def construct_feed_dict(self, X_b, y_b=None, w_b=None, training=True):
"""Get initial information about task normalization"""
# TODO(rbharath): I believe this is total amount of data
n_samples = len(X_b)
if y_b is None:
y_b = np.zeros((n_samples, self.n_tasks))
if w_b is None:
w_b = np.zeros((n_samples, self.n_tasks))
targets_dict = {self.label_placeholder : y_b,
self.weight_placeholder : w_b}
# Get graph information
atoms_dict = self.graph_topology.batch_to_feed_dict(X_b)
# TODO (hraut->rhbarath): num_datapoints should be a vector, with ith element being
# the number of labeled data points in target_i. This is to normalize each task
# num_dat_dict = {self.num_datapoints_placeholder : self.}
# Get other optimizer information
# TODO(rbharath): Figure out how to handle phase appropriately
#keras_dict = {K.learning_phase() : training}
keras_dict = {}
feed_dict = merge_dicts([targets_dict, atoms_dict,
keras_dict])
return feed_dict
def construct_feed_dict(self, X_b, y_b=None, w_b=None, training=True):
"""Get initial information about task normalization"""
# TODO(rbharath): I believe this is total amount of data
n_samples = len(X_b)
if y_b is None:
y_b = np.zeros((n_samples, self.n_tasks))
if w_b is None:
w_b = np.zeros((n_samples, self.n_tasks))
targets_dict = {
self.label_placeholder: y_b,
self.weight_placeholder: w_b
}
# Get graph information
atoms_dict = self.graph_topology.batch_to_feed_dict(X_b)
# TODO (hraut->rhbarath): num_datapoints should be a vector, with ith element being
# the number of labeled data points in target_i. This is to normalize each task
# num_dat_dict = {self.num_datapoints_placeholder : self.}
# Get other optimizer information
# TODO(rbharath): Figure out how to handle phase appropriately
#keras_dict = {K.learning_phase() : training}
keras_dict = {}
feed_dict = merge_dicts([targets_dict, atoms_dict, keras_dict])
return feed_dict
def construct_feed_dict(self, test, support, training=True, add_phase=False):
"""Constructs tensorflow feed from test/support sets."""
# Generate dictionary elements for support
feed_dict = (
self.model.support_graph_topology.batch_to_feed_dict(support.X))
feed_dict[self.support_label_placeholder] = np.squeeze(support.y)
# Get graph information for test
batch_topo_dict = (
self.model.test_graph_topology.batch_to_feed_dict(test.X))
feed_dict = merge_dicts([batch_topo_dict, feed_dict])
# Generate dictionary elements for test
feed_dict[self.test_label_placeholder] = np.squeeze(test.y)
feed_dict[self.test_weight_placeholder] = np.squeeze(test.w)
# Get information for keras
if add_phase:
feed_dict[K.learning_phase()] = training
return feed_dict
def construct_feed_dict(self,
test,
support,
training=True,
add_phase=False):
"""Constructs tensorflow feed from test/support sets."""
# Generate dictionary elements for support
feed_dict = (self.model.support_graph_topology.batch_to_feed_dict(
support.X))
feed_dict[self.support_label_placeholder] = np.squeeze(support.y)
# Get graph information for test
batch_topo_dict = (self.model.test_graph_topology.batch_to_feed_dict(
test.X))
feed_dict = merge_dicts([batch_topo_dict, feed_dict])
# Generate dictionary elements for test
feed_dict[self.test_label_placeholder] = np.squeeze(test.y)
feed_dict[self.test_weight_placeholder] = np.squeeze(test.w)
# Get information for keras
if add_phase:
feed_dict[K.learning_phase()] = training
return feed_dict
