class Linear(Layer):
"""
Dense layer with optional bias weights
Simple weights init weighted by input dimensions
"""
def __init__(self, n_inputs, n_outputs, bias=True):
super().__init__()
self.use_bias = bias
W = np.random.randn(n_inputs, n_outputs) * np.sqrt(2.0 / n_inputs)
self.weight = Tensor(W, autograd=True)
if self.use_bias:
self.bias = Tensor(np.zeros(n_outputs), autograd=True)
self.parameters.append(self.weight)
if self.use_bias:
self.parameters.append(self.bias)
def forward(self, input_matrix):
""" Input dot Weights + bias"""
if self.use_bias:
return input_matrix.dot(self.weight) + self.bias.expand(
0, len(input_matrix.data))
return input_matrix.dot(self.weight)
class Linear(LayerCore):
def __init__(self, n_inputs, n_outputs):
super(Linear, self).__init__()
W = np.random.rand(n_inputs, n_outputs) * np.sqrt(2.0 / n_inputs)
self.weight = Tensor(W, requires_grad = True)
self.bias = Tensor(np.zeros(n_outputs), requires_grad = True)
self.parameters.append(self.weight)
self.parameters.append(self.bias)
def __call__(self, input: Tensor) -> Tensor:
n_samples = input.data.shape[0]
return input.mm(self.weight) + self.bias.expand(repeat = n_samples, axis = 0)
