# variable_summaries(W_interaction)
A_B = tf.map_fn(lambda W: h_A_4 @ W, w_tensor, dtype=tf.float32)
A_B = tf.transpose(A_B, [1, 0, 2])
A_B = A_B @ tf.expand_dims(h_B_4, -1)
A_B = tf.reduce_sum(A_B, axis=2)
A_B = tf.concat([A_B, tf.matmul(tf.concat([h_A_4, h_B_4], 1), W_interaction) + b_tensor], axis=-1)
fully_size = 2 * tensor_size
if pairwise_node_comparison:
with tf.name_scope('pairwise_node_comparison'):
# def sgm(x):
# return 1/(1+np.exp(-x))
def hist(a,b):
return tf.histogram_fixed_width(tf.nn.sigmoid([email protected](b)), [0.0,1.0], nbins=n_bins)
atomA=tf.dynamic_partition(outputA,graphA_topology.get_membership_placeholder(),batch_size)
atomB=tf.dynamic_partition(outputB,graphB_topology.get_membership_placeholder(),batch_size)
res=[]
for i in range(batch_size):
res.append(hist(atomA[i],atomB[i]))
res=tf.to_float(tf.convert_to_tensor(res))
res=tf.stop_gradient(res)
A_B=tf.concat([A_B,res],-1)
fully_size+=n_bins
# A_B_interaction = h_A_4 + h_B_4
# A_B_interaction = tf.concat([h_A_3, h_B_3], 1)
# A_B = tf.nn.dropout(A_B, 1 - dropout3 * training)
# interaction_size = 2 * Dense_size
# interaction_size = Dense_size*Dense_size
# interaction_size = (conv_size+Dense_size)*2
b_tensor
],
axis=-1)
fully_size = 2 * tensor_size
if pairwise_node_comparison:
with tf.name_scope('pairwise_node_comparison'):
# def sgm(x):
# return 1/(1+np.exp(-x))
def hist(a, b):
return tf.histogram_fixed_width(tf.nn.sigmoid(
a @ tf.transpose(b)), [0.0, 1.0],
nbins=n_bins)
atomA = tf.dynamic_partition(
outputA, graphA_topology.get_membership_placeholder(),
batch_size)
atomB = tf.dynamic_partition(
outputB, graphB_topology.get_membership_placeholder(),
batch_size)
res = []
for i in range(batch_size):
res.append(hist(atomA[i], atomB[i]))
res = tf.to_float(tf.convert_to_tensor(res))
res = tf.stop_gradient(res)
A_B = tf.concat([A_B, res], -1)
fully_size += n_bins
# A_B_interaction = h_A_4 + h_B_4
# A_B_interaction = tf.concat([h_A_3, h_B_3], 1)
# A_B = tf.nn.dropout(A_B, 1 - dropout3 * training)
