def _check_params(self, parameters):
"""Checks for mistakes in 'parameters'
Args :
parameters: dict, parameters to be checked
Raises :
ValueError: if any parameter is not a valid argument for the target function
or the target function is not defined
TypeError: if argument parameters is not iterable
"""
a_valid_fn = []
if self.target_fn is None:
if callable(self):
a_valid_fn.append(self.__call__)
else:
raise TypeError(
'invalid argument: tested object is not callable,\
please provide a valid target_fn')
elif isinstance(self.target_fn, types.FunctionType) \
or isinstance(self.target_fn, types.MethodType):
a_valid_fn.append(self.target_fn)
else:
a_valid_fn.append(self.target_fn.__call__)
if not isinstance(parameters, str):
for p in parameters:
for fn in a_valid_fn:
if has_arg(fn, p):
pass
else:
raise ValueError(
'{} is not a valid parameter'.format(p))
else:
raise TypeError('invalid argument: list or dictionnary expected')
def _check_params(self, parameters):
a_valid_fn = []
if self.target_fn is None:
if callable(self):
a_valid_fn.append(self.__call__)
else:
raise TypeError(
'invalid argument: tested object is not callable,\
please provide a valid target_fn')
elif isinstance(self.target_fn, types.FunctionType) \
or isinstance(self.target_fn, types.MethodType):
a_valid_fn.append(self.target_fn)
else:
a_valid_fn.append(self.target_fn.__call__)
if not isinstance(parameters, str):
for p in parameters:
for fn in a_valid_fn:
if has_arg(fn, p):
pass
else:
raise ValueError(
'{} is not a valid parameter'.format(p))
else:
raise TypeError('invalid argument: list or dictionnary expected')
def filter_params(self, fn, override=None):
override = override or {}
result = {}
for name, value in self.target_params.items():
if has_arg(fn, name):
result.update({name: value})
result.update(override)
return result
def filter_params(self, fn, override=None):
"""Filters `target_params` and return those in `fn`'s arguments.
Args:
fn : arbitrary function
override: dict, values to override target_params
Returns:
result : dict, dictionary containing variables
in both target_params and fn's arguments.
"""
override = override or {}
result = {}
for name, value in self.target_params.items():
if has_arg(fn, name):
result.update({name: value})
result.update(override)
return result
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)
try:
expected_output_shape = layer.compute_output_shape(input_shape)
except:
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