# 定義網(wǎng)絡(luò) define neural network
	class DigitModule(Module):
	 def __init__(self):
	  # 計(jì)算順序就會(huì)按照這里定義的順序進(jìn)行
	  sequential = Sequential([
	layers.Linear2(in_dim=ROW_NUM * COLUM_NUM, out_dim=16, coe=2),
	layers.Relu(16),
	layers.Linear2(in_dim=16, out_dim=16, coe=2),
	layers.Relu(16),
	layers.Linear2(in_dim=16, out_dim=CLASS_NUM, coe=1),
	layers.Sigmoid(CLASS_NUM)
	  ])
	  super(DigitModule, self).__init__(sequential)
	
	
	module = DigitModule()  # 創(chuàng)建模型 create module
	loss_func = SquareLoss(backward_func=module.backward)  # 定義損失函數(shù) define loss function
	optimizer = SGD(module, lr=learning_rate)  # 定義優(yōu)化器 define optimizer
	
	
	for i in range(EPOCH_NUM):  # 共訓(xùn)練EPOCH_NUM輪
	 trainning_loss = 0  # 計(jì)算一下當(dāng)前一輪訓(xùn)練的loss值，可以沒有
	 for data in train_loader:  # 遍歷所有樣本，train_loader是可迭代對(duì)象，保存了數(shù)據(jù)集中所有的數(shù)據(jù)
	  imgs, targets = data  # 將數(shù)據(jù)拆分成圖片和標(biāo)簽
	  outputs = module(imgs)  # 將樣本的輸入值輸入到自己的神經(jīng)網(wǎng)絡(luò)中
	  loss = loss_func(outputs, targets, transform=True)  # 計(jì)算loss / calculate loss
	  trainning_loss += loss.value
	  loss.backward()  # 通過反向傳播計(jì)算梯度 / calculate gradiant through back propagation
	  optimizer.step()  # 通過優(yōu)化器調(diào)整模型參數(shù) / adjust the weights of network through optimizer
	 if i % TEST_STEP == 0:  # 每訓(xùn)練TEST_STEP輪就測(cè)試一下當(dāng)前訓(xùn)練的成果
	  show_effect(i, module, loss_func, test_loader, i // TEST_STEP)
	  print("{} turn finished, loss of train set = {}".format(i, trainning_loss))

	def forward(self, x):
	 out = x
	 for layer in self.layers:
	  out = layer(out)
	 return out
	
	def backward(self, output_gradiant):
	 layer_num = len(self.layers)
	 delta = output_gradiant
	 for i in range(layer_num - 1, -1, -1):
	  # 反向遍歷各個(gè)層, 將期望改變量反向傳播
	  delta = self.layers[i].backward(delta)
	
	def step(self, lr):
	 for layer in self.layers:
	  layer.step(lr)

 def forward(self, inputs):
	  """
	  :param inputs: input = (h0, x) h0.shape == (batch, out_dim) x.shape == (seq, batch, in_dim)
	  :return: outputs: outputs.shape == (seq, batch, out_dim)
	  """
	  h = inputs[0]  # 輸入的inputs由兩部分組成
	  X = inputs[1]
	  if X.shape[2] != self.in_dim or h.shape[1] != self.out_dim:
	# 檢查輸入的形狀是否有問題
	raise ShapeNotMatchException(self, "forward: wrong shape: h0 = {}, X = {}".format(h.shape, X.shape))
	
	  self.seq_len = X.shape[0]  # 時(shí)間序列的長(zhǎng)度
	  self.inputs = X  # 保存輸入，之后的反向傳播還要用
	  output_list = []  # 保存每個(gè)時(shí)間點(diǎn)的輸出
	  for x in X:
	# 按時(shí)間序列遍歷input
	# x.shape == (batch, in_dim), h.shape == (batch, out_dim)
	h = self.activation(self.linear(np.c_[h, x]))
	output_list.append(h)
	  self.outputs = np.stack(output_list, axis=0)  # 將列表轉(zhuǎn)換成一個(gè)矩陣保存起來(lái)
	  return self.outputs

	def backward(self, output_gradiant):
	 """
	 :param output_gradiant: shape == (seq, batch, out_dim)
	 :return: input_gradiant
	 """
	 if output_gradiant.shape != self.outputs.shape:
	  # 期望得到(seq, batch, out_dim)形狀
	  raise ShapeNotMatchException(self, "__backward: expected {}, but we got "
	"{}".format(self.outputs.shape, output_gradiant.shape))
	
	 input_gradients = []
	 # 每個(gè)time_step上的虛擬weight_gradient, 最后求平均值就是總的weight_gradient
	 weight_gradients = np.zeros(self.linear.weights_shape())
	 bias_gradients = np.zeros(self.linear.bias_shape())
	 batch_size = output_gradiant.shape[1]
	
	 # total_gradient: 前向傳播的時(shí)候是將x, h合成為一個(gè)矩陣，所以反向傳播也先計(jì)算這個(gè)大矩陣的梯度再拆分為x_grad, h_grad
	 total_gradient = np.zeros((batch_size, self.out_dim + self.in_dim))
	 h_gradient = None
	 
	 # 反向遍歷各個(gè)時(shí)間層，計(jì)算該層的梯度值
	 for i in range(self.seq_len - 1, -1, -1):
	  # 前向傳播順序: x, h -> z -> h
	  # 所以反向傳播計(jì)算順序：h_grad -> z_grad -> x_grad, h_grad, w_grad, b_grad
	
	  # %%%%%%%%%%%%%%計(jì)算平均值的版本%%%%%%%%%%%%%%%%%%%%%%%
	  # h_gradient = (output_gradiant[i] + total_gradient[:, 0:self.out_dim]) / 2
	  # %%%%%%%%%%%%%%不計(jì)算平均值的版本%%%%%%%%%%%%%%%%%%%%%%%
	  #  計(jì)算h_grad: 這一時(shí)間點(diǎn)的h_grad包括輸出的grad和之前的時(shí)間點(diǎn)計(jì)算所得grad兩部分
	  h_gradient = output_gradiant[i] + total_gradient[:, 0:self.out_dim]  
	
	  # w_grad和b_grad是在linear.backward()內(nèi)計(jì)算的，不用手動(dòng)再計(jì)算了
	  z_gradient = self.activation.backward(h_gradient)  # 計(jì)算z_grad
	  total_gradient = self.linear.backward(z_gradient)  # 計(jì)算x_grad和h_grad合成的大矩陣的梯度
	
	  # total_gradient 同時(shí)包含了h和x的gradient, shape == (batch, out_dim + in_dim)
	  x_gradient = total_gradient[:, self.out_dim:]
	
	  input_gradients.append(x_gradient)  
	  weight_gradients += self.linear.gradients["w"]
	  bias_gradients += self.linear.gradients["b"]
	
	 # %%%%%%%%%%%%%%%%%%計(jì)算平均值的版本%%%%%%%%%%%%%%%%%%%%%%%
	 # self.linear.set_gradients(w=weight_gradients / self.seq_len, b=bias_gradients / self.seq_len)
	 # %%%%%%%%%%%%%%%%%%不計(jì)算平均值的版本%%%%%%%%%%%%%%%%%%%%%%%
	 self.linear.set_gradients(w=weight_gradients, b=bias_gradients)  # 設(shè)置梯度值
	 
	 list.reverse(input_gradients)  # input_gradients是逆序的，最后輸出時(shí)需要reverse一下
	 print("sum(weight_gradients) = {}".format(np.sum(weight_gradients)))
	 
	 # np.stack的作用是將列表轉(zhuǎn)變成一個(gè)矩陣
	 return np.stack(input_gradients), h_gradient