def __init__(self, kernel_sizes, in_channels, out_channels):
super(Conv2D, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.kernels = [
Variable.random(*kernel_sizes) for _ in range(out_channels)
]
self.biases = [
Variable.random(*kernel_sizes) for _ in range(out_channels)
]
self._parameters.update(
{f"kernel_{i}": kernel
for i, kernel in enumerate(self.kernels)})
self._parameters.update(
{f"bias_{i}": bias
for i, bias in enumerate(self.biases)})
def __init__(self, input_size, output_size):
super(Dense, self).__init__()
self.weights = Variable.random(input_size, output_size)
self.bias = Variable.random(output_size, sign='+')
self._parameters = {'weights': self.weights, 'bias': self.bias}