0. 问题
前馈神经网络（Feedforward Neural Network， FNN）分类。利用 MNIST 数据训练一分类前馈神经网络, 并手写任意一个 0 至 9 的数字, 拍照, 读取该手写数字, 输入训练好的分类网络进行预测。注意:MNIST 数据集的每张图片是 28 × 28 像素的灰度图像, 另外可能需要对你手写照片进行灰度反转。
1. 问题分析
题目要求，利用MNIST数据训练一分类前馈神经网络，然后手写任意一个0至9的数字,拍照,读取该手写数字,输入训练好的分类网络进行预测。
因此首先要下载MNIST数据，然后根据分类需求设计一个前馈神经网络，接着将MNIST数据输入前馈神经网络内进行迭代训练，直到准确度提升至一定程度后停止训练，最后将处理后的手写数字图片输入训练好的前馈神经网络内进行预测。
2. 实现步骤
首先读入MNIST数据。
(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, one_hot_label=True)这里使用到了一个别人已经编写好的读入MNIST数据的函数。
读取进MNIST数据后可知，MNIST的图像数据是28*28的灰度图像，各个像素的取值在0到255之间。因为我们在利用load_mnist函数读入图像数据时，函数的第一个参数设置为True，意味着将图像的像素归一化处理了，因此读入后的图像数据的各个像素的取值在0.0到1.0之间。还需要说明的是，函数的第二个参数设置为True，意味着将图像数据的标签用one-hot表示，即仅正确解的标签为1，其余为0；另外，该函数在读取图像数据时，默认将读入的图像数据展开为一维数组，因此读取的图像会保存为由784(28*28)个元素构成的一维数组。
然后开始设计前馈神经网络。参考网络上的神经网络后，决定采用两层前馈神经网络，由于读入的图像为784个元素构成的输入，因此设计输入层的神经元数为784，分类的结果共有10种，因此设计输出层的神经元数为10，隐藏层的神经元数暂时先设定为50。设计好的前馈神经网络如图所示。
如上图所示，隐藏层的神经元个数暂且先设定为50个，但实际上只要设定一个合适的值即可，不一定非要设定50个神经元。
以下为初始化神经网络的代码。
network = TwoLayerNet(input_size=784, hidden_size=50, output_size=10)第一个参数表示设定输入层的神经元数为784个，第二个参数表示设定隐藏层的神经元数为50个，第三个参数表示设定输出层的神经元数为10个。
初始化完神经网络后，就可以开始学习了。这里采用的学习方法是mini-batch学习。学习过程是：首先从训练数据中随机选择一部分数据(即mini-batch)，再以这些mini-batch为对象，使用梯度法更新参数。
并且为了随时掌握神经网络的学习的效果，需要及时记录训练数据和测试数据的识别精度。因此，我们定义了一个epoch，epoch指的是：学习中所有训练数据均被使用过一次时的更新次数，每经过一个epoch就记录一次训练数据和测试数据的识别精度，例如总共有5000笔训练数据，mini-batch的大小设定为100，那么需要50(5000/100)次重复训练(即重复随机梯度下降法)，这5000笔训练数据才能均被使用过一次，因此在这里，epoch的值就为50，也就意味着每训练50次，就要记录一次训练数据和测试数据的识别精度。
具体代码如下所示
iters_num = 10000 # 适当设定循环的次数
train_size = x_train.shape[0]
batch_size = 100
learning_rate = 0.1
train_loss_list = []
train_acc_list = []
test_acc_list = []
iter_per_epoch = max(train_size / batch_size, 1)
for i in range(iters_num):
batch_mask = np.random.choice(train_size, batch_size)
x_batch = x_train[batch_mask]
t_batch = t_train[batch_mask]
# 计算梯度
grad = network.gradient(x_batch, t_batch)
# 更新参数
for key in ('W1', 'b1', 'W2', 'b2'):
network.params[key] -= learning_rate * grad[key]
loss = network.loss(x_batch, t_batch)
train_loss_list.append(loss)
if i % iter_per_epoch == 0:
train_acc = network.accuracy(x_train, t_train)
test_acc = network.accuracy(x_test, t_test)
train_acc_list.append(train_acc)
test_acc_list.append(test_acc)
print("train acc, test acc | " + str(train_acc) + ", " + str(test_acc))这里我们选取的训练次数为10000，mini-batch的大小为100，学习率设定为0.1。需要每次从60000个训练数据中随机取出100个数据(包括图像数据和正确解标签数据)。然后，对这个包含100个数据的mini-batch求梯度，使用随机梯度下降法(SGD)更新参数。这里选取的梯度法的更新次数为10000次。每更新一次，都对训练数据计算损失函数的值。并且每过一个epoch，就对训练数据和测试数据的识别精度进行一次记录。
最后输出一下训练数据和测试数据随着epoch的变化图，代码如下所示。
# 绘制图形
markers = {'train': 'o', 'test': 's'}
x = np.arange(len(train_acc_list))
plt.plot(x, train_acc_list, label='train acc')
plt.plot(x, test_acc_list, label='test acc', linestyle='--')
plt.xlabel("epochs")
plt.ylabel("accuracy")
plt.ylim(0, 1.0)
plt.legend(loc='lower right')
plt.show()
print('分类前馈网络训练完毕，最终测试集的准确度为：' + str(test_acc))输出图如下图所示。
训练数据和测试数据的识别精度变化输出结果见下图。
从图中可以看出，总共经历了大约16次epoch，并且识别精度也在提高，最终趋近于1，说明神经网络训练的学习效果是很好的。
最后将手写数字输入进该训练好的神经网络内。预处理后的手写数字图像见下图。
代码如下所示。
#对手写数字进行预测
num = cv2.imread('2_process.png', 0) / 255
y = network.predict(num.flatten())
print('对手写数字的预测结果为：' + str(np.argmax(y)))3. 实验结果和分析
最后预测结果如下图所示。
说明此神经网络能够很好地完成对手写数字的分类。
4. 源代码
train_neuralnet.py
# -*- coding: utf-8 -*-
"""
Created on Thurs Dec 24 22:54:15 2020
@author: jiawei
"""
import numpy as np
import matplotlib.pyplot as plt
from mnist import load_mnist
from two_layer_net import TwoLayerNet
import cv2
# 读入数据
(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, one_hot_label=True)
# 初始化神经网络
network = TwoLayerNet(input_size=784, hidden_size=50, output_size=10)
iters_num = 10000 # 适当设定循环的次数
train_size = x_train.shape[0]
batch_size = 100
learning_rate = 0.1
train_loss_list = []
train_acc_list = []
test_acc_list = []
iter_per_epoch = max(train_size / batch_size, 1)
for i in range(iters_num):
batch_mask = np.random.choice(train_size, batch_size)
x_batch = x_train[batch_mask]
t_batch = t_train[batch_mask]
# 计算梯度
grad = network.gradient(x_batch, t_batch)
# 更新参数
for key in ('W1', 'b1', 'W2', 'b2'):
network.params[key] -= learning_rate * grad[key]
loss = network.loss(x_batch, t_batch)
train_loss_list.append(loss)
if i % iter_per_epoch == 0:
train_acc = network.accuracy(x_train, t_train)
test_acc = network.accuracy(x_test, t_test)
train_acc_list.append(train_acc)
test_acc_list.append(test_acc)
print("train acc, test acc | " + str(train_acc) + ", " + str(test_acc))
# 绘制图形
markers = {'train': 'o', 'test': 's'}
x = np.arange(len(train_acc_list))
plt.plot(x, train_acc_list, label='train acc')
plt.plot(x, test_acc_list, label='test acc', linestyle='--')
plt.xlabel("epochs")
plt.ylabel("accuracy")
plt.ylim(0, 1.0)
plt.legend(loc='lower right')
plt.show()
print('分类前馈网络训练完毕，最终测试集的准确度为：' + str(test_acc))
#对手写数字进行预测
num = cv2.imread('2_process.png', 0) / 255
y = network.predict(num.flatten())
print('对手写数字的预测结果为：' + str(np.argmax(y)))two_layer_net.py
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 25 00:24:44 2020
@author: jiawei
"""
from functions import *
from gradient import numerical_gradient
class TwoLayerNet:
def __init__(self, input_size, hidden_size, output_size, weight_init_std=0.01):
# 初始化权重
self.params = {}
self.params['W1'] = weight_init_std * np.random.randn(input_size, hidden_size)
self.params['b1'] = np.zeros(hidden_size)
self.params['W2'] = weight_init_std * np.random.randn(hidden_size, output_size)
self.params['b2'] = np.zeros(output_size)
def predict(self, x):
W1, W2 = self.params['W1'], self.params['W2']
b1, b2 = self.params['b1'], self.params['b2']
a1 = np.dot(x, W1) + b1
z1 = sigmoid(a1)
a2 = np.dot(z1, W2) + b2
y = softmax(a2)
return y
# x:输入数据, t:监督数据
def loss(self, x, t):
y = self.predict(x)
return cross_entropy_error(y, t)
def accuracy(self, x, t):
y = self.predict(x)
y = np.argmax(y, axis=1)
t = np.argmax(t, axis=1)
accuracy = np.sum(y == t) / float(x.shape[0])
return accuracy
# x:输入数据, t:监督数据
def numerical_gradient(self, x, t):
loss_W = lambda W: self.loss(x, t)
grads = {}
grads['W1'] = numerical_gradient(loss_W, self.params['W1'])
grads['b1'] = numerical_gradient(loss_W, self.params['b1'])
grads['W2'] = numerical_gradient(loss_W, self.params['W2'])
grads['b2'] = numerical_gradient(loss_W, self.params['b2'])
return grads
def gradient(self, x, t):
W1, W2 = self.params['W1'], self.params['W2']
b1, b2 = self.params['b1'], self.params['b2']
grads = {}
batch_num = x.shape[0]
# forward
a1 = np.dot(x, W1) + b1
z1 = sigmoid(a1)
a2 = np.dot(z1, W2) + b2
y = softmax(a2)
# backward
dy = (y - t) / batch_num
grads['W2'] = np.dot(z1.T, dy)
grads['b2'] = np.sum(dy, axis=0)
da1 = np.dot(dy, W2.T)
dz1 = sigmoid_grad(a1) * da1
grads['W1'] = np.dot(x.T, dz1)
grads['b1'] = np.sum(dz1, axis=0)
return gradsmnist.py
# -*- coding: utf-8 -*-
"""
Created on Thurs Dec 24 23:47:23 2020
@author: jiawei
"""
try:
import urllib.request
except ImportError:
raise ImportError('You should use Python 3.x')
import os.path
import gzip
import pickle
import os
import numpy as np
url_base = 'http://yann.lecun.com/exdb/mnist/'
key_file = {
'train_img':'train-images-idx3-ubyte.gz',
'train_label':'train-labels-idx1-ubyte.gz',
'test_img':'t10k-images-idx3-ubyte.gz',
'test_label':'t10k-labels-idx1-ubyte.gz'
}
dataset_dir = os.path.dirname(os.path.abspath(__file__))
save_file = dataset_dir + "/mnist.pkl"
train_num = 60000
test_num = 10000
img_dim = (1, 28, 28)
img_size = 784
def _download(file_name):
file_path = dataset_dir + "/" + file_name
if os.path.exists(file_path):
return
print("Downloading " + file_name + " ... ")
urllib.request.urlretrieve(url_base + file_name, file_path)
print("Done")
def download_mnist():
for v in key_file.values():
_download(v)
def _load_label(file_name):
file_path = dataset_dir + "/" + file_name
print("Converting " + file_name + " to NumPy Array ...")
with gzip.open(file_path, 'rb') as f:
labels = np.frombuffer(f.read(), np.uint8, offset=8)
print("Done")
return labels
def _load_img(file_name):
file_path = dataset_dir + "/" + file_name
print("Converting " + file_name + " to NumPy Array ...")
with gzip.open(file_path, 'rb') as f:
data = np.frombuffer(f.read(), np.uint8, offset=16)
data = data.reshape(-1, img_size)
print("Done")
return data
def _convert_numpy():
dataset = {}
dataset['train_img'] = _load_img(key_file['train_img'])
dataset['train_label'] = _load_label(key_file['train_label'])
dataset['test_img'] = _load_img(key_file['test_img'])
dataset['test_label'] = _load_label(key_file['test_label'])
return dataset
def init_mnist():
download_mnist()
dataset = _convert_numpy()
print("Creating pickle file ...")
with open(save_file, 'wb') as f:
pickle.dump(dataset, f, -1)
print("Done!")
def _change_one_hot_label(X):
T = np.zeros((X.size, 10))
for idx, row in enumerate(T):
row[X[idx]] = 1
return T
def load_mnist(normalize=True, flatten=True, one_hot_label=False):
"""读入MNIST数据集
Parameters
----------
normalize : 将图像的像素值正规化为0.0~1.0
one_hot_label :
one_hot_label为True的情况下，标签作为one-hot数组返回
one-hot数组是指[0,0,1,0,0,0,0,0,0,0]这样的数组
flatten : 是否将图像展开为一维数组
Returns
-------
(训练图像, 训练标签), (测试图像, 测试标签)
"""
if not os.path.exists(save_file):
init_mnist()
with open(save_file, 'rb') as f:
dataset = pickle.load(f)
if normalize:
for key in ('train_img', 'test_img'):
dataset[key] = dataset[key].astype(np.float32)
dataset[key] /= 255.0
if one_hot_label:
dataset['train_label'] = _change_one_hot_label(dataset['train_label'])
dataset['test_label'] = _change_one_hot_label(dataset['test_label'])
if not flatten:
for key in ('train_img', 'test_img'):
dataset[key] = dataset[key].reshape(-1, 1, 28, 28)
return (dataset['train_img'], dataset['train_label']), (dataset['test_img'], dataset['test_label'])
if __name__ == '__main__':
init_mnist()gradient_simplenet.py
# coding: utf-8
import sys, os
sys.path.append(os.pardir) # 为了导入父目录中的文件而进行的设定
import numpy as np
from common.functions import softmax, cross_entropy_error
from common.gradient import numerical_gradient
class simpleNet:
def __init__(self):
self.W = np.random.randn(2,3)
def predict(self, x):
return np.dot(x, self.W)
def loss(self, x, t):
z = self.predict(x)
y = softmax(z)
loss = cross_entropy_error(y, t)
return loss
x = np.array([0.6, 0.9])
t = np.array([0, 0, 1])
net = simpleNet()
f = lambda w: net.loss(x, t)
dW = numerical_gradient(f, net.W)
print(dW)gradient_method.py
# coding: utf-8
import numpy as np
import matplotlib.pylab as plt
from gradient_2d import numerical_gradient
def gradient_descent(f, init_x, lr=0.01, step_num=100):
x = init_x
x_history = []
for i in range(step_num):
x_history.append( x.copy() )
grad = numerical_gradient(f, x)
x -= lr * grad
return x, np.array(x_history)
def function_2(x):
return x[0]**2 + x[1]**2
init_x = np.array([-3.0, 4.0])
lr = 0.1
step_num = 20
x, x_history = gradient_descent(function_2, init_x, lr=lr, step_num=step_num)
plt.plot( [-5, 5], [0,0], '--b')
plt.plot( [0,0], [-5, 5], '--b')
plt.plot(x_history[:,0], x_history[:,1], 'o')
plt.xlim(-3.5, 3.5)
plt.ylim(-4.5, 4.5)
plt.xlabel("X0")
plt.ylabel("X1")
plt.show()gradient_2d.py
# coding: utf-8
# cf.http://d.hatena.ne.jp/white_wheels/20100327/p3
import numpy as np
import matplotlib.pylab as plt
from mpl_toolkits.mplot3d import Axes3D
def _numerical_gradient_no_batch(f, x):
h = 1e-4 # 0.0001
grad = np.zeros_like(x)
for idx in range(x.size):
tmp_val = x[idx]
x[idx] = float(tmp_val) + h
fxh1 = f(x) # f(x+h)
x[idx] = tmp_val - h
fxh2 = f(x) # f(x-h)
grad[idx] = (fxh1 - fxh2) / (2*h)
x[idx] = tmp_val # 还原值
return grad
def numerical_gradient(f, X):
if X.ndim == 1:
return _numerical_gradient_no_batch(f, X)
else:
grad = np.zeros_like(X)
for idx, x in enumerate(X):
grad[idx] = _numerical_gradient_no_batch(f, x)
return grad
def function_2(x):
if x.ndim == 1:
return np.sum(x**2)
else:
return np.sum(x**2, axis=1)
def tangent_line(f, x):
d = numerical_gradient(f, x)
print(d)
y = f(x) - d*x
return lambda t: d*t + y
if __name__ == '__main__':
x0 = np.arange(-2, 2.5, 0.25)
x1 = np.arange(-2, 2.5, 0.25)
X, Y = np.meshgrid(x0, x1)
X = X.flatten()
Y = Y.flatten()
grad = numerical_gradient(function_2, np.array([X, Y]) )
plt.figure()
plt.quiver(X, Y, -grad[0], -grad[1], angles="xy",color="#666666")#,headwidth=10,scale=40,color="#444444")
plt.xlim([-2, 2])
plt.ylim([-2, 2])
plt.xlabel('x0')
plt.ylabel('x1')
plt.grid()
plt.legend()
plt.draw()
plt.show()gradient_1d_20190731_173642.py
# coding: utf-8
import numpy as np
import matplotlib.pylab as plt
def numerical_diff(f, x):
h = 1e-4 # 0.0001
return (f(x+h) - f(x-h)) / (2*h)
def function_1(x):
return 0.01*x**2 + 0.1*x
def tangent_line(f, x):
d = numerical_diff(f, x)
print(d)
y = f(x) - d*x
return lambda t: d*t + y
x = np.arange(0.0, 20.0, 0.1)
y = function_1(x)
plt.xlabel("x")
plt.ylabel("f(x)")
tf = tangent_line(function_1, 5)
y2 = tf(x)
plt.plot(x, y)
plt.plot(x, y2)
plt.show()gradient.py
# coding: utf-8
import numpy as np
def _numerical_gradient_1d(f, x):
h = 1e-4 # 0.0001
grad = np.zeros_like(x)
for idx in range(x.size):
tmp_val = x[idx]
x[idx] = float(tmp_val) + h
fxh1 = f(x) # f(x+h)
x[idx] = tmp_val - h
fxh2 = f(x) # f(x-h)
grad[idx] = (fxh1 - fxh2) / (2*h)
x[idx] = tmp_val # 还原值
return grad
def numerical_gradient_2d(f, X):
if X.ndim == 1:
return _numerical_gradient_1d(f, X)
else:
grad = np.zeros_like(X)
for idx, x in enumerate(X):
grad[idx] = _numerical_gradient_1d(f, x)
return grad
def numerical_gradient(f, x):
h = 1e-4 # 0.0001
grad = np.zeros_like(x)
it = np.nditer(x, flags=['multi_index'], op_flags=['readwrite'])
while not it.finished:
idx = it.multi_index
tmp_val = x[idx]
x[idx] = float(tmp_val) + h
fxh1 = f(x) # f(x+h)
x[idx] = tmp_val - h
fxh2 = f(x) # f(x-h)
grad[idx] = (fxh1 - fxh2) / (2*h)
x[idx] = tmp_val # 还原值
it.iternext()
return gradfunctions.py
# coding: utf-8
import numpy as np
def identity_function(x):
return x
def step_function(x):
return np.array(x > 0, dtype=np.int)
def sigmoid(x):
return 1 / (1 + np.exp(-x))
def sigmoid_grad(x):
return (1.0 - sigmoid(x)) * sigmoid(x)
def relu(x):
return np.maximum(0, x)
def relu_grad(x):
grad = np.zeros(x)
grad[x>=0] = 1
return grad
def softmax(x):
if x.ndim == 2:
x = x.T
x = x - np.max(x, axis=0)
y = np.exp(x) / np.sum(np.exp(x), axis=0)
return y.T
x = x - np.max(x) # 溢出对策
return np.exp(x) / np.sum(np.exp(x))
def mean_squared_error(y, t):
return 0.5 * np.sum((y-t)**2)
def cross_entropy_error(y, t):
if y.ndim == 1:
t = t.reshape(1, t.size)
y = y.reshape(1, y.size)
# 监督数据是one-hot-vector的情况下，转换为正确解标签的索引
if t.size == y.size:
t = t.argmax(axis=1)
batch_size = y.shape[0]
return -np.sum(np.log(y[np.arange(batch_size), t] + 1e-7)) / batch_size
def softmax_loss(X, t):
y = softmax(X)
return cross_entropy_error(y, t)
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