我正在尝试完成使用pytorch的神经网络类的实现。 但是升级步骤会导致弹出与“无类型”相关的错误。 我正在使用Jupyter Notebook将Pytorch Pkg与Python 3.73一起使用。 问题出在步骤中，我必须进行权重更新步骤，然后将梯度值归零。
class NNet(torch.nn.Module):
def __init__(self, n_inputs, n_hiddens_per_layer, n_outputs, act_func='tanh'):
super().__init__() # call parent class (torch.nn.Module) constructor
# Set self.n_hiddens_per_layer to [] if argument is 0, [], or [0]
if n_hiddens_per_layer == 0 or n_hiddens_per_layer == [] or n_hiddens_per_layer == [0]:
self.n_hiddens_per_layer = []
else:
self.n_hiddens_per_layer = n_hiddens_per_layer
self.hidden_layers = torch.nn.ModuleList() # necessary for model.to('cuda')
for nh in self.n_hiddens_per_layer:
self.hidden_layers.append( torch.nn.Sequential(
torch.nn.Linear(n_inputs, nh),
torch.nn.Tanh() if act_func == 'tanh' else torch.nn.ReLU()))
n_inputs = nh
self.output_layer = torch.nn.Linear(n_inputs, n_outputs)
self.Xmeans = None
self.Xstds = None
self.Tmeans = None
self.Tstds = None
self.error_trace = []
def forward(self, X):
Y = X
for hidden_layer in self.hidden_layers:
Y = hidden_layer(Y)
Y = self.output_layer(Y)
return Y
def train(self, X, T, n_epochs, learning_rate, verbose=True):
# Set data matrices to torch.tensors if not already.
if not isinstance(X, torch.Tensor):
X = torch.from_numpy(X).float()
if not isinstance(T, torch.Tensor):
T = torch.from_numpy(T).float()
W = torch.zeros((2, 1), requires_grad=True)
print(W.requires_grad)
# Calculate standardization parameters if not already calculated
if self.Xmeans is None:
self.Xmeans = X.mean(0)
self.Xstds = X.std(0)
self.Xstds[self.Xstds == 0] = 1
self.Tmeans = T.mean(0)
self.Tstds = T.std(0)
self.Tstds[self.Tstds == 0] = 1
# Standardize inputs and targets
X = (X - self.Xmeans) / self.Xstds
T = (T - self.Tmeans) / self.Tstds
# Set optimizer to Adam and loss functions to MSELoss
optimizer = torch.optim.Adam(self.parameters(), lr=learning_rate)
mse_func = torch.nn.MSELoss()
# For each epoch:
# Do forward pass to calculate output Y.
# Calculate mean squared error loss, mse.
# Calculate gradient of mse with respect to all weights by calling mse.backward().
# Take weight update step, then zero the gradient values.
# Unstandardize the mse error and save in self.error_trace
# Print epoch+1 and unstandardized error if verbose is True and
# (epoch+1 is n_epochs or epoch+1 % (n_epochs // 10) == 0)
for epoch in range(n_epochs):
# Do forward pass to calculate output Y.
Y = self.forward(X)
print("Y = \n",Y)
# Calculate mean squared error loss, mse.
mse = ((T - Y)**2).mean()
#mse = torch.mean((T - Y[-1]) ** 2)
print("Y shape = \n",Y.shape)
print("Tshape = \n",T.shape)
print("MSE = \n",mse)
# Calculate gradient of mse with respect to all weights by calling mse.backward().
#W.retain_grad()
mse.backward(torch.ones(100))
#print("mse.backward(torch.ones(100))",mse.backward(torch.ones(100)))
# Take weight update step, then zero the gradient values.
#print("W.grad = ",W.grad())
with torch.no_grad():
W = learning_rate*W.grad()
print("kuttu",W.requires_grad)
W -= learning_rate * W.grad()
W.grad.zero_()
# Unstandardize the mse error and save in self.error_trace
self.error_trace = mse * self.Tstds
#. . .
def use(self, X):
# Set input matrix to torch.tensors if not already.
if not isinstance(X, torch.Tensor):
X = torch.from_numpy(X).float()
# Standardize X
print("here=\n",type(X))
X = (X - torch.mean(X)) / self.Xstds
# Do forward pass and unstandardize resulting output. Assign to variable Y.
# Return output Y after detaching from computation graph and converting to numpy
return Y.detach().numpy()
*<ipython-input-20-6e1e577f866d> in train(self, X, T, n_epochs, learning_rate, verbose)
86 # Take weight update step, then zero the gradient values.
87 with torch.no_grad():
---> 88 W = learning_rate*W.grad()
89 print("w",W.requires_grad)
90 W -= learning_rate * W.grad()*
TypeError：“ NoneType”对象不可调用