neupy.init module
- class neupy.init.Initializer[source]
Base class for parameter initialization.
Methods
sample(shape, return_array=False) Returns tensorflow’s tensor or numpy array with specified shape. Type of the object depends on the return_array value. Numpy array will be returned when return_array=True and tensor otherwise. - inherit_method_docs = True[source]
- sample(shape, return_array=False)[source]
Returns tensorflow’s tensor with specified shape.
Parameters: - shape : tuple
Parameter shape.
- return_array : bool
Returns numpy’s array when equal to True and tensorflow’s tensor when equal to False. Defaults to False.
Returns: - array-like or Tensor
- class neupy.init.Constant[source]
Initialize parameter that has constant values.
Parameters: - value : float, int
All parameters in the tensor will be equal to this value. Defaults to 0.
Methods
sample(shape, return_array=False) Returns tensorflow’s tensor or numpy array with specified shape. Type of the object depends on the return_array value. Numpy array will be returned when return_array=True and tensor otherwise. - sample(shape, return_array=False)[source]
Returns tensorflow’s tensor with specified shape.
Parameters: - shape : tuple
Parameter shape.
- return_array : bool
Returns numpy’s array when equal to True and tensorflow’s tensor when equal to False. Defaults to False.
Returns: - array-like or Tensor
- class neupy.init.Normal[source]
Initialize parameter sampling from the normal distribution.
Parameters: - mean : int, float
Mean of the normal distribution.
- std : int, float
Standard deviation of the normal distribution.
- seed : None or int
Random seed. Integer value will make results reproducible. Defaults to None.
Methods
sample(shape, return_array=False) Returns tensorflow’s tensor or numpy array with specified shape. Type of the object depends on the return_array value. Numpy array will be returned when return_array=True and tensor otherwise. - sample(shape, return_array=False)[source]
Returns tensorflow’s tensor with specified shape.
Parameters: - shape : tuple
Parameter shape.
- return_array : bool
Returns numpy’s array when equal to True and tensorflow’s tensor when equal to False. Defaults to False.
Returns: - array-like or Tensor
- class neupy.init.Uniform[source]
Initialize parameter sampling from the uniform distribution.
Parameters: - minval : int, float
Minimum possible value.
- maxval : int, float
Maximum possible value.
- seed : None or int
Random seed. Integer value will make results reproducible. Defaults to None.
Methods
sample(shape, return_array=False) Returns tensorflow’s tensor or numpy array with specified shape. Type of the object depends on the return_array value. Numpy array will be returned when return_array=True and tensor otherwise. - sample(shape, return_array=False)[source]
Returns tensorflow’s tensor with specified shape.
Parameters: - shape : tuple
Parameter shape.
- return_array : bool
Returns numpy’s array when equal to True and tensorflow’s tensor when equal to False. Defaults to False.
Returns: - array-like or Tensor
- class neupy.init.Orthogonal[source]
Initialize matrix with orthogonal basis.
Parameters: - scale : int, float
Scales output matrix by a specified factor. Defaults to 1.
- seed : None or int
Random seed. Integer value will make results reproducible. Defaults to None.
Raises: - ValueError
In case if tensor shape has more than 2 dimensions.
Methods
sample(shape, return_array=False) Returns tensorflow’s tensor or numpy array with specified shape. Type of the object depends on the return_array value. Numpy array will be returned when return_array=True and tensor otherwise. - sample(shape, return_array=False)[source]
Returns tensorflow’s tensor with specified shape.
Parameters: - shape : tuple
Parameter shape.
- return_array : bool
Returns numpy’s array when equal to True and tensorflow’s tensor when equal to False. Defaults to False.
Returns: - array-like or Tensor
- class neupy.init.HeNormal[source]
Kaiming He parameter initialization method based on the normal distribution.
Parameters: - gain : float
Scales variance of the distribution by this factor. Value 2 is a suitable choice for layers that have Relu non-linearity. Defaults to 1.
- seed : None or int
Random seed. Integer value will make results reproducible. Defaults to None.
References
- [1] Kaiming He, Xiangyu Zhan, Shaoqing Ren, Jian Sun.
- Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, 2015.
Methods
sample(shape, return_array=False) Returns tensorflow’s tensor or numpy array with specified shape. Type of the object depends on the return_array value. Numpy array will be returned when return_array=True and tensor otherwise. - sample(shape, return_array=False)[source]
Returns tensorflow’s tensor with specified shape.
Parameters: - shape : tuple
Parameter shape.
- return_array : bool
Returns numpy’s array when equal to True and tensorflow’s tensor when equal to False. Defaults to False.
Returns: - array-like or Tensor
- class neupy.init.HeUniform[source]
Kaiming He parameter initialization method based on the uniformal distribution.
Parameters: - gain : float
Scales variance of the distribution by this factor. Value 2 is a suitable choice for layers that have Relu non-linearity. Defaults to 1.
- seed : None or int
Random seed. Integer value will make results reproducible. Defaults to None.
References
- [1] Kaiming He, Xiangyu Zhan, Shaoqing Ren, Jian Sun.
- Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, 2015.
Methods
sample(shape, return_array=False) Returns tensorflow’s tensor or numpy array with specified shape. Type of the object depends on the return_array value. Numpy array will be returned when return_array=True and tensor otherwise. - sample(shape, return_array=False)[source]
Returns tensorflow’s tensor with specified shape.
Parameters: - shape : tuple
Parameter shape.
- return_array : bool
Returns numpy’s array when equal to True and tensorflow’s tensor when equal to False. Defaults to False.
Returns: - array-like or Tensor
- class neupy.init.XavierNormal[source]
Xavier Glorot parameter initialization method based on normal distribution.
Parameters: - gain : float
Scales variance of the distribution by this factor. Value 2 is a suitable choice for layers that have Relu non-linearity. Defaults to 1.
- seed : None or int
Random seed. Integer value will make results reproducible. Defaults to None.
References
- [1] Xavier Glorot, Y Bengio. Understanding the difficulty
- of training deep feedforward neural networks, 2010.
Methods
sample(shape, return_array=False) Returns tensorflow’s tensor or numpy array with specified shape. Type of the object depends on the return_array value. Numpy array will be returned when return_array=True and tensor otherwise. - sample(shape, return_array=False)[source]
Returns tensorflow’s tensor with specified shape.
Parameters: - shape : tuple
Parameter shape.
- return_array : bool
Returns numpy’s array when equal to True and tensorflow’s tensor when equal to False. Defaults to False.
Returns: - array-like or Tensor
- class neupy.init.XavierUniform[source]
Xavier Glorot parameter initialization method based on uniform distribution.
Parameters: - gain : float
Scales variance of the distribution by this factor. Value 2 is a suitable choice for layers that have Relu non-linearity. Defaults to 1.
- seed : None or int
Random seed. Integer value will make results reproducible. Defaults to None.
References
- [1] Xavier Glorot, Y Bengio. Understanding the difficulty
- of training deep feedforward neural networks, 2010.
Methods
sample(shape, return_array=False) Returns tensorflow’s tensor or numpy array with specified shape. Type of the object depends on the return_array value. Numpy array will be returned when return_array=True and tensor otherwise. - sample(shape, return_array=False)[source]
Returns tensorflow’s tensor with specified shape.
Parameters: - shape : tuple
Parameter shape.
- return_array : bool
Returns numpy’s array when equal to True and tensorflow’s tensor when equal to False. Defaults to False.
Returns: - array-like or Tensor