sciai.architecture.MLP
- class sciai.architecture.MLP(layers, weight_init='xavier_trunc_normal', bias_init='zeros', activation='tanh', last_activation=None)[source]
Multi-layer perceptron. The last layer is without activation function.
The first value in layers in Args should be equal to the size of last axis in_channels in input Tensor.
- Parameters
layers (Union(tuple[int], list[int])) – List of numbers of neurons in each layer, e.g., [2, 10, 10, 1].
weight_init (Union[str, Initializer]) – The weight_init parameter for Dense. The dtype is the same as x. The values of str refer to the function initializer. Default: ‘xavier_trunc_normal’.
bias_init (Union[str, Initializer]) – The bias_init parameter for Dense. The dtype is same as x. The values of str refer to the function initializer. Default: ‘zeros’.
activation (Union[str, Cell, Primitive, FunctionType, None]) – Activation function applied to the output of each fully connected layer excluding the last layer. Both activation name, e.g. ‘relu’, and mindspore activation function, e.g. nn.ReLU(), are supported. Default: ‘tanh’.
last_activation (Union[str, Cell, Primitive, FunctionType, None]) – Activation function applied to the output of the last dense layer. The type rule is the same as activation.
- Inputs:
x (Tensor) - Tensor of shape (∗, in_channels).
- Outputs:
Union(Tensor, tuple[Tensor]), Output Tensor of the network.
- Raises
TypeError – If layers is not one of list, tuple, or elements in layers are not ints.
TypeError – If activation is not one of str, Cell, Primitive, None.
TypeError – If last_activation is not one of str, Cell, Primitive, None.
ValueError – If weight_init is not one of str, Initializer.
ValueError – If bias_init is not one of str, Initializer.
- Supported Platforms:
AscendGPUCPU
Examples
>>> import mindspore as ms >>> import numpy as np >>> from sciai.architecture import MLP >>> x = ms.Tensor(np.array([[180, 234, 154], [244, 48, 247]]), ms.float32) >>> net = MLP((3, 10, 4)) >>> output = net(x) >>> print(output.shape) (2, 4)