tutorial - redes neuronales python pdf

Enfoque de matriz completa para la propagación inversa en la red neuronal artificial

Question

Estoy aprendiendo Artificial Neural Network (ANN) recientemente y tengo un código que funciona y se ejecuta en Python por el mismo basado en entrenamiento de mini lotes. Seguí el libro de Neural Networks and Deep Learning de Michael Nilson donde hay una explicación paso a paso de cada uno de los algoritmos para principiantes. También hay un código completamente funcional para el reconocimiento de dígitos escritos a mano que también funciona bien para mí.

Sin embargo, estoy tratando de modificar un poco el código al pasar todo el mini-lote juntos para entrenar por retropropagación en la forma de la matriz. También he desarrollado un código de trabajo para eso, pero el código funciona muy lento cuando se ejecuta. ¿Hay alguna manera de que pueda implementar un enfoque completo basado en la matriz para el aprendizaje de mini-lotes de la red basado en el algoritmo de propagación de retorno?

import numpy as np
import pandas as pd
class Network:
def __init__(self, sizes):
self.layers = len(sizes)
self.sizes = sizes
self.biases = [np.random.randn(y, 1) for y in sizes[1:]]
self.weights = [np.random.randn(y, x) for y, x in zip(sizes[1:], sizes[:-1])]
def feed_forward(self, a):
for w, b in zip(self.weights, self.biases):
a = sigmoid(np.dot(w,a) + b)
return a
# Calculate the cost derivative (Gradient of C w.r.t. ''a'' - Nabla C(a))
def cost_derivative(self, output_activation, y):
return (output_activation - y)
def update_mini_batch(self, mini_batch, eta):
from scipy.linalg import block_diag
n = len(mini_batch)
xs = [x for x, y in mini_batch]
features = block_diag(*xs)
ys = [y for x, y in mini_batch]
responses = block_diag(*ys)
ws = [a for a in self.weights for i in xrange(n)]
new_list = []
k = 0
while (k < len(ws)):
new_list.append(ws[k: k + n])
k += n
weights = [block_diag(*elems) for elems in new_list]
bs = [b for b in self.biases for i in xrange(n)]
new_list2 = []
j = 0
while (j < len(bs)):
new_list2.append(bs[j : j + n])
j += n
biases = [block_diag(*elems) for elems in new_list2]
baises_dim_1 = [np.dot(np.ones((n*b.shape[0], b.shape[0])), b) for b in self.biases]
biases_dim_2 = [np.dot(b, np.ones((b.shape[1], n*b.shape[1]))) for b in baises_dim_1]
weights_dim_1 = [np.dot(np.ones((n*w.shape[0], w.shape[0])), w) for w in self.weights]
weights_dim_2 = [np.dot(w, np.ones((w.shape[1], n*w.shape[1]))) for w in weights_dim_1]
nabla_b = [np.zeros(b.shape) for b in biases_dim_2]
nabla_w = [np.zeros(w.shape) for w in weights_dim_2]
delta_b = [np.zeros(b.shape) for b in self.biases]
delta_w = [np.zeros(w.shape) for w in self.weights]
zs = []
activation = features
activations = [features]
for w, b in zip(weights, biases):
z = np.dot(w, activation) + b
zs.append(z)
activation = sigmoid(z)
activations.append(activation)
delta = self.cost_derivative(activations[-1], responses) * sigmoid_prime(zs[-1])
nabla_b[-1] = delta
nabla_w[-1] = np.dot(delta, activations[-2].transpose())
for l in xrange(2, self.layers):
z = zs[-l] # the weighted input for that layer
activation_prime = sigmoid_prime(z) # the derivative of activation for the layer
delta = np.dot(weights[-l + 1].transpose(), delta) * activation_prime # calculate the adjustment term (delta) for that layer
nabla_b[-l] = delta # calculate the bias adjustments - by means of using eq-BP3.
nabla_w[-l] = np.dot(delta, activations[-l-1].transpose()) # calculate the weight adjustments - by means of using eq-BP4.
delta_b = [self.split_cases(b, n) for b in nabla_b]
delta_w = [self.split_cases(w, n) for w in nabla_w]
self.weights = [w - (eta/n) * nw for w, nw in zip(self.weights, delta_w)]
self.biases = [b - (eta/ n) * nb for b, nb in zip(self.biases, delta_b)]
def split_cases(self, mat, mini_batch_size):
i = 0
j = 0
dim1 = mat.shape[0]/mini_batch_size
dim2 = mat.shape[1]/mini_batch_size
sum_samples = np.zeros((dim1, dim2))
while i < len(mat):
sum_samples = sum_samples + mat[i: i + dim1, j : j + dim2]
i += dim1
j += dim2
return sum_samples
"""Stochastic Gradient Descent for training in epochs"""
def SGD(self, training_data, epochs, mini_batch_size, eta, test_data = None):
n = len(training_data)
if test_data:
n_test = len(test_data)
for j in xrange(epochs):
np.random.shuffle(training_data) # for each epochs the mini-batches are selected randomly
mini_batches = [training_data[k: k+mini_batch_size] for k in xrange(0, n, mini_batch_size)] # select equal sizes of mini-batches for the epochs (last mini_batch size might differ however)
c = 1
for mini_batch in mini_batches:
print "Updating mini-batch {0}".format(c)
self.update_mini_batch(mini_batch, eta)
c += 1
if test_data:
print "Epoch {0}: {1}/{2}".format(j, self.evaluate(test_data), n_test)
else:
print "Epoch {0} completed.".format(j)
def evaluate(self, test_data):
test_results = [(np.argmax(self.feed_forward(x)), y) for (x, y) in test_data]
return (sum(int(x == y) for x, y in test_results))
def export_results(self, test_data):
results = [(np.argmax(self.feed_forward(x)), y) for (x, y) in test_data]
k = pd.DataFrame(results)
k.to_csv(''net_results.csv'')
# Global functions
## Activation function (sigmoid)
@np.vectorize
def sigmoid(z):
return 1.0/(1.0 + np.exp(-z))
## Activation derivative (sigmoid_prime)
@np.vectorize
def sigmoid_prime(z):
return sigmoid(z)*(1 - sigmoid(z))

Answer 1

Aquí está mi código. El tiempo necesario para iterar 30 épocas se reduce de 800+ segundos a más de 200 segundos en mi máquina.

Como soy nuevo en Python, uso lo que está disponible. Este fragmento solo requiere Numpy para ejecutarse.

Darle una oportunidad.

def feedforward2(self, a):
zs = []
activations = [a]
activation = a
for b, w in zip(self.biases, self.weights):
z = np.dot(w, activation) + b
zs.append(z)
activation = sigmoid(z)
activations.append(activation)
return (zs, activations)
def update_mini_batch2(self, mini_batch, eta):
batch_size = len(mini_batch)
# transform to (input x batch_size) matrix
x = np.asarray([_x.ravel() for _x, _y in mini_batch]).transpose()
# transform to (output x batch_size) matrix
y = np.asarray([_y.ravel() for _x, _y in mini_batch]).transpose()
nabla_b, nabla_w = self.backprop2(x, y)
self.weights = [w - (eta / batch_size) * nw for w, nw in zip(self.weights, nabla_w)]
self.biases = [b - (eta / batch_size) * nb for b, nb in zip(self.biases, nabla_b)]
return
def backprop2(self, x, y):
nabla_b = [0 for i in self.biases]
nabla_w = [0 for i in self.weights]
# feedforward
zs, activations = self.feedforward2(x)
# backward pass
delta = self.cost_derivative(activations[-1], y) * sigmoid_prime(zs[-1])
nabla_b[-1] = delta.sum(1).reshape([len(delta), 1]) # reshape to (n x 1) matrix
nabla_w[-1] = np.dot(delta, activations[-2].transpose())
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.sum(1).reshape([len(delta), 1]) # reshape to (n x 1) matrix
nabla_w[-l] = np.dot(delta, activations[-l - 1].transpose())
return (nabla_b, nabla_w)