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training.PY
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#第四个PY文件，加载处理好的数据和模型，并训练
#加载模型
import tensorflow as tf
import tensorflow.contrib.slim.python.slim.nets.inception_v3 as inception_v3
import tensorflow.contrib.slim as slim
import input
import csv
import pandas as pd
import numpy as np
#加载处理好的数据
TRAINING_FILE = './training.tfrecords' #输出文件
TESTING_FILE = './testing.tfrecords'
VALIDATION_FILE = './validation.tfrecords'
#保存训练好的模型路径
TRAIN_FILE = './model/save_model.ckpt'
write_csv = "train.csv"
#加载谷歌提供的模型数据
CKPT_FILE = './inception_v3.ckpt'
data_counts = [1018,39,139,47,90,248,77,54,43,35,209,140]
#训练参数
LEARNING_RATE = 0.0001
STEPS = 1800
BATCH = 32
N_CLASSES = 12 #????
Training_batch = 5
#全连接层参数前缀
CHECKPOINT_EXCLUDE_SCOPES = 'InceptionV3/Logits,InceptionV3/AuxLogits'
TRAINABLE_SCOPES = 'InceptionV3/Logits,InceptionV3/AuxLogits'
#从谷歌模型中加载参数
def get_tuned_variables():
exclusions = [scope.strip() for scope in CHECKPOINT_EXCLUDE_SCOPES.split(',')]
variables_to_restore = []
for var in slim.get_model_variables():
excluded = True
for exclusion in exclusions:
if var.op.name.startswith(exclusion):
excluded = False
break
if not excluded:
variables_to_restore.append(var)
return variables_to_restore
#获取所有需要训练的变量列表
def get_trainable_variables():
scopes = [scope.strip() for scope in TRAINABLE_SCOPES.split(',')]
variables_to_train = []
#枚举所有需要训练的变量前缀，找到所有参数
for scope in scopes:
variables = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES,scope)
variables_to_train.extend(variables)
return variables_to_train
def get_loss_bias():
bias = [[data_counts[j]/data_counts[i] for j in range(N_CLASSES)] for i in range(N_CLASSES)]
return bias
def get_loss_weight(batch_size,labels_size,labels,weight_array):
weight = [weight_array[labels[i]] for i in range(batch_size)]
return weight
def main():
tf.reset_default_graph()
#加载与处理好的数据
images = tf.placeholder(tf.float32,[None,299,299,3],name='input')
labels = tf.placeholder(tf.int64,[None],name='labels')
names = tf.placeholder(tf.string,[None],name='name')
loss_weights = tf.placeholder(tf.float32,[None,12],name='loss_weight')
with slim.arg_scope(inception_v3.inception_v3_arg_scope()):
logits,_ = inception_v3.inception_v3(
images,num_classes=N_CLASSES
)
trainable_variables = get_trainable_variables()
'''
tf.losses.softmax_cross_entropy(
tf.one_hot(labels,N_CLASSES),logits,weights=1.0
)
'''
with tf.name_scope('losses'):
loss = tf.reduce_mean((tf.one_hot(labels,N_CLASSES) - logits)**2 * loss_weights)#
tf.losses.add_loss(loss)
tf.summary.scalar('losses',tf.losses.get_total_loss())
train_step = tf.train.RMSPropOptimizer(LEARNING_RATE).minimize(tf.losses.get_total_loss())
with tf.name_scope('V_evaluation'):
correct_prediction = tf.equal(tf.argmax(logits,1),labels)
evaluation_step = tf.reduce_mean(tf.cast(
correct_prediction,tf.float32
))
exclusions = ['InceptionV3/Logits',
'InceptionV3/AuxLogits']
inception_except_logits = slim.get_variables_to_restore(exclude=exclusions)
load_fn = slim.assign_from_checkpoint_fn(
CKPT_FILE,
inception_except_logits,
ignore_missing_vars=True
)
images_feed,labels_feed,_ = input.get_batch("./training.tfrecords",Training_batch,299,299,2000,1999)
images_E,labels_E,_ = input.get_batch("./validation.tfrecords",100,299,299,2000,1999)
image_T,labels_T,name_T = input.get_batch("./testing.tfrecords",10,299,299,2000,1,if_shuffer=False)
saver = tf.train.Saver()
merged = tf.summary.merge_all()
with tf.Session() as sess:
'''
log_writer = tf.summary.FileWriter("./log/",sess.graph)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess = sess,coord=coord)
print('建立输入队列')
init = tf.global_variables_initializer()
sess.run(init)
load_fn(sess)
print('载入模型数据')
IF_TRAIN = False
print('训练开始')
for i in range(STEPS):
images_feeds,labels_feeds = sess.run([images_feed,labels_feed])
weights_feed = get_loss_weight(Training_batch,N_CLASSES,labels_feeds,get_loss_bias())
#print(np.array(weights_feed).shape)
sess.run(train_step,feed_dict={images:images_feeds,
labels:labels_feeds,
loss_weights:weights_feed})
summary = sess.run(merged,feed_dict={images:images_feeds,
labels:labels_feeds,
loss_weights:weights_feed})
print('第 %d 轮完成,损失为：' % i,sess.run(tf.losses.get_total_loss(),feed_dict={images:images_feeds,
labels:labels_feeds,
loss_weights:weights_feed}))
if i % 30 ==0 or i + 1 == STEPS: #输出日志
saver.save(sess,"./model/save_model.ckpt")
images_Es,labels_Es = sess.run([images_E,labels_E])
validation_accuracy = sess.run(evaluation_step,feed_dict={images:images_Es,
labels:labels_Es
})
print('第 %d 论训练后，正确率估计：%.1f%%' % (i,validation_accuracy * 100))
print(sess.run(tf.arg_max(logits,1),feed_dict={images:images_Es,labels:labels_Es}))
#print(sess.run(labels,feed_dict={images:images_Es,labels:labels_Es}))
log_writer.add_summary(summary,i)
coord.request_stop()
coord.join(threads)
log_writer.close()
'''
print('建立输入队列')
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess = sess,coord=coord)
print('载入已训练模型数据')
model_file=tf.train.latest_checkpoint('./model')
saver.restore(sess,model_file)
head = True
data_bool = [1 for x in range(440)]
print('分类开始')
for i in range(100):
print('第%d组' % i)
images_Es,labels_Es,names_ES = sess.run([image_T,labels_T,name_T])
#print('第 %d 论训练后，正确率估计：%.1f%%' % (0,validation_accuracy * 100))
L = sess.run(tf.argmax(logits,1),feed_dict={images:images_Es})
N = sess.run(names,feed_dict={names:names_ES})
for j in range(10):
not_finish = 1 in data_bool
if not not_finish:
break
numb = str(N[j])[2:-5]
numb = int(numb)
if data_bool[numb] == 1:
if 'defect'+str(L[j]) == 'defect0':
L_b = 'norm'
else:
L_b = 'defect'+str(L[j])
print('第%d个' % j)
print(str(N[j])[2:-1],L_b)
df = pd.DataFrame(({'defect1':[L_b]}),index = [str(N[j])[2:-1]])
if head:
head = False
df.to_csv('out_put.csv', mode='w', header=None)
else:
df.to_csv('out_put.csv', mode='a', header=None)
data_bool[numb] = 0
if not not_finish:
break
coord.request_stop()
coord.join(threads)
if __name__ == '__main__':
main()