mindspore.common.initializer
Initializer for cell parameters.
- class mindspore.common.initializer.Constant(value)[source]
Initialize a constant.
- Parameters
value (Union[int, numpy.ndarray]) – The value to initialize.
- Returns
Array, an array after being assigned.
- class mindspore.common.initializer.HeNormal(negative_slope=0, mode='fan_in', nonlinearity='leaky_relu')[source]
Initialize the array with He kaiming Normal algorithm, and from a normal distribution collect samples within N(0, sigma).
- class mindspore.common.initializer.HeUniform(negative_slope=0, mode='fan_in', nonlinearity='leaky_relu')[source]
Initialize the array with He kaiming uniform algorithm, and from a uniform distribution collect samples within U[-boundary, boundary] The boundary is defined as :
where \(boundary = \sqrt{\frac{6}{(1 + a^2) \times \text{fan_in}}}\).
- class mindspore.common.initializer.Initializer(**kwargs)[source]
The base class of the initializer. Initialization of tensor basic attributes and model weight values.
- Parameters
kwargs (dict) – Keyword arguments for Initializer.
- Returns
Array, an array after being initialized.
- class mindspore.common.initializer.Normal(sigma=0.01)[source]
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)[source]
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)[source]
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)[source]
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)[source]
Initialize the array with xavier uniform algorithm, and from a uniform distribution collect samples within U[-boundary, boundary] The boundary is defined as :
where \(boundary = gain * \sqrt{\frac{6}{n_{in} + n_{out}}}\).
where \(n_{in}\) is the number of input units in the weight tensor. where \(n_{out}\) is the number of output units in the weight tensor.
- Parameters
gain (Array) – The array to be assigned. Default: 1.
- Returns
Array, assigned array.
- class mindspore.common.initializer.Zero(**kwargs)[source]
Initialize the array to zero.
- Parameters
arr (Array) – The array to be assigned.
- Returns
Array, an array after being assigned.
- mindspore.common.initializer.initializer(init, shape=None, dtype=mindspore.float32)[source]
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, MetaTensor], When init is Tensor, the return is Tensor object, otherwise the return is Initialize object.
Examples
>>> tensor = initializer('ones', [1, 2, 3], mindspore.float32) >>> tensor = initializer(One(), [1, 2, 3], mindspore.float32) >>> tensor = initializer(0, [1, 2, 3], mindspore.float32)