Tensorflow 筆記用 GoogLeNet 做模式識別（轉）

阿新 • • 發佈：2018-12-27

GoogLeNet, 2014 年 ILSVRC 挑戰賽冠軍,這個 model 證明了一件事：用更多的卷積，更深的層次可以得到更好的結構。（當然，它並沒有證明淺的層次不能達到這樣的效果）

通過使用 NiN （ Network-in-network ）結構拓寬卷積網路的寬度和深度，其中將稀疏矩陣合併成稠密矩陣的方法和路徑具有相當的工程價值。

本帖使用這個 NiN 結構的複合濾波器對 HS300ETF 進行技術分析因子預測。並通過疊加不同指數，嘗試尋找‘指數輪動’可能存在的相關關係。

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 一種典型的卷積網路是。當年美國大多數銀行用它來識別支票上面的手寫數字的。

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

1.2 NiN 結構的 Inception module, GoogleLeNet 核心卷積模組，一個拓寬寬度的濾波器 ,相當於一個高度非線性的濾波器

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

1.3 GoogleLeNet 拓撲結構圖，可以看到 GoogleLeNet 在 LeNet 網路結構上面大量使用 Inception_unit 濾波器拓寬加深 LeNet 網路

Going deeper with convolutions 論文中 Inception_unit 濾波器將稀疏矩陣合併成稠密矩陣的方法和路徑具有相當的工程價值

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

1.4 GoogleLeNet 拓撲結構程式碼在我使用 Tensorflow 復現論文(Going deeper with convolutions)發現 SAME 演算法填充（ 0 ）要比 VALID 效果好一些，很穩定的好一些

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 連線的方向（維度）， values 是要連線的 tensor 連結串列， name 是操作名。 cancat_dim 維度可以不一樣，其他維度的尺寸必須一樣。
    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 輸入資料
    x = tf.reshape(x,[-1,224,224,4])  # 調整輸入資料維度格式

    # A1  Conv 7x7_S2
    x = tf.nn.conv2d(x, weights['conv1_7x7_S2'], strides=[1,2,2,1], padding='SAME')
    # 卷積層 卷積核 7*7 掃描步長 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)
    # 啟用函式
    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)
    # 區域性響應歸一化

    # 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)

    # 池化層
    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)
    # 引出第一條分支
    #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)
    # 引出第二條分支
    #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

    # 後連線
    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_normal([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 技術分析指標資料影象表示

2.1 處理技術分析指標生成時間序列的多因子資料,本帖使用前 56 天資料預測後 14 天漲跌，下資料處理

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']

# 預測 14 天漲跌
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.datetime.strptime(end,'%Y-%m-%d') # 將 STR 轉換為 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 直觀影象 取某個交易日技術分析指標引數合成圖片畫素資料 CNN 一般用來設計機器視覺，簡單說就是專門處理影象和視訊的，下圖為按照 CV 觀點來看輸入的多因子資料因為前面技術分析因子進行標準化（歸一化處理），這裡對因子資料進行縮放和偏置

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 使用上證指數、中證 500 、創業板指 疊加 HS300 在做技術分析的時候，通常行情和單隻股票走勢，採用疊加噪音的方式探索是否幾個指數存在可量化的關係這裡採用多圖疊加的方法新增噪音進行探索，參考 RGB 3 基色合成彩色圖片

本帖設定使用幾個指數線的 56 天技術分析因子作為訓練資料，使用 CNN 網路卷積進行提取特徵，將 HS300 指數按照未來 14 天漲跌情況分成下跌 -1 平穩 0 上漲 1 ，使用卷積提取特徵**，這一部分因為字數原因省略了。

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