Python3.6 deep learning first step

# coding=utf-8
import mnist_loader
import network,random,numpy
'''检验数据格式
training_data, valid_data, test_data=mnist_loader.load_data()
I = list(training_data)[0][3]
print(len(training_data[0]),len(valid_data[0]),len(test_data[0]),len(training_data[0][0]))
'''
training_data, valid_data, test_data=mnist_loader.load_data_wrapper()
'''检验图片的操作
from PIL import Image
I = list(training_data)[3][0] 用zip完成行列交换，变成每行一个样本
I.resize((28, 28))
im = Image.fromarray((I*256).astype('uint8'))
im.show()
for i in range(28):for k in range(28):if float(I[i,k]) > 0.8:print (1,end='')else: print (0,end='')print ('')
'''
net = network.Network([784, 30, 10])
net.SGD(list(training_data), 30, 10, 3, test_data=list(test_data))
'''检验洗牌的效果
test_data=list(test_data) #zip对象需要变成list对象才能取下标，同map对象,!!注意list后原test_data zip对象=【】
print(numpy.shape(test_data))
x=test_data.copy()
random.shuffle(x)
for i in range(len(x)):if x[i][1]!=test_data[i][1]:print(x[i][1],test_data[i][1])
'''

"""
mnist_loader
~~~~~~~~~~~~A library to load the MNIST image data.  For details of the data
structures that are returned, see the doc strings for ``load_data``
and ``load_data_wrapper``.  In practice, ``load_data_wrapper`` is the
function usually called by our neural network code.
"""
# coding=utf-8#### Libraries
# Standard library
import pickle
import gzip# Third-party libraries
import 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."""with gzip.open(r'C:\Users\li\Downloads\neural-networks-and-deep-learning-master\data\mnist.pkl.gz','rb')as f:training_data, validation_data, test_data = pickle.load(f, encoding='iso-8859-1')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

# coding=utf-8
"""
network.py
~~~~~~~~~~A module to implement the stochastic gradient descent learning
algorithm for a feedforward neural network.  Gradients are calculated
using backpropagation.  Note that I have focused on making the code
simple, easily readable, and easily modifiable.  It is not optimized,
and omits many desirable features.
"""#### Libraries
# Standard library
import random# Third-party libraries
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 = sizesself.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 range(epochs):random.shuffle(training_data)mini_batches = [training_data[k:k+mini_batch_size]for k in range(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))*nwfor w, nw in zip(self.weights, nabla_w)]self.biases = [b-(eta/len(mini_batch))*nbfor 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 range(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):"""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)#### Miscellaneous functions
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))

Python3.6 deep learning first step相关推荐

《Deep Learning With Python second edition》英文版读书笔记：第十一章DL for text: NLP、Transformer、Seq2Seq
文章目录第十一章:Deep learning for text 11.1 Natural language processing: The bird's eye view 11.2 Preparin ...
Deep Learning Chapter02：Python基础语法回顾
Deep Learning Chapter02:Python基础语法回顾由于一年多没有接触python,现在恶补了下python基础语法,为以后的深度学习打下基础.现总结如下,希望对大家有所帮助. ...
《neural networks and deep learning》读书笔记
neural networks and deep learning 项目地址:https://github.com/mnielsen/neural-networks-and-deep-learning ...
加法神经网络--AdderNet: DoWe Really Need Multiplications in Deep Learning?
AdderNet: DoWe Really Need Multiplications in Deep Learning? CVPR2020 https://arxiv.org/abs/1912.132 ...
Deep Learning 9_深度学习UFLDL教程：linear decoder_exercise（斯坦福大学深度学习教程）...
前言实验内容:Exercise:Learning color features with Sparse Autoencoders.即:利用线性解码器,从100000张8*8的RGB图像块中提取颜色特 ...
(zhuan) Where can I start with Deep Learning?
Where can I start with Deep Learning? By Rotek Song, Deep Reinforcement Learning/Robotics/Computer V ...
Deep Learning – Review by LeCun, Bengio, and Hinton
FROM:https://news.ycombinator.com/item?id=9613810 thisisdave 7 days ago Has LeCun changed his positi ...
Deep learning：二十二(linear decoder练习)
前言: 本节是练习Linear decoder的应用,关于Linear decoder的相关知识介绍请参考:Deep learning:十七(Linear Decoders,Convolution和P ...
The Wide and Deep Learning Model（译文+Tensorlfow源码解析）原创 2017年11月03日 22:14:47 标签：深度学习 / 谷歌 / tensorf
The Wide and Deep Learning Model(译文+Tensorlfow源码解析) 原创 2017年11月03日 22:14:47 标签: 深度学习 / 谷歌 / tensorfl ...

Python3.6 deep learning first step

Python3.6 deep learning first step相关推荐

最新文章

热门文章