def _execute(self, x):
if not self._is_initialized:
self.initialize()
n = x.shape[0]
if n > 1:
bias = numx.tile(self.b, (n, 1))
else:
bias = self.b
y = self.transfer_func.f(mult(x, self.w) + bias)
return y
def _execute(self, x):
if not self._is_initialized:
self.initialize()
n = x.shape[0]
if n > 1:
bias = numx.tile(self.b, (n, 1))
else:
bias = self.b
y = self.transfer_func.f(mult(x, self.w) + bias)
return y
def _switchboard_grad(self, x):
grad = numx.zeros((self.output_dim, self.input_dim))
grad[range(self.output_dim), self.connections] = 1
return numx.tile(grad, (len(x), 1, 1))
def _switchboard_grad(self, x):
grad = numx.zeros((self.output_dim, self.input_dim))
grad[range(self.output_dim), self.connections] = 1
return numx.tile(grad, (len(x), 1, 1))
