def get_scores(self):
"""Adds tensor operations for computing scores.
Computes prediction yhat (eqn (1) in Matching networks) of class for test
compounds.
"""
# Get featurization for test
# Shape (n_test, n_feat)
test_feat = self.model.get_test_output()
# Get featurization for support
# Shape (n_support, n_feat)
support_feat = self.model.get_support_output()
# Computes the inner part c() of the kernel
# (the inset equation in section 2.1.1 of Matching networks paper).
# Normalize
g = model_ops.cosine_distances(test_feat, support_feat)
# Note that gram matrix g has shape (n_test, n_support)
# soft corresponds to a(xhat, x_i) in eqn (1) of Matching Networks paper
# https://arxiv.org/pdf/1606.04080v1.pdf
# Computes softmax across axis 1, (so sums distances to support set for
# each test entry) to get attention vector
# Shape (n_test, n_support)
attention = tf.nn.softmax(g) # Renormalize
# Weighted sum of support labels
# Shape (n_support, 1)
support_labels = tf.expand_dims(self.support_label_placeholder, 1)
# pred is yhat in eqn (1) of Matching Networks.
# Shape squeeze((n_test, n_support) * (n_support, 1)) = (n_test,1)
pred = tf.matmul(attention, support_labels)
return pred
def get_scores(self):
"""Adds tensor operations for computing scores.
Computes prediction yhat (eqn (1) in Matching networks) of class for test
compounds.
"""
# Get featurization for test
# Shape (n_test, n_feat)
test_feat = self.model.get_test_output()
# Get featurization for support
# Shape (n_support, n_feat)
support_feat = self.model.get_support_output()
# Computes the inner part c() of the kernel
# (the inset equation in section 2.1.1 of Matching networks paper).
# Normalize
if self.similarity == 'cosine':
g = model_ops.cosine_distances(test_feat, support_feat)
else:
raise ValueError("Only cosine similarity is supported.")
# TODO(rbharath): euclidean kernel is broken!
#elif self.similarity == 'euclidean':
# g = model_ops.euclidean_distance(test_feat, support_feat)
# Note that gram matrix g has shape (n_test, n_support)
# soft corresponds to a(xhat, x_i) in eqn (1) of Matching Networks paper
# https://arxiv.org/pdf/1606.04080v1.pdf
# Computes softmax across axis 1, (so sums distances to support set for
# each test entry) to get attention vector
# Shape (n_test, n_support)
attention = tf.nn.softmax(g) # Renormalize
# Weighted sum of support labels
# Shape (n_support, 1)
support_labels = tf.expand_dims(self.support_label_placeholder, 1)
# pred is yhat in eqn (1) of Matching Networks.
# Shape squeeze((n_test, n_support) * (n_support, 1)) = (n_test,)
pred = tf.squeeze(tf.matmul(attention, support_labels), [1])
# Clip softmax probabilities to range [epsilon, 1-epsilon]
# Shape (n_test,)
pred = tf.clip_by_value(pred, 1e-7, 1. - 1e-7)
# Convert to logit space using inverse sigmoid (logit) function
# logit function: log(pred) - log(1-pred)
# Used to invoke tf.nn.sigmoid_cross_entropy_with_logits
# in Cross Entropy calculation.
# Shape (n_test,)
scores = tf.log(pred) - tf.log(
tf.constant(1., dtype=tf.float32) - pred)
return pred, scores
def get_scores(self):
"""Adds tensor operations for computing scores.
Computes prediction yhat (eqn (1) in Matching networks) of class for test
compounds.
"""
# Get featurization for test
# Shape (n_test, n_feat)
test_feat = self.model.get_test_output()
# Get featurization for support
# Shape (n_support, n_feat)
support_feat = self.model.get_support_output()
# Computes the inner part c() of the kernel
# (the inset equation in section 2.1.1 of Matching networks paper).
# Normalize
if self.similarity == 'cosine':
g = model_ops.cosine_distances(test_feat, support_feat)
else:
raise ValueError("Only cosine similarity is supported.")
# TODO(rbharath): euclidean kernel is broken!
#elif self.similarity == 'euclidean':
# g = model_ops.euclidean_distance(test_feat, support_feat)
# Note that gram matrix g has shape (n_test, n_support)
# soft corresponds to a(xhat, x_i) in eqn (1) of Matching Networks paper
# https://arxiv.org/pdf/1606.04080v1.pdf
# Computes softmax across axis 1, (so sums distances to support set for
# each test entry) to get attention vector
# Shape (n_test, n_support)
attention = tf.nn.softmax(g) # Renormalize
# Weighted sum of support labels
# Shape (n_support, 1)
support_labels = tf.expand_dims(self.support_label_placeholder, 1)
# pred is yhat in eqn (1) of Matching Networks.
# Shape squeeze((n_test, n_support) * (n_support, 1)) = (n_test,)
pred = tf.squeeze(tf.matmul(attention, support_labels), [1])
# Clip softmax probabilities to range [epsilon, 1-epsilon]
# Shape (n_test,)
pred = tf.clip_by_value(pred, 1e-7, 1. - 1e-7)
# Convert to logit space using inverse sigmoid (logit) function
# logit function: log(pred) - log(1-pred)
# Used to invoke tf.nn.sigmoid_cross_entropy_with_logits
# in Cross Entropy calculation.
# Shape (n_test,)
scores = tf.log(pred) - tf.log(tf.constant(1., dtype=tf.float32) - pred)
return pred, scores