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()
#设定损失函数
sgd=SGD(lr=learning_rate,decay=1e-5)
model_vgg_train.compile(loss='categorical_crossentropy',
optimizer=sgd,metrics=['accuracy'])
#加载数据
def get_train_batch(X_train,Y_train,img_w,img_h):
i=0
while 1:
x=[]
y=[]
image=Image.open(X_train[i])
image=image.resize([img_w,img_h])
x.append(np.array(image))
y.append(to_categorical(Y_train[i],N_CLASSES))
i=(i+1)%len(X_train)
x=np.array(x)
y=np.array(y)
yield(x,y)
train,train_label=image_P.get_files(train_dir)
model_vgg_train.fit_generator(get_train_batch(train,
train_label,IMG_W,IMG_H),
steps_per_epoch=BATCH_SIZE,
epochs=MAX_STEP)
model_vgg_train.save(log_dir)
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()
sgd = SGD(lr=learning_rate, decay=1e-5)
model_vgg_train.compile(loss='categorical_crossentropy',
optimizer=sgd,
metrics=['accuracy'])
#加载数据
def get_train_batch(X_train, Y_train, img_w, img_h):
i = 0
while 1:
x = []
y = []
image = Image.open(X_train[i])
image = image.resize([img_w, img_h])
x.append(np.array(image))
y.append(to_categorical(Y_train[i], N_CLASSES))
i = (i + 1) % len(X_train)
x = np.array(x)
y = np.array(y)
yield (x, y)
train, train_label = image_P.get_files(train_dir, CAPACITY)
model_vgg_train.fit_generator(get_train_batch(train, train_label, IMG_W,
IMG_H),
steps_per_epoch=BATCH_SIZE,
epochs=MAX_STEP)
model_vgg_train.save(log_dir)