def evaluate(input_x, input_y):
'''
评价 文本分类
:return
result:预测的结果,哪一维更大
accuracy:精确度
'''
graph = tf.Graph()
with graph.as_default(), tf.Session() as sess:
# 恢复模型
features = tf.placeholder(tf.int32, [None, cnnc.SEQUENCE_LENGTH])
labels = tf.placeholder(tf.int32, [None, cnnc.FLAGS.num_class])
logits = cnnc.inference(features)
predictions = tf.arg_max(logits, 1)
correct_predictions = tf.equal(predictions, tf.arg_max(labels, 1))
accuracy = tf.reduce_mean(tf.cast(correct_predictions,
dtype=tf.float32))
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print("SUCESS")
else:
print("No checkpoint file found")
result, accuracy = sess.run([predictions, accuracy], feed_dict={features: input_x, labels: input_y})
return result, accuracy
def train():
'''
训练 cnnc 模型
'''
with tf.Graph().as_default():
global_step = tf.Variable(0, trainable=False)
# 传入数据所在的文件夹
features, labels = cnnc.distorted_inputs(FLAGS.train_dir)
logits = cnnc.inference(features)
loss = cnnc.loss(logits, labels)
train_op = cnnc.train(loss, global_step)
# saver
saver = tf.train.Saver(tf.all_variables())
# 构建总结操作
summary_op = tf.merge_all_summaries()
# 初始化操作
init = tf.initialize_all_variables()
sess = tf.Session(config=tf.ConfigProto(log_device_placement=FLAGS.log_device_placement))
sess.run(init)
tf.train.start_queue_runners(sess=sess)
summary_writer = tf.train.SummaryWriter(FLAGS.train_dir, graph_def=sess.graph_def)
for step in range(FLAGS.max_steps):
start_time = time.time()
_, loss_value = sess.run([train_op, loss])
duration = time.time() - start_time
# 模型收敛
assert not np.isnan(loss_value), "Model diverged with loss = NAN"
if step % 10 == 0:
num_examples_per_step = FLAGS.batch_size
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)
format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
'sec/batch)')
print(format_str % (datetime.now(), step, loss_value,
examples_per_sec, sec_per_batch))
if step % 100 == 0:
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, step)
if step % 1000 == 0 or (step + 1) == FLAGS.max_steps:
checkpoint_path = os.path.join(FLAGS.train_dir, "model.ckpt")
saver.save(sess, checkpoint_path, global_step=step)
