本文整理汇总了Python中tensorpack.models.regularize_cost方法的典型用法代码示例。如果您正苦于以下问题：Python models.regularize_cost方法的具体用法？Python models.regularize_cost怎么用？Python models.regularize_cost使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorpack.models的用法示例。
在下文中一共展示了models.regularize_cost方法的7个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: build_graph
# 需要导入模块: from tensorpack import models [as 别名]
# 或者: from tensorpack.models import regularize_cost [as 别名]
def build_graph(self, image, label):
image = ImageNetModel.image_preprocess(image, bgr=self.image_bgr)
assert self.data_format in ['NCHW', 'NHWC']
if self.data_format == 'NCHW':
image = tf.transpose(image, [0, 3, 1, 2])
logits = self.get_logits(image)
print('self.label_smoothing', self.label_smoothing)
loss = ImageNetModel.compute_loss_and_error(logits, label, self.label_smoothing)
if self.weight_decay > 0:
wd_loss = regularize_cost(self.weight_decay_pattern,
tf.contrib.layers.l2_regularizer(self.weight_decay),
name='l2_regularize_loss')
add_moving_summary(loss, wd_loss)
total_cost = tf.add_n([loss, wd_loss], name='cost')
else:
total_cost = tf.identity(loss, name='cost')
add_moving_summary(total_cost)
if self.loss_scale != 1.:
logger.info("Scaling the total loss by {} ...".format(self.loss_scale))
return total_cost * self.loss_scale
else:
return total_cost 示例2: build_graph
# 需要导入模块: from tensorpack import models [as 别名]
# 或者: from tensorpack.models import regularize_cost [as 别名]
def build_graph(self, image, label):
image = self.image_preprocess(image)
assert self.data_format == 'NCHW'
image = tf.transpose(image, [0, 3, 1, 2])
logits = self.get_logits(image)
loss = ImageNetModel.compute_loss_and_error(
logits, label, label_smoothing=self.label_smoothing)
if self.weight_decay > 0:
wd_loss = regularize_cost(self.weight_decay_pattern,
tf.contrib.layers.l2_regularizer(self.weight_decay),
name='l2_regularize_loss')
add_moving_summary(loss, wd_loss)
total_cost = tf.add_n([loss, wd_loss], name='cost')
else:
total_cost = tf.identity(loss, name='cost')
add_moving_summary(total_cost)
if self.loss_scale != 1.:
logger.info("Scaling the total loss by {} ...".format(self.loss_scale))
return total_cost * self.loss_scale
else:
return total_cost 示例3: _build_graph
# 需要导入模块: from tensorpack import models [as 别名]
# 或者: from tensorpack.models import regularize_cost [as 别名]
def _build_graph(self, inputs):
image, label = inputs
image = ImageNetModel.image_preprocess(image, bgr=True)
if self.data_format == 'NCHW':
image = tf.transpose(image, [0, 3, 1, 2])
logits = self.get_logits(image)
loss = ImageNetModel.compute_loss_and_error(logits, label)
if self.weight_decay > 0:
wd_loss = regularize_cost('.*/W', tf.contrib.layers.l2_regularizer(self.weight_decay),
name='l2_regularize_loss')
add_moving_summary(loss, wd_loss)
self.cost = tf.add_n([loss, wd_loss], name='cost')
else:
self.cost = tf.identity(loss, name='cost')
add_moving_summary(self.cost) 示例4: build_graph
# 需要导入模块: from tensorpack import models [as 别名]
# 或者: from tensorpack.models import regularize_cost [as 别名]
def build_graph(self, image, label):
image = self.image_preprocess(image)
assert self.data_format in ['NCHW', 'NHWC']
if self.data_format == 'NCHW':
image = tf.transpose(image, [0, 3, 1, 2])
logits = self.get_logits(image)
loss = ImageNetModel.compute_loss_and_error(
logits, label, label_smoothing=self.label_smoothing)
if self.weight_decay > 0:
wd_loss = regularize_cost(self.weight_decay_pattern,
tf.contrib.layers.l2_regularizer(self.weight_decay),
name='l2_regularize_loss')
add_moving_summary(loss, wd_loss)
total_cost = tf.add_n([loss, wd_loss], name='cost')
else:
total_cost = tf.identity(loss, name='cost')
add_moving_summary(total_cost)
if self.loss_scale != 1.:
logger.info("Scaling the total loss by {} ...".format(self.loss_scale))
return total_cost * self.loss_scale
else:
return total_cost 示例5: build_graph
# 需要导入模块: from tensorpack import models [as 别名]
# 或者: from tensorpack.models import regularize_cost [as 别名]
def build_graph(self,
image,
label):
image = self.image_preprocess(image)
if is_channels_first(self.data_format):
image = tf.transpose(image, [0, 3, 1, 2], name="image_transpose")
# tf.summary.image('input_image_', image)
# tf.summary.tensor_summary('input_tensor_', image)
# with tf.name_scope('tmp1_summaries'):
# add_tensor_summary(image, ['histogram', 'rms', 'sparsity'], name='tmp1_tensor')
is_training = get_current_tower_context().is_training
logits = self.model_lambda(
x=image,
training=is_training)
loss = ImageNetModel.compute_loss_and_error(
logits=logits,
label=label,
label_smoothing=self.label_smoothing)
if self.weight_decay > 0:
wd_loss = regularize_cost(
regex=self.weight_decay_pattern,
func=tf.contrib.layers.l2_regularizer(self.weight_decay),
name="l2_regularize_loss")
add_moving_summary(loss, wd_loss)
total_cost = tf.add_n([loss, wd_loss], name="cost")
else:
total_cost = tf.identity(loss, name="cost")
add_moving_summary(total_cost)
if self.loss_scale != 1.0:
logger.info("Scaling the total loss by {} ...".format(self.loss_scale))
return total_cost * self.loss_scale
else:
return total_cost 示例6: _build_graph
# 需要导入模块: from tensorpack import models [as 别名]
# 或者: from tensorpack.models import regularize_cost [as 别名]
def _build_graph(self, inputs):
image, label = inputs
image = ImageNetModel.image_preprocess(image, bgr=self.image_bgr)
if self.data_format == 'NCHW':
image = tf.transpose(image, [0, 3, 1, 2])
logits = self.get_logits(image)
loss = ImageNetModel.compute_loss_and_error(logits, label)
wd_loss = regularize_cost(self.weight_decay_pattern,
tf.contrib.layers.l2_regularizer(self.weight_decay),
name='l2_regularize_loss')
add_moving_summary(loss, wd_loss)
self.cost = tf.add_n([loss, wd_loss], name='cost') 示例7: build_graph
# 需要导入模块: from tensorpack import models [as 别名]
# 或者: from tensorpack.models import regularize_cost [as 别名]
def build_graph(self, image, label):
image = self.image_preprocess(image)
assert self.data_format in ['NCHW', 'NHWC']
if self.data_format == 'NCHW':
image = tf.transpose(image, [0, 3, 1, 2])
logits = self.get_logits(image)
tf.nn.softmax(logits, name='prob')
loss = ImageNetModel.compute_loss_and_error(
logits, label, label_smoothing=self.label_smoothing)
if self.weight_decay > 0:
wd_loss = regularize_cost(self.weight_decay_pattern,
l2_regularizer(self.weight_decay),
name='l2_regularize_loss')
add_moving_summary(loss, wd_loss)
total_cost = tf.add_n([loss, wd_loss], name='cost')
else:
total_cost = tf.identity(loss, name='cost')
add_moving_summary(total_cost)
if self.loss_scale != 1.:
logger.info("Scaling the total loss by {} ...".format(self.loss_scale))
return total_cost * self.loss_scale
else:
return total_cost