Linear.py

class Linear(Module):
    """ Applies a linear transformation to the incoming data: y=Ax+b
    Parameters
    ----------
    in_features : int
        size of each input sample
    out_features : int
        size of each output sample
    Variables
    ----------
    weight : array-like, shape = [out_features x in_features]
        the learnable weights of the module
    bias : array-like, shape = [out_features]
        the learnable bias of the module
    """
    def __init__(self, in_features, out_features):
        law_bound = 1/np.sqrt(in_features)
        self._bias = np.random.uniform(-law_bound,law_bound,size=(out_features)).astype(np.float32)
        self._weight = np.random.uniform(-law_bound,law_bound,size=(out_features, in_features)).astype(np.float32)

    def forward(self, X):
        self._last_input = X
        return np.matmul(X, self._weight.T) + np.tile(self._bias, (X.shape[0],1))

    def backward(self, output_grad):
        self._grad_bias = np.sum(output_grad,axis=0)
        self._grad_weight = np.dot(output_grad.T, self._last_input)
        return np.dot(output_grad, self._weight)