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_leaky_relu(x, opt):
r""""See [Xu, et al. 2015](https://arxiv.org/pdf/1505.00853v2.pdf)
Args:
x: A tensor
opt:
name: A name for the operation (optional).
Returns:
A `Tensor` with the same type and shape as `x`.
"""
return tf.select(tf.greater(x, 0), x, 0.01 * x, name=opt.name)
def sg_summary_activation(tensor, prefix=None, name=None):
r"""Register `tensor` to summary report as `activation`
Args:
tensor: A `Tensor` to log as activation
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
_scalar(name + '/ratio',
tf.reduce_mean(tf.cast(tf.greater(tensor, 0), tf.sg_floatx)))
_histogram(name + '/ratio-h', tensor)
def sg_leaky_relu(x, opt):
return tf.select(tf.greater(x, 0), x, 0.01 * x, name=opt.name)
def sg_leaky_relu(x, opt):
r""""See Xu, et al. 2015 `https://arxiv.org/pdf/1505.00853v2.pdf`
"""
return tf.select(tf.greater(x, 0), x, 0.01 * x, name=opt.name)