sugartensor.sg_initializer module

sugartensor.sg_initializer.constant(name, shape, value=0, dtype=tf.float32, summary=True)

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.

Returns:

A Variable.

sugartensor.sg_initializer.external(name, value, dtype=tf.float32, summary=True)

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.

Returns:

A Variable. Has the same contents as value of dtype.

sugartensor.sg_initializer.glorot_uniform(name, shape, scale=1, dtype=tf.float32, summary=True)

See [Glorot & Bengio. 2010.](http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf)

Args:

name: The name of new variable shape: A tuple/list of integers. scale: A Python scalar. Scale to initialize. Default is 1. dtype: The data type. Default is float32. summary: If True, add this constant to tensor board summary.

Returns:

A Variable.

sugartensor.sg_initializer.he_uniform(name, shape, scale=1, dtype=tf.float32, summary=True)

See [He et al. 2015](http://arxiv.org/pdf/1502.01852v1.pdf)

Args:

name: The name of new variable shape: A tuple/list of integers. scale: A Python scalar. Scale to initialize. Default is 1. dtype: The data type. Default is float32. summary: If True, add this constant to tensor board summary.

Returns:

A Variable.

sugartensor.sg_initializer.identity(name, dim, scale=1, dtype=tf.float32, summary=True)

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.

Returns:

A 2-D Variable.

sugartensor.sg_initializer.orthogonal(name, shape, scale=1.1, dtype=tf.float32, summary=True)

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.

Returns:

A Variable.

sugartensor.sg_initializer.uniform(name, shape, scale=0.05, dtype=tf.float32, summary=True)

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.