def call(self, argument, mask=None):
"""Execute this layer on input tensors.
Parameters
----------
argument: list
List of two tensors (X, Xp). X should be of shape (n_test, n_feat) and
Xp should be of shape (n_support, n_feat) where n_test is the size of
the test set, n_support that of the support set, and n_feat is the number
of per-atom features.
Returns
-------
list
Returns two tensors of same shape as input. Namely the output shape will
be [(n_test, n_feat), (n_support, n_feat)]
"""
self.build()
x, xp = argument
# Get initializations
p = self.p_init
q = self.q_init
# Rename support
z = xp
states = self.support_states_init
x_states = self.test_states_init
for d in range(self.max_depth):
# Process support xp using attention
e = cos(z + q, xp)
a = tf.nn.softmax(e)
# Get linear combination of support set
r = model_ops.dot(a, xp)
# Not sure if it helps to place the update here or later yet. Will
# decide
#z = r
# Process test x using attention
x_e = cos(x + p, z)
x_a = tf.nn.softmax(x_e)
s = model_ops.dot(x_a, z)
# Generate new support attention states
qr = model_ops.concatenate([q, r], axis=1)
q, states = self.support_lstm([qr] + states)
# Generate new test attention states
ps = model_ops.concatenate([p, s], axis=1)
p, x_states = self.test_lstm([ps] + x_states)
# Redefine
z = r
#return [x+p, z+q]
return [x + p, xp + q]
def call(self, argument, mask=None):
"""Execute this layer on input tensors.
Parameters
----------
argument: list
List of two tensors (X, Xp). X should be of shape (n_test, n_feat) and
Xp should be of shape (n_support, n_feat) where n_test is the size of
the test set, n_support that of the support set, and n_feat is the number
of per-atom features.
Returns
-------
list
Returns two tensors of same shape as input. Namely the output shape will
be [(n_test, n_feat), (n_support, n_feat)]
"""
self.build()
x, xp = argument
# Get initializations
p = self.p_init
q = self.q_init
# Rename support
z = xp
states = self.support_states_init
x_states = self.test_states_init
for d in range(self.max_depth):
# Process support xp using attention
e = cos(z + q, xp)
a = tf.nn.softmax(e)
# Get linear combination of support set
r = model_ops.dot(a, xp)
# Not sure if it helps to place the update here or later yet. Will
# decide
#z = r
# Process test x using attention
x_e = cos(x + p, z)
x_a = tf.nn.softmax(x_e)
s = model_ops.dot(x_a, z)
# Generate new support attention states
qr = model_ops.concatenate([q, r], axis=1)
q, states = self.support_lstm([qr] + states)
# Generate new test attention states
ps = model_ops.concatenate([p, s], axis=1)
p, x_states = self.test_lstm([ps] + x_states)
# Redefine
z = r
#return [x+p, z+q]
return [x + p, xp + q]
def call(self, x_xp, mask=None):
"""Execute this layer on input tensors.
Parameters
----------
x_xp: list
List of two tensors (X, Xp). X should be of shape (n_test, n_feat) and
Xp should be of shape (n_support, n_feat) where n_test is the size of
the test set, n_support that of the support set, and n_feat is the number
of per-atom features.
Returns
-------
list
Returns two tensors of same shape as input. Namely the output shape will
be [(n_test, n_feat), (n_support, n_feat)]
"""
# x is test set, xp is support set.
x, xp = x_xp
## Initializes trainable weights.
n_feat = self.n_feat
self.lstm = LSTMStep(n_feat, 2 * n_feat)
self.q_init = model_ops.zeros([self.n_test, n_feat])
self.r_init = model_ops.zeros([self.n_test, n_feat])
self.states_init = self.lstm.get_initial_states([self.n_test, n_feat])
self.trainable_weights = [self.q_init, self.r_init]
### Performs computations
# Get initializations
q = self.q_init
#r = self.r_init
states = self.states_init
for d in range(self.max_depth):
# Process using attention
# Eqn (4), appendix A.1 of Matching Networks paper
e = cos(x + q, xp)
a = tf.nn.softmax(e)
r = model_ops.dot(a, xp)
# Generate new aattention states
y = model_ops.concatenate([q, r], axis=1)
q, states = self.lstm([y] + states) #+ self.lstm.get_constants(x)
return [x + q, xp]
def call(self, x_xp, mask=None):
"""Execute this layer on input tensors.
Parameters
----------
x_xp: list
List of two tensors (X, Xp). X should be of shape (n_test, n_feat) and
Xp should be of shape (n_support, n_feat) where n_test is the size of
the test set, n_support that of the support set, and n_feat is the number
of per-atom features.
Returns
-------
list
Returns two tensors of same shape as input. Namely the output shape will
be [(n_test, n_feat), (n_support, n_feat)]
"""
# x is test set, xp is support set.
x, xp = x_xp
## Initializes trainable weights.
n_feat = self.n_feat
self.lstm = LSTMStep(n_feat, 2 * n_feat)
self.q_init = model_ops.zeros([self.n_test, n_feat])
self.r_init = model_ops.zeros([self.n_test, n_feat])
self.states_init = self.lstm.get_initial_states([self.n_test, n_feat])
self.trainable_weights = [self.q_init, self.r_init]
### Performs computations
# Get initializations
q = self.q_init
#r = self.r_init
states = self.states_init
for d in range(self.max_depth):
# Process using attention
# Eqn (4), appendix A.1 of Matching Networks paper
e = cos(x + q, xp)
a = tf.nn.softmax(e)
r = model_ops.dot(a, xp)
# Generate new aattention states
y = model_ops.concatenate([q, r], axis=1)
q, states = self.lstm([y] + states) #+ self.lstm.get_constants(x)
return [x + q, xp]
