"f1": 0
}
start = time.time()
train_head_hierarchy = train_head_deepwalk
train_tail_hierarchy = train_tail_deepwalk
test_head_hierarchy = test_head_deepwalk
test_tail_hierarchy = test_tail_deepwalk
with tf.Session() as sess:
sess.run(tf.local_variables_initializer())
sess.run(tf.global_variables_initializer())
for epoch in range(epochs):
train_head_data, train_head_hierarchy, train_head_context,\
train_tail_data, train_tail_hierarchy, train_tail_context, \
train_labels = permute_dataset((train_head_data, train_head_hierarchy, train_head_context,
train_tail_data, train_tail_hierarchy, train_tail_context,
train_labels))
runner.train_model(
train_op,
result.loss,
train_batch_num,
feed_vars=(head_data_placeholder, head_hierarchy_placeholder,
head_context_placeholder, tail_data_placeholder,
tail_hierarchy_placeholder, tail_context_placeholder,
labels_placeholder),
feed_data=pt.train.feed_numpy(batch_size, train_head_data,
train_head_hierarchy,
train_head_context, train_tail_data,
train_tail_hierarchy,
train_tail_context, train_labels),
phase=pt.Phase.test)
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
train_op = pt.apply_optimizer(optimizer, losses=[result.loss])
#save_path = '/data/cdy/ykq/checkpoints/model_conv2d_{}-{}.cpkt'.format(
# learning_rate, time.strftime("%m-%d-%H%M%S", time.localtime()))
#print("model has been saved: " + save_path)
#runner = pt.train.Runner(save_path)
runner = pt.train.Runner()
best_accuracy = 0
best_epoch = 0
with tf.Session() as sess:
# print(epochs)
for epoch in range(epochs):
train_data, train_labels = permute_dataset((train_data, train_labels))
# 并没有保存最佳的model
runner.train_model(train_op,
result.loss,
num_batches,
feed_vars=(data_placeholder, labels_placeholder),
feed_data=pt.train.feed_numpy(
train_batch_size, train_data, train_labels))
classification_accuracy = runner.evaluate_model(
accuracy,
num_batches,
feed_vars=(data_placeholder, labels_placeholder),
feed_data=pt.train.feed_numpy(test_batch_size, test_data,
test_labels))
best_epoch = 0
start = time.time()
train_head_hierarchy = train_head_deepwalk
train_tail_hierarchy = train_tail_deepwalk
test_head_hierarchy = test_head_deepwalk
test_tail_hierarchy = test_tail_deepwalk
train_head_word = train_head_word2vec
train_tail_word = train_tail_word2vec
test_head_word = test_head_word2vec
test_tail_word = test_tail_word2vec
with tf.Session() as sess:
for epoch in range(epochs):
train_head_data, train_head_word, train_head_hierarchy, \
train_tail_data, train_tail_word, train_tail_hierarchy, \
train_labels = permute_dataset((train_head_data, train_head_word, train_head_hierarchy,
train_tail_data, train_tail_word, train_tail_hierarchy,
train_labels))
runner.train_model(
train_op,
result.loss,
train_batch_num,
feed_vars=(head_data_placeholder, head_word2vec_placeholder,
head_hierarchy_placeholder, tail_data_placeholder,
tail_word2vec_placeholder, tail_hierarchy_placeholder,
labels_placeholder),
feed_data=pt.train.feed_numpy(batch_size, train_head_data,
train_head_word,
train_head_hierarchy,
train_tail_data, train_tail_word,
train_tail_hierarchy, train_labels),