def compute_mask(self, inputs, mask=None):
if mask is None:
return None
if not isinstance(mask, list):
raise ValueError('`mask` should be a list.')
if not isinstance(inputs, list):
raise ValueError('`inputs` should be a list.')
if len(mask) != len(inputs):
raise ValueError('The lists `inputs` and `mask` '
'should have the same length.')
if all([m is None for m in mask]):
return None
# Make a list of masks while making sure
# the dimensionality of each mask
# is the same as the corresponding input.
masks = []
for input_i, mask_i in zip(inputs, mask):
if mask_i is None:
# Input is unmasked. Append all 1s to masks,
masks.append(array_ops.ones_like(input_i, dtype='bool'))
elif K.ndim(mask_i) < K.ndim(input_i):
# Mask is smaller than the input, expand it
masks.append(array_ops.expand_dims(mask_i, axis=-1))
else:
masks.append(mask_i)
concatenated = K.concatenate(masks, axis=self.axis)
return K.all(concatenated, axis=-1, keepdims=False)
def compute_mask(self, inputs, mask=None):
if mask is None:
return None
if not isinstance(mask, (tuple, list)):
raise ValueError('`mask` should be a list.')
if not isinstance(inputs, (tuple, list)):
raise ValueError('`inputs` should be a list.')
if len(mask) != len(inputs):
raise ValueError('The lists `inputs` and `mask` '
'should have the same length.')
if all(m is None for m in mask):
return None
# Make a list of masks while making sure
# the dimensionality of each mask
# is the same as the corresponding input.
masks = []
for input_i, mask_i in zip(inputs, mask):
if mask_i is None:
# Input is unmasked. Append all 1s to masks,
masks.append(array_ops.ones_like(input_i, dtype='bool'))
elif backend.ndim(mask_i) < backend.ndim(input_i):
# Mask is smaller than the input, expand it
masks.append(array_ops.expand_dims(mask_i, axis=-1))
else:
masks.append(mask_i)
concatenated = backend.concatenate(masks, axis=self.axis)
return backend.all(concatenated, axis=-1, keepdims=False)
def compute_mask(self, inputs, mask=None):
if isinstance(inputs, list):
memory = inputs[0]
else:
memory = inputs
if len(K.int_shape(memory)) > 3 and mask is not None:
return K.all(mask, axis=-1)
else:
return None
def compute_mask(self, inputs, mask=None):
if mask is None:
return None
if not isinstance(mask, list):
raise ValueError('`mask` should be a list.')
if not isinstance(inputs, list):
raise ValueError('`inputs` should be a list.')
if len(mask) != len(inputs):
raise ValueError('The lists `inputs` and `mask` '
'should have the same length.')
if all(m is None for m in mask):
return None
masks = [array_ops.expand_dims(m, axis=0) for m in mask if m is not None]
return K.all(K.concatenate(masks, axis=0), axis=0, keepdims=False)
def tp_score(y_true, y_pred, threshold=0.1):
tp_3d = K.concatenate([
K.cast(K.expand_dims(K.flatten(y_true)), 'bool'),
K.cast(
K.expand_dims(K.flatten(K.greater(y_pred, K.constant(threshold)))),
'bool'),
K.cast(K.ones_like(K.expand_dims(K.flatten(y_pred))), 'bool')
],
axis=1)
tp = K.sum(K.cast(K.all(tp_3d, axis=1), 'int32'))
return tp
def compute_mask(self, inputs, mask=None):
if mask is None:
return None
if not isinstance(mask, list):
raise ValueError('`mask` should be a list.')
if not isinstance(inputs, list):
raise ValueError('`inputs` should be a list.')
if len(mask) != len(inputs):
raise ValueError('The lists `inputs` and `mask` '
'should have the same length.')
if all([m is None for m in mask]):
return None
masks = [array_ops.expand_dims(m, axis=0) for m in mask if m is not None]
return K.all(K.concatenate(masks, axis=0), axis=0, keepdims=False)
def fn_score(y_true, y_pred, threshold=0.1):
fn_3d = K.concatenate([
K.cast(K.expand_dims(K.flatten(y_true)), 'bool'),
K.cast(
K.expand_dims(
K.flatten(
K.abs(
K.cast(K.greater(y_pred, K.constant(threshold)),
'float') - K.ones_like(y_pred)))), 'bool'),
K.cast(K.ones_like(K.expand_dims(K.flatten(y_pred))), 'bool')
],
axis=1)
fn = K.sum(K.cast(K.all(fn_3d, axis=1), 'int32'))
return fn
def update_state(self, y_true, y_pred, sample_weight=None):
"""
:param y_true: Tensor shape: (B, T)
:param y_pred: Tensor shape (B, T)
:param sample_weight: None or Tensor shape (B, T)
:return:
"""
judge = K.equal(y_pred, y_true) # shape: (B, T)
value = K.all(judge, axis=1, keepdims=False) # shape: (B, )
# # # DEBUG: output training value
# print_op = tf.print(value)
# with tf.control_dependencies([print_op]):
# value = tf.identity(value)
super(SequenceCorrectness, self).update_state(value)
