def FProp(self, theta, inputs, paddings, state0=None, labels=None):
"""Computes xent loss given the language model input activations.
Args:
theta: A `.NestedMap` object containing weights' values of this layer and
its children layers.
inputs: Input ids. An int32 tensor of shape [time, batch].
paddings: A 0/1 tensor of shape [time, batch].
state0: Not used for Transformer.
labels: If not None, a `.NestedMap` containing the following fields: -
class_weights, a tensor with shape [time, batch] containing the weights
for each target word. - class_ids, a tensor with shape [time, batch] of
int32 dtype containing the target class labels. - class_probabilities, a
tensor with shape [time, batch, vocab_size] of float values indicating
class-membership probabilities.
Returns:
If `labels` is not None, returns (xent_output, state1), where
`xent_output` is a `.NestedMap` as defined by `SoftmaxLayer`'s return
value and `state1` is the next recurrent state. Otherwise,
`xent_output` only contains the softmax logits.
"""
p = self.params
ids = py_utils.HasRank(inputs, 2)
paddings = py_utils.HasShape(paddings, tf.shape(ids))
per_example_xent, logits = self.stack.FProp(
theta.stack, ids, paddings, None, None, None, None,
tf.cast(labels.class_ids, py_utils.FPropDtype(p)), labels.class_weights)
per_example_argmax = py_utils.ArgMax(logits)
total_xent = tf.reduce_sum(per_example_xent * labels.class_weights)
total_weights = tf.reduce_sum(labels.class_weights)
xent_output = py_utils.NestedMap(
total_weight=total_weights,
per_example_xent=per_example_xent,
logits=logits,
per_example_argmax=per_example_argmax,
avg_xent=total_xent / total_weights,
total_xent=total_xent)
return xent_output, {}
def Compute(x):
with self.session(graph=tf.Graph()) as sess:
x = tf.constant(x)
y = py_utils.ArgMax(x)
return sess.run([x, y])