基于LeNet-5卷积神经网络的手绘数字即时识别项目

import numpy as np import random # 线性数据集、过原点 def get_beans(counts): xs = np.random.rand(counts) xs = np.sort(xs) ys = [1.2 * x + np.random.rand() / 10 for x in xs] return xs, ys # 线性数据集、过原点 带正负号 def get_beans2(counts): xs = np.random.rand(counts) * 10 - 5 xs = np.sort(xs) ys = [2.3 * x + np.random.rand() for x in xs] return xs, ys # 线性数据集、带截距 def get_beans3(counts): xs = np.random.rand(counts) xs = np.sort(xs) ys = np.array([(0.7 * x + (0.5 - np.random.rand()) / 5 + 0.5) for x in xs]) return xs, ys # def get_beans4(counts): xs = np.random.rand(counts) xs = np.sort(xs) ys = np.zeros(counts) for i in range(counts): x = xs[i] yi = 0.7 * x + (0.5 - np.random.rand()) / 50 + 0.5 if yi > 0.8: ys[i] = 1 return xs, ys def get_beans5(counts): xs = np.random.rand(counts) * 2 xs = np.sort(xs) ys = np.zeros(counts) for i in range(counts): x = xs[i] yi = 0.7 * x + (0.5 - np.random.rand()) / 50 + 0.5 if yi > 0.8 and yi < 1.4: ys[i] = 1 return xs, ys def get_beans6(counts): xs = np.random.rand(counts, 2) * 2 ys = np.zeros(counts) for i in range(counts): x = xs[i] if (x[0] - 0.5 * x[1] - 0.1) > 0: ys[i] = 1 return xs, ys def get_beans7(counts): xs = np.random.rand(counts, 2) * 2 ys = np.zeros(counts) for i in range(counts): x = xs[i] if (np.power(x[0] - 1, 2) + np.power(x[1] - 0.3, 2)) < 0.5: ys[i] = 1 return xs, ys # 定义函数get_house_prices，参数为样本数量counts def get_house_prices(counts): # 生成counts个随机数表示房屋的面积，并赋值给sizes（假设面积在50到200平方米之间） sizes = np.random.uniform(50, 200, counts) # 对sizes进行排序 sizes = np.sort(sizes) # 生成prices，其中每个值为3000倍的sizes加上一个在0到50000之间的随机数 prices = [3000 * size + np.random.uniform(0, 50000) for size in sizes] # 返回sizes和prices return sizes, prices def get_beans8(counts): posX, posY = genSpiral(int(counts / 2), 0, 1) negX, negY = genSpiral(int(counts / 2), np.pi, 0) X = np.vstack((posX, negX)) Y = np.hstack((posY, negY)) return X, Y def genSpiral(counts, deltaT, label): X = np.zeros((counts, 2)) Y = np.zeros(counts) for i in range(counts): r = i / counts * 5 t = 1.75 * i / counts * 2 * np.pi + deltaT; x1 = r * np.sin(t) + random.uniform(-0.1, 0.1) x2 = r * np.cos(t) + random.uniform(-0.1, 0.1) X[i] = np.array([x1, x2]) Y[i] = label return X, Y def dist(a, b): dx = a['x'] - b['x']; dy = a['y'] - b['y']; return np.sqrt(dx * dx + dy * dy); def getCircleLabel(p, center): radius = 1; if dist(p, center) < (radius * 0.5): return 1 else: return 0 def randUniform(a=-1, b=1): return np.random.rand() * (b - a) + a; def classifyCircleData(numSamples=100, noise=0): points = []; Y = [] X = [] radius = 1; num = int(numSamples / 2) for i in range(num): r = randUniform(0, radius * 0.5); angle = randUniform(0, 2 * np.pi); x = r * np.sin(angle); y = r * np.cos(angle); noiseX = randUniform(-radius, radius) * noise; noiseY = randUniform(-radius, radius) * noise; label = getCircleLabel({'x': x + noiseX, 'y': y + noiseY}, {'x': 0, 'y': 0}); X.append([x + 1, y + 1]) Y.append(label) for i in range(num): r = randUniform(radius * 0.7, radius); angle = randUniform(0, 2 * np.pi); x = r * np.sin(angle); y = r * np.cos(angle); noiseX = randUniform(-radius, radius) * noise; noiseY = randUniform(-radius, radius) * noise; label = getCircleLabel({'x': x + noiseX, 'y': y + noiseY}, {'x': 0, 'y': 0}); X.append([x + 1, y + 1]) Y.append(label) X = np.array(X) Y = np.array(Y) return X, Y