def backward(ctx, grad_output):
a, b = ctx.saved_tensors
grad_a = grad_output.data / b.data
grad_b = (-1) * a.data * grad_output.data / (b.data**2)
grad_a = MLlib.Tensor(unbroadcast(grad_a, a.shape))
grad_b = MLlib.Tensor(unbroadcast(grad_b, b.shape))
return grad_a, grad_b
def backward(ctx, grad_output):
shape_a, shape_b = ctx.shape_a, ctx.shape_b
grad_a = np.ones(shape_a) * grad_output.data
grad_b = np.ones(shape_b) * grad_output.data * (-1)
grad_a = MLlib.Tensor(unbroadcast(grad_a, shape_a))
grad_b = MLlib.Tensor(unbroadcast(grad_b, shape_b))
return grad_a, grad_b
def backward(ctx, grad_output):
grad_output = grad_output.data
a, b = ctx.saved_tensors
grad_a = (grad_output) @ (b.data.T)
grad_b = (a.data.T) @ (grad_output)
grad_a = MLlib.Tensor(unbroadcast(grad_a, a.shape))
grad_b = MLlib.Tensor(unbroadcast(grad_b, b.shape))
return grad_a, grad_b
def backward(ctx, grad_output):
a, b, output = ctx.saved_tensors
grad_a = grad_b = None
if ctx.a_req_grad:
grad_a = b.data * np.power(a.data, b.data - 1) * grad_output.data
grad_a = MLlib.Tensor(unbroadcast(grad_a, a.shape))
if ctx.b_req_grad:
grad_b = output.data * np.log(a.data) * grad_output.data
grad_b = MLlib.Tensor(unbroadcast(grad_b, b.shape))
return grad_a, grad_b
def backward(ctx, grad_output):
o = ctx.saved_tensors[0]
grad_o = o.data * (1 - o.data) * grad_output.data
grad_o = MLlib.Tensor(unbroadcast(grad_o, o.shape))
return grad_o
def backward(ctx, grad_output):
o = ctx.saved_tensors[0]
grad_o = np.greater(o.data, 0).astype(int) * grad_output.data
grad_o = MLlib.Tensor(unbroadcast(grad_o, o.shape))
return grad_o
def backward(ctx, grad_output):
grad_output = grad_output.data
a, b = ctx.saved_tensors
if len(grad_output.shape) > 0:
grad_a = (grad_output).dot(b.data.T)
grad_b = (a.data.T).dot(grad_output)
grad_a = MLlib.Tensor(unbroadcast(grad_a, a.shape))
grad_b = MLlib.Tensor(unbroadcast(grad_b, b.shape))
else:
grad_a = (grad_output) * (b.data.T)
grad_b = (a.data.T) * (grad_output)
grad_a = MLlib.Tensor(unbroadcast(grad_a, a.shape))
grad_b = MLlib.Tensor(unbroadcast(grad_b, b.shape))
return grad_a, grad_b
def test_DivSum():
a = np.random.randn(4, 6, 8)
b = np.random.randn(6, 8)
ma, mb = gen_mT(a, b)
mo = (ma / mb).sum()
mo.backward()
if not_close(mb.grad.data, unbroadcast(-a / (b**2), b.shape)):
raise AssertionError
if not_close(ma.grad.data, 1 / (mb.data)):
raise AssertionError
def backward(ctx, grad_output):
derivative = ctx.derivative_core
grad_prediction = (derivative / derivative.shape[0]) * grad_output.data
return Tensor(unbroadcast(grad_prediction, derivative.shape))
