def __init__(self, train_config):
self.learing_rate = float(train_config['learning_rate'])
self.train_iterior = int(train_config['train_iteration'])
self._train_logger_init()
net_config = parser_cfg_file('./config/rnn_net.cfg')
self.rnn_net = AFD_RNN(net_config)
self.predict = self.rnn_net.build_net_graph()
self.label = tf.placeholder(
tf.float32, [None, self.rnn_net.time_step, self.rnn_net.class_num])
def __init__(self, mode_dir, time_step=5, batch_size=1):
self.batch_size = batch_size
self.time_step = time_step
ckpt = tf.train.get_checkpoint_state(mode_dir)
if ckpt is None:
raise FileExistsError(str(mode_dir, '没有模型可以加载'))
net_config = parser_cfg_file('./config/rnn_net.cfg')
self.rnn_net = AFD_RNN(net_config, batch_size, time_step)
predict = self.rnn_net.build_net_graph()
self._predict_tensor = tf.argmax(predict, axis=2)
saver = tf.train.Saver()
self._sess = tf.Session()
# 加载参数
saver.restore(self._sess, ckpt.model_checkpoint_path)
class AFD_RNN_Train(object):
def __init__(self, train_config):
self.learing_rate = float(train_config['learning_rate'])
self.train_iterior = int(train_config['train_iteration'])
self._train_logger_init()
net_config = parser_cfg_file('./config/rnn_net.cfg')
self.rnn_net = AFD_RNN(net_config)
self.predict = self.rnn_net.build_net_graph()
self.label = tf.placeholder(
tf.float32, [None, self.rnn_net.time_step, self.rnn_net.class_num])
def _compute_loss(self):
with tf.name_scope('loss'):
# [batchszie, time_step, class_num] ==> [time_step][batchsize, class_num]
predict = tf.unstack(self.predict, axis=0)
label = tf.unstack(self.label, axis=1)
loss = [
tf.nn.softmax_cross_entropy_with_logits(labels=label[i],
logits=predict[i])
for i in range(self.rnn_net.time_step)
]
loss = tf.reduce_mean(loss)
train_op = tf.train.AdamOptimizer(self.learing_rate).minimize(loss)
return loss, train_op
def train_rnn(self):
loss, train_op = self._compute_loss()
with tf.name_scope('accuracy'):
predict = tf.transpose(self.predict, [1, 0, 2])
correct_pred = tf.equal(tf.argmax(self.label, 2),
tf.argmax(predict, axis=2))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
dataset = DataLoad('./dataset/train/',
time_step=self.rnn_net.time_step,
class_num=self.rnn_net.class_num)
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for step in range(1, self.train_iterior + 1):
x, y = dataset.get_batch(self.rnn_net.batch_size)
if step == 1:
feed_dict = {self.rnn_net.input_tensor: x, self.label: y}
else:
feed_dict = {
self.rnn_net.input_tensor: x,
self.label: y,
self.rnn_net.cell_state: state
}
_, compute_loss, state = sess.run(
[train_op, loss, self.rnn_net.cell_state],
feed_dict=feed_dict)
if step % 10 == 0:
compute_accuracy = sess.run(accuracy, feed_dict=feed_dict)
self.train_logger.info(
'train step = %d,loss = %f,accuracy = %f' %
(step, compute_loss, compute_accuracy))
if step % 1000 == 0:
save_path = saver.save(sess, './model/model.ckpt')
self.train_logger.info(
"train step = %d ,model save to =%s" %
(step, save_path))
def _train_logger_init(self):
"""
初始化log日志
:return:
"""
self.train_logger = logging.getLogger('train')
self.train_logger.setLevel(logging.DEBUG)
# 添加文件输出
log_file = './train_logs/' + time.strftime(
'%Y%m%d%H%M', time.localtime(time.time())) + '.logs'
file_handler = logging.FileHandler(log_file, mode='w')
file_handler.setLevel(logging.DEBUG)
file_formatter = logging.Formatter(
'%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s'
)
file_handler.setFormatter(file_formatter)
self.train_logger.addHandler(file_handler)
# 添加控制台输出
consol_handler = logging.StreamHandler()
consol_handler.setLevel(logging.DEBUG)
consol_formatter = logging.Formatter('%(message)s')
consol_handler.setFormatter(consol_formatter)
self.train_logger.addHandler(consol_handler)
class Run_AFD_RNN(object):
def __init__(self, mode_dir, time_step=5, batch_size=1):
self.batch_size = batch_size
self.time_step = time_step
ckpt = tf.train.get_checkpoint_state(mode_dir)
if ckpt is None:
raise FileExistsError(str(mode_dir, '没有模型可以加载'))
net_config = parser_cfg_file('./config/rnn_net.cfg')
self.rnn_net = AFD_RNN(net_config, batch_size, time_step)
predict = self.rnn_net.build_net_graph()
self._predict_tensor = tf.argmax(predict, axis=2)
saver = tf.train.Saver()
self._sess = tf.Session()
# 加载参数
saver.restore(self._sess, ckpt.model_checkpoint_path)
def run(self, data):
data = np.reshape(
data,
[self.batch_size, self.time_step, self.rnn_net.senor_data_num])
predict = self._sess.run(self._predict_tensor,
feed_dict={self.rnn_net.input_tensor: data})
return predict
def run_stop(self):
self._sess.close()
def _update_show_data(self, data, step, update_data):
for i in range(step):
data.pop(0)
data.append(update_data[i])
def draw_flow(self, test_data, test_label):
data_size = test_data.shape[0]
x = [_ for _ in range(150)]
ax = [0 for _ in range(150)]
ay = [0 for _ in range(150)]
az = [0 for _ in range(150)]
sum = 0
run_step = 10
num = int(data_size / run_step)
print(num)
start_time = time.time()
#plt.axis([0, 151, -20, 20])
#plt.ion()
for i in range(num):
if i > int(time_step / run_step):
predict = run.run(test_data[i * run_step - time_step:i *
run_step, :])
title = 'correct:' + Label[test_label[
i * run_step]] + ' predict:' + Label[predict[int(
time_step - 1)][0]]
print(Label[test_label[i * run_step]])
print(Label[predict[int(time_step - 1)][0]])
if Label[test_label[i *
run_step]] is Label[predict[int(time_step -
1)][0]]:
sum = sum + 1
else:
title = 'correct:' + Label[test_label[
i * run_step]] + ' predict:' + 'unknow'
self._update_show_data(
ax, run_step, test_data[i * run_step:i * run_step + run_step,
0])
self._update_show_data(
ay, run_step, test_data[i * run_step:i * run_step + run_step,
1])
self._update_show_data(
az, run_step, test_data[i * run_step:i * run_step + run_step,
2])
# plt.cla()
# plt.plot(x, ax)
# plt.plot(x, ay)
# plt.plot(x, az)
#
# plt.title(title)
# plt.draw()
# plt.pause(0.001)
during = str(time.time() - start_time)
print(sum / num)
print('检测耗时=', during)