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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.

Examples

>>> import mindspore
>>> from mindspore.common.initializer import initializer
>>> tensor1 = initializer(0, [1, 2, 3], mindspore.float32)
>>> tensor2 = initializer(5, [1, 2, 3], mindspore.float32)

class mindspore.common.initializer.HeNormal(negative_slope=0, mode="fan_in", nonlinearity="leaky_relu")

Initialize the array with HeKaiming Normal algorithm, and from a normal distribution collect samples within $N(0,{\text{sigma}}^2)$ where

$$sigma=\frac{gain}{\sqrt{mode}}$$

• where $gain$ is an optional scaling factor.

• where $mode$ is the number of input units or output units in the weight tensor.

For details of HeUniform algorithm, please check https://arxiv.org/abs/1502.01852.

Parameters

• negative_slope (int, float, bool) – The negative slope of the rectifier used after this layer (only used when nonlinearity is ‘leaky_relu’). Default: 0.

• mode (str) – Either ‘fan_in’ or ‘fan_out’. Choosing ‘fan_in’ preserves the magnitude of the variance of the weights in the forward pass. Choosing ‘fan_out’ preserves the magnitudes in the backwards pass. Default: fan_in.

• nonlinearity (str) – The non-linear function, recommended to use only with ‘relu’ or ‘leaky_relu’. Default: leaky_relu.

Examples

>>> import mindspore
>>> from mindspore.common.initializer import initializer, HeNormal
>>> tensor1 = initializer(HeNormal(), [1, 2, 3], mindspore.float32)
>>> tensor2 = initializer('he_normal', [1, 2, 3], mindspore.float32)

class mindspore.common.initializer.HeUniform(negative_slope=0, mode="fan_in", nonlinearity="leaky_relu")

Initialize the array with HeKaiming Uniform algorithm, and from a uniform distribution collect samples within $U(-\text{boundary},\text{boundary})$ where

$$boundary=\sqrt{\frac{6}{(1+a^2)\times \text{fan\_in}}}$$

• where $-boundary$ the lower bound of the HeUniform distribution.

• where $boundary$ the upper bound of the HeUniform distribution.

For details of HeUniform algorithm, please check https://arxiv.org/abs/1502.01852.

Parameters

• negative_slope (int, float, bool) – The negative slope of the rectifier used after this layer (only used when nonlinearity is ‘leaky_relu’). Default: 0.

• mode (str) – Either ‘fan_in’ or ‘fan_out’. Choosing ‘fan_in’ preserves the magnitude of the variance of the weights in the forward pass. Choosing ‘fan_out’ preserves the magnitudes in the backwards pass. Default: fan_in.

• nonlinearity (str) – The non-linear function, recommended to use only with ‘relu’ or ‘leaky_relu’. Default: leaky_relu.

Examples

>>> import mindspore
>>> from mindspore.common.initializer import initializer, HeUniform
>>> tensor1 = initializer(HeUniform(), [1, 2, 3], mindspore.float32)
>>> tensor2 = initializer('he_uniform', [1, 2, 3], mindspore.float32)

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

The base class of the initializer. Initialization of tensor basic attributes and model weight values.

Parameters

kwargs (dict) – Keyword arguments for Initializer.

class mindspore.common.initializer.Normal(sigma=0.01, mean=0.0)

Initialize a normal array, and obtain values $N(\text{sigma},\text{mean})$ from the normal distribution to fill the input tensor.

$$f(x)=\frac{1}{\sqrt{2\ast \pi }\ast sigma}exp(-\frac{(x-mean{)}^2}{2\ast {sigma}^2})$$

Parameters

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

• mean (float) – The mean of the array. Default: 0.0.

Examples

>>> import mindspore
>>> from mindspore.common.initializer import initializer, Normal
>>> tensor1 = initializer(Normal(), [1, 2, 3], mindspore.float32)
>>> tensor2 = initializer('normal', [1, 2, 3], mindspore.float32)

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

Fills the input array with the values one.

Parameters

arr (Array) – The array to be assigned.

Examples

>>> import mindspore
>>> from mindspore.common.initializer import initializer, One
>>> tensor1 = initializer(One(), [1, 2, 3], mindspore.float32)
>>> tensor2 = initializer('ones', [1, 2, 3], mindspore.float32)

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

Initialize a truncated normal distribution which is a bounded normal distribution within $N(\text{low},\text{high})$.

Parameters

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

Examples

>>> import mindspore
>>> from mindspore.common.initializer import initializer, TruncatedNormal
>>> tensor1 = initializer(TruncatedNormal(), [1, 2, 3], mindspore.float32)
>>> tensor2 = initializer('truncatedNormal', [1, 2, 3], mindspore.float32)

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

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

Parameters

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

Examples

>>> import mindspore
>>> from mindspore.common.initializer import initializer, Uniform
>>> tensor1 = initializer(Uniform(), [1, 2, 3], mindspore.float32)
>>> tensor2 = initializer('uniform', [1, 2, 3], mindspore.float32)

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

Initialize the array with xavier uniform algorithm, and from a uniform distribution collect samples within $U(-\text{boundary},\text{boundary})$ where:

$$boundary=gain\ast \sqrt{\frac{6}{n_{in}+n_{out}}}$$

• where $gain$ is an optional scaling factor.

• 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.

For details of XavierUniform algorithm, please check http://proceedings.mlr.press/v9/glorot10a.html.

Parameters

gain (float) – An optional scaling factor. Default: 1.

Examples

>>> import mindspore
>>> from mindspore.common.initializer import initializer, XavierUniform
>>> tensor1 = initializer(XavierUniform(), [1, 2, 3], mindspore.float32)
>>> tensor2 = initializer('xavier_uniform', [1, 2, 3], mindspore.float32)

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

Fills the input array with the values zero.

Parameters

arr (Array) – The array to be assigned.

Examples

>>> import mindspore
>>> from mindspore.common.initializer import initializer, Zero
>>> tensor1 = initializer(Zero(), [1, 2, 3], mindspore.float32)
>>> tensor2 = initializer('zeros', [1, 2, 3], mindspore.float32)

mindspore.common.initializer.initializer(init, shape=None, dtype=mstype.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. The value of ‘init’ can be “normal”, “ones” or “zeros”, etc.

  • 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], return is Tensor object.

Examples

>>> import mindspore
>>> from mindspore.common.initializer import initializer, One
>>> tensor1 = initializer('ones', [1, 2, 3], mindspore.float32)
>>> tensor2 = initializer(One(), [1, 2, 3], mindspore.float32)
>>> tensor3 = initializer(0, [1, 2, 3], mindspore.float32)