def _fc_layer(self, embedding):
"""The fully connected layer to be finetuned."""
with tf.variable_scope('fc_finetune', reuse=tf.AUTO_REUSE):
logits = functional_classifiers.linear_classifier(
embedding, self.logit_dim, self.cosine_classifier,
self.cosine_logits_multiplier, self.use_weight_norm)
return logits
def forward_pass_fc(self, embeddings):
"""Passes the provided embeddings through the fc layer to get the logits.
Args:
embeddings: A Tensor of the penultimate layer activations as computed by
BaselineLearner.forward_pass.
Returns:
The fc layer activations.
"""
with tf.variable_scope('fc', reuse=tf.AUTO_REUSE):
# Always maps to a space whose dimensionality is the number of classes
# at meta-training time.
logits = functional_classifiers.linear_classifier(
embeddings, self.logit_dim, self.cosine_classifier,
self.cosine_logits_multiplier, self.use_weight_norm)
return logits
