在变换一个矩阵/V之后执行矩阵乘法

Question

我正在尝试完成反向传播的代码，最后一步是计算权重和偏差的变化（使用平方成本）。这一步包括在对一个数组进行转置之后对两个数组执行矩阵乘法。你知道吗

# necessary functions for this example
def sigmoid(z):
    return 1.0/(1.0+np.exp(-z))

def prime(z):
    return sigmoid(z) * (1-sigmoid(z))

def cost_derivative(output_activations, y):
    return (output_activations-y)

# Mock weight and bias matrices
weights = [np.array([[ 1, 0, 2], 
                     [2, -1, 0], 
                     [4, -1, 0], 
                     [1, 3, -2],
                     [0, 0, -1]]), 
           np.array([[2, 0, -1, -1, 2],
                     [0, 2, -1, -1, 0]])]

biases = [np.array([-1, 2, 0, 0, 4]), np.array([-2, 1])]

# The mock training example
q = [(np.array([1, -2, 3]), np.array([0, 1])), 
     (np.array([2, -3, 5]), np.array([1, 0])),
     (np.array([3, 6, -1]), np.array([1, 0])),
     (np.array([4, -1, -1]), np.array([0, 0]))]

nabla_b = [np.zeros(b.shape) for b in biases]
nabla_w = [np.zeros(w.shape) for w in weights]

for x, y in q:
        activation = x
        activations = [x]
        zs = []
        for w, b in zip(weights, biases): 
            z = np.dot(w, activation) + b
            zs.append(z)
            activation = sigmoid(z)
            activations.append(activation)

    # Computation of last layer
    delta = cost_derivative(activations[-1], y) * prime(zs[-1])
    nabla_b[-1] = delta
    nabla_w[-1] = np.dot(np.transpose(activations[-2]), delta) + biases

我已经打印了delta的输出，第一个实例给出了[ 0.14541528 -0.14808645]，它是一个1x2矩阵，并且

activations[-2] = [9.97527377e-01   9.97527377e-01   9.97527377e-01   1.67014218e-05   7.31058579e-01]

这是一个1x5矩阵。现在换位activations[-2]应该得到一个1x5，得到的乘法应该得到一个5x2矩阵，但是没有