我正在使用Theano创建一个神经网络，但当我尝试 return two lists of tensors at the same time in a list 时，我收到错误：

#This is the line that causes the error

#type(nabla_w) ==

#type(nabla_w[0]) ==

backpropagate = function(func_inputs, [nabla_w, nabla_b])

TypeError: Outputs must be theano Variable or Out instances. Received [dot.0, dot.0, dot.0, dot.0] of type

我应该使用什么样的Theano结构将两个张量一起返回到数组中，以便我可以像这样检索它们：

nabla_w, nabla_b = backpropagate(*args)

我尝试使用我在_2868665中找到的一些东西，但没有一个工作 . (例如我尝试了堆栈或堆栈列表)

这是我使用theano.tensor.stack或stacklists的错误：

ValueError: all the input array dimensions except for the concatenation axis must match exactly

Apply node that caused the error: Join(TensorConstant{0}, Rebroadcast{0}.0, Rebroadcast{0}.0, Rebroadcast{0}.0, Rebroadcast{0}.0)

Inputs shapes: [(), (1, 10, 50), (1, 50, 100), (1, 100, 200), (1, 200, 784)]

Inputs strides: [(), (4000, 400, 8), (40000, 800, 8), (160000, 1600, 8), (1254400, 6272, 8)]

Inputs types: [TensorType(int8, scalar), TensorType(float64, 3D), TensorType(float64, 3D), TensorType(float64, 3D), TensorType(float64, 3D)]

Use the Theano flag 'exception_verbosity=high' for a debugprint of this apply node.

代码的一些额外上下文：

weights = [T.dmatrix('w'+str(x)) for x in range(0, len(self.weights))]

biases = [T.dmatrix('b'+str(x)) for x in range(0, len(self.biases))]

nabla_b = []

nabla_w = []

# feedforward

x = T.dmatrix('x')

y = T.dmatrix('y')

activations = []

inputs = []

activations.append(x)

for i in xrange(0, self.num_layers-1):

inputt = T.dot(weights[i], activations[i])+biases[i]

activation = 1 / (1 + T.exp(-inputt))

activations.append(activation)

inputs.append(inputt)

delta = activations[-1]-y

nabla_b.append(delta)

nabla_w.append(T.dot(delta, T.transpose(inputs[-2])))

for l in xrange(2, self.num_layers):

z = inputs[-l]

spv = (1 / (1 + T.exp(-z))*(1 - (1 / (1 + T.exp(-z)))))

delta = T.dot(T.transpose(weights[-l+1]), delta) * spv

nabla_b.append(delta)

nabla_w.append(T.dot(delta, T.transpose(activations[-l-1])))

T.set_subtensor(nabla_w[-l], T.dot(delta, T.transpose(inputs[-l-1])))

func_inputs = list(weights)

func_inputs.extend(biases)

func_inputs.append(x)

func_inputs.append(y)

backpropagate = function(func_inputs, [nabla_w, nabla_b])