Lukasz - 2 months ago 12x

Python Question

I'm attempting to write a backpropagation algorithm and I'm encountering an error when attempting to perform a matrix multiplication.

I've created the following simple example to work with

`# 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],

[-3, 2, 0, 1, -1]])]

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])),

(np.array([2, -3, 5]), np.array([1])),

(np.array([3, 6, -1]), np.array([1])),

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

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)

delta = cost_derivative(activations[-1], y) * prime(zs[-1])

print(np.dot(np.transpose(weights[-1])), delta)

I get the following error:

`TypeError: Required argument 'b' (pos 2) not found`

I've printed the outputs of both the

`weights`

`delta`

`np.transpose(weights[-1]) = [[ 2 -3]`

[ 0 2]

[-1 0]

[-1 1]

[ 2 -1]]

and

`delta = [-0.14342712 -0.03761959]`

so the multiplication should work and produce a 5x1 matrix

Answer

There is a misplaced parenthesis on your last line. It should be

```
print(np.dot(np.transpose(weights[-1]), delta))
```

instead of

```
print(np.dot(np.transpose(weights[-1])), delta)
```

Source (Stackoverflow)

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