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TensorFlow

Stanford University CS224d

这是一个创建于 3254 天前的主题，其中的信息可能已经有所发展或是发生改变。

GoogLeNet, 2014 年 ILSVRC 挑战赛冠军,这个 model 证明了一件事：用更多的卷积，更深的层次可以得到更好的结构。（当然，它并没有证明浅的层次不能达到这样的效果）

通过使用 NiN （ Network-in-network ）结构拓宽卷积网络的宽度和深度，其中将稀疏矩阵合并成稠密矩阵的方法和路径具有相当的工程价值。

本帖使用这个 NiN 结构的复合滤波器对 HS300ETF 进行技术分析因子预测。并通过叠加不同指数，尝试寻找‘指数轮动’可能存在的相关关系。

import zipfile import matplotlib.pyplot as plt import matplotlib.image as mpimg import numpy as np import pandas as pd fig = zipfile.ZipFile('fig1.zip','r') for file in fig.namelist(): fig.extract(file,'tmp/')

1.1 LeNet-5 一种典型的卷积网络是。当年美国大多数银行用它来识别支票上面的手写数字的。

image = mpimg.imread("tmp/A2.png") plt.figure(figsize=(16,16)) plt.axis("off") plt.imshow(image) plt.show()

1.2 NiN 结构的 Inception module, GoogleLeNet 核心卷积模块，一个拓宽宽度的滤波器 ,相当于一个高度非线性的滤波器

image = mpimg.imread("tmp/A3.png") plt.figure(figsize=(16,46)) plt.axis("off") plt.imshow(image) plt.show()

1.3 GoogleLeNet 拓扑结构图，可以看到 GoogleLeNet 在 LeNet 网络结构上面大量使用 Inception_unit 滤波器拓宽加深 LeNet 网络

Going deeper with convolutions 论文中 Inception_unit 滤波器将稀疏矩阵合并成稠密矩阵的方法和路径具有相当的工程价值

image = mpimg.imread("tmp/A1.png") plt.figure(figsize=(16,46)) plt.axis("off") plt.imshow(image) plt.show()

1.4 GoogleLeNet 拓扑结构代码 在我使用 Tensorflow 复现论文(Going deeper with convolutions)发现 SAME 算法填充（ 0 ）要比 VALID 效果好一些，很稳定的好一些

def inception_unit(inputdata, weights, biases): # A3 inception 3a inception_in = inputdata # Conv 1x1+S1 inception_1x1_S1 = tf.nn.conv2d(inception_in, weights['inception_1x1_S1'], strides=[1,1,1,1], padding='SAME') inception_1x1_S1 = tf.nn.bias_add(inception_1x1_S1, biases['inception_1x1_S1']) inception_1x1_S1 = tf.nn.relu(inception_1x1_S1) # Conv 3x3+S1 inception_3x3_S1_reduce = tf.nn.conv2d(inception_in, weights['inception_3x3_S1_reduce'], strides=[1,1,1,1], padding='SAME') inception_3x3_S1_reduce = tf.nn.bias_add(inception_3x3_S1_reduce, biases['inception_3x3_S1_reduce']) inception_3x3_S1_reduce = tf.nn.relu(inception_3x3_S1_reduce) inception_3x3_S1 = tf.nn.conv2d(inception_3x3_S1_reduce, weights['inception_3x3_S1'], strides=[1,1,1,1], padding='SAME') inception_3x3_S1 = tf.nn.bias_add(inception_3x3_S1, biases['inception_3x3_S1']) inception_3x3_S1 = tf.nn.relu(inception_3x3_S1) # Conv 5x5+S1 inception_5x5_S1_reduce = tf.nn.conv2d(inception_in, weights['inception_5x5_S1_reduce'], strides=[1,1,1,1], padding='SAME') inception_5x5_S1_reduce = tf.nn.bias_add(inception_5x5_S1_reduce, biases['inception_5x5_S1_reduce']) inception_5x5_S1_reduce = tf.nn.relu(inception_5x5_S1_reduce) inception_5x5_S1 = tf.nn.conv2d(inception_5x5_S1_reduce, weights['inception_5x5_S1'], strides=[1,1,1,1], padding='SAME') inception_5x5_S1 = tf.nn.bias_add(inception_5x5_S1, biases['inception_5x5_S1']) inception_5x5_S1 = tf.nn.relu(inception_5x5_S1) # MaxPool inception_MaxPool = tf.nn.max_pool(inception_in, ksize=[1,3,3,1], strides=[1,1,1,1], padding='SAME') inception_MaxPool = tf.nn.conv2d(inception_MaxPool, weights['inception_MaxPool'], strides=[1,1,1,1], padding='SAME') inception_MaxPool = tf.nn.bias_add(inception_MaxPool, biases['inception_MaxPool']) inception_MaxPool = tf.nn.relu(inception_MaxPool) # Concat #tf.concat(concat_dim, values, name='concat') #concat_dim 是 tensor 连接的方向（维度）， values 是要连接的 tensor 链表， name 是操作名。 cancat_dim 维度可以不一样，其他维度的尺寸必须一样。 inception_out = tf.concat(concat_dim=3, values=[inception_1x1_S1, inception_3x3_S1, inception_5x5_S1, inception_MaxPool]) return inception_out def GoogleLeNet_topological_structure(x, weights, biases, conv_weights_3a, conv_biases_3a, conv_weights_3b, conv_biases_3b , conv_weights_4a, conv_biases_4a, conv_weights_4b, conv_biases_4b, conv_weights_4c, conv_biases_4c, conv_weights_4d, conv_biases_4d, conv_weights_4e, conv_biases_4e, conv_weights_5a, conv_biases_5a, conv_weights_5b, conv_biases_5b, dropout=0.8): # A0 输入数据 x = tf.reshape(x,[-1,224,224,4]) # 调整输入数据维度格式 # A1 Conv 7x7_S2 x = tf.nn.conv2d(x, weights['conv1_7x7_S2'], strides=[1,2,2,1], padding='SAME') # 卷积层 卷积核 7*7 扫描步长 2*2 x = tf.nn.bias_add(x, biases['conv1_7x7_S2']) #print (x.get_shape().as_list()) # 偏置向量 x = tf.nn.relu(x) # 激活函数 x = tf.nn.max_pool(x, ksize=pooling['pool1_3x3_S2'], strides=[1,2,2,1], padding='SAME') # 池化取最大值 x = tf.nn.local_response_normalization(x, depth_radius=5/2.0, bias=2.0, alpha=1e-4, beta= 0.75) # 局部响应归一化 # A2 x = tf.nn.conv2d(x, weights['conv2_1x1_S1'], strides=[1,1,1,1], padding='SAME') x = tf.nn.bias_add(x, biases['conv2_1x1_S1']) x = tf.nn.conv2d(x, weights['conv2_3x3_S1'], strides=[1,1,1,1], padding='SAME') x = tf.nn.bias_add(x, biases['conv2_3x3_S1']) x = tf.nn.local_response_normalization(x, depth_radius=5/2.0, bias=2.0, alpha=1e-4, beta= 0.75) x = tf.nn.max_pool(x, ksize=pooling['pool2_3x3_S2'], strides=[1,2,2,1], padding='SAME') # inception 3 inception_3a = inception_unit(inputdata=x, weights=conv_W_3a, biases=conv_B_3a) inception_3b = inception_unit(inception_3a, weights=conv_W_3b, biases=conv_B_3b) # 池化层 x = inception_3b x = tf.nn.max_pool(x, ksize=pooling['pool3_3x3_S2'], strides=[1,2,2,1], padding='SAME' ) # inception 4 inception_4a = inception_unit(inputdata=x, weights=conv_W_4a, biases=conv_B_4a) # 引出第一条分支 #softmax0 = inception_4a inception_4b = inception_unit(inception_4a, weights=conv_W_4b, biases=conv_B_4b) inception_4c = inception_unit(inception_4b, weights=conv_W_4c, biases=conv_B_4c) inception_4d = inception_unit(inception_4c, weights=conv_W_4d, biases=conv_B_4d) # 引出第二条分支 #softmax1 = inception_4d inception_4e = inception_unit(inception_4d, weights=conv_W_4e, biases=conv_B_4e) # 池化 x = inception_4e x = tf.nn.max_pool(x, ksize=pooling['pool4_3x3_S2'], strides=[1,2,2,1], padding='SAME' ) # inception 5 inception_5a = inception_unit(x, weights=conv_W_5a, biases=conv_B_5a) inception_5b = inception_unit(inception_5a, weights=conv_W_5b, biases=conv_B_5b) softmax2 = inception_5b # 后连接 softmax2 = tf.nn.avg_pool(softmax2, ksize=[1,7,7,1], strides=[1,1,1,1], padding='SAME') softmax2 = tf.nn.dropout(softmax2, keep_prob=0.4) softmax2 = tf.reshape(softmax2, [-1,weights['FC2'].get_shape().as_list()[0]]) softmax2 = tf.nn.bias_add(tf.matmul(softmax2,weights['FC2']),biases['FC2']) #print(softmax2.get_shape().as_list()) return softmax2 weights = { 'conv1_7x7_S2': tf.Variable(tf.random_normal([7,7,4,64])), 'conv2_1x1_S1': tf.Variable(tf.random_normal([1,1,64,64])), 'conv2_3x3_S1': tf.Variable(tf.random_normal([3,3,64,192])), 'FC2': tf.Variable(tf.random_normal([7*7*1024, 3])) } biases = { 'conv1_7x7_S2': tf.Variable(tf.random_normal([64])), 'conv2_1x1_S1': tf.Variable(tf.random_normal([64])), 'conv2_3x3_S1': tf.Variable(tf.random_normal([192])), 'FC2': tf.Variable(tf.random_normal([3])) } pooling = { 'pool1_3x3_S2': [1,3,3,1], 'pool2_3x3_S2': [1,3,3,1], 'pool3_3x3_S2': [1,3,3,1], 'pool4_3x3_S2': [1,3,3,1] } conv_W_3a = { 'inception_1x1_S1': tf.Variable(tf.random_normal([1,1,192,64])), 'inception_3x3_S1_reduce': tf.Variable(tf.random_normal([1,1,192,96])), 'inception_3x3_S1': tf.Variable(tf.random_normal([1,1,96,128])), 'inception_5x5_S1_reduce': tf.Variable(tf.random_normal([1,1,192,16])), 'inception_5x5_S1': tf.Variable(tf.random_normal([5,5,16,32])), 'inception_MaxPool': tf.Variable(tf.random_noral([1,1,192,32])) } conv_B_3a = { 'inception_1x1_S1': tf.Variable(tf.random_normal([64])), 'inception_3x3_S1_reduce': tf.Variable(tf.random_normal([96])), 'inception_3x3_S1': tf.Variable(tf.random_normal([128])), 'inception_5x5_S1_reduce': tf.Variable(tf.random_normal([16])), 'inception_5x5_S1': tf.Variable(tf.random_normal([32])), 'inception_MaxPool': tf.Variable(tf.random_normal([32])) } conv_W_3b = { 'inception_1x1_S1': tf.Variable(tf.random_normal([1,1,256,128])), 'inception_3x3_S1_reduce': tf.Variable(tf.random_normal([1,1,256,128])), 'inception_3x3_S1': tf.Variable(tf.random_normal([1,1,128,192])), 'inception_5x5_S1_reduce': tf.Variable(tf.random_normal([1,1,256,32])), 'inception_5x5_S1': tf.Variable(tf.random_normal([5,5,32,96])), 'inception_MaxPool': tf.Variable(tf.random_normal([1,1,256,64])) } conv_B_3b = { 'inception_1x1_S1': tf.Variable(tf.random_normal([128])), 'inception_3x3_S1_reduce': tf.Variable(tf.random_normal([128])), 'inception_3x3_S1': tf.Variable(tf.random_normal([192])), 'inception_5x5_S1_reduce': tf.Variable(tf.random_normal([32])), 'inception_5x5_S1': tf.Variable(tf.random_normal([96])), 'inception_MaxPool': tf.Variable(tf.random_normal([64])) } conv_W_4a = { 'inception_1x1_S1': tf.Variable(tf.random_normal([1,1,480,192])), 'inception_3x3_S1_reduce': tf.Variable(tf.random_normal([1,1,480,96])), 'inception_3x3_S1': tf.Variable(tf.random_normal([1,1,96,208])), 'inception_5x5_S1_reduce': tf.Variable(tf.random_normal([1,1,480,16])), 'inception_5x5_S1': tf.Variable(tf.random_normal([5,5,16,48])), 'inception_MaxPool': tf.Variable(tf.random_normal([1,1,480,64])) } conv_B_4a = { 'inception_1x1_S1': tf.Variable(tf.random_normal([192])), 'inception_3x3_S1_reduce': tf.Variable(tf.random_normal([96])), 'inception_3x3_S1': tf.Variable(tf.random_normal([208])), 'inception_5x5_S1_reduce': tf.Variable(tf.random_normal([16])), 'inception_5x5_S1': tf.Variable(tf.random_normal([48])), 'inception_MaxPool': tf.Variable(tf.random_normal([64])) } conv_W_4b = { 'inception_1x1_S1': tf.Variable(tf.random_normal([1,1,512,160])), 'inception_3x3_S1_reduce': tf.Variable(tf.random_normal([1,1,512,112])), 'inception_3x3_S1': tf.Variable(tf.random_normal([1,1,112,224])), 'inception_5x5_S1_reduce': tf.Variable(tf.random_normal([1,1,512,24])), 'inception_5x5_S1': tf.Variable(tf.random_normal([5,5,24,64])), 'inception_MaxPool': tf.Variable(tf.random_normal([1,1,512,64])) } conv_B_4b = { 'inception_1x1_S1': tf.Variable(tf.random_normal([160])), 'inception_3x3_S1_reduce': tf.Variable(tf.random_normal([112])), 'inception_3x3_S1': tf.Variable(tf.random_normal([224])), 'inception_5x5_S1_reduce': tf.Variable(tf.random_normal([24])), 'inception_5x5_S1': tf.Variable(tf.random_normal([64])), 'inception_MaxPool': tf.Variable(tf.random_normal([64])) } conv_W_4c = { 'inception_1x1_S1': tf.Variable(tf.random_normal([1,1,512,128])), 'inception_3x3_S1_reduce': tf.Variable(tf.random_normal([1,1,512,128])), 'inception_3x3_S1': tf.Variable(tf.random_normal([1,1,128,256])), 'inception_5x5_S1_reduce': tf.Variable(tf.random_normal([1,1,512,24])), 'inception_5x5_S1': tf.Variable(tf.random_normal([5,5,24,64])), 'inception_MaxPool': tf.Variable(tf.random_normal([1,1,512,64])) } conv_B_4c = { 'inception_1x1_S1': tf.Variable(tf.random_normal([128])), 'inception_3x3_S1_reduce': tf.Variable(tf.random_normal([128])), 'inception_3x3_S1': tf.Variable(tf.random_normal([256])), 'inception_5x5_S1_reduce': tf.Variable(tf.random_normal([24])), 'inception_5x5_S1': tf.Variable(tf.random_normal([64])), 'inception_MaxPool': tf.Variable(tf.random_normal([64])) } conv_W_4d = { 'inception_1x1_S1': tf.Variable(tf.random_normal([1,1,512,112])), 'inception_3x3_S1_reduce': tf.Variable(tf.random_normal([1,1,512,144])), 'inception_3x3_S1': tf.Variable(tf.random_normal([1,1,144,288])), 'inception_5x5_S1_reduce': tf.Variable(tf.random_normal([1,1,512,32])), 'inception_5x5_S1': tf.Variable(tf.random_normal([5,5,32,64])), 'inception_MaxPool': tf.Variable(tf.random_normal([1,1,512,64])) } conv_B_4d = { 'inception_1x1_S1': tf.Variable(tf.random_normal([112])), 'inception_3x3_S1_reduce': tf.Variable(tf.random_normal([144])), 'inception_3x3_S1': tf.Variable(tf.random_normal([288])), 'inception_5x5_S1_reduce': tf.Variable(tf.random_normal([32])), 'inception_5x5_S1': tf.Variable(tf.random_normal([64])), 'inception_MaxPool': tf.Variable(tf.random_normal([64])) } conv_W_4e = { 'inception_1x1_S1': tf.Variable(tf.random_normal([1,1,528,256])), 'inception_3x3_S1_reduce': tf.Variable(tf.random_normal([1,1,528,160])), 'inception_3x3_S1': tf.Variable(tf.random_normal([1,1,160,320])), 'inception_5x5_S1_reduce': tf.Variable(tf.random_normal([1,1,528,32])), 'inception_5x5_S1': tf.Variable(tf.random_normal([5,5,32,128])), 'inception_MaxPool': tf.Variable(tf.random_normal([1,1,528,128])) } conv_B_4e = { 'inception_1x1_S1': tf.Variable(tf.random_normal([256])), 'inception_3x3_S1_reduce': tf.Variable(tf.random_normal([160])), 'inception_3x3_S1': tf.Variable(tf.random_normal([320])), 'inception_5x5_S1_reduce': tf.Variable(tf.random_normal([32])), 'inception_5x5_S1': tf.Variable(tf.random_normal([128])), 'inception_MaxPool': tf.Variable(tf.random_normal([128])) } conv_W_5a = { 'inception_1x1_S1': tf.Variable(tf.random_normal([1,1,832,256])), 'inception_3x3_S1_reduce': tf.Variable(tf.random_normal([1,1,832,160])), 'inception_3x3_S1': tf.Variable(tf.random_normal([1,1,160,320])), 'inception_5x5_S1_reduce': tf.Variable(tf.random_normal([1,1,832,32])), 'inception_5x5_S1': tf.Variable(tf.random_normal([5,5,32,128])), 'inception_MaxPool': tf.Variable(tf.random_normal([1,1,832,128])) } conv_B_5a = { 'inception_1x1_S1': tf.Variable(tf.random_normal([256])), 'inception_3x3_S1_reduce': tf.Variable(tf.random_normal([160])), 'inception_3x3_S1': tf.Variable(tf.random_normal([320])), 'inception_5x5_S1_reduce': tf.Variable(tf.random_normal([32])), 'inception_5x5_S1': tf.Variable(tf.random_normal([128])), 'inception_MaxPool': tf.Variable(tf.random_normal([128])) } conv_W_5b = { 'inception_1x1_S1': tf.Variable(tf.random_normal([1,1,832,384])), 'inception_3x3_S1_reduce': tf.Variable(tf.random_normal([1,1,832,192])), 'inception_3x3_S1': tf.Variable(tf.random_normal([1,1,192,384])), 'inception_5x5_S1_reduce': tf.Variable(tf.random_normal([1,1,832,48])), 'inception_5x5_S1': tf.Variable(tf.random_normal([5,5,48,128])), 'inception_MaxPool': tf.Variable(tf.random_normal([1,1,832,128])) } conv_B_5b = { 'inception_1x1_S1': tf.Variable(tf.random_normal([384])), 'inception_3x3_S1_reduce': tf.Variable(tf.random_normal([192])), 'inception_3x3_S1': tf.Variable(tf.random_normal([384])), 'inception_5x5_S1_reduce': tf.Variable(tf.random_normal([48])), 'inception_5x5_S1': tf.Variable(tf.random_normal([128])), 'inception_MaxPool': tf.Variable(tf.random_normal([128])) }

2. HS300 技术分析指标数据图像表示

2.1 处理技术分析指标生成时间序列的多因子数据,本帖使用前 56 天数据预测后 14 天涨跌，下数据处理

import datetime import lib.TAFPL as TAF HS300 = pd.read_csv('NHS300.csv') del HS300['Unnamed: 0'] HS300 = TAF.Technical_Analysis_Factor_Normalization(inputdata= HS300, rolling=16*40, Tdropna= True) Dtmp = pd.read_csv('NDHS300.csv') del Dtmp['Unnamed: 0'] # 预测 14 天涨跌 Dtmp['actual_future_rate_of_return'] = Dtmp.closePrice.shift(-14)/Dtmp.closePrice - 1.0 Dtmp = Dtmp.dropna() Dtmp = Dtmp[-200:] Dtmp['Direction_Label'] = 0 Dtmp.actual_future_rate_of_return.describe() Dtmp.loc[Dtmp.actual_future_rate_of_return>0.025,'Direction_Label'] = 1 Dtmp.loc[Dtmp.actual_future_rate_of_return<-0.01,'Direction_Label'] = -1 Dtmp.reset_index(drop= True , inplace= True) start = Dtmp.tradingPeriod.values[0] end = Dtmp.tradingPeriod.values[-1] end = datetime.dateime.strptime(end,'%Y-%m-%d') # 将 STR 转换为 datetime end = end + datetime.timedelta(days=1) # 增加一天 end = end.strftime('%Y-%m-%d') fac_HS300 = HS300.ix[(HS300.tradingPeriod.values>start) & (HS300.tradingPeriod.values<end)].reset_index(drop=True) fac_list = TAF.get_Multifactor_list(fac_HS300) fac_list = fac_list[:56] fe = 56 # 回溯日期 tmp_HS300 = np.zeros((1,fe*16*56)) for i in np.arange(fe,int(len(fac_HS300)/16)): tmp = fac_HS300.ix[16*(i-fe):16*i-1][fac_list] tmp = np.array(tmp).ravel(order='C').transpose() tmp_HS300 = np.vstack((tmp_HS300,tmp)) tmp_HS300 = np.delete(tmp_HS300,0,axis=0)

('Number of data losses', 726, 'Ratio : 0.0000000')

2.2 直观图像 取某个交易日技术分析指标参数合成图片像素数据 CNN 一般用来设计机器视觉，简单说就是专门处理图像和视频的，下图为按照 CV 观点来看输入的多因子数据因为前面技术分析因子进行标准化（归一化处理），这里对因子数据进行缩放和偏置

import matplotlib.pyplot as plt import matplotlib.image as mping plt.figure(figsize=(8,8)) shpig = tmp_HS300[1] shpig = shpig.reshape(224,224) shpig +=4 shpig *=26 plt.axis("off") plt.imshow(shpig) plt.show()

from mpl_toolkits.mplot3d import axes3d import matplotlib.pyplot as plt x = range(shpig.shape[0]) y = range(shpig.shape[1]) xm, ym = np.meshgrid(x,y) fig = plt.figure(figsize=(12,12)) ax = fig.gca(projection='3d') ax.plot_wireframe(xm, ym, shpig, rstride=10, cstride=0) plt.show()

from mpl_toolkits.mplot3d import Axes3D from matplotlib import cbook from matplotlib import cm from matplotlib.colors import LightSource import matplotlib.pyplot as plt x = xm y = ym z = shpig fig,ax = plt.subplots(figsize=(15,15),subplot_kw=dict(projection='3d')) ls = LightSource(270, 45) rgb = ls.shade(z, cmap=cm.gist_earth, vert_exag=0.1, blend_mode='soft') surf = ax.plot_surface(x, y, z, rstride=1, cstride=1, facecolors=rgb, linewidth=0, antialiased=False, shade=False) plt.show()

2.3 使用上证指数、中证 500 、创业板指叠加 HS300 在做技术分析的时候，通常行情和单只股票走势，采用叠加噪音的方式探索是否几个指数存在可量化的关系这里采用多图叠加的方法添加噪音进行探索，参考 RGB 3 基色合成彩色图片（字数限制）

本帖设定使用几个指数线的 56 天技术分析因子作为训练数据，使用 CNN 网络卷积进行提取特征，将 HS300 指数按照未来 14 天涨跌情况分成下跌 -1 平稳 0 上涨 1 ，使用卷积提取特征**，这一部分因为字数原因省略了。

V2EX 不能贴图，而且有字数限制，看原文中的逻辑图可能会更加清晰一点，原文地址： https://uqer.io/community/share/58777d8289e3ba004defe973

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