def val():
num_test = 10000
data_dir = '/home/rong/something_for_deep/cifar-10-batches-bin'
train_log_dir = './logs/train'
image_batch, label_batch = input_data.read_cifar10(data_dir,
is_train=False,
batch_size=BATCH_SIZE,
shuffle=False)
vgg16 = model.VGG16()
logits = vgg16.build(image_batch, NUM_CLASSES, False)
saver = tf.train.Saver()
correct_per_batch = tools.num_correct_prediction(logits, label_batch)
with tf.Session() as sess:
print('Reading checkpoints')
ckpt = tf.train.get_checkpoint_state(train_log_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
saver.restore(sess, './logs/train/model.ckpt-8000')
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
print('\nEvaluating......')
num_step = int(math.floor(num_test / BATCH_SIZE))
num_sample = num_step * BATCH_SIZE
step = 0
total_correct = 0
while step < num_step and not coord.should_stop():
batch_correct = sess.run(correct_per_batch)
total_correct += np.sum(batch_correct)
step += 1
if step % 10 == 0:
print('Testing samples: %d' % (step * BATCH_SIZE))
print('Correct predictions: %d' % total_correct)
print('Average accuracy: %.2f%%' % (total_correct /
(step * BATCH_SIZE)))
print('Total testing samples: %d' % num_sample)
print('Total correct predictions: %d' % total_correct)
print('Average accuracy: %.2f%%' %
(100 * total_correct / num_sample))
except Exception as e:
coord.request_stop(e)
finally:
coord.request_stop()
coord.join(threads)
def tower_loss(scope):
images, labels = input_data.read_cifar10(FLAGS.data_dir, True,
FLAGS.batch_size, True)
logits = resnet.inference(images, FLAGS.num_units_per_block,
FLAGS.is_training)
_ = resnet.loss(logits, labels)
losses = tf.get_collection('losses', scope)
total_loss = tf.add_n(losses, name='total_loss')
with tf.name_scope(None) as scope:
tf.summary.scalar("total_loss", total_loss)
return total_loss
def evaluate():
with tf.Graph().as_default():
# log_dir = 'C://Users//kevin//Documents//tensorflow//VGG//logsvgg//train//'
# log_dir = 'C:/Users/kevin/Documents/tensorflow/VGG/logs/train/'
log_dir = 'C:/Users/YuanGuoliang/Desktop/3_5th/VGG_model1/logs/train/'
test_dir = './/data//cifar-10-batches-bin//'
n_test = 10000
images, labels = input_data.read_cifar10(data_dir=test_dir,
is_train=False,
batch_size=BATCH_SIZE,
shuffle=False)
logits = VGG.VGG16N(images, N_CLASSES, IS_PRETRAIN)
correct = tools.num_correct_prediction(logits, labels)
saver = tf.train.Saver(tf.global_variables())
with tf.Session() as sess:
print("Reading checkpoints...")
ckpt = tf.train.get_checkpoint_state(log_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:
print('\nEvaluating......')
num_step = int(math.floor(n_test / BATCH_SIZE))
num_sample = num_step * BATCH_SIZE
step = 0
total_correct = 0
while step < num_step and not coord.should_stop():
batch_correct = sess.run(correct)
total_correct += np.sum(batch_correct)
step += 1
print('Total testing samples: %d' % num_sample)
print('Total correct predictions: %d' % total_correct)
print('Average accuracy: %.2f%%' %
(100 * total_correct / num_sample))
except Exception as e:
coord.request_stop(e)
finally:
coord.request_stop()
coord.join(threads)
def evaluate():
with tf.Graph().as_default():
log_dir = './logs2/train/'
test_dir = '/content/data/'
n_test = 10000
test_iamge_batch, test_label_batch = input_data.read_cifar10(
test_dir, is_train=False, batch_size=BATCH_SIZE, shuffle=False)
logits = VGG.MyResNet(test_iamge_batch, N_CLASSES, IS_PRETRAIN)
correct = tools.num_correct_prediction(logits, test_label_batch)
saver = tf.train.Saver(tf.global_variables())
with tf.Session() as sess:
print('Reading checkpoint...')
ckpt = tf.train.get_checkpoint_state(log_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('Load success, global step: %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:
print('\nEvaluating...')
num_step = int(math.ceil(n_test / BATCH_SIZE))
num_example = num_step * BATCH_SIZE
step = 0
total_correct = 0
while step < num_step and not coord.should_stop():
batch_correct = sess.run(correct)
total_correct += np.sum(batch_correct)
step += 1
print("Total test examples: %d" % num_example)
print("Total correct predictions: %d" % total_correct)
print("Average accuracy: %.2f%%" %
(100 * total_correct / num_example))
except Exception as e:
coord.request_stop(e)
finally:
coord.request_stop()
coord.join(threads)
def evaluate():
with tf.Graph().as_default():
# log_dir = 'C://Users//kevin//Documents//tensorflow//VGG//logsvgg//train//'
log_dir = 'C:/Users/kevin/Documents/tensorflow/VGG/logs/train/'
test_dir = './/data//cifar-10-batches-bin//'
n_test = 10000
images, labels = input_data.read_cifar10(data_dir=test_dir,
is_train=False,
batch_size= BATCH_SIZE,
shuffle=False)
logits = VGG.VGG16N(images, N_CLASSES, IS_PRETRAIN)
correct = tools.num_correct_prediction(logits, labels)
saver = tf.train.Saver(tf.global_variables())
with tf.Session() as sess:
print("Reading checkpoints...")
ckpt = tf.train.get_checkpoint_state(log_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:
print('\nEvaluating......')
num_step = int(math.floor(n_test / BATCH_SIZE))
num_sample = num_step*BATCH_SIZE
step = 0
total_correct = 0
while step < num_step and not coord.should_stop():
batch_correct = sess.run(correct)
total_correct += np.sum(batch_correct)
step += 1
print('Total testing samples: %d' %num_sample)
print('Total correct predictions: %d' %total_correct)
print('Average accuracy: %.2f%%' %(100*total_correct/num_sample))
except Exception as e:
coord.request_stop(e)
finally:
coord.request_stop()
coord.join(threads)
def evaluate():
with tf.Graph().as_default():
log_dir = './logs/vgg16_logs/train/'
data_dir = './data/cifar-10-batches-bin/'
n_test = 10000
images, labels = input_data.read_cifar10(data_dir=data_dir,
is_train=False,
batch_size=BATCH_SIZE,
shuffle=False)
logits = VGG.VGG16N(images, N_CLASSES, IS_PRETRAIN) # shape of logits: [Batch_size, n_classes]
correct = tools.num_correct_prediction(logits, labels) # 得到准确率，类型是浮点型
saver = tf.train.Saver(tf.global_variables())
with tf.Session() as sess:
print("Reading checkpoints...")
ckpt = tf.train.get_checkpoint_state(log_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:
print('\nEvaluating......')
# math.floor()返回小于或等于一个给定数字的最大整数
num_step = int(math.floor(n_test / BATCH_SIZE)) # num_step = 312
num_sample = num_step*BATCH_SIZE # num_sample=9984
step = 0
total_correct = 0
while step < num_step and not coord.should_stop():
batch_correct = sess.run(correct) # 得到在一个batch中正确的个数
total_correct += np.sum(batch_correct) # 得到总共的正确数量
step += 1
print('Total testing samples: %d' % num_sample)
print('Total correct predictions: %d' % total_correct)
print('Average accuracy: %.2f%%' % (100*total_correct/num_sample))
except Exception as e:
coord.request_stop(e)
finally:
coord.request_stop()
coord.join(threads)
def test():
with tf.Graph().as_default():
n_test = 10000
images, labels = input_data.read_cifar10(data_dir=data_dir,
is_train=False,
batch_size=BATCH_SIZE,
shuffle=False)
logits = models.VGG16(images, N_CLASSES, IS_PRETRAIN)
correct = utils.num_correct_prediction(logits, labels)
saver = tf.train.Saver(tf.global_variables())
with tf.Session() as sess:
print("Reading checkpoints...")
ckpt = tf.train.get_checkpoint_state(train_log_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:
print('Testing......')
num_step = int(math.floor(n_test / BATCH_SIZE))
num_sample = num_step * BATCH_SIZE
step = 0
total_correct = 0
while step < num_step and not coord.should_stop():
batch_correct = sess.run(correct)
total_correct += np.sum(batch_correct)
step += 1
print('Total testing samples: %d' % num_sample)
print('Total correct predictions: %d' % total_correct)
print('Average accuracy: %.2f%%' %
(100 * total_correct / num_sample))
except Exception as e:
coord.request_stop(e)
finally:
coord.request_stop()
coord.join(threads)
def evaluate(batch_size):
with tf.Graph().as_default():
log_dir = 'F:\\DL\\mxnet_cifar10\\trian_logs'
test_img_dir = 'F:\\DL\\mxnet_cifar10\\data\\cifar-10-batches-py'
n_test = 20000
images, labels = input_data.read_cifar10(data_dir=test_img_dir,
is_train=False,
batch_size=batch_size,
shuffle=False)
logits, is_trian = resnet.resnet18(images, n_claases=10)
correct = num_correct_prediction(logits, labels)
saver = tf.train.Saver(tf.global_variables())
with tf.Session() as sess:
print("Reading checkpoints...")
ckpt = tf.train.get_checkpoint_state(log_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")
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
num_step = int(math.floor(n_test / batch_size))
num_sample = num_step * batch_size
step = 0
total_correct = 0
while step < num_step and not coord.should_stop():
batch_correct = sess.run(correct,
feed_dict={is_trian: False})
total_correct += np.sum(batch_correct)
step += 1
print('Total testing samples: %d' % num_sample)
print('Total correct predictions: %d' % total_correct)
print('Average accuracy: %.2f%%' %
(100 * total_correct / num_sample))
except Exception as e:
coord.request_stop(e)
finally:
coord.request_stop()
coord.join(threads)
def evaluate():
with tf.Graph().as_default():
test_images, test_labels = input_data.read_cifar10(
FLAGS.DATA_DIR, False, FLAGS.BATCH_SIZE, False)
logits = resnet.inference(test_images, 7, is_training=False)
init = tf.global_variables_initializer()
acc_num = num_correct_predition(logits, test_labels)
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
print('reading checkpoints...')
checkpoint = tf.train.latest_checkpoint(FLAGS.train_dir)
if checkpoint:
saver.restore(sess, checkpoint)
print("restore from the checkpoint {0}".format(checkpoint))
else:
print('No checkpoint file found')
try:
print('\nEvaluating...')
num_step = int(math.floor(FLAGS.num_test / FLAGS.BATCH_SIZE))
num_sample = num_step * FLAGS.BATCH_SIZE
step = 0
total_correct = 0
print(num_step, num_sample)
while step < num_step:
batch_correct = sess.run(acc_num)
total_correct += np.sum(batch_correct)
step += 1
print(step)
print('Total testing samples: %d' % num_sample)
print('Total correct predictions: %d' % total_correct)
print('Average accuracy: %.2f%%' %
(100 * total_correct / num_sample))
except Exception as e:
pass
finally:
coord.request_stop()
coord.join(threads)
def train():
pre_trained_weights = './/vgg16_pretrain//vgg16.npy'
data_dir = './/data//cifar-10-batches-bin//'
train_log_dir = './/logs//train//'
val_log_dir = './/logs//val//'
with tf.name_scope('input'):
tra_image_batch, tra_label_batch = input_data.read_cifar10(data_dir=data_dir,
is_train=True,
batch_size= BATCH_SIZE,
shuffle=True)
val_image_batch, val_label_batch = input_data.read_cifar10(data_dir=data_dir,
is_train=False,
batch_size= BATCH_SIZE,
shuffle=False)
logits = VGG.VGG16N(tra_image_batch, N_CLASSES, IS_PRETRAIN)
loss = tools.loss(logits, tra_label_batch)
accuracy = tools.accuracy(logits, tra_label_batch)
my_global_step = tf.Variable(0, name='global_step', trainable=False)
train_op = tools.optimize(loss, learning_rate, my_global_step)
x = tf.placeholder(tf.float32, shape=[BATCH_SIZE, IMG_W, IMG_H, 3])
y_ = tf.placeholder(tf.int16, shape=[BATCH_SIZE, N_CLASSES])
saver = tf.train.Saver(tf.global_variables())
summary_op = tf.summary.merge_all()
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
# load the parameter file, assign the parameters, skip the specific layers
tools.load_with_skip(pre_trained_weights, sess, ['fc6','fc7','fc8'])
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
tra_summary_writer = tf.summary.FileWriter(train_log_dir, sess.graph)
val_summary_writer = tf.summary.FileWriter(val_log_dir, sess.graph)
try:
for step in np.arange(MAX_STEP):
if coord.should_stop():
break
tra_images,tra_labels = sess.run([tra_image_batch, tra_label_batch])
_, tra_loss, tra_acc = sess.run([train_op, loss, accuracy],
feed_dict={x:tra_images, y_:tra_labels})
if step % 50 == 0 or (step + 1) == MAX_STEP:
print ('Step: %d, loss: %.4f, accuracy: %.4f%%' % (step, tra_loss, tra_acc))
summary_str = sess.run(summary_op)
tra_summary_writer.add_summary(summary_str, step)
if step % 200 == 0 or (step + 1) == MAX_STEP:
val_images, val_labels = sess.run([val_image_batch, val_label_batch])
val_loss, val_acc = sess.run([loss, accuracy],
feed_dict={x:val_images,y_:val_labels})
print('** Step %d, val loss = %.2f, val accuracy = %.2f%% **' %(step, val_loss, val_acc))
summary_str = sess.run(summary_op)
val_summary_writer.add_summary(summary_str, step)
if step % 2000 == 0 or (step + 1) == MAX_STEP:
checkpoint_path = os.path.join(train_log_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 train():
#with tf.device('/device:GPU:0'):
with tf.Graph().as_default():
# log_dir = 'C://Users//kevin//Documents//tensorflow//VGG//logsvgg//train//'
log_dir = './vgg/logs/train/'
#input where you put your binary cifar10 image
Data_dir = '/home/tony/Desktop/Datasets/cifar_10_batches_binary_version/'
train_log_dir = './devise_logs/train/'
val_log_dir = './devise_logs/val/'
#n_test the number of image data
n_test = 10000
with tf.name_scope('input'):
tra_image_batch, tra_label_batch = input_data.read_cifar10(
data_dir=Data_dir,
is_train=True,
batch_size=BATCH_SIZE,
shuffle=True)
val_image_batch, val_label_batch = input_data.read_cifar10(
data_dir=Data_dir,
is_train=False,
batch_size=BATCH_SIZE,
shuffle=False)
#images
x = tf.placeholder(tf.float32,
shape=[BATCH_SIZE, IMG_W, IMG_H, 3],
name='input_x')
#labels
y_ = tf.placeholder(tf.int16,
shape=[BATCH_SIZE, N_CLASSES],
name='input_y_')
logits = VGG.VGG16N(x, N_CLASSES, is_pretrain=False)
saver = tf.train.Saver(tf.global_variables())
id_array_tensor = tf.placeholder(tf.int32, [N_CLASSES])
softmax_accuracy = tools.accuracy(logits, y_)
vgg_predict = tf.argmax(logits, -1)
vgg_predict_id = tf.gather_nd(id_array_tensor, vgg_predict)
get_label_string_tensor = tf.placeholder(
tf.float32, shape=[None, word_vector_dim],
name="label_string") ##word_vector_dim
tmp = tf.matmul(tf.cast(y_, tf.float32), get_label_string_tensor)
#print(tmp.shape)
initializer = tf.contrib.layers.variance_scaling_initializer()
fc7 = tf.get_default_graph().get_tensor_by_name("VGG16/fc7/Relu:0")
fc8 = tf.layers.dense(inputs=fc7,
units=1024,
kernel_initializer=initializer,
name="combination_hidden1")
image_feature_output = tf.layers.dense(inputs=fc8,
units=word_vector_dim,
kernel_initializer=initializer,
name="combination_hidden2")
#devise_loss
tmparray_tensor = tf.placeholder(tf.float32,
shape=[None, word_vector_dim],
name='tmparray')
margin = 0.1
#tMV here means that max (tJ *M* V - tLabel *M *V ,0 ) in essay
#tmparray_tensor mearns tJ and tmp means tmp
tMV = tf.nn.relu(margin + tf.matmul(
(tmparray_tensor -
tmp), tf.transpose(tf.cast(image_feature_output, tf.float32))))
hinge_loss = tf.reduce_mean(tf.reduce_sum(tMV, 0), name='hinge_loss')
train_step1 = tf.train.AdamOptimizer(
0.0001, name="optimizer").minimize(hinge_loss)
#tMV here means that tJ *M* V in essay
tMV_ = tf.matmul(
tmparray_tensor,
tf.transpose(tf.cast(image_feature_output, tf.float32)))
#accuracy
predict_label = tf.argmax(tMV_, 0)
predict_label = tf.cast(predict_label, tf.int32)
predict_label = tf.reshape(predict_label, [-1, 1],
name='predict_label_text')
#id_array_tensor = tf.placeholder(tf.int32 ,[N_CLASSES])
select_id = tf.cast(tf.argmax(input=y_, axis=-1), tf.int32)
select_id = tf.reshape(select_id, [1])
y_label = tf.gather_nd(id_array_tensor, select_id)
y_label = tf.reshape(y_label, [-1, 1], name='true_label_text')
#y_label = tf.argmax(tf.matmul(tmparray_tensor , tf.transpose(tmp)), 0) #(2000,word_vector_dim)*(word_vector_dim,1)
#y_label = tf.reshape(y_label,[-1,1])
print(y_label.shape)
print(predict_label.shape)
acc, acc_op = tf.metrics.accuracy(labels=y_label,
predictions=predict_label,
weights=None,
metrics_collections=None,
updates_collections=None,
name="acc")
summary_op = tf.summary.merge_all()
saver2 = tf.train.Saver(tf.global_variables())
with tf.Session() as sess:
tra_summary_writer = tf.summary.FileWriter(train_log_dir,
sess.graph)
val_summary_writer = tf.summary.FileWriter(val_log_dir, sess.graph)
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
print("Reading checkpoints...")
ckpt = tf.train.get_checkpoint_state(log_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)
saver.restore(
sess,
"/home/tony/Desktop/DeVise/vgg/logs/train/model.ckpt-14999"
)
print('Loading success, global_step is %s' % global_step)
else:
print('No checkpoint file found')
return
##---------------------------------------------------------------Training-------------------------------------------------------------------------------------------------
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
print('\Triaining....')
try:
vgg_total_correct = 0
for step in tqdm(range(MAX_STEP)):
if coord.should_stop():
break
#print("step %d"%step)
tra_images, tra_labels = sess.run(
[tra_image_batch, tra_label_batch])
loss, _, accuracy_, acc_operator, predict_label_, y_label_, summary_str, vgg_correct, vgg_predict_id_ = sess.run(
[
hinge_loss, train_step1, acc, acc_op,
predict_label, y_label, summary_op,
softmax_accuracy, vgg_predict_id
],
feed_dict={
get_label_string_tensor: label_string_vector,
tmparray_tensor: tmparray,
id_array_tensor: id_array,
x: tra_images,
y_: tra_labels
})
#print(vgg_correct)
if vgg_correct > 50:
vgg_total_correct = vgg_total_correct + 1
if step % 100 == 0:
print("step %d" % step)
print('%d / %d steps' % (step, MAX_STEP), 'loss = ',
loss, ' acc = ', accuracy_, '\n\n')
print('vgg predict acc ',
vgg_total_correct * 1.0 / (step + 1),
' ---->vgg predict ',
keys[int(vgg_predict_id_)])
print(' devise predict_label',
predict_label_, ' ---->DeVise predict ',
keys[int(predict_label_)])
print(' y_label ', y_label_,
' ---->ground truth is', keys[int(y_label_)],
'\n\n-------\n\n')
if step % 2000 == 0 or (step + 1) == MAX_STEP:
checkpoint_path = os.path.join(train_log_dir,
'model.ckpt')
saver2.save(sess, checkpoint_path, global_step=step)
except tf.errors.OutOfRangeError:
print('Done training -- epoch limit reached')
finally:
coord.request_stop()
coord.join(threads)
print("end training\n\n\n------------------------------------------\n")
##-----------------------------------------------------------------Testing---------------------------------------
with tf.Session() as sess:
print('----Testing----')
#input num of data
print("Reading checkpoints...")
ckpt = tf.train.get_checkpoint_state(train_log_dir)
print(ckpt)
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(global_step)
print(ckpt.model_checkpoint_path)
saver2.restore(sess, ckpt.model_checkpoint_path)
print('Loading success, global_step is %s' % global_step)
else:
print('No checkpoint file found')
return
sess.run(tf.local_variables_initializer())
num_of_test = MAX_STEP
get_acc = -1
try:
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
for step in tqdm(range(MAX_STEP)):
if coord.should_stop():
break
#print("step %d"%step)
val_images, val_labels = sess.run(
[val_image_batch, val_label_batch])
loss, accuracy_, acc_operator, predict_label_, y_label_ = sess.run(
[hinge_loss, acc, acc_op, predict_label, y_label],
feed_dict={
get_label_string_tensor: label_string_vector,
tmparray_tensor: tmparray,
id_array_tensor: id_array,
x: val_images,
y_: val_labels
})
#print('%d / %d steps'%(step,MAX_STEP),'loss = ',loss,' acc = ',accuracy_,'\n\n')
#print ('predict_label',predict_label_,' ----> I predict ',keys[int(predict_label_)])
#print ('y_label ',y_label_, ' ----> true ans',keys[int(y_label_)],'\n\n-------\n\n')
get_acc = accuracy_
except tf.errors.OutOfRangeError:
print('Done test -- epoch limit reached')
finally:
print('test acc = ', get_acc)
coord.request_stop()
coord.join(threads)
def train():
with tf.name_scope('inputs'):
train_images, train_labels = input_data.read_cifar10(
FLAGS.data_dir, True, FLAGS.batch_size, True)
test_images, test_labels = input_data.read_cifar10(
FLAGS.data_dir, False, FLAGS.batch_size, False)
xs = tf.placeholder(dtype=tf.float32, shape=(FLAGS.batch_size, 32, 32, 3))
ys = tf.placeholder(dtype=tf.int32, shape=(FLAGS.batch_size, 10))
global_step = tf.Variable(0, trainable=False)
lerning_rate = tf.train.exponential_decay(FLAGS.lr,
global_step,
32000,
0.1,
staircase=False)
tf.summary.scalar('lerning_rate', lerning_rate)
logits = resnet.inference(xs, FLAGS.num_units_per_block, FLAGS.is_training)
loss = resnet.loss(logits, ys)
tf.summary.scalar('loss', loss)
opt = tf.train.MomentumOptimizer(lerning_rate, 0.9)
update_op = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_op):
train = opt.minimize(loss, global_step=global_step)
acc_op = resnet.accurracy(logits, ys)
tf.summary.scalar('accuracy', acc_op)
err_op = resnet.error(logits, ys)
tf.summary.scalar('error', err_op)
summary_op = tf.summary.merge_all()
saver = tf.train.Saver(tf.all_variables())
init = tf.global_variables_initializer()
coord = tf.train.Coordinator()
with tf.Session() as sess:
sess.run(init)
train_summary_writer = tf.summary.FileWriter(FLAGS.train_dir,
sess.graph)
test_summary_writer = tf.summary.FileWriter(FLAGS.test_dir, sess.graph)
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
start_step = 0
checkpoint = tf.train.latest_checkpoint(FLAGS.train_dir)
if checkpoint:
saver.restore(sess, checkpoint)
print("restore from the checkpoint {0}".format(checkpoint))
start_step += int(checkpoint.split('-')[-1])
print("start training...")
try:
for step in range(start_step, FLAGS.max_steps):
if coord.should_stop():
break
tra_images_batch, tra_labels_batch = sess.run(
[train_images, train_labels])
tes_images_batch, tes_labels_batch = sess.run(
[test_images, test_labels])
_ = sess.run(train,
feed_dict={
xs: tra_images_batch,
ys: tra_labels_batch
})
if step % 50 == 0 or (step + 1) == FLAGS.max_steps:
tra_los, tra_acc = sess.run([loss, acc_op],
feed_dict={
xs: tra_images_batch,
ys: tra_labels_batch
})
print('Step: %d, loss: %.6f, accuracy: %.4f' %
(step, tra_los, tra_acc))
if step % 200 == 0 or (step + 1) == FLAGS.max_steps:
tes_los, tes_acc = sess.run([loss, acc_op],
feed_dict={
xs: tes_images_batch,
ys: tes_labels_batch
})
print(
'***test_loss***Step: %d, loss: %.6f, accuracy: %.4f' %
(step, tes_los, tes_acc))
if step % 300 == 0 or (step + 1) == FLAGS.max_steps:
summary_str1 = sess.run(summary_op,
feed_dict={
xs: tra_images_batch,
ys: tra_labels_batch
})
summary_str2 = sess.run(summary_op,
feed_dict={
xs: tes_images_batch,
ys: tes_labels_batch
})
train_summary_writer.add_summary(summary_str1, step)
test_summary_writer.add_summary(summary_str2, step)
if step % 2000 == 0 or (step + 1) == FLAGS.max_steps:
checkpoint_path = os.path.join(FLAGS.train_dir,
'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
except tf.errors.OutOfRangeError:
print('Done training -- epoch limit reached')
coord.request_stop()
coord.join()
finally:
coord.request_stop()
coord.join(threads)
def train(lr, batch_size, max_step, n_classes):
train_log_dir = 'F:\\DL\\mxnet_cifar10\\trian_logs'
data_dir = 'F:\\DL\\mxnet_cifar10\\data\\cifar-10-batches-py'
with tf.name_scope('input'):
train_img_batch, train_label_batch = input_data.read_cifar10(
data_dir, is_train=True, batch_size=batch_size, shuffle=True)
val_img_batch, val_label_batch = input_data.read_cifar10(
data_dir, is_train=False, batch_size=batch_size, shuffle=False)
features = tf.placeholder(tf.float32, [batch_size, 32, 32, 3])
labels = tf.placeholder(tf.int64, [batch_size, 10])
logits, is_train = resnet.resnet18(features, n_classes)
cross_entropy = loss(logits, labels)
acc = accuracy(logits, labels)
with tf.name_scope('adam_optimizer'):
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_step = tf.train.AdamOptimizer(lr).minimize(cross_entropy)
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
try:
for step in range(max_step):
if coord.should_stop():
break
tra_img, tra_label = sess.run(
[train_img_batch, train_label_batch])
_, tra_loss, tra_acc = sess.run(
[train_step, cross_entropy, acc],
feed_dict={
features: tra_img,
labels: tra_label,
is_train: True
})
if step % 50 == 0 or (step + 1) == max_step:
print('Step: %d, loss: %.4f, accuracy: %.4f%%' %
(step, tra_loss, tra_acc))
if step % 200 == 0 or (step + 1) == max_step:
val_img, val_label = sess.run(
[val_img_batch, val_label_batch])
val_loss, val_acc = sess.run([cross_entropy, acc],
feed_dict={
features: val_img,
labels: val_label,
is_train: False
})
print(
'** Step %d, val loss = %.2f, val accuracy = %.2f%% **'
% (step, val_loss, val_acc))
if step % 2000 == 0 or (step + 1) == max_step:
checkpoint_path = os.path.join(train_log_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)
def load_mnist(dset, n_labeled=-1, data_root=os.path.join('..', 'data')):
data_path = os.path.join(data_root, dset)
if dset=='digits': return input_data.read_mnist(data_path, one_hot=True, SOURCE_URL=input_data.SOURCE_DIGITS, n_labeled=n_labeled)
if dset=='fashion': return input_data.read_mnist(data_path, one_hot=True, SOURCE_URL=input_data.SOURCE_FASHION, n_labeled=n_labeled)
if dset=='fashion_2d': return input_data.read_mnist(os.path.join(data_root, 'fashion'), one_hot=True, SOURCE_URL=input_data.SOURCE_FASHION, n_labeled=n_labeled, binary_zero=[0,2,3,4,6])
if dset=='cifar10': return input_data.read_cifar10(data_path)
def evaluate1():
with tf.Graph().as_default():
log_dir = './logs/train_st2/' # 'C:/3_5th/VGG_model/logs/train/' #训练日志，即训练参数
# test_dir = './/cifar10_data//cifar-10-batches-bin//'
test_dir = './data'
n_test = 3000
input_images, input_labels, input_labels1 = input_data.read_cifar10(
data_dir=test_dir,
is_train=True,
batch_size=BATCH_SIZE,
shuffle=True)
# mean_data = np.mean(mnist.train.images, axis=0)
logits, features = VGG.VGG16N(input_images, N_CLASSES, IS_PRETRAIN)
correct = tools.num_correct_prediction(logits, input_labels)
saver = tf.train.Saver(tf.global_variables())
with tf.Session() as sess:
print("Reading checkpoints...")
ckpt = tf.train.get_checkpoint_state(log_dir)
if ckpt and ckpt.model_checkpoint_path:
print("找到文件啦")
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("没有找到文件")
print('No checkpoint file found')
return
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
print('\nEvaluating......')
# j=0
d = []
tt = np.array([])
for step in np.arange(1):
if coord.should_stop():
break
feat = np.array([])
images, labels = sess.run([input_images, input_labels])
mean_data = np.mean(images, axis=0)
# label=[]
# np.concatenate((array,q),axis=0)
b = np.transpose(np.nonzero(labels))[:, 1]
# d = b
d = np.concatenate((d, b), axis=0)
# labels=labels.tolist()
# print(d)
if step == 0:
tt = sess.run(
features,
feed_dict={input_images: images - mean_data})
else:
feat = sess.run(
features,
feed_dict={input_images: images - mean_data})
if step != 0:
tt = np.concatenate([tt, feat])
fig = plt.figure(figsize=(16, 9))
# fig = plt.figure()
# ax = Axes3D(fig)
#aa = TSNE(n_components=2).fit_transform(tt)
pca = PCA(n_components=2)
pca.fit(tt)
aa = pca.transform(tt)
#io.savemat('zongtnse.mat', {'matrix': aa})
#lda = LinearDiscriminantAnalysis(n_components=2)
#lda.fit(tt, d)
#aa = lda.transform(tt)
np.save('save_pca', aa)
#aa = TSNE(n_components=2).fit_transform(tt)
#print(aa[d==0,0].flatten())
#np.save('vgg-9', aa)
# ax.scatter(aa[:,0],aa[:,1],aa[:,2],c=labels1)
# f = plt.figure()
c = [
'#ff0000', '#ffff00', '#00ff00', '#00ffff', '#0000ff',
'#ff00ff', '#990000', '#999900', '#009900'
]
for i in range(9):
plt.plot((aa[d == i, 0].flatten()) / 100.0,
(aa[d == i, 1].flatten()) / 100.0,
'.',
markersize=10,
c=c[i])
plt.legend(['1', '2', '3', '4', '5', '6', '7', '8', '9'])
# plt.xlim(-10, 10)
# plt.ylim(-10,10)
plt.grid()
plt.show()
# plt.close(fig)
except Exception as e:
coord.request_stop(e)
finally:
coord.request_stop()
coord.join(threads)
def train2(retrain=False):
data_dir = '/home/rong/something_for_deep/cifar-10-batches-bin'
train_image_batch, train_label_batch = input_data.read_cifar10(
data_dir=data_dir, is_train=True, batch_size=BATCH_SIZE, shuffle=True)
# 宣布图片batch和标签batch的占位符
x = tf.placeholder(tf.float32,
shape=[BATCH_SIZE, IMG_W, IMG_H, IMG_CHANNELS],
name='X')
y_ = tf.placeholder(tf.int16, shape=[BATCH_SIZE, NUM_CLASSES])
with open('vgg_6000.pb', 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
logits = tf.import_graph_def(graph_def,
input_map={'X': x},
return_elements=['fc8/relu:0'])
# 宣布损失,精确度等关键节点
loss = tools.loss(logits, y_)
accuracy = tools.accuracy(logits, y_)
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
coord = tf.train.Coordinator() #宣布线程管理器
threads = tf.train.start_queue_runners(
sess=sess, coord=coord) #线程负责把文件加入队列(input_data那个file队列)
train_images, train_labels = sess.run(
[train_image_batch, train_label_batch])
loss2, accuracy = sess.run([loss, accuracy],
feed_dict={
x: train_images,
y_: train_labels
})
print(loss2, accuracy)
coord.request_stop()
coord.join(threads)
'''
if retrain == False:
print('Reading checkpoints')
ckpt = tf.train.get_checkpoint_state(train_log_dir)
if ckpt and ckpt.model_checkpoint_path:
global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
saver.restore(sess, './logs/train/model.ckpt-10000')
print('Loading success, global_step is %s' % global_step)
else:
print('No checkpoint file found')
return
saver.restore(sess, './logs/train/model.ckpt-10000')
for step in range(50):
train_images, train_labels = sess.run([train_image_batch, train_label_batch])
_, train_loss, train_acc = sess.run([train_op2, loss, accuracy],
feed_dict={x: train_images, y_: train_labels})
print('Step: %d, loss: %.4f, accuracy: %.4f%%' % (step, train_loss, train_acc))
saver.restore(sess, './logs/train/model.ckpt-14999')
'''
'''
#下面的try语句可以当做模板使用
try:
for step in np.arange(MAX_STEP):
if coord.should_stop():
break
#运行计算节点,从计算节点中得到真实的image,label
train_images, train_labels = sess.run([train_image_batch, train_label_batch])
#运行损失, 精确度计算节点, 得到具体数值
_, train_loss, train_acc = sess.run([train_op, loss, accuracy],
feed_dict={x: train_images, y_: train_labels})
#每到50步或者最后一步就当前batch的损失值大小和准确度大小
if step % 50 == 0 or (step + 1) == MAX_STEP:
print('Step: %d, loss: %.4f, accuracy: %.4f%%' % (step, train_loss, train_acc))
#summary_str = sess.run(summary_op)
#tra_summary_writer.add_summary(summary_str, step)
#每到200步或者最后一步就从测试集取一个batch, 计算损失值大小和准确度
if step % 200 == 0 or (step + 1) == MAX_STEP:
val_images, val_labels = sess.run([val_image_batch, val_label_batch])
val_loss, val_acc = sess.run([loss, accuracy],
feed_dict={x: val_images, y_: val_labels})
print('** Step %d, val loss = %.2f, val accuracy = %.2f%% **' % (step, val_loss, val_acc))
#summary_str = sess.run(summary_op)
#val_summary_writer.add_summary(summary_str, step)
#每到2000步就保存一次
if step % 2000 == 0 or (step + 1) == MAX_STEP:
if step == 0:
continue
checkpoint_path = os.path.join(train_log_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 train(retrain=False):
data_dir = '/home/rong/something_for_deep/cifar-10-batches-bin'
npy_dir = '/home/rong/something_for_deep/vgg16.npy'
train_log_dir = './logs/train'
val_log_dir = './logs/val'
train_image_batch, train_label_batch = input_data.read_cifar10(
data_dir=data_dir, is_train=True, batch_size=BATCH_SIZE, shuffle=True)
val_image_batch, val_label_batch = input_data.read_cifar10(
data_dir=data_dir,
is_train=False,
batch_size=BATCH_SIZE,
shuffle=False)
#宣布图片batch和标签batch的占位符
x = tf.placeholder(tf.float32,
shape=[BATCH_SIZE, IMG_W, IMG_H, IMG_CHANNELS])
y_ = tf.placeholder(tf.int16, shape=[BATCH_SIZE, NUM_CLASSES])
#宣布VGG16类型的变量
vgg = model.VGG16()
#宣布损失,精确度等关键节点
logits = vgg.build(x, NUM_CLASSES, IS_PRETRAIN)
loss = tools.loss(logits, y_)
accuracy = tools.accuracy(logits, y_)
my_global_step = tf.Variable(0, name='global_step', trainable=False)
train_op = tools.optimize(loss, learning_rate, my_global_step)
train_op2 = tools.optimize2(loss, learning_rate)
saver = tf.train.Saver() #括号那个参数不知道是干什么的
summary_op = tf.summary.merge_all()
#初始化所有的variable,之前我看过另外一种写法,那种写法好像废弃了
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
#从npy文件加载除了全连接之外,其他层的权重
tools.load_with_skip(npy_dir, sess, ['fc6', 'fc7', 'fc8'])
saver.restore(sess, './logs/train/model.ckpt-6000')
output_graph_def = convert_variables_to_constants(
sess, sess.graph_def, output_node_names=['fc8/relu'])
with tf.gfile.FastGFile('vgg_6000.pb', mode='wb') as f:
f.write(output_graph_def.SerializeToString())
'''
#下面的和多线程有关,暂时不懂
coord = tf.train.Coordinator() #宣布线程管理器
threads = tf.train.start_queue_runners(sess=sess, coord=coord) #线程负责把文件加入队列(input_data那个file队列)
tra_summary_writer = tf.summary.FileWriter(train_log_dir, sess.graph)
val_summary_writer = tf.summary.FileWriter(val_log_dir, sess.graph)
'''
'''
if retrain == False:
print('Reading checkpoints')
ckpt = tf.train.get_checkpoint_state(train_log_dir)
if ckpt and ckpt.model_checkpoint_path:
global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
saver.restore(sess, './logs/train/model.ckpt-10000')
print('Loading success, global_step is %s' % global_step)
else:
print('No checkpoint file found')
return
saver.restore(sess, './logs/train/model.ckpt-10000')
for step in range(50):
train_images, train_labels = sess.run([train_image_batch, train_label_batch])
_, train_loss, train_acc = sess.run([train_op2, loss, accuracy],
feed_dict={x: train_images, y_: train_labels})
print('Step: %d, loss: %.4f, accuracy: %.4f%%' % (step, train_loss, train_acc))
saver.restore(sess, './logs/train/model.ckpt-14999')
'''
'''
#下面的try语句可以当做模板使用
try:
for step in np.arange(MAX_STEP):
if coord.should_stop():
break
#运行计算节点,从计算节点中得到真实的image,label
train_images, train_labels = sess.run([train_image_batch, train_label_batch])
#运行损失, 精确度计算节点, 得到具体数值
_, train_loss, train_acc = sess.run([train_op, loss, accuracy],
feed_dict={x: train_images, y_: train_labels})
#每到50步或者最后一步就当前batch的损失值大小和准确度大小
if step % 50 == 0 or (step + 1) == MAX_STEP:
print('Step: %d, loss: %.4f, accuracy: %.4f%%' % (step, train_loss, train_acc))
#summary_str = sess.run(summary_op)
#tra_summary_writer.add_summary(summary_str, step)
#每到200步或者最后一步就从测试集取一个batch, 计算损失值大小和准确度
if step % 200 == 0 or (step + 1) == MAX_STEP:
val_images, val_labels = sess.run([val_image_batch, val_label_batch])
val_loss, val_acc = sess.run([loss, accuracy],
feed_dict={x: val_images, y_: val_labels})
print('** Step %d, val loss = %.2f, val accuracy = %.2f%% **' % (step, val_loss, val_acc))
#summary_str = sess.run(summary_op)
#val_summary_writer.add_summary(summary_str, step)
#每到2000步就保存一次
if step % 2000 == 0 or (step + 1) == MAX_STEP:
if step == 0:
continue
checkpoint_path = os.path.join(train_log_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 train():
pre_trained_weights = './VGG16_pretrain/vgg16.npy'
data_dir = config.dataPath
train_log_dir = './logs2/train/'
val_log_dir = './logs2/val/'
with tf.name_scope('input'):
train_image_batch, train_label_batch = input_data.read_cifar10(
data_dir, is_train=True, batch_size=BATCH_SIZE, shuffle=True)
val_image_batch, val_label_batch = input_data.read_cifar10(
data_dir, is_train=False, batch_size=BATCH_SIZE, shuffle=False)
logits = VGG.VGG16(train_image_batch, N_CLASSES, IS_PRETRAIN)
loss = tools.loss(logits, train_label_batch)
accuracy = tools.accuracy(logits, train_label_batch)
my_global_step = tf.Variable(0, trainable=False, name='global_step')
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = tools.optimize(loss, learning_rate, my_global_step)
x = tf.placeholder(dtype=tf.float32, shape=[BATCH_SIZE, IMG_H, IMG_W, 3])
y_ = tf.placeholder(dtype=tf.int32, shape=[BATCH_SIZE, N_CLASSES])
tf.summary.image('input', x, 10)
saver = tf.train.Saver(tf.global_variables())
summary_op = tf.summary.merge_all()
'''if exit checkpoint
restore
else:
init
'''
print('Reading checkpoint...')
ckpt = tf.train.get_checkpoint_state(train_log_dir)
sess = tf.Session()
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('Load success, global step: %s' % global_step)
else:
init = tf.global_variables_initializer()
sess.run(init)
# load pretrain weights
tools.load_with_skip(pre_trained_weights, sess, ['fc6', 'fc7', 'fc8'])
print('Load pre_trained_weights success!!!')
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
train_summary_writer = tf.summary.FileWriter(train_log_dir, sess.graph)
val_summary_writer = tf.summary.FileWriter(val_log_dir, sess.graph)
try:
for step in np.arange(MAX_STEP):
if coord.should_stop():
break
train_images, train_labels = sess.run(
[train_image_batch, train_label_batch])
_, train_loss, train_accuracy = sess.run(
[train_op, loss, accuracy],
feed_dict={
x: train_images,
y_: train_labels
})
if step % 50 == 0 or (step + 1) == MAX_STEP:
print("Step: %d, loss: %.4f, accuracy: %.4f%%" %
(step, train_loss, train_accuracy))
summary_str = sess.run(summary_op,
feed_dict={
x: train_images,
y_: train_labels
})
train_summary_writer.add_summary(summary_str, step)
if step % 200 == 0 or (step + 1) == MAX_STEP:
val_images, val_labels = sess.run(
[val_image_batch, val_label_batch])
val_loss, val_accuracy = sess.run([loss, accuracy],
feed_dict={
x: val_images,
y_: val_labels
})
print("** Step: %d, loss: %.4f, accuracy: %.4f%%" %
(step, val_loss, val_accuracy))
summary_str = sess.run(summary_op,
feed_dict={
x: train_images,
y_: train_labels
})
val_summary_writer.add_summary(summary_str, step)
if step % 2000 == 0 or (step + 1) == MAX_STEP:
checkpoint_path = os.path.join(train_log_dir, 'model.ckpt')
saver.save(sess, save_path=checkpoint_path, global_step=step)
except tf.errors.OutOfRangeError:
print('Done training -- epoch limited reached')
finally:
coord.request_stop()
coord.join(threads)
sess.close()
