import numpy as npclass Tensor(object):def __init__(self, data,autograd=False,creators=None,creation_op=None,id=None):self.data = np.array(data)self.autograd = autogradself.grad = Noneif (id is None):self.id = np.random.randint(0, 100000)else:self.id = idself.creators = creatorsself.creation_op = creation_opself.children = {}if (creators is not None):for c in creators:if (self.id not in c.children):c.children[self.id] = 1else:c.children[self.id] += 1def all_children_grads_accounted_for(self):for id, cnt in self.children.items():if (cnt != 0):return Falsereturn Truedef backward(self, grad=None, grad_origin=None):if (self.autograd):if (grad is None):grad = Tensor(np.ones_like(self.data))if (grad_origin is not None):if (self.children[grad_origin.id] == 0):raise Exception("cannot backprop more than once")else:self.children[grad_origin.id] -= 1if (self.grad is None):self.grad = gradelse:self.grad += grad# grads must not have grads of their ownassert grad.autograd == False# only continue backpropping if there's something to# backprop into and if all gradients (from children)# are accounted for override waiting for children if# "backprop" was called on this variable directlyif (self.creators is not None and(self.all_children_grads_accounted_for() orgrad_origin is None)):if (self.creation_op == "add"):self.creators[0].backward(self.grad, self)self.creators[1].backward(self.grad, self)if (self.creation_op == "sub"):self.creators[0].backward(Tensor(self.grad.data), self)self.creators[1].backward(Tensor(self.grad.__neg__().data), self)if (self.creation_op == "mul"):new = self.grad * self.creators[1]self.creators[0].backward(new, self)new = self.grad * self.creators[0]self.creators[1].backward(new, self)if (self.creation_op == "mm"):c0 = self.creators[0]c1 = self.creators[1]new = self.grad.mm(c1.transpose())c0.backward(new)new = self.grad.transpose().mm(c0).transpose()c1.backward(new)if (self.creation_op == "transpose"):self.creators[0].backward(self.grad.transpose())if ("sum" in self.creation_op):dim = int(self.creation_op.split("_")[1])self.creators[0].backward(self.grad.expand(dim,self.creators[0].data.shape[dim]))if ("expand" in self.creation_op):dim = int(self.creation_op.split("_")[1])self.creators[0].backward(self.grad.sum(dim))if (self.creation_op == "neg"):self.creators[0].backward(self.grad.__neg__())#加法def __add__(self, other):if (self.autograd and other.autograd):return Tensor(self.data + other.data,autograd=True,creators=[self, other],creation_op="add")return Tensor(self.data + other.data)#取负def __neg__(self):if (self.autograd):return Tensor(self.data * -1,autograd=True,creators=[self],creation_op="neg")return Tensor(self.data * -1)#减法def __sub__(self, other):if (self.autograd and other.autograd):return Tensor(self.data - other.data,autograd=True,creators=[self, other],creation_op="sub")return Tensor(self.data - other.data)#乘法def __mul__(self, other):if (self.autograd and other.autograd):return Tensor(self.data * other.data,autograd=True,creators=[self, other],creation_op="mul")return Tensor(self.data * other.data)#求和def sum(self, dim):if (self.autograd):return Tensor(self.data.sum(dim), #即 Tensor 对象所存储的 numpy 数组数据）在指定维度 dim 上进行求和操作autograd=True,creators=[self],creation_op="sum_" + str(dim))return Tensor(self.data.sum(dim))#扩展def expand(self, dim, copies):trans_cmd = list(range(0, len(self.data.shape)))trans_cmd.insert(dim, len(self.data.shape))new_data = self.data.repeat(copies).reshape(list(self.data.shape) + [copies]).transpose(trans_cmd)if (self.autograd):return Tensor(new_data,autograd=True,creators=[self],creation_op="expand_" + str(dim))return Tensor(new_data)#转置def transpose(self):if (self.autograd):return Tensor(self.data.transpose(),autograd=True,creators=[self],creation_op="transpose")return Tensor(self.data.transpose())#矩阵乘法def mm(self, x):if (self.autograd):return Tensor(self.data.dot(x.data),autograd=True,creators=[self, x],creation_op="mm")return Tensor(self.data.dot(x.data))def __repr__(self):return str(self.data.__repr__())def __str__(self):return str(self.data.__str__())class SGD(object):def __init__(self, parameters, alpha=0.1):self.parameters = parametersself.alpha = alphadef zero(self):for p in self.parameters:p.grad.data *= 0def step(self, zero=True):for p in self.parameters:p.data -= p.grad.data * self.alphaif (zero):p.grad.data *= 0class Layer(object):def __init__(self):self.parameters = list()def get_parameters(self):return self.parametersclass Linear(Layer):def __init__(self, n_inputs, n_outputs):super().__init__()W = np.random.randn(n_inputs, n_outputs) * np.sqrt(2.0 / (n_inputs))self.weight = Tensor(W, autograd=True)self.bias = Tensor(np.zeros(n_outputs), autograd=True)self.parameters.append(self.weight)self.parameters.append(self.bias)def forward(self, input):return input.mm(self.weight) + self.bias.expand(0, len(input.data))class Sequential(Layer):def __init__(self, layers=list()):super().__init__()self.layers = layersdef add(self, layer):self.layers.append(layer)def forward(self, input):for layer in self.layers:input = layer.forward(input)return inputdef get_parameters(self):params = list()for l in self.layers:params += l.get_parameters()return paramsnp.random.seed(1)data = Tensor(np.array([[0, 0], [0, 1], [1, 0], [1, 1]]), autograd=True)
target = Tensor(np.array([[0], [1], [0], [1]]), autograd=True)w = list()
'''
w.append(Tensor(np.random.rand(2, 3), autograd=True)) 这行代码的主要功能是创建一个形状为 (2, 3) 的随机张量（Tensor），
并将其添加到列表 w 中。这个随机张量的数据是从均匀分布 [0, 1) 中随机采样得到的，同时开启了自动求导（autograd=True）功能，
意味着后续可以对该张量进行梯度计算，常用于深度学习模型的参数初始化。
'''
weights_0_1 = np.array([[0.1, 0.2, 0.3],[0.2, 0.3, 0.4]])weights_1_2 = np.array([[0.1], [0.2], [0.3]])
w.append(Tensor(weights_0_1, autograd=True))
w.append(Tensor(weights_1_2, autograd=True))model = Sequential([Linear(2, 3), Linear(3, 1)])optim = SGD(parameters=model.get_parameters(), alpha=0.05)for i in range(10):# Predictpred = model.forward(data)# Compareloss = ((pred - target) * (pred - target)).sum(0)# Learnloss.backward(Tensor(np.ones_like(loss.data)))optim.step()print(loss)'''
[4.33222765]
[0.06584977]
[0.01869537]
[0.01068846]
[0.00609207]
[0.00360451]
[0.00210719]
[0.00126275]
[0.00075884]
[0.00046488]
'''