def test_check_bad_shape():
a = np.random.random((2, 3, 5))
with pytest.raises(Exception) as exc:
check_loss_and_target_compatibility([a], [K.categorical_crossentropy],
[(2, 3, 6)])
assert "targets to have the same shape" in str(exc)
def test_check_last_is_one():
a = np.random.random((2, 3, 1))
with pytest.raises(Exception) as exc:
check_loss_and_target_compatibility([a], [K.categorical_crossentropy],
[a.shape])
assert "You are passing a target array" in str(exc)
def _standardize_user_data(model,
x,
y,
sample_weight=None,
class_weight=None,
check_batch_dim=True,
batch_size=None):
if not hasattr(model, 'optimizer'):
raise Exception('You must compile a model before training/testing.'
' Use `model.compile(optimizer, loss)`.')
output_shapes = []
for output_shape, loss_fn in zip(model.internal_output_shapes,
model.loss_functions):
if loss_fn.__name__ == 'sparse_categorical_crossentropy':
output_shapes.append(output_shape[:-1] + (1, ))
elif getattr(objectives, loss_fn.__name__, None) is None:
output_shapes.append(None)
else:
output_shapes.append(output_shape)
x = standardize_input_data(x,
model.input_names,
model.internal_input_shapes,
check_batch_dim=False,
exception_prefix='model input')
y = standardize_input_data(y,
model.output_names,
output_shapes,
check_batch_dim=False,
exception_prefix='model target')
sample_weights = standardize_sample_weights(sample_weight,
model.output_names)
class_weights = standardize_class_weights(class_weight, model.output_names)
sample_weights = [
standardize_weights(ref, sw, cw, mode) for (ref, sw, cw, mode) in zip(
y, sample_weights, class_weights, model.sample_weight_modes)
]
'''
We only need to comment out check_array_lengeh(x, y, weights) in the next line to
let the model compile and train.
'''
# check_array_lengths(x, y, sample_weights)
check_loss_and_target_compatibility(y, model.loss_functions,
model.internal_output_shapes)
if model.stateful and batch_size:
if x[0].shape[0] % batch_size != 0:
raise Exception('In a stateful network, '
'you should only pass inputs with '
'a number of samples that can be '
'divided by the batch size. Found: ' +
str(x[0].shape[0]) + ' samples')
return x, y, sample_weights
def _standardize_user_data(self, x, y, sample_weight, class_weight,
input_names, input_shapes, output_names,
output_shapes, check_batch_dim, batch_size):
"""Standardizes given user data."""
if not hasattr(self, 'optimizer'):
raise RuntimeError('You must compile a model before training or '
'testing. Use `model.compile`.')
x = self._convert_input_to_list(x, input_names)
y = self._convert_input_to_list(y, output_names)
# Calculates the number of training samples.
nb_train_samples = None
for arr in x + y:
if is_numpy_array(arr):
nb_train_samples = arr.shape[0]
break
else:
raise ValueError('At least one of the fed inputs must be a Numpy '
'array (usually the real training data).')
x = self._standardize_input_data(x,
input_names,
input_shapes,
check_batch_dim=False,
exception_prefix='model input')
y = self._standardize_input_data(y,
output_names,
output_shapes,
check_batch_dim=False,
exception_prefix='model output')
y_exp = [
y_inst(nb_train_samples) if hasattr(y_inst, '__call__') else y_inst
for y_inst in y
]
sample_weights = keras_training.standardize_sample_weights(
sample_weight, output_names)
class_weights = keras_training.standardize_class_weights(
class_weight, output_names)
sample_weights = [
keras_training.standardize_weights(ref, sw, cw, mode)
for ref, sw, cw, mode in zip(y_exp, sample_weights, class_weights,
self.sample_weight_modes)
]
keras_training.check_loss_and_target_compatibility(
y_exp, self.loss_functions, output_shapes)
return x, y, sample_weights, nb_train_samples
def _standardize_user_data(model, x, y,
sample_weight=None, class_weight=None,
check_batch_dim=True, batch_size=None):
if not hasattr(model, 'optimizer'):
raise Exception('You must compile a model before training/testing.'
' Use `model.compile(optimizer, loss)`.')
output_shapes = []
for output_shape, loss_fn in zip(model.internal_output_shapes, model.loss_functions):
if loss_fn.__name__ == 'sparse_categorical_crossentropy':
output_shapes.append(output_shape[:-1] + (1,))
elif getattr(objectives, loss_fn.__name__, None) is None:
output_shapes.append(None)
else:
output_shapes.append(output_shape)
x = standardize_input_data(x, model.input_names,
model.internal_input_shapes,
check_batch_dim=False,
exception_prefix='model input')
y = standardize_input_data(y, model.output_names,
output_shapes,
check_batch_dim=False,
exception_prefix='model target')
sample_weights = standardize_sample_weights(sample_weight,
model.output_names)
class_weights = standardize_class_weights(class_weight,
model.output_names)
sample_weights = [standardize_weights(ref, sw, cw, mode)
for (ref, sw, cw, mode)
in zip(y, sample_weights, class_weights, model.sample_weight_modes)]
'''
We only need to comment out check_array_lengeh(x, y, weights) in the next line to
let the model compile and train.
'''
# check_array_lengths(x, y, sample_weights)
check_loss_and_target_compatibility(y, model.loss_functions, model.internal_output_shapes)
if model.stateful and batch_size:
if x[0].shape[0] % batch_size != 0:
raise Exception('In a stateful network, '
'you should only pass inputs with '
'a number of samples that can be '
'divided by the batch size. Found: ' +
str(x[0].shape[0]) + ' samples')
return x, y, sample_weights
def test_check_bad_shape():
a = np.random.random((2, 3, 5))
with pytest.raises(Exception) as exc:
check_loss_and_target_compatibility([a], [K.categorical_crossentropy], [(2, 3, 6)])
assert "targets to have the same shape" in str(exc)
def test_check_last_is_one():
a = np.random.random((2, 3, 1))
with pytest.raises(Exception) as exc:
check_loss_and_target_compatibility([a], [K.categorical_crossentropy], [a.shape])
assert "You are passing a target array" in str(exc)
def test_check_not_failing():
a = np.random.random((2, 1, 3))
check_loss_and_target_compatibility([a], [K.categorical_crossentropy], [a.shape])
check_loss_and_target_compatibility([a], [K.categorical_crossentropy], [(2, None, 3)])
def test_check_not_failing():
a = np.random.random((2, 1, 3))
check_loss_and_target_compatibility([a], [K.categorical_crossentropy],
[a.shape])
check_loss_and_target_compatibility([a], [K.categorical_crossentropy],
[(2, None, 3)])
def test_check_not_last_is_one():
a = np.random.random((2, 1, 3))
check_loss_and_target_compatibility([a], [K.categorical_crossentropy],
[a.shape])
def test_check_not_last_is_one():
a = np.random.random((2, 1, 3))
check_loss_and_target_compatibility([a], [K.categorical_crossentropy], [a.shape])
