evaluate函数在python_Python test.evaluate方法代碼示例

# 需要導入模塊: import test [as 別名]

# 或者: from test import evaluate [as 別名]

def main(train_tensor, dev_tensor, candidates_tensor, model, config):

logger.info('Run main with config {}'.format(config))

epochs = config['epochs']

batch_size = config['batch_size']

negative_cand = config['negative_cand']

save_dir = config['save_dir']

# TODO: Add LR decay

optimizer = tf.train.AdamOptimizer(config['lr']).minimize(model.loss)

prev_best_accuracy = 0

saver = tf.train.Saver()

config = tf.ConfigProto()

config.gpu_options.allow_growth = True

with tf.Session(config=config) as sess:

sess.run(tf.global_variables_initializer())

for epoch in range(epochs):

avg_loss = _train(train_tensor, batch_size, negative_cand, model, optimizer, sess)

# TODO: Refine dev loss calculation

avg_dev_loss = _forward_all(dev_tensor, model, sess)

logger.info('Epoch: {}; Train loss: {}; Dev loss: {};'.format(epoch, avg_loss, avg_dev_loss))

if epoch % 2 == 0:

dev_eval = evaluate(dev_tensor, candidates_tensor, sess, model)

logger.info('Evaluation: {}'.format(dev_eval))

accuracy = dev_eval[2]

if accuracy >= prev_best_accuracy:

logger.debug('Saving checkpoint')

prev_best_accuracy = accuracy

saver.save(sess, save_dir)

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