test instantiation from layer config
layer_config = layer.get_config()
layer_config['batch_input_shape'] = input_shape
layer = layer.__class__.from_config(layer_config)
# for further checks in the caller function
return actual_output