Вы, я пытался запустить этот скрипт, но я получаю ошибку отступа в конце
функции backprop (x, y). Я был бы очень признателен за любую помощь !!
import cPickle
import gzip
def load_data():
f = gzip.open('mnist.pkl.gz', 'rb')
training_data, validation_data, test_data = cPickle.load(f)
f.close()
return (training_data, validation_data, test_data)
import numpy as np
class Network(object):
def __init__(self, layers):
self.layers = layers
self.biases = [np.random.randn(y,1) for y
in layers[1:]]
self.weights = [np.transpose(np.random.randn(x,y))
for x,y
in zip(layers[:-1],layers[1:])]
self.num_layers = len(layers)
def backprop(self, x, y):
nabla_b = [np.zeros(b.shape) for b in self.biases]
nabla_w = [np.zeros(w.shape) for w in self.weights]
# feedforward
activation = x
activations = [x] # list to store all the activations, layer by layer
zs = [] # list to store all the z vectors, layer by layer
for b, w in zip(self.biases, self.weights):
z = np.dot(w, activation)+b
zs.append(z)
activation = sigmoid(z)
activations.append(activation)
# backward pass
delta = self.cost_derivative(activations[-1], y) * \
sigmoid_prime(zs[-1])#set first delta
nabla_b[-1] = delta#set last dC/db to delta vector
nabla_w[-1] = np.dot(delta, activations[-2].transpose())
#calculate nabla_b, nabla_w for the rest of the layers
for l in xrange(2, self.num_layers):
z = zs[-l]
sp = sigmoid_prime(z)
delta = np.dot(self.weights[-l+1].transpose(), delta) * sp
nabla_b[-l] = delta
nabla_w[-l] = np.dot(delta, activations[-l-1].transpose())
#this is where python says there is an indent error!
return (nabla_b, nabla_w)