from __future__ import absolute_import
import sugartensor as tf
# noinspection PyPackageRequirements
import numpy as np
__author__ = 'namju.kim@kakaocorp.com'
[docs]def constant(name, shape, value=0, dtype=tf.sg_floatx, summary=True, regularizer=None, trainable=True):
r"""Creates a tensor variable of which initial values are `value` and shape is `shape`.
Args:
name: The name of new variable.
shape: A tuple/list of integers or an integer.
If shape is an integer, it is converted to a list.
value: A Python scalar. All elements of the initialized variable
will be set to this value. Default is 0.
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.constant_initializer(value),
regularizer=regularizer, trainable=trainable)
# add summary
if summary:
tf.sg_summary_param(x)
return x
[docs]def identity(name, dim, scale=1, dtype=tf.sg_floatx, summary=True, regularizer=None, trainable=True):
r"""Creates a tensor variable of which initial values are of
an identity matrix.
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.
For example,
```
identity("identity", 3, 2) =>
[[2. 0. 0.]
[0. 2. 0.]
[0. 0. 2.]]
```
Args:
name: The name of new variable.
dim: An int. The size of the first and second dimension of the output tensor.
scale: A Python scalar. The value on the diagonal.
dtype: The type of the elements of the resulting tensor.
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 2-D `Variable`.
"""
x = tf.get_variable(name,
initializer=tf.constant(np.eye(dim) * scale, dtype=dtype),
regularizer=regularizer, trainable=trainable)
# add summary
if summary:
tf.sg_summary_param(x)
return x
[docs]def orthogonal(name, shape, scale=1.1, dtype=tf.sg_floatx, summary=True, regularizer=None, trainable=True):
r"""Creates a tensor variable of which initial values are of
an orthogonal ndarray.
See [Saxe et al. 2014.](http://arxiv.org/pdf/1312.6120.pdf)
Args:
name: The name of new variable.
shape: A tuple/list of integers.
scale: A Python scalar.
dtype: Either float32 or float64.
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`.
"""
flat_shape = (shape[0], np.prod(shape[1:]))
a = np.random.normal(0.0, 1.0, flat_shape)
u, _, v = np.linalg.svd(a, full_matrices=False)
# pick the one with the correct shape
q = u if u.shape == flat_shape else v
q = q.reshape(shape)
# create variable
x = tf.get_variable(name,
initializer=tf.constant(scale * q[:shape[0], :shape[1]], dtype=dtype),
regularizer=regularizer, trainable=trainable)
# add summary
if summary:
tf.sg_summary_param(x)
return x
[docs]def external(name, value, dtype=tf.sg_floatx, summary=True, regularizer=None, trainable=True):
r"""Creates a tensor variable of which initial values are `value`.
For example,
```
external("external", [3,3,1,2])
=> [3. 3. 1. 2.]
```
Args:
name: The name of new variable.
value: A constant value (or list) of output type `dtype`.
dtype: The type of the elements of the resulting tensor.
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`. Has the same contents as `value` of `dtype`.
"""
# create variable
x = tf.get_variable(name,
initializer=tf.constant(value, dtype=dtype),
regularizer=regularizer, trainable=trainable)
# add summary
if summary:
tf.sg_summary_param(x)
return x
def _get_fans(shape):
r"""Returns the size of input dimension and output dimension, given `shape`.
Args:
shape: A list of integers.
Returns:
fan_in: An int. The value of input dimension.
fan_out: An int. The value of output dimension.
"""
if len(shape) == 2:
fan_in = shape[0]
fan_out = shape[1]
elif len(shape) == 4 or len(shape) == 5:
# assuming convolution kernels (2D or 3D).
kernel_size = np.prod(shape[:2])
fan_in = shape[-2] * kernel_size
fan_out = shape[-1] * kernel_size
else:
# no specific assumptions
fan_in = np.sqrt(np.prod(shape))
fan_out = np.sqrt(np.prod(shape))
return fan_in, fan_out