def test_weighted_masked_objective():
a = Input(shape=(3,), name='input_a')
# weighted_masked_objective
def mask_dummy(y_true=None, y_pred=None, weight=None):
return K.placeholder(y_true.shape)
weighted_function = training_utils.weighted_masked_objective(
losses.categorical_crossentropy)
weighted_function(a, a, None)
def test_weighted_masked_objective():
a = Input(shape=(3,), name='input_a')
# weighted_masked_objective
def mask_dummy(y_true=None, y_pred=None, weight=None):
return K.placeholder(y_true.shape)
weighted_function = training_utils.weighted_masked_objective(
losses.categorical_crossentropy)
weighted_function(a, a, None)
def __init__(self,
session,
max_seq_length,
num_symbols,
batch_generator,
metrics_name_postfix="unknown",
inspector=None,
num_samples_to_inspect=5,
one_hot_encoding=True,
**kwargs):
super().__init__(batch_generator, metrics_name_postfix, **kwargs)
self._sess = session
self._one_hot_encoding = one_hot_encoding
if self._one_hot_encoding:
self._target_batch_placeholder = K.placeholder(
(None, max_seq_length + 2, num_symbols))
else:
self._target_batch_placeholder = K.placeholder(
(None, max_seq_length + 2, 1))
self._output_batch_placeholder = K.placeholder(
(None, max_seq_length + 2, num_symbols))
self._sample_weights_batch_placeholder = K.placeholder(
(None, max_seq_length + 2))
loss = categorical_crossentropy if self._one_hot_encoding else sparse_categorical_crossentropy
self._weighted_categorical_cross_entropy_op = weighted_masked_objective(
loss)(self._target_batch_placeholder,
self._output_batch_placeholder,
self._sample_weights_batch_placeholder)
metric = categorical_accuracy if self._one_hot_encoding else sparse_categorical_accuracy
self._weighted_categorical_accuracy_op = weighted_masked_objective(
metric)(self._target_batch_placeholder,
self._output_batch_placeholder,
self._sample_weights_batch_placeholder)
self._inspector = inspector
self._num_samples_to_inspect = num_samples_to_inspect
def test_loss_masking():
weighted_loss = weighted_masked_objective(losses.get('mae'))
shape = (3, 4, 2)
x = np.arange(24).reshape(shape)
y = 2 * x
# Normally the trailing 1 is added by standardize_weights
weights = np.ones((3, ))
mask = np.ones((3, 4))
mask[1, 0] = 0
out = K.eval(
weighted_loss(K.variable(x), K.variable(y), K.variable(weights),
K.variable(mask)))
def test_loss_masking():
weighted_loss = weighted_masked_objective(losses.get('mae'))
shape = (3, 4, 2)
x = np.arange(24).reshape(shape)
y = 2 * x
# Normally the trailing 1 is added by standardize_weights
weights = np.ones((3,))
mask = np.ones((3, 4))
mask[1, 0] = 0
out = K.eval(weighted_loss(K.variable(x),
K.variable(y),
K.variable(weights),
K.variable(mask)))