def sg_summary_param(tensor, prefix='40. parameters'):
# defaults
prefix = '' if prefix is None else prefix + '/'
# summary name
name = prefix + _pretty_name(tensor)
# summary statistics
with tf.name_scope('summary'):
tf.scalar_summary(name + '/norm', tf.global_norm([tensor]))
tf.histogram_summary(name, tensor)
def sg_summary_gradient(tensor, gradient, prefix='50. gradient'):
# defaults
prefix = '' if prefix is None else prefix + '/'
# summary name
name = prefix + _pretty_name(tensor)
# summary statistics
with tf.name_scope('summary'):
tf.scalar_summary(name + '/norm', tf.global_norm([gradient]))
tf.histogram_summary(name, gradient)
def sg_summary_activation(tensor, prefix='30. activation'):
# defaults
prefix = '' if prefix is None else prefix + '/'
# summary name
name = prefix + _pretty_name(tensor)
# summary statistics
with tf.name_scope('summary'):
tf.scalar_summary(name + '/norm', tf.global_norm([tensor]))
tf.scalar_summary(
name + '/ratio',
tf.reduce_mean(tf.cast(tf.greater(tensor, 0), tf.sg_floatx)))
tf.histogram_summary(name, tensor)
def sg_summary_gradient(tensor, gradient, prefix='50. gradient'):
r"""Writes the normalized gradient value
Args:
tensor: A `Tensor` variable.
gradient: A `Tensor`. Gradient of `tensor`.
"""
# defaults
prefix = '' if prefix is None else prefix + '/'
# summary name
name = prefix + _pretty_name(tensor)
# summary statistics
with tf.name_scope('summary'):
try:
tf.scalar_summary(name + '/norm', tf.global_norm([gradient]))
tf.histogram_summary(name, gradient)
except:
pass
def sg_summary_param(tensor, prefix=None, name=None):
r"""Register `tensor` to summary report as `parameters`
Args:
tensor: A `Tensor` to log as parameters
prefix: A `string`. A prefix to display in the tensor board web UI.
name: A `string`. A name to display in the tensor board web UI.
Returns:
None
"""
# defaults
prefix = '' if prefix is None else prefix + '/'
# summary name
name = prefix + _pretty_name(tensor) if name is None else prefix + name
# summary statistics
# noinspection PyBroadException
try:
tf.summary.scalar(name + '/norm', tf.global_norm([tensor]))
tf.summary.histogram(name + '/norm-h', tensor)
except:
pass
def sg_summary_gradient(tensor, gradient, prefix=None, name=None):
r"""Register `tensor` to summary report as `gradient`
Args:
tensor: A `Tensor` to log as gradient
gradient: A 0-D `Tensor`. A gradient to log
prefix: A `string`. A prefix to display in the tensor board web UI.
name: A `string`. A name to display in the tensor board web UI.
Returns:
None
"""
# defaults
prefix = '' if prefix is None else prefix + '/'
# summary name
name = prefix + _full_name(tensor) if name is None else prefix + name
# summary statistics
# noinspection PyBroadException
try:
tf.summary.scalar(name + '/grad', tf.global_norm([gradient]))
tf.summary.histogram(name + '/grad-h', gradient)
except:
pass