mindspore.common.initializer

Initializer for cell parameters.

class mindspore.common.initializer.Constant(value)

Initialize a constant.

Parameters

value (Union[int, numpy.ndarray]) – The value to initialize.

Returns

Array, initialize array.

class mindspore.common.initializer.HeUniform(**kwargs)

Initialize the array with He kaiming uniform algorithm, and from a uniform distribution collect samples within U[-boundary, boundary] where \(boundary = \sqrt{\frac{6}{n_{in}}}\) where \(n_{in}\) is the number of input units in the weight tensor.

Parameters

arr (Array) – The array to be assigned.

Returns

Array, assigned array.

class mindspore.common.initializer.Initializer(**kwargs)

The base class of the initializer.

Parameters

kwargs (dict) – Keyword arguments for Initializer.

Returns

Array, assigned array.

to_tensor(slice_index=None, shape=None)

Get the tensor format data of this Initializer.

Parameters

• slice_index (int) – Slice index of a parameter’s slices. Used when initialize a slice of a parameter, it guarantee that devices use the same slice can generate the same tensor.

• shape (list[int]) – Shape of the slice, used when initialize a slice of the parameter.

class mindspore.common.initializer.Normal(sigma=0.01)

Initialize a normal array, and obtain values N(0, sigma) from the uniform distribution to fill the input tensor.

Parameters

sigma (float) – The sigma of the array. Default: 0.01.

Returns

Array, normal array.

class mindspore.common.initializer.One(**kwargs)

Initialize the array to one.

Parameters

arr (Array) – The array to be assigned.

Returns

Array, assigned array.

class mindspore.common.initializer.TruncatedNormal(sigma=0.01)

Initialize a truncated normal distribution which is a bounded normal distribution within N(low, high).

Parameters

sigma (float) – The sigma of the array. Default: 0.01.

Returns

Array, truncated normal array.

class mindspore.common.initializer.Uniform(scale=0.07)

Initialize a uniform array, and obtain values U(-scale, scale) from the uniform distribution to fill the input tensor.

Parameters

scale (float) – The scale of the array. Default: 0.07.

Returns

Array, uniform array.

class mindspore.common.initializer.XavierUniform(gain=1)

Initialize the array with xavier uniform algorithm, and from a uniform distribution collect samples within U[-boundary, boundary] where \(boundary = gain * \sqrt{\frac{6}{n_{in} + n_{out}}}\).

Parameters

gain (Array) – The array to be assigned. Default: 1.

Returns

Array, assigned array.

class mindspore.common.initializer.Zero(**kwargs)

Initialize the array to zero.

Parameters

arr (Array) – The array to be assigned.

Returns

Array, assigned array.

mindspore.common.initializer.initializer(init, shape=None, dtype=mindspore.float32)

Create and initialize a tensor.

Parameters

• init (Union[Tensor, str, Initializer, numbers.Number]) –

  Initialize value.

  • str: The init should be the alias of the class inheriting from Initializer and the corresponding class will be called.

  • Initializer: The init should be the class inheriting from Initializer to initialize tensor.

  • numbers.Number: The Constant will be called to initialize tensor.

• shape (Union[tuple, list, int]) – A list of integers, a tuple of integers or an integer as the shape of output. Default: None.

• dtype (mindspore.dtype) – The type of data in initialized tensor. Default: mindspore.float32.

Returns

Union[Tensor, Initializer], When init is Tensor, the return is Tensor object, otherwise the return is Initialize object.

Examples

>>> tensor = initializer('ones', [1, 2, 3], mindspore.float32)