def val(val_data_set, load_model_path, phases_names):
x = tf.placeholder(tf.float32,
shape=[
None, sub_Config.IMAGE_W, sub_Config.IMAGE_H,
sub_Config.IMAGE_CHANNEL * len(phases_names)
],
name='input_x')
y_ = tf.placeholder(tf.float32, shape=[
None,
])
global_step = tf.Variable(0, trainable=False)
is_training = tf.placeholder('bool', [], name='is_training')
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('data_dir', '/tmp/cifar-data',
'where to store the dataset')
tf.app.flags.DEFINE_boolean(
'use_bn', True, 'use batch normalization. otherwise use biases')
y = inference_small(x,
is_training=is_training,
num_classes=sub_Config.OUTPUT_NODE,
use_bias=FLAGS.use_bn,
phase_names=phases_names,
num_blocks=3)
with tf.variable_scope('accuracy'):
accuracy_tensor = tf.reduce_mean(
tf.cast(tf.equal(x=tf.argmax(y, 1), y=tf.cast(y_, tf.int64)),
tf.float32))
tf.summary.scalar('accuracy', accuracy_tensor)
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
if load_model_path:
saver.restore(sess, load_model_path)
validation_images, validation_labels = val_data_set.get_next_batch(
None, None)
validation_accuracy, logits = sess.run([accuracy_tensor, y],
feed_dict={
x: validation_images,
y_: validation_labels
})
calculate_acc_error(logits=np.argmax(logits, 1),
label=validation_labels,
show=True)
binary_acc = acc_binary_acc(
logits=np.argmax(logits, 1),
label=validation_labels,
)
print 'accuracy is %g, binary_acc is %g' % \
(validation_accuracy, binary_acc)
def train(train_data_set, val_data_set, load_model_path, save_model_path):
x = tf.placeholder(tf.float32,
shape=[
None, sub_Config.IMAGE_W, sub_Config.IMAGE_H,
sub_Config.IMAGE_CHANNEL
],
name='input_x')
y_ = tf.placeholder(tf.float32, shape=[
None,
])
tf.summary.histogram('label', y_)
global_step = tf.Variable(0, trainable=False)
# variable_average = tf.train.ExponentialMovingAverage(
# sub_Config.MOVING_AVERAGE_DECAY,
# global_step
# )
# vaeriable_average_op = variable_average.apply(tf.trainable_variables())
# regularizer = tf.contrib.layers.l2_regularizer(sub_Config.REGULARIZTION_RATE)
is_training = tf.placeholder('bool', [], name='is_training')
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('data_dir', '/tmp/cifar-data',
'where to store the dataset')
tf.app.flags.DEFINE_boolean(
'use_bn', True, 'use batch normalization. otherwise use biases')
y = inference_small(x,
is_training=is_training,
num_classes=sub_Config.OUTPUT_NODE,
use_bias=FLAGS.use_bn,
num_blocks=3)
tf.summary.histogram('logits', tf.argmax(y, 1))
loss_ = loss(logits=y, labels=tf.cast(y_, np.int32))
tf.summary.scalar('loss', loss_)
train_op = tf.train.GradientDescentOptimizer(
learning_rate=sub_Config.LEARNING_RATE).minimize(
loss=loss_, global_step=global_step)
# with tf.control_dependencies([train_step, vaeriable_average_op]):
# train_op = tf.no_op(name='train')
with tf.variable_scope('accuracy'):
accuracy_tensor = tf.reduce_mean(
tf.cast(tf.equal(x=tf.argmax(y, 1), y=tf.cast(y_, tf.int64)),
tf.float32))
tf.summary.scalar('accuracy', accuracy_tensor)
saver = tf.train.Saver()
merge_op = tf.summary.merge_all()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
if load_model_path:
saver.restore(sess, load_model_path)
writer = tf.summary.FileWriter('./log/fine_tuning/train',
tf.get_default_graph())
val_writer = tf.summary.FileWriter('./log/fine_tuning/val',
tf.get_default_graph())
for i in range(sub_Config.ITERATOE_NUMBER):
images, labels = train_data_set.get_next_batch(
sub_Config.BATCH_SIZE, sub_Config.BATCH_DISTRIBUTION)
images = changed_shape(images, [
len(images), sub_Config.IMAGE_W, sub_Config.IMAGE_W,
sub_Config.IMAGE_CHANNEL
])
_, loss_value, accuracy_value, summary, global_step_value = sess.run(
[train_op, loss_, accuracy_tensor, merge_op, global_step],
feed_dict={
x: images,
y_: labels
})
writer.add_summary(summary=summary, global_step=global_step_value)
if i % 500 == 0 and i != 0 and save_model_path is not None:
# 保存模型
import os
saveedpath = os.path.join(save_model_path,
str(global_step_value))
if not os.path.exists(saveedpath):
os.mkdir(saveedpath)
saveedpath += '/model.ckpt'
saver.save(sess, saveedpath, global_step=global_step_value)
if i % 100 == 0:
validation_images, validation_labels = val_data_set.get_next_batch(
sub_Config.BATCH_SIZE, sub_Config.BATCH_DISTRIBUTION)
validation_images = changed_shape(validation_images, [
len(validation_images), sub_Config.IMAGE_W,
sub_Config.IMAGE_W, 1
])
validation_accuracy, validation_loss, summary, logits = sess.run(
[accuracy_tensor, loss_, merge_op, y],
feed_dict={
x: validation_images,
y_: validation_labels
})
calculate_acc_error(logits=np.argmax(logits, 1),
label=validation_labels,
show=True)
binary_acc = acc_binary_acc(
logits=np.argmax(logits, 1),
label=validation_labels,
)
val_writer.add_summary(summary, global_step_value)
print 'step is %d,training loss value is %g, accuracy is %g ' \
'validation loss value is %g, accuracy is %g, binary_acc is %g' % \
(global_step_value, loss_value, accuracy_value, validation_loss, validation_accuracy, binary_acc)
writer.close()
val_writer.close()
def train(train_data_set, val_data_set, load_model_path, save_model_path):
x = tf.placeholder(tf.float32,
shape=[
None, sub_Config.IMAGE_W, sub_Config.IMAGE_H,
sub_Config.IMAGE_CHANNEL
],
name='input_x')
if sub_Config.NEED_MUL:
tf.summary.image('input_x', x * 120, max_outputs=5)
else:
tf.summary.image('input_x', x)
y_ = tf.placeholder(tf.float32, shape=[
None,
])
tf.summary.histogram('label', y_)
# global_step = tf.Variable(0, trainable=False)
# variable_average = tf.train.ExponentialMovingAverage(
# sub_Config.MOVING_AVERAGE_DECAY,
# global_step
# )
# vaeriable_average_op = variable_average.apply(tf.trainable_variables())
regularizer = tf.contrib.layers.l2_regularizer(
sub_Config.REGULARIZTION_RATE)
y = inference(x, regularizer)
tf.summary.histogram('logits', tf.argmax(y, 1))
loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=y, labels=tf.cast(y_, tf.int32))) + tf.add_n(
tf.get_collection('losses'))
tf.summary.scalar('loss', loss)
train_op = tf.train.GradientDescentOptimizer(
learning_rate=sub_Config.LEARNING_RATE).minimize(
loss=loss,
# global_step=global_step
)
# with tf.control_dependencies([train_step, vaeriable_average_op]):
# train_op = tf.no_op(name='train')
with tf.variable_scope('accuracy'):
accuracy_tensor = tf.reduce_mean(
tf.cast(tf.equal(x=tf.argmax(y, 1), y=tf.cast(y_, tf.int64)),
tf.float32))
tf.summary.scalar('accuracy', accuracy_tensor)
saver = tf.train.Saver()
merge_op = tf.summary.merge_all()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
if load_model_path:
saver.restore(sess, load_model_path)
writer = tf.summary.FileWriter('./log/fine_tuning/train',
tf.get_default_graph())
val_writer = tf.summary.FileWriter('./log/fine_tuning/val',
tf.get_default_graph())
for i in range(sub_Config.ITERATOE_NUMBER):
images, labels = train_data_set.images, train_data_set.labels
images = changed_shape(images, [
len(images), sub_Config.IMAGE_W, sub_Config.IMAGE_W,
sub_Config.IMAGE_CHANNEL
])
if i == 0:
from PIL import Image
image = Image.fromarray(
np.asarray(images[0, :, :, 0], np.uint8))
image.show()
_, loss_value, accuracy_value, summary = sess.run(
[train_op, loss, accuracy_tensor, merge_op],
feed_dict={
x: images,
y_: labels
})
writer.add_summary(summary=summary, global_step=i)
if i % 1000 == 0 and i != 0 and save_model_path is not None:
# 保存模型
saver.save(sess, save_model_path)
if i % 100 == 0:
validation_images, validation_labels = val_data_set.images, val_data_set.labels
validation_images = changed_shape(validation_images, [
len(validation_images), sub_Config.IMAGE_W,
sub_Config.IMAGE_W, 1
])
validation_accuracy, validation_loss, summary, logits = sess.run(
[accuracy_tensor, loss, merge_op, y],
feed_dict={
x: validation_images,
y_: validation_labels
})
calculate_acc_error(logits=np.argmax(logits, 1),
label=validation_labels,
show=True)
binary_acc = acc_binary_acc(
logits=np.argmax(logits, 1),
label=validation_labels,
)
val_writer.add_summary(summary, i)
print 'step is %d,training loss value is %g, accuracy is %g ' \
'validation loss value is %g, accuracy is %g, binary_acc is %g' % \
(i, loss_value, accuracy_value, validation_loss, validation_accuracy, binary_acc)
writer.close()
val_writer.close()
def val(val_data_set, load_model_path, phases_names):
x_ROI = tf.placeholder(tf.float32,
shape=[
None, net_config.ROI_SIZE_W,
net_config.ROI_SIZE_H,
net_config.IMAGE_CHANNEL * len(phases_names)
],
name='input_x')
x_EXPAND = tf.placeholder(tf.float32,
shape=[
None, net_config.EXPAND_SIZE_W,
net_config.EXPAND_SIZE_H,
net_config.IMAGE_CHANNEL * len(phases_names)
])
y_ = tf.placeholder(tf.float32, shape=[
None,
])
tf.summary.histogram('label', y_)
global_step = tf.Variable(0, trainable=False)
# variable_average = tf.train.ExponentialMovingAverage(
# sub_Config.MOVING_AVERAGE_DECAY,
# global_step
# )
# vaeriable_average_op = variable_average.apply(tf.trainable_variables())
# regularizer = tf.contrib.layers.l2_regularizer(sub_Config.REGULARIZTION_RATE)
is_training = tf.placeholder('bool', [], name='is_training')
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('data_dir', '/tmp/cifar-data',
'where to store the dataset')
tf.app.flags.DEFINE_boolean(
'use_bn', True, 'use batch normalization. otherwise use biases')
y = inference_small([x_ROI, x_EXPAND],
is_training=is_training,
num_classes=net_config.OUTPUT_NODE,
use_bias=FLAGS.use_bn,
phase_names=phases_names,
num_blocks=3)
tf.summary.histogram('logits', tf.argmax(y, 1))
loss_ = loss(logits=y, labels=tf.cast(y_, np.int32))
tf.summary.scalar('loss', loss_)
with tf.variable_scope('accuracy'):
accuracy_tensor = tf.reduce_mean(
tf.cast(tf.equal(x=tf.argmax(y, 1), y=tf.cast(y_, tf.int64)),
tf.float32))
tf.summary.scalar('accuracy', accuracy_tensor)
saver = tf.train.Saver()
merge_op = tf.summary.merge_all()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
if load_model_path:
saver.restore(sess, load_model_path)
validation_images, validation_images_expand, validation_labels = val_data_set.get_next_batch(
)
validation_accuracy, validation_loss, summary, logits = sess.run(
[accuracy_tensor, loss_, merge_op, y],
feed_dict={
x_ROI: validation_images,
x_EXPAND: validation_images_expand,
y_: validation_labels
})
calculate_acc_error(logits=np.argmax(logits, 1),
label=validation_labels,
show=True)
binary_acc = acc_binary_acc(
logits=np.argmax(logits, 1),
label=validation_labels,
)
print 'validation loss value is %g, accuracy is %g, binary_acc is %g' % \
(validation_loss, validation_accuracy, binary_acc)
