"""Generic utilities for machine learning.
"""
import code
import os
import tensorflow as tf
from ccn.cfg import get_config
CFG = get_config()
def shuffle_together(*tensors):
idxs = tf.range(tensors[0].shape[0])
out = []
for tensor in tensors:
out.append(tf.gather(tensor, idxs))
return tuple(out)
def update_data_dict(data_dict, batch_dict):
for name in batch_dict.keys():
if name not in data_dict:
data_dict[name] = batch_dict[name]
else:
data_dict[name] += batch_dict[name]
return data_dict
def normalize_data_dict(data_dict, num_batches):
for name, value in data_dict.items():
"""Brute force graph matching with TensorFlow code. This works for small graphs.
(For graphs with >6 max nodes, it becomes pretty much unfeasible.)
TODO: Hungarian algorithm
"""
import code
import itertools
import numpy as np
import tensorflow as tf
from ccn.cfg import get_config; CFG = get_config()
permutations = {
3: tf.convert_to_tensor(np.array(list(itertools.permutations(range(3))))),
4: tf.convert_to_tensor(np.array(list(itertools.permutations(range(4))))),
5: tf.convert_to_tensor(np.array(list(itertools.permutations(range(5))))),
6: tf.convert_to_tensor(np.array(list(itertools.permutations(range(6))))),
7: tf.convert_to_tensor(np.array(list(itertools.permutations(range(7))))),
8: tf.convert_to_tensor(np.array(list(itertools.permutations(range(8))))),
}
def loss_fn(adj, nf, possible_adjs, possible_nfs):
acc = {}
permute_dim = possible_adjs.shape[1]
# calculate losses along last axis (per node)
lfn = tf.keras.losses.mean_squared_error if CFG['use_mse_loss'] else \