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execute_cora.py
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execute_cora.py
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import time
import numpy as np
import tensorflow as tf
from models import GAT
from utils import process
checkpt_file = 'pre_trained/cora/mod_cora.ckpt'
dataset = 'cora'
# training params
batch_size = 1
nb_epochs = 100000
patience = 100
lr = 0.005 # learning rate
l2_coef = 0.0005 # weight decay
hid_units = [8] # numbers of hidden units per each attention head in each layer
n_heads = [8, 1] # additional entry for the output layer
residual = False
nonlinearity = tf.nn.elu
model = GAT
print('Dataset: ' + dataset)
print('----- Opt. hyperparams -----')
print('lr: ' + str(lr))
print('l2_coef: ' + str(l2_coef))
print('----- Archi. hyperparams -----')
print('nb. layers: ' + str(len(hid_units)))
print('nb. units per layer: ' + str(hid_units))
print('nb. attention heads: ' + str(n_heads))
print('residual: ' + str(residual))
print('nonlinearity: ' + str(nonlinearity))
print('model: ' + str(model))
adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask = process.load_data(dataset)
features, spars = process.preprocess_features(features)
nb_nodes = features.shape[0]
ft_size = features.shape[1]
nb_classes = y_train.shape[1]
adj = adj.todense()
features = features[np.newaxis]
adj = adj[np.newaxis]
y_train = y_train[np.newaxis]
y_val = y_val[np.newaxis]
y_test = y_test[np.newaxis]
train_mask = train_mask[np.newaxis]
val_mask = val_mask[np.newaxis]
test_mask = test_mask[np.newaxis]
biases = process.adj_to_bias(adj, [nb_nodes], nhood=1)
with tf.Graph().as_default():
with tf.name_scope('input'):
ftr_in = tf.placeholder(dtype=tf.float32, shape=(batch_size, nb_nodes, ft_size))
bias_in = tf.placeholder(dtype=tf.float32, shape=(batch_size, nb_nodes, nb_nodes))
lbl_in = tf.placeholder(dtype=tf.int32, shape=(batch_size, nb_nodes, nb_classes))
msk_in = tf.placeholder(dtype=tf.int32, shape=(batch_size, nb_nodes))
attn_drop = tf.placeholder(dtype=tf.float32, shape=())
ffd_drop = tf.placeholder(dtype=tf.float32, shape=())
is_train = tf.placeholder(dtype=tf.bool, shape=())
model=model()
logits = model.inference(ftr_in, nb_classes, nb_nodes, is_train,
attn_drop, ffd_drop,
bias_mat=bias_in,
hid_units=hid_units, n_heads=n_heads,
residual=residual, activation=nonlinearity)
log_resh = tf.reshape(logits, [-1, nb_classes])
lab_resh = tf.reshape(lbl_in, [-1, nb_classes])
msk_resh = tf.reshape(msk_in, [-1])
loss = model.masked_softmax_cross_entropy(log_resh, lab_resh, msk_resh)
accuracy = model.masked_accuracy(log_resh, lab_resh, msk_resh)
train_op = model.training(loss, lr, l2_coef)
saver = tf.train.Saver()
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
vlss_mn = np.inf
vacc_mx = 0.0
curr_step = 0
with tf.Session() as sess:
sess.run(init_op)
writer = tf.summary.FileWriter('./graph', sess.graph)
train_loss_avg = 0
train_acc_avg = 0
val_loss_avg = 0
val_acc_avg = 0
for epoch in range(nb_epochs):
tr_step = 0
tr_size = features.shape[0]
while tr_step * batch_size < tr_size:
_, loss_value_tr, acc_tr = sess.run([train_op, loss, accuracy],
feed_dict={
ftr_in: features[tr_step*batch_size:(tr_step+1)*batch_size],
bias_in: biases[tr_step*batch_size:(tr_step+1)*batch_size],
lbl_in: y_train[tr_step*batch_size:(tr_step+1)*batch_size],
msk_in: train_mask[tr_step*batch_size:(tr_step+1)*batch_size],
is_train: True,
attn_drop: 0.6, ffd_drop: 0.6})
train_loss_avg += loss_value_tr
train_acc_avg += acc_tr
tr_step += 1
vl_step = 0
vl_size = features.shape[0]
while vl_step * batch_size < vl_size:
loss_value_vl, acc_vl = sess.run([loss, accuracy],
feed_dict={
ftr_in: features[vl_step*batch_size:(vl_step+1)*batch_size],
bias_in: biases[vl_step*batch_size:(vl_step+1)*batch_size],
lbl_in: y_val[vl_step*batch_size:(vl_step+1)*batch_size],
msk_in: val_mask[vl_step*batch_size:(vl_step+1)*batch_size],
is_train: False,
attn_drop: 0.0, ffd_drop: 0.0})
val_loss_avg += loss_value_vl
val_acc_avg += acc_vl
vl_step += 1
print('Training: loss = %.5f, acc = %.5f | Val: loss = %.5f, acc = %.5f' %
(train_loss_avg/tr_step, train_acc_avg/tr_step,
val_loss_avg/vl_step, val_acc_avg/vl_step))
if val_acc_avg/vl_step >= vacc_mx or val_loss_avg/vl_step <= vlss_mn:
if val_acc_avg/vl_step >= vacc_mx and val_loss_avg/vl_step <= vlss_mn:
vacc_early_model = val_acc_avg/vl_step
vlss_early_model = val_loss_avg/vl_step
saver.save(sess, checkpt_file)
vacc_mx = np.max((val_acc_avg/vl_step, vacc_mx))
vlss_mn = np.min((val_loss_avg/vl_step, vlss_mn))
curr_step = 0
else:
curr_step += 1
if curr_step == patience:
print('Early stop! Min loss: ', vlss_mn, ', Max accuracy: ', vacc_mx)
print('Early stop model validation loss: ', vlss_early_model, ', accuracy: ', vacc_early_model)
break
train_loss_avg = 0
train_acc_avg = 0
val_loss_avg = 0
val_acc_avg = 0
saver.restore(sess, checkpt_file)
ts_size = features.shape[0]
ts_step = 0
ts_loss = 0.0
ts_acc = 0.0
while ts_step * batch_size < ts_size:
loss_value_ts, acc_ts = sess.run([loss, accuracy],
feed_dict={
ftr_in: features[ts_step*batch_size:(ts_step+1)*batch_size],
bias_in: biases[ts_step*batch_size:(ts_step+1)*batch_size],
lbl_in: y_test[ts_step*batch_size:(ts_step+1)*batch_size],
msk_in: test_mask[ts_step*batch_size:(ts_step+1)*batch_size],
is_train: False,
attn_drop: 0.0, ffd_drop: 0.0})
ts_loss += loss_value_ts
ts_acc += acc_ts
ts_step += 1
print('Test loss:', ts_loss/ts_step, '; Test accuracy:', ts_acc/ts_step)
sess.close()