def run_training(txt_name):
logs_train_dir = './face72/faceIDcard/'
X_data = read_image()
# print(X_data.shape)
graph = face_net(BATCH_SIZE, IMG_H, IMG_W, N_CLASSES, learning_rate, 15, 3)
sess = tf.Session()
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state(logs_train_dir)
if ckpt and ckpt.model_checkpoint_path:
print('读取模型成功')
global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
saver.restore(sess, ckpt.model_checkpoint_path)
print(global_step)
for step in np.arange(MAX_STEP):
# for i in range(BATCH_SIZE):
# xb= (step%400)*16+i
# ximage=np.array(X_data[xb]*255, dtype='uint8')
# for xxi in range(72):
# cv.circle(ximage,(int(Y_data[xb][2+2*xxi]*96),int(Y_data[xb][2+2*xxi+1]*96)),2,(0, 255, 255), -1)
# cv.imshow('ximage',ximage)
# cv.waitKey()
_, tra_loss, sed_pos, sed_neg = sess.run(
[graph['optimize'], graph['loss'], graph['d_pos'], graph['d_neg']],
feed_dict={
graph['x']:
np.reshape(X_data[(step % 400) * 16:(step % 400) * 16 + 16],
(16, 3, 64, 64, 3))
}) #,graph['y']: np.reshape(Y_data[xb], (1, 30))
# print(se_anchor,se_positive,se_anchor)
# print('se_anchor',se_anchor)
# print('se_positive', se_positive)
# print('se_negative', se_negative)
if step % 50 == 0:
print('同一个人', sed_pos, sess.run(tf.reduce_mean(sed_pos)))
print('不同一个人', sed_neg, sess.run(tf.reduce_mean(sed_neg)))
print('Step %d,train loss = %.5f' % (step, tra_loss))
constant_graph = graph_util.convert_variables_to_constants(
sess, sess.graph_def, ['output/output'])
with tf.gfile.FastGFile(logs_train_dir + 'face72.pb',
mode='wb') as f:
f.write(constant_graph.SerializeToString())
# 每迭代50次，打印出一次结果
# summary_str = sess.run(summary_op)
# train_writer.add_summary(summary_str, step)
if step % 200 == 0 or (step + 1) == MAX_STEP:
checkpoint_path = os.path.join(logs_train_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
# 每迭代200次，利用saver.save()保存一次模型文件，以便测试的时候使用
sess.close()
def run_training(txt_name):
imgs = draw_form(MAX_STEP)
logs_train_dir = './face72/faceID/'
X_data = read_image()
graph= face_net(BATCH_SIZE, IMG_H,IMG_W, N_CLASSES,learning_rate,margin=10,run_train=True)
sess = tf.Session()
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state(logs_train_dir)
y_step=0
if ckpt and ckpt.model_checkpoint_path:
global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
saver.restore(sess, ckpt.model_checkpoint_path)
print(global_step)
y_step = int(float(global_step))
loss_list ={}
loss_list['x']=[]
loss_list['y'] = []
for step in np.arange(MAX_STEP):
loss_avg = 0.0
for i in range(BATCH_SIZE):
xb= (step%200)*32+i
_, tra_loss, sess_pos, sess_neg = sess.run([graph['optimize'],graph['loss'],graph['d_pos'],graph['d_neg']],feed_dict={
graph['x']: np.reshape(X_data[xb], (3, 64, 64, 3))})
loss_avg+=tra_loss
avg_loss =loss_avg/BATCH_SIZE
loss_list['x'].append(step+y_step)
loss_list['y'].append(avg_loss)
loss_list['x'].append(step+y_step)
loss_list['y'].append(tra_loss)
drow_spot(imgs,step, tra_loss, MAX_STEP)
print('同:',sess_pos,'不同:',sess_neg,'距离差',sess_neg-sess_pos)
if step % 50 == 0:
# print('同一个人',sess.run(tf.reduce_mean(sess_pos)),'\t',sess_pos)
# print('不同一个人',sess.run(tf.reduce_mean(sess_neg)),'\t',sess_neg)
print('Step %d,train loss = %.5f' % (step+y_step, tra_loss))
constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph_def,
['output/output'])
with tf.gfile.FastGFile(logs_train_dir + 'face72.pb', mode='wb') as f:
f.write(constant_graph.SerializeToString())
# 每迭代50次，打印出一次结果
if step % 200 == 0 or (step + 1) == MAX_STEP:
checkpoint_path = os.path.join(logs_train_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step+y_step)
# 每迭代200次，利用saver.save()保存一次模型文件，以便测试的时候使用
sess.close()
'-')[-1]
saver.restore(sess, ckpt.model_checkpoint_path)
# 调用saver.restore()函数，加载训练好的网络模型
# print('Loading success')
else:
print('没有保存的模型')
pos_d, neg_d = sess.run([graph['d_pos'], graph['d_neg']],
feed_dict={
graph['x']:
np.reshape(image_arr,
(1, 3, 64, 64, 3))
})
return pos_d, neg_d
X_data = read_image()
for i in range(200):
start_time = datetime.datetime.now()
pos_d, neg_d = val(X_data[6400 + i])
print('耗时：', datetime.datetime.now() - start_time)
img = (X_data[6400 + i][0] * 256 + 128).astype(np.uint8)
img = cv.resize(img, (480, 480), interpolation=cv.INTER_CUBIC)
print(pos_d, neg_d)
# biaoq ='None'
# if prediction[0][0]>= 0.8 and prediction[0][0]<1.6:
# biaoq = 'Smile'
# elif prediction[0][0]>=1.6:
# biaoq = 'Laugh'
# biaoq+=':' + str(prediction[0][1])