class SoftmaxReadout(MergeReadout, Random):
def __init__(self, num_tokens, **kwargs):
kwargs['post_merge_dim'] = num_tokens
super(SoftmaxReadout, self).__init__(**kwargs)
self.num_tokens = num_tokens
self.softmax = NDimensionalSoftmax()
self.children += [self.softmax]
self.costs.inputs = [
'prediction', 'prediction_mask',
'groundtruth', 'groundtruth_mask']
self.all_scores.inputs = ['prediction']
self.scores.inputs = []
self.sample.inputs = []
for application_method in [self.costs, self.all_scores,
self.scores, self.sample]:
application_method.inputs += self.input_names
self.sample.outputs = ['samples', 'scores']
@application
def costs(self, prediction, prediction_mask,
groundtruth, groundtruth_mask, **inputs):
log_probs = self.all_scores(
prediction, self.merge(**dict_subset(inputs, self.merge_names)))
if not prediction_mask:
prediction_mask = 1
return -(log_probs * prediction_mask).sum(axis=0)
@application
def all_scores(self, prediction, merged):
return -self.softmax.categorical_cross_entropy(
prediction, merged, extra_ndim=1)
@application
def scores(self, **inputs):
return self.softmax.log_probabilities(self.merge(
**dict_subset(inputs, self.merge_names)))
@application
def sample(self, **inputs):
scores = self.scores(**inputs)
probs = tensor.exp(scores)
sample = self.theano_rng.multinomial(pvals=probs).argmax(axis=1)
return sample, scores[tensor.arange(probs.shape[0]), sample]
def get_dim(self, name):
if name == 'samples' or name == 'scores':
return 0
return super(SoftmaxReadout, self).get_dim(name)
class SoftmaxReadout(MergeReadout, Random):
def __init__(self, num_tokens, **kwargs):
kwargs['post_merge_dim'] = num_tokens
super(SoftmaxReadout, self).__init__(**kwargs)
self.num_tokens = num_tokens
self.softmax = NDimensionalSoftmax()
self.children += [self.softmax]
self.costs.inputs = [
'prediction', 'prediction_mask', 'groundtruth', 'groundtruth_mask'
]
self.all_scores.inputs = ['prediction']
self.scores.inputs = []
self.sample.inputs = []
for application_method in [
self.costs, self.all_scores, self.scores, self.sample
]:
application_method.inputs += self.input_names
self.sample.outputs = ['samples', 'scores']
@application
def costs(self, prediction, prediction_mask, groundtruth, groundtruth_mask,
**inputs):
log_probs = self.all_scores(
prediction, self.merge(**dict_subset(inputs, self.merge_names)))
if not prediction_mask:
prediction_mask = 1
return -(log_probs * prediction_mask).sum(axis=0)
@application
def all_scores(self, prediction, merged):
return -self.softmax.categorical_cross_entropy(
prediction, merged, extra_ndim=1)
@application
def scores(self, **inputs):
return self.softmax.log_probabilities(
self.merge(**dict_subset(inputs, self.merge_names)))
@application
def sample(self, **inputs):
scores = self.scores(**inputs)
probs = tensor.exp(scores)
sample = self.theano_rng.multinomial(pvals=probs).argmax(axis=1)
return sample, scores[tensor.arange(probs.shape[0]), sample]
def get_dim(self, name):
if name == 'samples' or name == 'scores':
return 0
return super(SoftmaxReadout, self).get_dim(name)
def __init__(self, num_tokens, **kwargs):
kwargs['post_merge_dim'] = num_tokens
super(SoftmaxReadout, self).__init__(**kwargs)
self.num_tokens = num_tokens
self.softmax = NDimensionalSoftmax()
self.children += [self.softmax]
self.costs.inputs = [
'prediction', 'prediction_mask', 'groundtruth', 'groundtruth_mask'
]
self.all_scores.inputs = ['prediction']
self.scores.inputs = []
self.sample.inputs = []
for application_method in [
self.costs, self.all_scores, self.scores, self.sample
]:
application_method.inputs += self.input_names
self.sample.outputs = ['samples', 'scores']
def __init__(self, num_tokens, **kwargs):
kwargs['post_merge_dim'] = num_tokens
super(SoftmaxReadout, self).__init__(**kwargs)
self.num_tokens = num_tokens
self.softmax = NDimensionalSoftmax()
self.children += [self.softmax]
self.costs.inputs = [
'prediction', 'prediction_mask',
'groundtruth', 'groundtruth_mask']
self.all_scores.inputs = ['prediction']
self.scores.inputs = []
self.sample.inputs = []
for application_method in [self.costs, self.all_scores,
self.scores, self.sample]:
application_method.inputs += self.input_names
self.sample.outputs = ['samples', 'scores']
