def construct_feed_dict(self, X_b, y_b=None, w_b=None, ids_b=None):
"""Construct a feed dictionary from minibatch data.
TODO(rbharath): ids_b is not used here. Can we remove it?
Args:
X_b: np.ndarray of shape (batch_size, n_features)
y_b: np.ndarray of shape (batch_size, n_tasks)
w_b: np.ndarray of shape (batch_size, n_tasks)
ids_b: List of length (batch_size) with datapoint identifiers.
"""
orig_dict = {}
orig_dict["mol_features"] = X_b
for task in range(self.n_tasks):
if y_b is not None:
orig_dict["labels_%d" % task] = to_one_hot(y_b[:, task])
else:
# Dummy placeholders
orig_dict["labels_%d" % task] = np.squeeze(to_one_hot(
np.zeros((self.batch_size,))))
if w_b is not None:
orig_dict["weights_%d" % task] = w_b[:, task]
else:
# Dummy placeholders
orig_dict["weights_%d" % task] = np.ones(
(self.batch_size,))
return TensorflowGraph.get_feed_dict(orig_dict)
def construct_feed_dict(self, X_b, y_b=None, w_b=None, ids_b=None):
"""Construct a feed dictionary from minibatch data.
TODO(rbharath): ids_b is not used here. Can we remove it?
Args:
X_b: np.ndarray of shape (batch_size, n_features)
y_b: np.ndarray of shape (batch_size, n_tasks)
w_b: np.ndarray of shape (batch_size, n_tasks)
ids_b: List of length (batch_size) with datapoint identifiers.
"""
orig_dict = {}
orig_dict["mol_features"] = X_b
for task in range(self.n_tasks):
if y_b is not None:
orig_dict["labels_%d" % task] = to_one_hot(y_b[:, task])
else:
# Dummy placeholders
orig_dict["labels_%d" % task] = np.squeeze(
to_one_hot(np.zeros((self.batch_size, ))))
if w_b is not None:
orig_dict["weights_%d" % task] = w_b[:, task]
else:
# Dummy placeholders
orig_dict["weights_%d" % task] = np.ones((self.batch_size, ))
return TensorflowGraph.get_feed_dict(orig_dict)
def construct_task_feed_dict(self,
this_task,
X_b,
y_b=None,
w_b=None,
ids_b=None):
"""Construct a feed dictionary from minibatch data.
TODO(rbharath): ids_b is not used here. Can we remove it?
Args:
X_b: np.ndarray of shape (batch_size, n_features)
y_b: np.ndarray of shape (batch_size, n_tasks)
w_b: np.ndarray of shape (batch_size, n_tasks)
ids_b: List of length (batch_size) with datapoint identifiers.
"""
orig_dict = {}
orig_dict["mol_features"] = X_b
n_samples = len(X_b)
for task in range(self.n_tasks):
if (this_task == task) and y_b is not None:
#orig_dict["labels_%d" % task] = np.reshape(y_b[:, task], (n_samples, 1))
orig_dict["labels_%d" % task] = np.reshape(y_b[:, task], (n_samples,))
else:
# Dummy placeholders
#orig_dict["labels_%d" % task] = np.zeros((n_samples, 1))
orig_dict["labels_%d" % task] = np.zeros((n_samples,))
if (this_task == task) and w_b is not None:
#orig_dict["weights_%d" % task] = np.reshape(w_b[:, task], (n_samples, 1))
orig_dict["weights_%d" % task] = np.reshape(w_b[:, task], (n_samples,))
else:
# Dummy placeholders
#orig_dict["weights_%d" % task] = np.zeros((n_samples, 1))
orig_dict["weights_%d" % task] = np.zeros((n_samples,))
return TensorflowGraph.get_feed_dict(orig_dict)
def construct_task_feed_dict(self, this_task, X_b, y_b=None, w_b=None, ids_b=None):
"""Construct a feed dictionary from minibatch data.
TODO(rbharath): ids_b is not used here. Can we remove it?
Args:
X_b: np.ndarray of shape (batch_size, n_features)
y_b: np.ndarray of shape (batch_size, n_tasks)
w_b: np.ndarray of shape (batch_size, n_tasks)
ids_b: List of length (batch_size) with datapoint identifiers.
"""
orig_dict = {}
orig_dict["mol_features"] = X_b
n_samples = len(X_b)
for task in range(self.n_tasks):
if (this_task == task) and y_b is not None:
#orig_dict["labels_%d" % task] = np.reshape(y_b[:, task], (n_samples, 1))
orig_dict["labels_%d" % task] = np.reshape(y_b[:, task], (n_samples,))
else:
# Dummy placeholders
#orig_dict["labels_%d" % task] = np.zeros((n_samples, 1))
orig_dict["labels_%d" % task] = np.zeros((n_samples,))
if (this_task == task) and w_b is not None:
#orig_dict["weights_%d" % task] = np.reshape(w_b[:, task], (n_samples, 1))
orig_dict["weights_%d" % task] = np.reshape(w_b[:, task], (n_samples,))
else:
# Dummy placeholders
#orig_dict["weights_%d" % task] = np.zeros((n_samples, 1))
orig_dict["weights_%d" % task] = np.zeros((n_samples,))
return TensorflowGraph.get_feed_dict(orig_dict)
def construct_feed_dict(self, X_b, y_b=None, w_b=None, ids_b=None):
orig_dict = {}
orig_dict["mol_features"] = X_b
for task in range(self.n_tasks):
if y_b is not None:
y_2column = to_one_hot(y_b[:, task])
# fix the size to be [?,1]
orig_dict["labels_%d" % task] = y_2column[:, 1:2]
else:
# Dummy placeholders
orig_dict["labels_%d" % task] = np.zeros((self.batch_size, 1))
if w_b is not None:
orig_dict["weights_%d" % task] = w_b[:, task]
else:
# Dummy placeholders
orig_dict["weights_%d" % task] = np.ones((self.batch_size,))
return TensorflowGraph.get_feed_dict(orig_dict)
def construct_feed_dict(self, X_b, y_b=None, w_b=None, ids_b=None):
orig_dict = {}
orig_dict["mol_features"] = X_b
for task in range(self.n_tasks):
if y_b is not None:
y_2column = to_one_hot(y_b[:, task])
# fix the size to be [?,1]
orig_dict["labels_%d" % task] = y_2column[:, 1:2]
else:
# Dummy placeholders
orig_dict["labels_%d" % task] = np.zeros((self.batch_size, 1))
if w_b is not None:
orig_dict["weights_%d" % task] = w_b[:, task]
else:
# Dummy placeholders
orig_dict["weights_%d" % task] = np.ones((self.batch_size, ))
return TensorflowGraph.get_feed_dict(orig_dict)
