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softmax_regression.[A+ GUIDE]

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# Do not use packages that are not in standard distribution of python import numpy as np from ._base_network import _baseNetwork class SoftmaxRegression(_baseNetwork): def __init__(self, input_siz ... e=28*28, num_classes=10): ''' A single layer softmax regression. The network is composed by: a linear layer without bias => (optional ReLU activation) => Softmax :param input_size: the input dimension :param num_classes: the number of classes in total ''' super().__init__(input_size, num_classes) self._weight_init() def _weight_init(self): ''' initialize weights of the single layer regression network. No bias term included. :return: None; self.weights is filled based on method - W1: The weight matrix of the linear layer of shape (num_features, hidden_size) ''' np.random. [Show More]

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