def backward(ctx, grad_output):
a, b = ctx.saved_tensors
#raise Exception("TODO: Implement '-' backward")
# calculate gradient of output w.r.t. each input
temp_a = (1. / b.data)
grad_a = temp_a * grad_output.data
temp_b = -(a.data / (b.data**2))
grad_b = temp_b * grad_output.data
if a.shape != b.shape:
if a.shape != grad_a.shape:
grad_a = unbroadcast(grad_a, a.shape)
if b.shape != grad_b.shape:
grad_b = unbroadcast(grad_b, b.shape)
# the order of gradients returned should match the order of the arguments
return tensor.Tensor(grad_a), tensor.Tensor(grad_b)
def forward(ctx, a, shape):
if not type(a).__name__ == 'Tensor':
raise Exception("Arg for Reshape must be tensor: {}".format(type(a).__name__))
ctx.shape = a.shape
requires_grad = a.requires_grad
c = tensor.Tensor(a.data.reshape(shape), requires_grad=requires_grad,
is_leaf=not requires_grad)
return c
def backward(ctx, grad_output):
predicted, target = ctx.saved_tensors
Softmax = ((np.exp(predicted.data)) /
(np.sum(np.exp(predicted.data), axis=1).reshape(-1, 1)))
one_hot = to_one_hot(target.data, Softmax.shape[-1])
grad_predicted = ((Softmax - one_hot.data) *
grad_output.data) / predicted.data.shape[0]
return (tensor.Tensor(grad_predicted), )
def backward(ctx, grad_output):
# TODO: Finish Conv1d backward pass. It's surprisingly similar to the forward pass.
x,weight,stride = ctx.saved_tensors
batch_size, in_channel, input_size = x.shape
out_channel, _, kernel_size = weight.shape
stride = stride.data.item()
output_size = grad_output.shape[-1]
#Or grad_w = np.zeros_like(weight) #since gradient is not transposed in pytorch contrary to math
grad_w = np.zeros(shape=(out_channel,in_channel,kernel_size))
grad_y = np.zeros(shape=(batch_size,in_channel,input_size))
grad_b = np.zeros(shape=(out_channel,))
#Grad_b
grad_b = np.einsum('ijk->j',grad_output.data)
##grad_w
#upsample
new_shape = (grad_output.shape[2]-1) * stride + 1
out_z = np.zeros((grad_output.shape[0],grad_output.shape[1],new_shape))
out_z[:,:,::stride] = grad_output.data
for t in range(input_size-out_z.shape[2]+1):
if t < grad_w.shape[2]:
seg_b = x.data[:,:,t:t+out_z.shape[2]]
grad_w[:,:,t] = np.einsum('ijk,ilk->lj',seg_b,out_z)
### grad_x
#For strided z from y|upsample
new_k = (grad_output.shape[2]-1) * stride + 1
out = np.zeros((grad_output.shape[0],grad_output.shape[1],new_k))
out[:,:,::stride] = grad_output.data
#Padding to restore kernel_size - 1 # but not for stride greater than 1, but we upsample and behave stride as 1
npad = ((0, 0), (0, 0), (kernel_size-1, kernel_size-1))
out_2 = np.pad(out,pad_width=npad, mode='constant', constant_values=0)
flip_weight = np.flip(weight.data,axis=2)
for idx,t in enumerate(range(out_2.shape[2]-kernel_size+1)):
seg_a = out_2[:,:,t:t+kernel_size]
grad_y[:,:,t] = np.einsum('ijk,jlk->il',seg_a,flip_weight)
return tensor.Tensor(grad_y,requires_grad=True,is_leaf=True), tensor.Tensor(grad_w,requires_grad=True,is_leaf=True) \
,tensor.Tensor(grad_b,requires_grad=True,is_leaf=True)
def backward(ctx, grad_output):
a = ctx.saved_tensors[0]
grad_output = tensor.Tensor(2 * a.data * grad_output.data)
#print("Shape of Power :",grad_output.shape)
# TODO: Implement more Functions below
#print("Power :",grad_output)
return grad_output, None
def forward(ctx, a):
if not type(a).__name__ == 'Tensor':
raise Exception("Arg for Log must be tensor: {}".format(type(a).__name__))
ctx.save_for_backward(a)
requires_grad = a.requires_grad
c = tensor.Tensor(np.log(a.data), requires_grad=requires_grad,
is_leaf=not requires_grad)
return c
def backward(ctx, grad_output):
a = ctx.saved_tensors
zero_check = np.zeros((1))
grad_output = np.multiply(
tensor.Tensor(np.where(a[0].data <= zero_check, 0, 1)),
grad_output)
#print("ReLU :",grad_output)
# TODO: Implement more Functions below
return grad_output, None
def backward(ctx, grad_output):
a, b = ctx.saved_tensors
#pdb.set_trace()
#raise Exception("TODO: Implement '-' backward")
# calculate gradient of output w.r.t. each input
grad_a = np.matmul(grad_output.data, b.T().data)
grad_b = np.matmul(a.T().data, grad_output.data)
if b.shape != grad_b.shape:
grad_b = unbroadcast(grad_b, b)
if a.shape != grad_a.shape:
grad_a = unbroadcast(grad_a, a)
grad_a = tensor.Tensor(grad_a)
grad_b = tensor.Tensor(grad_b)
# TODO: Implement more Functions below
#print('grad_a and grad_b from Matmul :',grad_a.shape,grad_b.shape)
return grad_a, grad_b
def forward(ctx, a):
if not len(a.shape) == 2:
raise Exception("Arg for Transpose must be 2D tensor: {}".format(
a.shape))
requires_grad = a.requires_grad
b = tensor.Tensor(a.data.T,
requires_grad=requires_grad,
is_leaf=not requires_grad)
return b
def backward(ctx, grad_output):
x, = ctx.saved_tensors
# calculate gradient of output w.r.t. each input
grad_x = np.where(x.data >= 0, np.ones(x.data.shape),
np.zeros(x.data.shape))
grad_x = grad_x * grad_output.data
#grad_x = unbroadcast(grad_x, grad_x.shape[1])
# the order of gradients returned should match the order of the arguments
return tensor.Tensor(grad_x)
def backward(ctx, grad_output):
a = ctx.saved_tensors[0]
grad_output = tensor.Tensor(np.exp(a.data) * grad_output.data)
#print("Root :",grad_output)
#print("Shape of Root",grad_output.shape)
# TODO: Implement more Functions below
#print('grad_output shape in Exp:',grad_output.shape)
return grad_output, None
def backward(ctx, grad_output):
a = ctx.saved_tensors[0]
grad_output = tensor.Tensor(0.5 * ((a.data)**(-0.5)) *
grad_output.data)
#print("Root :",grad_output)
#print("Shape of Root",grad_output.shape)
# TODO: Implement more Functions below
return grad_output, None
def backward(ctx, grad_output):
x, weight, output_size, bias, stride = ctx.saved_tensors
batch_size, in_channel, input_size = x.shape
out_channel, _, kernel_size = weight.shape
dz = grad_output.data #np.asarray([[[1,1],[2,1],[1,2]]]) #np.asarray([[[1,1,1,1],[2,1,2,1],[1,2,1,2]]]) #
#print('dz:',dz)
if stride.data > 1:
dz = upsample(dz, stride.data, input_size, kernel_size)
dzpad = np.zeros(
(batch_size, out_channel, (input_size + 2 * (kernel_size - 1))))
Wflip = weight.data
for i in range(weight.shape[0]):
Wflip[i] = np.fliplr(weight.data[i])
for i in range(batch_size):
for j in range(out_channel):
dzpad[i, j, kernel_size - 1:input_size] = dz[i,
j, :] #center map
dy = np.zeros((batch_size, in_channel, input_size))
for i in range(batch_size):
for j in range(in_channel):
for k in range(input_size):
segment = dzpad[:, :, k:k + kernel_size]
dy[i, j, k] = np.sum(
Wflip[:, j, :] *
segment[i, :, :]) #np.tensordot(Wflip, segment)
dw = np.zeros((out_channel, in_channel, kernel_size))
for i in range(out_channel):
for j in range(in_channel):
for k in range(kernel_size):
dw[i, j, k] = np.sum(dz[:, i, :] *
x.data[:, j, k:(k + input_size) -
(kernel_size - 1)])
grad_data = grad_output.data
#grad_b = (np.sum(grad_data, axis=2)[0]*grad_data.shape[0])
grad_b = np.sum(grad_data, axis=(0, 2))
return tensor.Tensor(dy), tensor.Tensor(dw), tensor.Tensor(
grad_b), None
def forward(ctx, a):
# Save inputs to access later in backward pass.
ctx.save_for_backward(a)
# Create addition output and sets `requires_grad and `is_leaf`
# (see appendix A for info on those params)
requires_grad = a.requires_grad
c = tensor.Tensor(np.maximum(a.data,0), requires_grad=requires_grad,
is_leaf=not requires_grad)
return c
def backward(ctx, grad_output):
a = ctx.saved_tensors[0]
#print("Shape of a:",a.shape)
#print("Shape of grad_ouput :",grad_output.shape)
grad_a = tensor.Tensor(np.ones(a.shape) * grad_output.data)
#print('grad_output shape in Sum_column:',grad_a.shape)
return grad_a, None
def backward(ctx, grad_output):
a, b = ctx.saved_tensors
#raise Exception("TODO: Implement '-' backward")
# calculate gradient of output w.r.t. each input
grad_a = np.multiply(np.divide(1, b.data), grad_output.data)
grad_b = -np.multiply(np.multiply(a.data, np.divide(1, b.data**2)),
grad_output.data)
if b.shape != grad_b.shape:
grad_b = tensor.Tensor(unbroadcast(grad_b, b))
if a.shape != grad_a.shape:
grad_a = tensor.Tensor(unbroadcast(grad_a, a))
# TODO: Implement more Functions below
#print('grad_a and grad_b from Division :',grad_a.shape,grad_b.shape)
return grad_a, grad_b
def backward(ctx, grad_output):
# retrieve forward inputs that we stored
a, b = ctx.saved_tensors
# calculate gradient of output w.r.t. each input
"""
grad_output := dl/dc i.e. gradient of loss w.r.t output (c)
grad_a := dl/da i.e. gradient of loss w.r.t a
dl/da = (dl/dc) . (dc/da)
dc/da = d/da(a + b) ==> 1
"""
grad_a = np.ones(a.shape) * grad_output.data
grad_b = np.ones(b.shape) * grad_output.data
if a.shape != b.shape:
if a.shape != grad_a.shape:
grad_a = unbroadcast(grad_a, a.shape)
if b.shape != grad_b.shape:
grad_b = unbroadcast(grad_b, b.shape)
# the order of gradients returned should match the order of the arguments
return tensor.Tensor(grad_a), tensor.Tensor(grad_b)
def forward(ctx, a, b):
if not (type(a).__name__ == 'Tensor' and type(b).__name__ == 'Tensor'):
raise Exception("Both args must be Tensors:{}, {}".format(type(a).__name__, type(b).__name__))
ctx.save_for_backward(a, b)
requires_grad = a.requires_grad or b.requires_grad
# 加法运算中只要有一项requires_grad=True
# 则结果相加结果的requires_grad也为True
# 开始创建的tensor is_leaf都为True
c = tensor.Tensor(a.data * b.data, requires_grad=requires_grad,
is_leaf=not requires_grad)
return c
def forward(ctx, a, b):
# Check that inputs are tensors of same shape
if not (type(a).__name__ == 'Tensor' and type(b).__name__ == 'Tensor') or \
a.data.shape != b.data.shape:
raise Exception("Both args must be Tensors: {}, {}".format(type(a).__name__, type(b).__name__))
ctx.save_for_backward(a, b)
requires_grad = a.requires_grad or b.requires_grad
c = tensor.Tensor(a.data - b.data, requires_grad=requires_grad,
is_leaf=not requires_grad)
return c
def backward(ctx, grad_output):
# retrieve forward inputs that we stored
a = ctx.saved_tensors[0]
# calculate gradient of output w.r.t. each input
# dL/da = dout/da * dL/dout
grad_relu = grad_output.data * np.where(a.data<0,0,1)
# the order of gradients returned should match the order of the arguments
grad_relu = tensor.Tensor(unbroadcast(grad_relu, a.shape))
return grad_relu
def forward(ctx, predicted, target):
ctx.save_for_backward(predicted, target)
LogSoftmax = np.log(
(np.exp(predicted.data)) /
(np.sum(np.exp(predicted.data), axis=1).reshape(-1, 1)))
one_hot = to_one_hot(target.data, LogSoftmax.shape[-1])
N = LogSoftmax.shape[0]
LLLoss = np.sum(LogSoftmax * one_hot.data) / N
requires_grad = predicted.requires_grad
return tensor.Tensor(-LLLoss,
requires_grad=requires_grad,
is_leaf=not requires_grad)
def backward(ctx, grad_output):
# retrieve forward inputs that we stored
a, b = ctx.saved_tensors
# calculate gradient of output w.r.t. each input
grad_a = np.ones(a.shape) * grad_output.data
grad_b = np.ones(b.shape) * grad_output.data
if b.shape != grad_b.shape:
grad_b = unbroadcast(grad_b, b)
if a.shape != grad_a.shape:
grad_a = unbroadcast(grad_a, a)
# the order of gradients returned should match the order of the arguments
grad_a = tensor.Tensor(grad_a)
grad_b = tensor.Tensor(grad_b)
# TODO: Implement more Functions below
#print('grad_a and grad_b from Add :',grad_a.shape,grad_b.shape)
return grad_a, grad_b
def backward(ctx, grad_output):
a, b = ctx.saved_tensors
#raise Exception("TODO: Implement '-' backward")
# calculate gradient of output w.r.t. each input
grad_a = np.ones(a.shape) * grad_output.data
grad_b = -1 * np.ones(b.shape) * grad_output.data
if len(b.shape) == 1:
grad_b = unbroadcast(grad_b, b)
if len(a.shape) == 1:
grad_a = unbroadcast(grad_a, a)
# TODO: Implement more Functions below
grad_a = tensor.Tensor(grad_a)
grad_b = tensor.Tensor(grad_b)
# TODO: Implement more Functions below
#print('grad_a and grad_b from Subtract :',grad_a.shape,grad_b.shape)
return grad_a, grad_b
def forward(ctx, a, axis, keepdims):
if not type(a).__name__ == 'Tensor':
raise Exception("Only log of tensor is supported")
ctx.axis = axis
ctx.shape = a.shape
if axis is not None:
ctx.len = a.shape[axis]
ctx.keepdims = keepdims
requires_grad = a.requires_grad
c = tensor.Tensor(a.data.sum(axis = axis, keepdims = keepdims), \
requires_grad=requires_grad, is_leaf=not requires_grad)
return c
def backward(ctx, grad_output):
# retrieve forward inputs that we stored
z = ctx.saved_tensors
# calculate gradient of output w.r.t. each input
# dL/da = dout/da * dL/dout
grad = tensor.Tensor(np.heaviside(z[0].data,0)*grad_output.data)
# the order of gradients returned should match the order of the arguments
return grad,
def backward(ctx, grad_output):
grad_out = grad_output.data
if (ctx.axis is not None) and (not ctx.keepdims):
grad_out = np.expand_dims(grad_output.data, axis=ctx.axis)
else:
grad_out = grad_output.data.copy()
grad = np.ones(ctx.shape) * grad_out
assert grad.shape == ctx.shape
# Take note that gradient tensors SHOULD NEVER have requires_grad = True.
return tensor.Tensor(grad), None, None
def backward(ctx, grad_output):
# retrieve forward inputs that we stored
x = ctx.saved_tensors
# calculate gradient of output w.r.t. each input
# dL/da = dout/da * dL/dout
# grad_a = np.matmul(grad_output.data,np.transpose(1/x[0].data))
# dL/db = dout/db * dL/dout
grad_a = grad_output.data * 1/x[0].data
grad_a = tensor.Tensor(grad_a)
# the order of gradients returned should match the order of the arguments
return grad_a,
def forward(ctx, a, b):
# Check that inputs are tensors of same shape
if not (type(a).__name__ == 'Tensor' and type(b).__name__ == 'Tensor') or \
a.data.shape != b.data.shape:
pass
# Save inputs to access later in backward pass.
ctx.save_for_backward(a, b)
# Create addition output and sets `requires_grad and `is_leaf`
# (see appendix A for info on those params)
requires_grad = a.requires_grad or b.requires_grad
c = tensor.Tensor(a.data / b.data, requires_grad=requires_grad,
is_leaf=not requires_grad)
return c
def forward(ctx, x, indices):
'''
Args:
x (tensor): Tensor object that we need to slice
indices (int,list,Slice): This is the key passed to the __getitem__ function of the Tensor object when it is sliced using [ ] notation.
'''
# raise NotImplementedError('Implemented Slice.forward')
ctx.x = x
ctx.indices = indices
requires_grad = x.requires_grad
sliced = x.data[indices]
return tensor.Tensor(sliced,
requires_grad=requires_grad,
is_leaf=not requires_grad)
def forward(ctx, x, p):
if not (type(x).__name__ == 'Tensor'):
raise Exception("Args must be Tensors: {}".format(
type(x).__name__))
ctx.save_for_backward(x, p)
result = np.float_power(x.data, p.data)
requires_grad = x.requires_grad
z = tensor.Tensor(result,
requires_grad=requires_grad,
is_leaf=not requires_grad)
return z
