def run_training():
# you need to change the directories to yours.
#train_dir = '/home/kevin/tensorflow/cats_vs_dogs/data/train/'
train_dir = 'D:/tensorflow/mydata/cat_dog2/' #My dir--20170727-csq
#logs_train_dir = '/home/kevin/tensorflow/cats_vs_dogs/logs/train/'
logs_train_dir = 'D:/tensorflow/mylog/cat_dog2/'
train, train_label = input_data.get_files(train_dir)
print(train)
print(train_label)
train_batch, train_label_batch = input_data.get_batch(
train, train_label, IMG_W, IMG_H, BATCH_SIZE, CAPACITY)
print(train_batch)
print(train_label_batch)
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()
print(summary_op)
with tf.Session() as sess:
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):
print(step)
if coord.should_stop():
break
_, tra_loss, tra_acc = sess.run(
[train_op, train_loss, train__acc])
if step % cnt_summary == 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 % cnt_cache == 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():
'''''Test one image against the saved models and parameters
'''
print('evaluate_one_image enter')
# you need to change the directories to yours.
#train_dir = '/home/kevin/tensorflow/cats_vs_dogs/data/train/'
train_dir = 'D:/tensorflow/mypic/cat.2.jpg'
#train, train_label = input_data.get_files(train_dir)
#image_array = get_one_image(train)
image_array = get_one_image1(train_dir)
print(image_array)
with tf.Graph().as_default():
BATCH_SIZE = 2
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 = '/home/kevin/tensorflow/cats_vs_dogs/logs/train/'
logs_train_dir = 'D:/tensorflow/mylog/cat_dog2/'
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])
#%%
##run_training()
#evaluate_one_image()
def evaluate():
'''''Test one image against the saved models and parameters
'''
# you need to change the directories to yours.
# img_dir = '/home/hjxu/PycharmProjects/01_cats_vs_dogs/222.jpg'
# image_array = get_one_img(img_dir)
with tf.Graph().as_default():
# you need to change the directories to yours.
logs_train_dir = 'recordstrain/'
tfrecords_file = 'tfrecords2'
train_batch, train_batch_d, train_label_batch = cr.read_and_decode(
tfrecords_file, batch_size=BATCH_SIZE)
train_batch = tf.cast(train_batch, dtype=tf.float32)
train_batch_d = tf.cast(train_batch_d, dtype=tf.float32)
train_label_batch = tf.cast(train_label_batch, dtype=tf.int64)
train_logits = model1.inference(model1, train_batch, BATCH_SIZE,
N_CLASSES)
train_loss = model1.losses(train_logits, train_label_batch)
train_op = model1.trainning(train_loss, learning_rate)
train__acc = model1.evaluation(train_logits, train_label_batch)
m = np.empty([31])
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
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')
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
for step in np.arange(MAX_STEP):
_, tra_loss, tra_acc = sess.run(
[train_op, train_loss, train__acc])
if coord.should_stop():
break
if step % 1 == 0:
print(
'test: train loss = %.2f, train accuracy = %.2f%%'
% (tra_loss, tra_acc * 100.0))
m[step] = tra_acc
_, tra_loss, tra_acc = sess.run(
[train_op, train_loss, train__acc])
finally:
coord.request_stop()
coord.join(threads)
sess.close()
print(np.mean(m))
def test_one_image():
"""
Test one image with the saved models and parameters
"""
test_image = get_one_image(test_dir)
with tf.Graph().as_default():
BATCH_SIZE = 1
N_CLASSES = 2
image = tf.cast(test_image, tf.float32)
image = tf.image.per_image_standardization(image)
image = tf.reshape(image, [1, 208, 208, 3])
logit = model1.inference(image, BATCH_SIZE, N_CLASSES)
logit = tf.nn.softmax(logit)
x = tf.placeholder(tf.float32, shape=[208, 208, 3])
saver = tf.train.Saver()
with tf.Session() as sess:
print("Reading checkpoints...")
ckpt = tf.train.get_checkpoint_state(train_logs_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: test_image})
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():
logs_train_dir = 'G:\\dataS-UNIWARD0.4\\logs\\train'
logs_val_dir = 'G:\\dataS-UNIWARD0.4\\logs\\val'
tfrecords_traindir = 'G:\\dataS-UNIWARD0.4\\S_UNIWARD0.4train.tfrecords'
tfrecords_valdir = 'G:\\dataS-UNIWARD0.4\\S_UNIWARD0.4val.tfrecords'
# 获得batch tfrecord方法
train_batch, train_label_batch = input_data1.read_and_decode(
tfrecords_traindir, BATCH_SIZE)
val_batch, val_label_batch = input_data1.read_and_decode(
tfrecords_valdir, BATCH_SIZE)
x = tf.placeholder(tf.float32, shape=[BATCH_SIZE, 256, 256, 1])
y_ = tf.placeholder(tf.int32, shape=[BATCH_SIZE])
logits = model1.inference(x, BATCH_SIZE, N_CLASSES)
loss = model1.losses(logits, y_)
acc = model1.evaluation(logits, y_)
train_op = model1.trainning(loss, learning_rate)
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)
summary_op = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(logs_train_dir, sess.graph)
val_writer = tf.summary.FileWriter(logs_val_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 % 2 == 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(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)
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 = model1.inference(train_batch, BATCH_SIZE, N_CLASSES)
loss = model1.losses(logits, train_label_batch)
train_op = model1.trainning(loss, learning_rate)
acc = model1.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_loss, val_acc = sess.run([loss, acc])
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)