def run_training():
# you need to change the directories to yours.
s_train_dir = '/home/hrz/projects/tensorflow/emotion/ck+/CK+YuanTu'
T_train_dir = '/home/hrz/projects/tensorflow/emotion/ck+/CK+X_mid'
logs_train_dir = '/home/hrz/projects/tensorflow/emotion/ck+'
s_train, s_train_label = input_data.get_files(s_train_dir)
s_train_batch, s_train_label_batch = input_data.get_batch(
s_train, s_train_label, IMG_W, IMG_H, BATCH_SIZE, CAPACITY)
T_train, T_train_label = input_data.get_files(T_train_dir)
T_train_batch, T_train_label_batch = input_data.get_batch(
T_train, T_train_label, IMG_W, IMG_H, BATCH_SIZE, CAPACITY)
train_logits = model.inference(s_train_batch, T_train_batch, BATCH_SIZE,
N_CLASSES)
train_loss = model.losses(train_logits, s_train_label_batch)
train_op = model.trainning(train_loss, learning_rate)
train__acc = model.evaluation(train_logits, s_train_label_batch)
summary_op = tf.summary.merge_all() #汇总操作
sess = tf.Session() #定义sess
train_writer = tf.summary.FileWriter(logs_train_dir, sess.graph) #
saver = tf.train.Saver() #保存操作
sess.run(tf.global_variables_initializer()) #初始化所有变量
coord = tf.train.Coordinator() #设置多线程协调器
threads = tf.train.start_queue_runners(
sess=sess, coord=coord) #开始Queue Runners(队列运行器)
#开始训练过程
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))
#运行汇总操作，写入汇总
summary_str = sess.run(summary_op)
train_writer.add_summary(summary_str, step)
if step % 800 == 0 or (step + 1) == MAX_STEP:
#保存当前模型和权重到 logs_train_dir,global_step为当前迭代次数
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 run_training(N_CLASSES, IMG_W, IMG_H, BATCH_SIZE, MAX_STEP, CAPACITY,
model1_data, learning_rate, total):
train, train_label, randomList = input_data.get_files(model1_data, total)
train_batch, train_label_batch = input_data.get_batch(
train, train_label, IMG_W, IMG_H, BATCH_SIZE, CAPACITY)
train_logits = model.inference(train_batch, BATCH_SIZE, N_CLASSES)
return train_logits, train_batch, train_label_batch, randomList
def run_training():
traindir = 'data/train/'
logs_train_dir = 'logs/train/'
train_image, train_label = input_data.get_files(traindir)
train_batch, train_label_batch = input_data.get_batch(
train_image, 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.training(train_loss, learning_rate)
train_acc = model.evaluation(train_logits, train_label_batch)
sess = tf.Session()
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 range(MAX_STEP):
if coord.should_stop():
break
_, tra_loss, tra_acc = sess.run([train_op, train_loss, train_acc])
if step % 20 == 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_one_image():
train_dir = '/home/xiaoyi/data/dogs_vs_cats/data/train/'
train, train_label = input_data.get_files(train_dir)
image_array = get_one_image(train)
with tf.Graph().as_default():
BATCH_SIZE = 1
N_CLASSES = 2
image = tf.cast(image_array, tf.float32)
image = tf.reshape(image, [1, 208, 208, 3])
logit = model.inference(image, BATCH_SIZE, N_CLASSES)
logit = tf.nn.softmax(logit)
x = tf.placeholder(tf.float32, shape=[208, 208, 3])
logs_train_dir = '/home/xiaoyi/data/dogs_vs_cats/logs/train/'
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 seccess,globol_step is %s' % global_step)
else:
print('No checkpoint file found')
prediction = sess.run(logit, feed_dict={x: image_array})
print(prediction)
max_index = np.argmax(prediction)
if max_index == 0:
print('This is a cat with possibility %.6f' % prediction[:, 0])
else:
print('This is a dog with possibility %.6f' % prediction[:, 1])
def run_training():
# 数据集
train_dir = r'D:\Python\mnist\test_my\big_0_1_4/' # My dir--20170727-csq
# logs_train_dir 存放训练模型的过程的数据，在tensorboard 中查看
logs_train_dir = r'D:\PyCharm_code\Ai\Tensorflow_mooc_note\6\MinstNew\logs\train/'
# 获取图片和标签集
train, train_label = input_data.get_files(train_dir)
# 生成批次
train_batch, train_label_batch = input_data.get_batch(train,
train_label,
IMG_W,
IMG_H,
BATCH_SIZE,
CAPACITY)
# 进入模型
train_logits = model.inference(train_batch, BATCH_SIZE, N_CLASSES)
# 获取 loss
train_loss = model.losses(train_logits, train_label_batch)
# 训练
train_op = model.trainning(train_loss, learning_rate)
# 获取准确率
train__acc = model.evaluation(train_logits, train_label_batch)
# 合并 summary
summary_op = tf.summary.merge_all()
sess = tf.Session()
# 保存summary
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):
ckpt = tf.train.get_checkpoint_state(logs_train_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
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:
# 每隔2000步保存一下模型，模型保存在 checkpoint_path 中
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_one_image():
#Test one image against the saved models and parameters
train_dir = 'E:/data/17_DEG/'
train, train_label = input_data.get_files(train_dir)
n = len(train)
ind = np.random.randint(0, n)
img_dir = train[ind]
image = Image.open(img_dir)
image = image.resize([128, 128])
#image = tf.random_crop(image, [96, 96, 1])# randomly crop the image size to 96 x 96
image = tf.image.random_flip_left_right(image)
#image = tf.image.random_brightness(image, max_delta=63)
image = tf.image.random_contrast(image, lower=0.2, upper=1.8)
plt.imshow(image)
image1 = np.array(image)
with tf.Graph().as_default():
BATCH_SIZE = 1
N_CLASSES = 3
image = tf.cast(image1, tf.float32)
image = tf.image.per_image_standardization(image)
image = tf.reshape(image, [1, 96, 96, 1])
logit = model.inference(image, BATCH_SIZE, N_CLASSES)
logit = tf.nn.softmax(logit)
x = tf.placeholder(tf.float32, shape=[96, 96, 1])
logs_train_dir = 'E:/data/logs/train/'
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')
prediction = sess.run(logit, feed_dict={x: image1})
max_index = np.argmax(prediction)
if max_index == 0:
print('This is BMP2 with possibility %.6f' % prediction[:, 0])
if max_index == 1:
print('This is BTR70 with possibility %.6f' % prediction[:, 1])
if max_index == 2:
print('This is T72 with possibility %.6f' % prediction[:, 2])
def evaluate_one_image():
'''Test one image against the saved models and parameters
'''
# you need to change the directories to yours.
train_dir = './train/'
train, train_label = input_data.get_files(train_dir)
image_array = get_one_image(train)
with tf.Graph().as_default():
BATCH_SIZE = 1
N_CLASSES = 5
image = tf.cast(image_array, tf.float32)
image = tf.image.per_image_standardization(image)
image = tf.reshape(image, [1, 208, 208, 3])
logit = model.inference(image, BATCH_SIZE, N_CLASSES)
logit = tf.nn.softmax(logit)
x = tf.placeholder(tf.float32, shape=[208, 208, 3])
# you need to change the directories to yours.
logs_train_dir = './train_logs/'
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')
prediction = sess.run(logit, feed_dict={x: image_array})
print(prediction)
max_index = np.argmax(prediction)
if max_index == 0:
print('This is a daisy with possibility %.6f' %
prediction[:, 0])
elif max_index == 1:
print('This is a roses with possibility %.6f' %
prediction[:, 1])
elif max_index == 2:
print('This is a sunflowers with possibility %.6f' %
prediction[:, 2])
elif max_index == 3:
print('This is a dandelion with possibility %.6f' %
prediction[:, 3])
else:
print('This is a tuplits with possibility %.6f' %
prediction[:, 4])
def evaluate_random_image():
'''Test one image against the saved models and parameters
'''
# you need to change the directories to yours.
train_dir = 'data/train/'
train, train_label = input_data.get_files(train_dir)
random_img = get_one_random_image(train)
evaluate_image(random_img)
def run_training():
'''
'''
'''
tf.train.Coordinator和tf.train.start_queue_runners貌似都要在sess.run之前使用,不然会无法运行
try:这些语法貌似是模板,直接使用就好
'''
# you need to change the directories to yours.
train_dir = '/home/kevin/tensorflow/cats_vs_dogs/data/train/'
logs_train_dir = '/home/kevin/tensorflow/cats_vs_dogs/logs/train/'
train, train_label = input_data.get_files(train_dir)
train_batch, train_label_batch = input_data.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() #和下面的queue_runners配合使用,发生错误可以正确关闭线程
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:
# When done, ask the threads to stop.
coord.request_stop()
# Wait for threads to finish.
coord.join(threads)
sess.close()
def run_training():
# dataset
train_dir = '/Users/xcliang/PycharmProjects/cats_vs_dogs/data/train/' # My dir--20170727-csq
# logs_train_dir store the data of the process of training model, view in tensorbpard
logs_train_dir = '/Users/xcliang/PycharmProjects/cats_vs_dogs/data/saveNet'
# Get images and tag sets
train, train_label = input_data.get_files(train_dir)
# Generate batch
train_batch, train_label_batch = input_data.get_batch(train,
train_label,
IMG_W,
IMG_H,
BATCH_SIZE,
CAPACITY)
# Entering the model
train_logits = model.inference(train_batch, BATCH_SIZE, N_CLASSES)
# Get loss
train_loss = model.losses(train_logits, train_label_batch)
# train
train_op = model.trainning(train_loss, learning_rate)
# Get accuracy
train__acc = model.evaluation(train_logits, train_label_batch)
# merge summary
summary_op = tf.summary.merge_all()
sess = tf.Session()
# save summary
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:
# Save the model every 2000 steps and save the model in checkpoint_path
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 run_evaluating():
eval_data, eval_label = input_data.get_files(FLAGS.eval_dir)
eval_batch, eval_label_batch = input_data.get_batch(
eval_data, eval_label, FLAGS.height, FLAGS.width, FLAGS.batch_size,
FLAGS.capacity)
keep_prob = tf.placeholder(tf.float32)
hypothesis, cross_entropy, eval_step = model.make_network(
eval_batch, eval_label_batch, keep_prob)
cost_sum = tf.summary.scalar("cost_eval", cross_entropy)
eval_accuracy = tf.nn.in_top_k(hypothesis, eval_label_batch, 1)
eval_acc = model.evaluation(hypothesis, eval_label_batch)
saver = tf.train.Saver()
print('Start Evaluation......')
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
total_sample_count = FLAGS.eval_steps * FLAGS.batch_size
true_count = 0
writer = tf.summary.FileWriter(FLAGS.log_dir)
writer.add_graph(sess.graph) # Show the graph
merge_sum = tf.summary.merge_all()
saver.restore(sess, './CNN_Homework/logs/model.ckpt-36000')
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
for step in np.arange(FLAGS.eval_steps + 1):
_, summary, eval_loss = sess.run(
[eval_step, merge_sum, cross_entropy],
feed_dict={keep_prob: 1.0})
predictions, accuracy = sess.run([eval_accuracy, eval_acc],
feed_dict={keep_prob: 1.0})
writer.add_summary(summary, global_step=step)
true_count = true_count + np.sum(predictions)
if step % 10 == 0:
print('step : %d, loss : %f, eval_accuracy : %f' %
(step, eval_loss, accuracy * 100))
coord.request_stop()
coord.join(threads)
print('precision : %f' % (true_count / total_sample_count))
sess.close()
def run_training():
# you need to change the directories to yours.
train_dir = 'D:/tensorflow/practicePlus/ResNet/train/'
# val_dir = 'D:/tensorflow/practicePlus/cats_vs_dogs/test'
logs_train_dir = 'D:/tensorflow/practicePlus/ResNet/save/'
train, train_label = input_data.get_files(train_dir)
# val, val_label = input_data.get_files(val_dir)
train_batch, train_label_batch = input_data.get_batch(train, train_label, IMG_W, IMG_H, BATCH_SIZE, CAPACITY)
# val_batch, val_label_batch = input_data.get_batch(val, val_label,IMG_W,IMG_H,BATCH_SIZE,CAPACITY)
# train
train_logits = model_resnet50.inference(train_batch, BATCH_SIZE, N_CLASSES)
train_loss = model_resnet50.losses(train_logits, train_label_batch)
train_op = model_resnet50.trainning(train_loss, learning_rate)
train_acc = model_resnet50.evaluation(train_logits, train_label_batch)
# validation
# test_logits = model.inference(val_batch,BATCH_SIZE,N_CLASSES)
# test_loss = model.losses(test_logits, val_label_batch)
# test_acc = model.evaluation(test_logits, val_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)
# batch trainning
try:
# one step one batch
for step in np.arange(MAX_STEP):
if coord.should_stop():
break
_, tra_loss, tra_acc = sess.run([train_op, train_loss, train_acc])
# print loss and acc each 10 step, record log and write at same time
if step % 10 == 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)
# save modle each 500 steps
if ((step == 500) 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()
def run_training():
#train set path
train_dir = '/raidHDD/experimentData/Dev/Knife/hackthon/upup7/'
#output model path
logs_train_dir = '/raidHDD/experimentData/Dev/Knife/hackthon/modelX'
if removeLogFIle:
if os.path.exists(logs_train_dir):
for logFile in os.listdir(logs_train_dir):
os.remove("{0}/{1}".format(logs_train_dir, logFile))
print("Delete Log file success...")
train, train_label = input_data.get_files(train_dir)
train_batch, train_label_batch = input_data.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()
config.gpu_options.per_process_gpu_memory_fraction = 0.8 #0.8 =GPU_memory usage
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())
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)
train_writer.flush()
#only save end model
if (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 main(_):
# train raw_data and label
train_data, train_label = input_data.get_files(WORK_DIRECTORY)
train_batch, train_label_batch = input_data.next_batch(
train_data, train_label)
# define train op
train_logits = model.inference(train_batch, N_LABELS, BATCH_SIZE)
train_loss = model.losses(train_logits, train_label_batch)
train_op = model.optimization(train_loss, LEARNING_RATE)
train_acc = model.evaluation(train_logits, train_label_batch)
# start logs
summary_op = tf.summary.merge_all()
# Save logs
saver = tf.train.Saver()
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# writen to logs
train_writer = tf.summary.FileWriter(LOGS_DIRECTORY, sess.graph)
# queue monitor
coord = tf.train.Coordinator()
# threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# train
try:
for step in np.arange(MAX_STEP):
if coord.should_stop():
break
# start op node
_, loss, accuracy = sess.run([train_op, train_loss, train_acc])
# print and write to logs.
if step % 2 == 0:
print(
f"Step [{step}/{MAX_STEP}] Loss {loss} Accuracy {accuracy * 100.0:.2f}%"
)
summary_str = sess.run(summary_op)
train_writer.add_summary(summary_str, step)
if step == MAX_STEP:
# Save logs
checkpoint_path = os.path.join(LOGS_DIRECTORY, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
break
print(f"Model saved! Global step = {step}")
except tf.errors.OutOfRangeError:
print('Done training -- epoch limit reached')
finally:
coord.request_stop()
sess.close()
def run_training():
train_dir = "/home/sxy/PycharmProjects/defect2/data/train"
logs_train_dir = "/home/sxy/PycharmProjects/defect2/logs/train-4"
tf.reset_default_graph()
train, train_label = input_data.get_files(train_dir)
train_batch, train_label_batch = input_data.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)
#测试准确率
# test_dir="/home/sxy/PycharmProjects/defect2/data/test"
# test,test_label=input_data.get_files(test_dir)
# test_batch,test_label_batch=input_data.get_batch(test,
# test_label,
# IMG_W,
# IMG_H,
# BATCH_SIZE,
# CAPACITY)
# test_logits = model.inference(test_batch, BATCH_SIZE, N_CLASSES)
# train_loss = model.losses(test_logits, test_label_batch)
# train_op = model.trainning(train_loss, learning_rate)
# train_acc = model.evaluation(test_logits, test_label_batch)
summary_op = tf.merge_all_summaries()
sess = tf.Session()
train_writer = tf.train.SummaryWriter(logs_train_dir, sess.graph)
saver = tf.train.Saver()
sess.run(tf.initialize_all_variables())
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))
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 run_training():
# 调用input_data文件的get_files()函数获得image_list, label_list
train, train_label = input_data.get_files(train_dir)
# 获得image_batch, label_batch
train_batch, train_label_batch = input_data.get_batch(
train, train_label, IMG_W, IMG_H, BATCH_SIZE, CAPACITY)
# 进行前向训练，获得回归值
train_logits = model.inference(train_batch, BATCH_SIZE, N_CLASSES)
# 计算获得损失值loss
train_loss = model.losses(train_logits, train_label_batch)
# 对损失值进行优化
train_op = model.trainning(train_loss, learning_rate)
# 根据计算得到的损失值，计算出分类准确率
train__acc = model.evaluation(train_logits, train_label_batch)
# 将图形、训练过程合并在一起
summary_op = tf.summary.merge_all()
# 新建会话
sess = tf.Session()
# 将训练日志写入到logs_train_dir的文件夹内
train_writer = tf.summary.FileWriter(logs_train_dir, sess.graph)
# 保存变量
saver = tf.train.Saver()
# 执行训练过程，初始化变量
sess.run(tf.global_variables_initializer())
# 创建一个线程协调器，用来管理之后在Session中启动的所有线程
coord = tf.train.Coordinator()
# 启动入队的线程，一般情况下，系统有多少个核，就会启动多少个入队线程（入队具体使用多少个线程在tf.train.batch中定义）;
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
for step in np.arange(MAX_STEP):
# 使用 coord.should_stop()来查询是否应该终止所有线程，当文件队列（queue）中的所有文件都已经读取出列的时候，
# 会抛出一个 OutofRangeError 的异常，这时候就应该停止Sesson中的所有线程了;
if coord.should_stop():
break
_, tra_loss, tra_acc = sess.run([train_op, train_loss, train__acc])
# 每50步打印一次损失值和准确率
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)
# 每2000步保存一次训练得到的模型
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)
# 如果读取到文件队列末尾会抛出此异常
except tf.errors.OutOfRangeError:
print('Done training -- epoch limit reached')
finally:
coord.request_stop() # 使用coord.request_stop()来发出终止所有线程的命令
coord.join(threads) # coord.join(threads)把线程加入主线程，等待threads结束
def run_training():
# you need to change the directories to yours.
train_dir = './data/train/train/'
logs_train_dir = './logs/'
train, train_label = input_data.get_files(train_dir)
train_batch, train_label_batch = input_data.get_batch(
train, train_label, IMG_W, IMG_H, BATCH_SIZE, CAPACITY)
with tf.name_scope("training"):
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 % 20 == 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) #保存模型和模型参数到logs_train_dir文件夹
except tf.errors.OutOfRangeError:
print('Done training -- epoch limit reached')
finally:
coord.request_stop()
coord.join(threads)
sess.close()
def evaluate_running():
with tf.Graph().as_default():
data_dir = './data/KTH_RGB/'
model_dir = './model/KTH_RGB6000/'
train_image, train_label, val_image, val_label, n_test = input_data.get_files(
data_dir, RATIO, ret_val_num=True)
train_batch, train_label_batch = input_data.get_batch(
train_image, train_label, IMG_W, IMG_H, BATCH_SIZE, CAPACITY)
val_batch, val_label_batch = input_data.get_batch(
val_image, val_label, IMG_W, IMG_H, BATCH_SIZE, CAPACITY)
#
logits = models.AlexNet(val_batch, N_CLASSES)
top_k_op = tf.nn.in_top_k(logits, val_label_batch, 1)
saver = tf.train.Saver(tf.global_variables())
with tf.Session() as sess:
print("Reading checkpoints...")
ckpt = tf.train.get_checkpoint_state(model_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')
return
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
num_iter = int(math.ceil(n_test / BATCH_SIZE))
true_count = 0
total_sample_count = num_iter * BATCH_SIZE
step = 0
while step < num_iter and not coord.should_stop():
val_images_, val_labels_ = sess.run(
[val_batch, val_label_batch])
predictions = sess.run([top_k_op])
true_count += np.sum(predictions)
step += 1
precision = true_count / total_sample_count
print('precision = %.3f' % precision)
except Exception as e:
coord.request_stop(e)
finally:
coord.request_stop()
coord.join(threads)
def evaluate_all_image():
start_time = time.time()
'''
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 = '/Users/sherry/Documents/Study/CS 6220/HW01/HW01_20190901/01_cats_vs_dogs/data/outlier_test/'
N_CLASSES = 2
print('-------------------------')
test, test_label = input_data.get_files(test_dir)
BATCH_SIZE = len(test)
print('There are %d test images totally..' % BATCH_SIZE)
print('-------------------------')
test_batch, test_label_batch = input_data.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 = '/Users/sherry/Documents/Study/CS 6220/HW01/HW01_20190901/01_cats_vs_dogs/data/logs_dataset1/train/'
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))
print(
"------------------------ Testing time is: %s seconds -----------------------"
% (time.time() - start_time))
coord.request_stop()
coord.join(threads)
sess.close()
print("--- Testing time is: %s seconds ---" % (time.time() - start_time))
def evaluate_one_image(test1_data, i, total, N_CLASSES):
'''Test one image against the saved models and parameters
'''
train, train_label, randomList = input_data.get_files(test1_data, total)
image_array = get_one_image(train, i)
image = tf.cast(image_array, tf.float32)
image = tf.image.per_image_standardization(image)
image = tf.reshape(image, [1, 32, 32, 3])
logit = model.inference(image, 1, N_CLASSES)
return logit, image_array, train_label, randomList
def evaluate_one_image():
'''Test one image against the saved models and parameters
'''
# you need to change the directories to yours.
train_dir = 'E:/Code/Dog vs Cat/test/'
train = input_data.get_files(train_dir)
image_array = get_one_image(train)
with tf.Graph().as_default():
BATCH_SIZE = 1
N_CLASSES = 2
image = tf.cast(image_array, tf.float32)
image = tf.image.per_image_standardization(image)
image = tf.reshape(image, [1, 208, 208, 3])
x = tf.placeholder(tf.float32, shape=[1, 208, 208, 3])
logit = model.inference(x, BATCH_SIZE, N_CLASSES)
logit = tf.nn.softmax(logit)
# you need to change the directories to yours.
logs_train_dir = 'E:/Code/Dog vs Cat/log/'
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')
image_ = sess.run(image)
prediction = sess.run(logit, feed_dict={x: image_})
print(prediction)
max_index = np.argmax(prediction)
if prediction[:, max_index] > 0.7:
if max_index == 0:
print('This is a cat with possibility %.6f' %
prediction[:, 0])
else:
print('This is a dog with possibility %.6f' %
prediction[:, 1])
else:
print('input error!')
def run_training():
train_dir = './train/' # 加载数据训练
logs_train_dir = './save_model/' # 储存训练好的位置
train, train_label = input_data.get_files(train_dir)
train_batch, train_label_batch = input_data.get_batch(train,
train_label,
IMG_W,
IMG_H,
BATCH_SIZE,
CAPACITY)
train_logits = model.inference(train_batch, BATCH_SIZE, N_CLASSES,True) # forward pass
train_loss = model.losses(train_logits, train_label_batch) # 设置损失函数
train_op = model.trainning(train_loss,learning_rate=) # training
train__acc = model.evaluation(train_logits, train_label_batch) # 验证正确率
summary_op = tf.summary.merge_all() # 定义合并变量操作，一次性生成所有摘要数据
sess = tf.Session() # 初始化会话
train_writer = tf.summary.FileWriter(logs_train_dir, sess.graph) # TensorBoard的记录
saver = tf.train.Saver() # 储存模型
sess.run(tf.global_variables_initializer()) # 所有变量初始化
coord = tf.train.Coordinator() # 多线程
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
csvfile = open('csv.csv', 'w', newline='')
writer = csv.writer(csvfile)
writer.writerow(['name','label'])
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 run_training():
train_dir = "./data/TRAIN/"
logs_train_dir = "./logs/"
train, train_label = input_data.get_files(train_dir)
train_batch, train_label_batch = input_data.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 % 100 == 0:
print('Step:', step, 'train loss:', tra_loss, 'train accuracy:', tra_acc)
summary_str = sess.run(summary_op)
train_writer.add_summary(summary_str, step)
if tra_acc > 0.95 and step>6000:
checkpoint_path = os.path.join(logs_train_dir, "model")
saver.save(sess, checkpoint_path, global_step=step)
print("train success!")
print('Step:', step, 'train loss:', tra_loss, 'train accuracy:', tra_acc)
coord.request_stop()
except tf.errors.OutOfRangeError:
print("Done training -- epoch limit reached.")
finally:
coord.request_stop()
coord.join(threads)
sess.close()
def run_training():
DIR_PRE = os.getcwd() + '/'
train_dir = DIR_PRE + 'data/train/'
logs_train_dir = DIR_PRE + 'logs/train/'
os.makedirs(train_dir, exist_ok=True)
os.makedirs(logs_train_dir, exist_ok=True)
# 获取所有图片文件列表和对应的标签列表
train, train_label = input_data.get_files(train_dir)
#
train_batch, train_label_batch = input_data.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 run_training():
# you need to change the directories to yours.
# train_dir = '/home/kevin/tensorflow/hams_vs_hots/data/train/'
train_dir = 'D:/workspace/uploadPicJudge3Class/train/'
# logs_train_dir = '/home/kevin/tensorflow/hams_vs_hots/logs/train/'
logs_train_dir = 'D:/workspace/uploadPicJudge3Class/logs/'
train, train_label = input_data.get_files(train_dir)
train_batch, train_label_batch = input_data.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 % 10 == 0:
print('Step %d, train loss = %.2f, train accuracy = %.2f%%' %
(step, tra_loss, tra_acc * 100.0) + ' ' +
datetime.datetime.now().strftime('%Y-%m-%d %H_%M_%S'))
summary_str = sess.run(summary_op)
train_writer.add_summary(summary_str, step)
if step % 100 == 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 run_training():
# you need to change the directories to yours.
train_dir = '/home/kevin/tensorflow/cats_vs_dogs/data/train/'
logs_train_dir = '/home/kevin/tensorflow/cats_vs_dogs/logs/train/'
train, train_label = input_data.get_files(train_dir)
train_batch, train_label_batch = input_data.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_one_image():
#测试原始图片
train_dir = "./data/ORIGIN_TEST/"
train, train_label = input_data.get_files(train_dir)
image_array, image_dir = get_one_origin_image(train)
with tf.Graph().as_default():
BATCH_SIZE = 1
image = tf.cast(image_array, tf.float32)
image = tf.reshape(image, [1, IMG_H, IMG_W, 3])
logit = model.inference(image, BATCH_SIZE, N_CLASSES)
logit = tf.nn.softmax(logit)
x = tf.placeholder(tf.float32, shape=[IMG_H, IMG_W, 3])
logs_train_dir = "./logs/"
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('The test picture is :', image_dir)
prediction = sess.run(logit, feed_dict={x: image_array})
print(prediction)
max_index = np.argmax(prediction)
if max_index == 0:
# print("This is a eosinophil cell with possibility %.6f" % prediction[:, 0])
print("This is a eosinophil cell")
elif max_index==1:
# print("This is a lymphocyte cell with possibility %.6f" % prediction[:, 1])
print("This is a lymphocyte cell")
elif max_index==2:
# print("This is a monocyte cell with possibility %.6f" % prediction[:, 1])
print("This is a monocyte cell")
elif max_index==3:
# print("This is a neutrophil cell with possibility %.6f" % prediction[:, 1])
print("This is a neutrophil cell")
else:
print('can not recognize the cell')
cv2.waitKey(0)
cv2.destroyAllWindows()
def evaluate_one_image():
'''Test one image against the saved models and parameters
'''
# you need to change the directories to yours.
train_dir = './data/train/train/'
train, train_label = input_data.get_files(train_dir)
image_array = get_one_image(train) # 任意选择一张图片
with tf.Graph().as_default():
BATCH_SIZE = 1
N_CLASSES = 2
image = tf.cast(image_array, tf.float32)
image = tf.image.per_image_standardization(image) # 图片标准化
image = tf.reshape(image, [1, 208, 208, 3])
logit = model.inference(image, BATCH_SIZE, N_CLASSES)
logit = tf.nn.softmax(logit) # 因为最后一层没有激活函数，所在此处应该加上激活函数
x = tf.placeholder(tf.float32, shape=[208, 208,
3]) #利用placeholder方式喂给数据
# you need to change the directories to yours.
logs_train_dir = './logs'
saver = tf.train.Saver()
with tf.Session() as sess:
print("Reading checkpoints...")
ckpt = tf.train.get_checkpoint_state(logs_train_dir) # 读取模型结构和参数
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')
# 模型已经准备就绪，准备预测图片的类型
prediction = sess.run(logit, feed_dict={x: image_array})
max_index = np.argmax(prediction) # 得到两个概率，取最大的概率
if max_index == 0:
print('This is a cat with possibility %.6f' %
prediction[:, 0]) # 猫
else:
print('This is a dog with possibility %.6f' %
prediction[:, 1]) # 狗
def run_training():
train_dir = 'C://Users/Sizhe/Desktop/CatsvsDogs/data/train/'
logs_train_dir = 'C://Users/Sizhe/Desktop/CatsvsDogs/data/logs/train/'
train, train_label = input_data.get_files(train_dir)
train_batch, train_label_batch = input_data.get_batch(train,
train_label,
image_width,
image_height,
batch_size,
capacity)
train_logits = model.inference(train_batch, batch_size, n_class)
train_loss = model.losses(train_logits, train_label_batch)
train_op = model.training(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])
### step%50 when training
if step%50 == 0:
print ('Step %d, train loss = %.2f, train accuracy = %.2f%%' %(step, tra_loss, tra_acc*100.00))
summary_str = sess.run(summary_op)
train_writer.add_summary(summary_str, step)
if step%2000 == 0 or step == max_step-1:
checkpoint_path = os.path.join(logs_train_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step= step)
except tf.errors.OutOfRangeError:
print ('Training finished -- epoch limit reached')
finally:
coord.request_stop()
coord.join(threads)
sess.close()
def evaluate_one_image():
'''Test one image against the saved models and parameters
'''
# you need to change the directories to yours.
train_dir = '/userDocs/user000/workspaces/2018-06-30-tensorflowCNN/Data/catVsDog/train/'
train, train_label = input_data.get_files(train_dir)
image_array = get_one_image(train)
with tf.Graph().as_default():
BATCH_SIZE = 1
N_CLASSES = 2
image = tf.cast(image_array, tf.float32)
image = tf.image.per_image_standardization(image)
image = tf.reshape(image, [1, 208, 208, 3])
logit = model.inference(image, BATCH_SIZE, N_CLASSES)
logit = tf.nn.softmax(logit)
x = tf.placeholder(tf.float32, shape=[208, 208, 3])
# you need to change the directories to yours.
logs_train_dir = '/userDocs/user000/workspaces/2018-06-30-tensorflowCNN/Data/catVsDog/trainedModels/'
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')
prediction = sess.run(logit, feed_dict={x: image_array})
max_index = np.argmax(prediction)
if max_index == 0:
print('This is a cat with possibility %.6f' % prediction[:, 0])
else:
print('This is a dog with possibility %.6f' % prediction[:, 1])
plt.imshow(image_array)
plt.show()
def run_training():
print 'let us begin....'
train_dir = '../data/train/'
logs_train_dir = './train/'
train, train_label = input_data.get_files(train_dir)
train_batch, train_label_batch = input_data.get_batch(
train, train_label, IMG_H, IMG_W, 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, lr)
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():
print 'coord 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\n' % (
step, tra_loss, tra_acc * 100.0)
summary_str = sess.run(summary_op)
train_writer.add_summary(summary_str, step)
if step % 2500 == 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 run_training():
# you need to change the directories to yours.
s_train_dir = '/home/hrz/projects/tensorflow/emotion/ck+/CK+YuanTu'
T_train_dir = '/home/hrz/projects/tensorflow/emotion/ck+/CK+X_mid'
logs_train_dir = '/home/hrz/projects/tensorflow/emotion/ck+'
s_train, s_train_label = input_data.get_files(s_train_dir)
s_train_batch, s_train_label_batch = input_data.get_batch(s_train,
s_train_label,
IMG_W,
IMG_H,
BATCH_SIZE,
CAPACITY)
T_train, T_train_label = input_data.get_files(T_train_dir)
T_train_batch, T_train_label_batch = input_data.get_batch(T_train,
T_train_label,
IMG_W,
IMG_H,
BATCH_SIZE,
CAPACITY)
train_logits = model.inference(s_train_batch,T_train_batch, BATCH_SIZE, N_CLASSES)
train_loss = model.losses(train_logits, s_train_label_batch)
train_op = model.trainning(train_loss, learning_rate)
train__acc = model.evaluation(train_logits, s_train_label_batch)
summary_op = tf.summary.merge_all() #汇总操作
sess = tf.Session() #定义sess
train_writer = tf.summary.FileWriter(logs_train_dir, sess.graph) #
saver = tf.train.Saver() #保存操作
sess.run(tf.global_variables_initializer())#初始化所有变量
coord = tf.train.Coordinator() #设置多线程协调器
threads = tf.train.start_queue_runners(sess=sess, coord=coord) #开始Queue Runners(队列运行器)
#开始训练过程
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))
#运行汇总操作，写入汇总
summary_str = sess.run(summary_op)
train_writer.add_summary(summary_str, step)
if step % 800 == 0 or (step + 1) == MAX_STEP:
#保存当前模型和权重到 logs_train_dir,global_step为当前迭代次数
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()
import input_data
import model
import sys
N_CLASSES = 6
IMG_W = 256 # resize the image, if the input image is too large, training will be very slow.
IMG_H = 256
BATCH_SIZE = 16
CAPACITY = 100000
MAX_STEP = 10000 # with current parameters, it is suggested to use MAX_STEP>10k
learning_rate = 0.0001 # with current parameters, it is suggested to use learning rate<0.0001
s_train_dir = '/home/hrz/projects/tensorflow/emotion/ck+/CK+YuanTu'
T_train_dir = '/home/hrz/projects/tensorflow/emotion/ck+/CK+X_mid'
logs_train_dir = '/home/hrz/projects/tensorflow/emotion/ck+'
s_train, s_train_label = input_data.get_files(s_train_dir)
#print(s_train)
s_train_batch, s_train_label_batch = input_data.get_batch(s_train,
s_train_label,
IMG_W,
IMG_H,
BATCH_SIZE,
CAPACITY)
#print(s_train_label_batch)
T_train, T_train_label = input_data.get_files(T_train_dir)
T_train_batch, T_train_label_batch = input_data.get_batch(T_train,
T_train_label,
IMG_W,
IMG_H,
BATCH_SIZE,
def training():
train, train_label, val, val_label = input_data.get_files(train_dir, RATIO)
train_batch, train_label_batch = input_data.get_batch(train,
train_label,
IMG_W,
IMG_H,
BATCH_SIZE,
CAPACITY)
val_batch, val_label_batch = input_data.get_batch(val,
val_label,
IMG_W,
IMG_H,
BATCH_SIZE,
CAPACITY)
logits = model.inference(train_batch, BATCH_SIZE, N_CLASSES)
loss = model.losses(logits, train_label_batch)
train_op = model.trainning(loss, learning_rate)
acc = model.evaluation(logits, train_label_batch)
x = tf.placeholder(tf.float32, shape=[BATCH_SIZE, IMG_W, IMG_H, 3])
y_ = tf.placeholder(tf.int16, shape=[BATCH_SIZE])
with tf.Session() as sess:
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess= sess, coord=coord)
summary_op = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(train_logs_dir, sess.graph)
val_writer = tf.summary.FileWriter(val_logs_dir, sess.graph)
try:
for step in np.arange(MAX_STEP):
if coord.should_stop():
break
tra_images,tra_labels = sess.run([train_batch, train_label_batch])
_, tra_loss, tra_acc = sess.run([train_op, loss, acc],
feed_dict={x:tra_images, y_:tra_labels})
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 % 200 == 0 or (step + 1) == MAX_STEP:
val_images, val_labels = sess.run([val_batch, val_label_batch])
val_loss, val_acc = sess.run([loss, acc],
feed_dict={x:val_images, y_:val_labels})
print('** Step %d, val loss = %.2f, val accuracy = %.2f%% **' %(step, val_loss, val_acc*100.0))
summary_str = sess.run(summary_op)
val_writer.add_summary(summary_str, step)
if step % 2000 == 0 or (step + 1) == MAX_STEP:
checkpoint_path = os.path.join(train_logs_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)