def test():
images_dir='./image/image/'
log_dir='./log/'
images_cat=open("imagelist.txt")
#保存所有图像经过模型计算之后的数组
images_tested=[]
with tf.Graph().as_default():
#重载模型
x=tf.placeholder(tf.float32,shape=[1,300,400,3])
p=model.inference(x,1,10)
logits=tf.nn.softmax(p)
sess=tf.Session()
tf.get_variable_scope().reuse_variables()
ckpt=tf.train.get_checkpoint_state(log_dir)
saver=tf.train.Saver()
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('Loading success!')
else:
print('Loading failed!')
for line in images_cat.readlines():
image_name=line.strip('\n')
image_array=get_one_image(images_dir+image_name)
image_array=np.reshape(image_array,[1,300,400,3])
prediction=sess.run(logits,feed_dict={x:image_array})
prediction=np.array(prediction,dtype='float32')
images_tested.append([image_name,prediction])
print(image_name)
print(prediction)
#测试单张图片
while (True):
test_file=input('输入测试图片:')
if(test_file=='z'):
break
image_name=test_file
image_array=get_one_image(images_dir+image_name)
image_array=np.reshape(image_array,[1,300,400,3])
prediction=sess.run(logits,feed_dict={x:image_array})
prediction=np.array(prediction,dtype='float32')
test_result=[]
for sample in images_tested:
distance=np.sqrt(np.sum(np.square(sample[1]-prediction)));
distance.astype('float32')
test_result.append([sample[0],distance])
#将结果排序
test_result=np.array(test_result)
test_result=test_result[np.lexsort(test_result.T)]
for i in range(10):
print(test_result[i][0])
images_show(test_result)
def run_training():
#目录
train_dir="./image/"
logs_train_dir ="./log"
train,train_label=image_P.get_files(train_dir)
train_batch,train_label_batch=image_P.get_batch(train,
train_label,
IMG_W,
IMG_H,
BATCH_SIZE,
CAPACITY)
train_logits=model.inference(train_batch,BATCH_SIZE,N_CLASSES)
train_loss=model.losses(train_logits,train_label_batch)
train_op=model.trainning(train_loss,learning_rate)
train_acc=model.evaluation(train_logits,train_label_batch)
summary_op=tf.summary.merge_all()
sess=tf.Session()
train_writer=tf.summary.FileWriter(logs_train_dir,sess.graph)
#保存模型
saver=tf.train.Saver()
#初始化
sess.run(tf.global_variables_initializer())
#使用多线程
coord=tf.train.Coordinator()
threads=tf.train.start_queue_runners(sess=sess,coord=coord)
try:
for step in np.arange(MAX_STEP):
if coord.should_stop():
break#线程停止
_,temp_loss,temp_acc=sess.run([train_op,train_loss,train_acc])
#每迭代50次打印一次结果
if step%50 == 0:
print('Step %d,train loss = %.2f,train accuracy = %.2f'%(step,temp_loss,temp_acc))
summary_str=sess.run(summary_op)
train_writer.add_summary(summary_str,step)
#每迭代200次或到达最后一次保存一次模型
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)
except tf.errors.OutOfRangeError:
print('Failed!')
finally:
coord.request_stop()
#结束线程
coord.join(threads)
sess.close()
def train(): with (tf.Graph().as_default()): global_step = tf.contrib.framework.get_or_create_global_step() images, display_images, labels = CNN_input.construct_inputs(False, True) logits = CNN_model.inference(True, images) loss = CNN_model.loss(logits, labels) train_op = CNN_model.train(loss, global_step) # Logs loss and runtime class _LoggerHook(tf.train.SessionRunHook): def begin(self): self._step = -1 self._estimated_time_array = [] for i in range(EST_TIME_SMOOTHING): self._estimated_time_array.append(0) def before_run(self, run_context): self._step += 1 self._start_time = time.time() return tf.train.SessionRunArgs(loss) def get_estimated_completion(self): total = 0 for i in range(EST_TIME_SMOOTHING): total += self._estimated_time_array[i] return (total / EST_TIME_SMOOTHING) def after_run(self, run_context, run_values): duration = time.time() - self._start_time loss_value = run_values.results self._estimated_time_array[self._step % EST_TIME_SMOOTHING] = float(duration * (FLAGS.max_steps - self._step)) / (60.0 * 60.0) if (self._step % LOGGING_STEP_INTERVAL == 0): log_value = '%s: step %d, loss = %.2f (%.1f examples/sec; %.3f sec/batch; %.2f est. hours to completion)' \ % (datetime.now(), self._step, loss_value, FLAGS.batch_size / duration, float(duration), self.get_estimated_completion()) print(log_value) return (log_value) with tf.train.MonitoredTrainingSession( checkpoint_dir=FLAGS.train_dir, hooks=[tf.train.StopAtStepHook(last_step=FLAGS.max_steps), tf.train.NanTensorHook(loss), _LoggerHook()], config=tf.ConfigProto( log_device_placement=FLAGS.log_device_placement)) as mon_sess: while (not mon_sess.should_stop()): mon_sess.run(train_op)
def run_training():
# log dir
logs_train_dir = logs_dir
# read data
test, train, validate, test_label, train_label, validate_label = rd.read_file(
)
# get batch
train_batch, train_label_batch = rd.get_batch(train, train_label, IMG_W,
IMG_H, BATCH_SIZE, CAPACITY)
train_logits = model.inference(train_batch, BATCH_SIZE, N_CLASSES)
train_loss = model.losses(train_logits, train_label_batch)
train_op = model.trainning(train_loss, learning_rate)
train__acc = model.evaluation(train_logits, train_label_batch)
summary_op = tf.summary.merge_all()
sess = tf.Session()
train_writer = tf.summary.FileWriter(logs_train_dir, sess.graph)
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
for step in np.arange(MAX_STEP):
if coord.should_stop():
break
_, tra_loss, tra_acc = sess.run([train_op, train_loss, train__acc])
if step % 50 == 0:
print('Step %d, train loss = %.2f, train accuracy = %.2f%%' %
(step, tra_loss, tra_acc * 100.0))
summary_str = sess.run(summary_op)
train_writer.add_summary(summary_str, step)
if step % 2000 == 0 or (step + 1) == MAX_STEP:
checkpoint_path = os.path.join(logs_train_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
except tf.errors.OutOfRangeError:
print('Done training -- epoch limit reached')
finally:
coord.request_stop()
coord.join(threads)
sess.close()
def evaluate_all_image():
'''
Test all image against the saved models and parameters.
Return global accuracy of test_image_set
##############################################
##Notice that test image must has label to compare the prediction and real
##############################################
'''
# you need to change the directories to yours.
# test_dir = '/home/kevin/tensorflow/cats_vs_dogs/data/test/'
# N_CLASSES = 2
print('-------------------------')
test, train, validate, test_label, train_label, validate_label = rd.read_file(
)
BATCH_SIZE = len(test)
print('There are %d test images totally..' % BATCH_SIZE)
print('-------------------------')
test_batch, test_label_batch = rd.get_batch(test, test_label, IMG_W, IMG_H,
BATCH_SIZE, CAPACITY)
logits = model.inference(test_batch, BATCH_SIZE, N_CLASSES)
testloss = model.losses(logits, test_label_batch)
testacc = model.evaluation(logits, test_label_batch)
logs_train_dir = logs_dir
saver = tf.train.Saver()
with tf.Session() as sess:
print("Reading checkpoints...")
ckpt = tf.train.get_checkpoint_state(logs_train_dir)
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('Loading success, global_step is %s' % global_step)
else:
print('No checkpoint file found')
print('-------------------------')
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
test_loss, test_acc = sess.run([testloss, testacc])
print('The model\'s loss is %.2f' % test_loss)
correct = int(BATCH_SIZE * test_acc)
print('Correct : %d' % correct)
print('Wrong : %d' % (BATCH_SIZE - correct))
print('The accuracy in test images are %.2f%%' % (test_acc * 100.0))
coord.request_stop()
coord.join(threads)
sess.close()
def main(argv=None):
CNN_input.extract()
images, display_images, labels = CNN_input.construct_inputs(True, True)
logits = CNN_model.inference(False, images)
variable_averages = tf.train.ExponentialMovingAverage(
CNN_model.MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
with tf.Session() as sess:
check_point = tf.train.get_checkpoint_state(FLAGS.train_dir)
if check_point and check_point.model_checkpoint_path:
saver.restore(
sess,
os.path.join(
FLAGS.train_dir,
list(
reversed(
check_point.model_checkpoint_path.split('\\',
-1)))[0]))
else:
raise IOError("No Checkpoint file found!")
coord = tf.train.Coordinator()
threads = []
try:
for queue_runner in tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS):
threads.extend(
queue_runner.create_threads(sess,
coord=coord,
daemon=True,
start=True))
session_result = sess.run(
[tf.nn.softmax(logits), labels, display_images])
ResultsPlot(session_result[2], session_result[1],
session_result[0])
coord.request_stop()
coord.join(threads, stop_grace_period_secs=10)
except Exception as e:
print(e)
coord.request_stop()
def evaluate(run_once):
with tf.Graph().as_default() as graph:
images, display_images, labels = CNN_input.construct_inputs(
True, False)
logits = CNN_model.inference(False, images)
top_k_op = tf.nn.in_top_k(logits, labels, 3)
variable_averages = tf.train.ExponentialMovingAverage(
CNN_model.MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
summary_op = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(FLAGS.eval_dir, graph)
while True:
eval_once(saver, summary_writer, top_k_op, summary_op)
if run_once:
break
time.sleep(FLAGS.eval_interval_secs)
def run_training():
train_dir = "./image/"
logs_train_dir = "./log_" + str(MAX_STEP) + "_cap_" + str(CAPACITY)
train, train_label = image_P.get_files(train_dir)
train_batch, train_label_batch = image_P.get_batch(train, train_label,
IMG_W, IMG_H,
BATCH_SIZE, CAPACITY)
train_logits = model.inference(train_batch, BATCH_SIZE, N_CLASSES)
train_loss = model.losses(train_logits, train_label_batch)
train_op = model.trainning(train_loss, learning_rate)
train_acc = model.evaluation(train_logits, train_label_batch)
summary_op = tf.summary.merge_all()
config = tf.ConfigProto(allow_soft_placement=True,
gpu_options=tf.GPUOptions(
per_process_gpu_memory_fraction=0.7,
allow_growth=True))
sess = tf.Session(config=config)
train_writer = tf.summary.FileWriter(logs_train_dir, sess.graph)
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
tf.train.start_queue_runners(sess=sess)
for step in np.arange(9999999):
sess.run([train_op, train_loss, train_acc])
print(step)
if step == MAX_STEP:
print("Finished")
break
log_dir = train_dir
images_dir = './image/'
images_cat = open("imagelist.txt")
#保存所有图像经过模型计算之后的数组
images_tested = []
num_photos = 0
outfile = open("test-new-" + str(MAX_STEP) + "-" + str(CAPACITY) + ".txt",
"w")
filelines = images_cat.readlines()
for line in filelines:
image_name = line.strip('\n')
image_array = get_one_image(images_dir + image_name)
image_array = np.reshape(image_array, [1, 300, 400, 3])
xName = tf.placeholder(tf.float32, shape=[1, 300, 400, 3])
prediction = sess.run(train_logits, feed_dict={xName: image_array})
prediction = np.array(prediction, dtype='float32')
images_tested.append([image_name, prediction])
num_photos += 1
print("Test:" + str(num_photos))
outfile.writelines(image_name)
t = str(prediction)
outfile.writelines(t)
outfile.close()
outfile = open(
"test-new-" + str(MAX_STEP) + "-" + str(CAPACITY) + ".txt", "a")
outfile.close()
outfile2 = open(
"nearesttest-" + str(MAX_STEP) + "-" + str(CAPACITY) + ".txt", "w")
outfile2.write("result = {\n")
outfile2.close()
num_photos = 0
for line in filelines:
num_photos += 1
print("Find Near:" + str(num_photos))
image_name = line.strip('\n')
image_array = get_one_image(images_dir + image_name)
image_array = np.reshape(image_array, [1, 300, 400, 3])
outfile2 = open(
"nearesttest-" + str(MAX_STEP) + "-" + str(CAPACITY) + ".txt", "a")
outfile2.write("'" + image_name + "': [\n")
xName = tf.placeholder(tf.float32, shape=[1, 300, 400, 3])
prediction = sess.run(train_logits, feed_dict={xName: image_array})
prediction = np.array(prediction, dtype='float32')
test_result = []
for sample in images_tested:
distance = np.sqrt(np.sum(np.square(sample[1] - prediction)))
distance.astype('float32')
test_result.append([sample[0], distance])
#将结果排序
test_result = np.array(test_result)
test_result = test_result[np.lexsort(test_result.T)]
for i in range(11):
outfile2.write("'" + test_result[i][0] + "', ")
outfile2.write("],\n")
outfile2.close()
outfile2 = open(
"nearesttest-" + str(MAX_STEP) + "-" + str(CAPACITY) + ".txt", "a")
outfile2.write("}\n")
outfile2.close()
sess.close()
def test(log_dir):
images_dir = './image/'
images_cat = open("imagelist.txt")
#保存所有图像经过模型计算之后的数组
images_tested = []
with tf.Graph().as_default():
#重载模型
x = tf.placeholder(tf.float32, shape=[1, 300, 400, 3])
p = model.inference(x, 1, 10)
logits = tf.nn.softmax(p)
sess = tf.Session()
tf.get_variable_scope().reuse_variables()
ckpt = tf.train.get_checkpoint_state(log_dir)
saver = tf.train.Saver()
num_photos = 0
outfile = open("test.txt", "w")
for line in images_cat.readlines():
image_name = line.strip('\n')
image_array = get_one_image(images_dir + image_name)
image_array = np.reshape(image_array, [1, 300, 400, 3])
prediction = sess.run(logits, feed_dict={x: image_array})
prediction = np.array(prediction, dtype='float32')
images_tested.append([image_name, prediction])
num_photos += 1
print(num_photos)
outfile.writelines(image_name)
t = str(prediction)
outfile.writelines(t)
#测试单张图片
while (True):
test_file = input('输入测试图片:')
if (test_file == 'z'):
break
image_name = test_file
image_array = get_one_image(images_dir + image_name)
image_array = np.reshape(image_array, [1, 300, 400, 3])
prediction = sess.run(logits, feed_dict={x: image_array})
prediction = np.array(prediction, dtype='float32')
test_result = []
for sample in images_tested:
distance = np.sqrt(np.sum(np.square(sample[1] - prediction)))
distance.astype('float32')
test_result.append([sample[0], distance])
#将结果排序
test_result = np.array(test_result)
test_result = test_result[np.lexsort(test_result.T)]
for i in range(11):
print(test_result[i][0])
images_show(test_result)