def uniform(name, shape, scale=0.05, dtype=tf.sg_floatx, summary=True, regularizer=None, trainable=True):
r"""Creates a tensor variable of which initial values are
random numbers based on uniform distribution.
Note that the default value of `scale` (=0.05) is different from
the min/max values (=0.0, 1.0) of tf.random_uniform_initializer.
Args:
name: The name of the new variable.
shape: A tuple/list of integers or an integer.
If shape is an integer, it's converted to a list.
scale: A Python scalar. All initial values should be in range `[-scale, scale)`. Default is .05.
dtype: The data type. Only floating point types are supported. Default is float32.
summary: If True, add this constant to tensor board summary.
regularizer: A (Tensor -> Tensor or None) function; the result of applying it on a newly created variable
will be added to the collection tf.GraphKeys.REGULARIZATION_LOSSES and can be used for regularization
trainable: If True, add this constant to trainable collection. Default is True.
Returns:
A `Variable`.
"""
shape = shape if isinstance(shape, (tuple, list)) else [shape]
x = tf.get_variable(name, shape, dtype=dtype,
initializer=tf.random_uniform_initializer(minval=-scale, maxval=scale),
regularizer=regularizer, trainable=trainable)
# add summary
if summary:
tf.sg_summary_param(x)
return x
def uniform(name, shape, scale=0.05, dtype=tf.sg_floatx, summary=True):
r"""Creates a tensor variable of which initial values are
random numbers based on uniform distribution.
Note that the default value of `scale` (=0.05) is different from
the min/max values (=0.0, 1.0) of tf.random_uniform_initializer.
Args:
name: The name of the new variable.
shape: A tuple/list of integers or an integer.
If shape is an integer, it's converted to a list.
scale: A Python scalar. All initial values should be in range `[-scale, scale)`. Default is .05.
dtype: The data type. Only floating point types are supported. Default is float32.
summary: If True, add this constant to tensor board summary.
Returns:
A `Variable`.
"""
shape = shape if isinstance(shape, (tuple, list)) else [shape]
x = tf.get_variable(name,
shape,
dtype=dtype,
initializer=tf.random_uniform_initializer(
minval=-scale, maxval=scale))
# add summary
if not tf.get_variable_scope().reuse and summary:
tf.sg_summary_param(x)
return x
def uniform(name, shape, scale=0.05, dtype=tf.sg_floatx):
shape = shape if isinstance(shape, (tuple, list)) else [shape]
x = tf.get_variable(name, shape, dtype=dtype,
initializer=tf.random_uniform_initializer(minval=-scale, maxval=scale))
# add summary
if not tf.get_variable_scope().reuse:
tf.sg_summary_param(x)
return x
def uniform(name, shape, scale=0.05, dtype=tf.sg_floatx):
r"""Returns an initializer of random numbers based on uniform distribution.
Note that the default value of `scale` (=0.05) is different from
the min/max values (=0.0, 1.0) of tf.random_uniform_initializer.
"""
shape = shape if isinstance(shape, (tuple, list)) else [shape]
x = tf.get_variable(name,
shape,
dtype=dtype,
initializer=tf.random_uniform_initializer(
minval=-scale, maxval=scale))
# add summary
if not tf.get_variable_scope().reuse:
tf.sg_summary_param(x)
return x