DL之NN：利用(本地數據集50000張數據集)調用自定義神經網絡network.py實現手寫數字圖片識別94%準確率

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目錄

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代碼設計

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輸出結果

更新……

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代碼設計

import mnist_loader import networktraining_data, validation_data, test_data = mnist_loader.load_data_wrapper() print("training_data") print(type(training_data)) print(list(training_data)) print(training_data[0][0].shape) print(training_data[0][1].shape) net = network.Network([784, 30, 10]) net.SGD(training_data, 30, 10, 3.0, test_data=test_data) import random import numpy as npclass Network(object): def __init__(self, sizes): """The list ``sizes`` contains the number of neurons in therespective layers of the network. For example, if the listwas [2, 3, 1] then it would be a three-layer network, with thefirst layer containing 2 neurons, the second layer 3 neurons,and the third layer 1 neuron. The biases and weights for thenetwork are initialized randomly, using a Gaussiandistribution with mean 0, and variance 1. Note that the firstlayer is assumed to be an input layer, and by convention wewon't set any biases for those neurons, since biases are onlyever used in computing the outputs from later layers."""self.num_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 x, y in zip(sizes[:-1], sizes[1:])]def feedforward(self, a): """Return the output of the network if ``a`` is input."""for b, w in zip(self.biases, self.weights):a = sigmoid(np.dot(w, a)+b)return adef SGD(self, training_data, epochs, mini_batch_size, eta, test_data=None):"""Train the neural network using mini-batch stochasticgradient descent. The ``training_data`` is a list of tuples``(x, y)`` representing the training inputs and the desiredoutputs. The other non-optional parameters areself-explanatory. If ``test_data`` is provided then thenetwork will be evaluated against the test data after eachepoch, and partial progress printed out. This is useful fortracking progress, but slows things down substantially."""if test_data: n_test = len(test_data) n = len(training_data) for j in xrange(epochs): random.shuffle(training_data) mini_batches = [training_data[k:k+mini_batch_size]for k in xrange(0, n, mini_batch_size)] for mini_batch in mini_batches: self.update_mini_batch(mini_batch, eta)if test_data: print ("Epoch {0}: {1} / {2}".format(j, self.evaluate(test_data), n_test))else: print ("Epoch {0} complete".format(j))def update_mini_batch(self, mini_batch, eta): """Update the network's weights and biases by applyinggradient descent using backpropagation to a single mini batch.The ``mini_batch`` is a list of tuples ``(x, y)``, and ``eta``is the learning rate."""nabla_b = [np.zeros(b.shape) for b in self.biases] nabla_w = [np.zeros(w.shape) for w in self.weights]for x, y in mini_batch: delta_nabla_b, delta_nabla_w = self.backprop(x, y) nabla_b = [nb+dnb for nb, dnb in zip(nabla_b, delta_nabla_b)] nabla_w = [nw+dnw for nw, dnw in zip(nabla_w, delta_nabla_w)]self.weights = [w-(eta/len(mini_batch))*nw for w, nw in zip(self.weights, nabla_w)]self.biases = [b-(eta/len(mini_batch))*nb for b, nb in zip(self.biases, nabla_b)]def backprop(self, x, y): """Return a tuple ``(nabla_b, nabla_w)`` representing thegradient for the cost function C_x. ``nabla_b`` and``nabla_w`` are layer-by-layer lists of numpy arrays, similarto ``self.biases`` and ``self.weights``."""nabla_b = [np.zeros(b.shape) for b in self.biases]nabla_w = [np.zeros(w.shape) for w in self.weights]# feedforwardactivation = xactivations = [x] # list to store all the activations, layer by layerzs = [] # list to store all the z vectors, layer by layerfor b, w in zip(self.biases, self.weights):z = np.dot(w, activation)+bzs.append(z)activation = sigmoid(z)activations.append(activation)# backward passdelta = self.cost_derivative(activations[-1], y) * \sigmoid_prime(zs[-1])nabla_b[-1] = deltanabla_w[-1] = np.dot(delta, activations[-2].transpose())# Note that the variable l in the loop below is used a little# differently to the notation in Chapter 2 of the book. Here,# l = 1 means the last layer of neurons, l = 2 is the# second-last layer, and so on. It's a renumbering of the# scheme in the book, used here to take advantage of the fact# that Python can use negative indices in lists.for l in xrange(2, self.num_layers):z = zs[-l]sp = sigmoid_prime(z)delta = np.dot(self.weights[-l+1].transpose(), delta) * spnabla_b[-l] = deltanabla_w[-l] = np.dot(delta, activations[-l-1].transpose())return (nabla_b, nabla_w)def evaluate(self, test_data):#評估，"""Return the number of test inputs for which the neuralnetwork outputs the correct result. Note that the neuralnetwork's output is assumed to be the index of whicheverneuron in the final layer has the highest activation."""test_results = [(np.argmax(self.feedforward(x)), y)for (x, y) in test_data]return sum(int(x == y) for (x, y) in test_results) def cost_derivative(self, output_activations, y): """Return the vector of partial derivatives \partial C_x /\partial a for the output activations."""return (output_activations-y)def sigmoid(z): """The sigmoid function."""return 1.0/(1.0+np.exp(-z))def sigmoid_prime(z):"""Derivative of the sigmoid function."""return sigmoid(z)*(1-sigmoid(z)) import pickle as cPickle import gzipimport numpy as npdef load_data():"""Return the MNIST data as a tuple containing the training data,the validation data, and the test data.The ``training_data`` is returned as a tuple with two entries.The first entry contains the actual training images. This is anumpy ndarray with 50,000 entries. Each entry is, in turn, anumpy ndarray with 784 values, representing the 28 * 28 = 784pixels in a single MNIST image.The second entry in the ``training_data`` tuple is a numpy ndarraycontaining 50,000 entries. Those entries are just the digitvalues (0...9) for the corresponding images contained in the firstentry of the tuple.The ``validation_data`` and ``test_data`` are similar, excepteach contains only 10,000 images.This is a nice data format, but for use in neural networks it'shelpful to modify the format of the ``training_data`` a little.That's done in the wrapper function ``load_data_wrapper()``, seebelow."""f = gzip.open('../data/mnist.pkl.gz', 'rb')training_data, validation_data, test_data = cPickle.load(f,encoding='bytes') #(f,encoding='bytes')f.close()return (training_data, validation_data, test_data)def load_data_wrapper():"""Return a tuple containing ``(training_data, validation_data,test_data)``. Based on ``load_data``, but the format is moreconvenient for use in our implementation of neural networks.In particular, ``training_data`` is a list containing 50,0002-tuples ``(x, y)``. ``x`` is a 784-dimensional numpy.ndarraycontaining the input image. ``y`` is a 10-dimensionalnumpy.ndarray representing the unit vector corresponding to thecorrect digit for ``x``.``validation_data`` and ``test_data`` are lists containing 10,0002-tuples ``(x, y)``. In each case, ``x`` is a 784-dimensionalnumpy.ndarry containing the input image, and ``y`` is thecorresponding classification, i.e., the digit values (integers)corresponding to ``x``.Obviously, this means we're using slightly different formats forthe training data and the validation / test data. These formatsturn out to be the most convenient for use in our neural networkcode."""tr_d, va_d, te_d = load_data()training_inputs = [np.reshape(x, (784, 1)) for x in tr_d[0]]training_results = [vectorized_result(y) for y in tr_d[1]]training_data = zip(training_inputs, training_results)validation_inputs = [np.reshape(x, (784, 1)) for x in va_d[0]]validation_data = zip(validation_inputs, va_d[1])test_inputs = [np.reshape(x, (784, 1)) for x in te_d[0]]test_data = zip(test_inputs, te_d[1])return (training_data, validation_data, test_data)def vectorized_result(j):"""Return a 10-dimensional unit vector with a 1.0 in the jthposition and zeroes elsewhere. This is used to convert a digit(0...9) into a corresponding desired output from the neuralnetwork."""e = np.zeros((10, 1))e[j] = 1.0return e

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DL之NN：利用(本地數據集50000張數據集)調用自定義神經網絡network.py實現手寫圖片識別94%

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