def test():
input_data = tf.placeholder(tf.int32, [batchSize, maxSeqLength])
labels = tf.placeholder(tf.int32, [batchSize])
data = tf.nn.embedding_lookup(wordVectors, input_data)
data = tf.reshape(data, [batchSize, maxSeqLength, numDimensions, 1])
train_logits = CNN_model.inference1(data, batchSize, N_CLASSES)
train_loss = CNN_model.losses(train_logits, labels)
train_acc = CNN_model.evaluation(train_logits, labels)
all_accuracy = 0
with tf.Session() as sess:
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
saver.restore(sess, './models/pretrained_CNN.ckpt-20000')
for i in range(2000):
train_batch, train_label_batch = input_text_data.get_allTest(i)
tra_loss, tra_acc = sess.run([train_loss, train_acc], {
input_data: train_batch,
labels: train_label_batch
})
all_accuracy = all_accuracy + tra_acc
print('All_accuracy:')
print(all_accuracy / 2000)
def training():
input_data = tf.placeholder(tf.int32, [batchSize, maxSeqLength])
labels = tf.placeholder(tf.int32, [batchSize])
data = tf.nn.embedding_lookup(wordVectors, input_data)
data = tf.reshape(data, [batchSize, maxSeqLength, numDimensions, 1])
train_logits = CNN_model.inference1(data, batchSize, N_CLASSES)
train_loss = CNN_model.losses(train_logits, labels)
train_op = CNN_model.trainning(train_loss, learning_rate)
train_acc = CNN_model.evaluation(train_logits, labels)
sess = tf.InteractiveSession()
tf.summary.scalar('Loss', train_loss)
tf.summary.scalar('Accuracy', train_acc)
merged = tf.summary.merge_all()
writer = tf.summary.FileWriter(logs_train_dir, sess.graph)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=1)
config = tf.ConfigProto(allow_soft_placement=True, gpu_options=gpu_options)
with tf.Session(config=config) as sess:
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
for i in range(MAX_STEP):
train_batch, train_label_batch = input_text_data.getTrainBatch()
_, tra_loss, tra_acc = sess.run([train_op, train_loss, train_acc],
{
input_data: train_batch,
labels: train_label_batch
})
# 汇总到Tensorboard
if i % 1000 == 0:
print("Step %d, train loss = %.2f, train accuracy = %.2f%%" %
(i, tra_loss, tra_acc))
test_batch, test_label_batch = input_text_data.getTestBatch()
summary = sess.run(merged, {
input_data: test_batch,
labels: test_label_batch
})
writer.add_summary(summary, i)
test_loss, test_acc = sess.run([train_loss, train_acc], {
input_data: test_batch,
labels: test_label_batch
})
print(
"*************, test loss = %.2f, test accuracy = %.2f%%" %
(test_loss, test_acc))
if i % 2000 == 0 or (i + 1) == MAX_STEP:
save_path = saver.save(sess,
"./models/pretrained_CNN.ckpt",
global_step=i)
print("saved to %s" % save_path)
writer.close()
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 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 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()