做文案策划需要看什么网站/什么是网络营销与直播电商
我试着用python中的Theano库来做一些关于深度信任网络的实验。我使用这个地址中的代码：DBN full code。此代码使用MNIST Handwritten database。此文件已采用pickle格式。
它未被勾入：列车组
有效配置集
测试集
这一点在以下几点上更为明确：train_set_x，train_set_y=列车组
有效集x，有效集y=有效集
test_set_x，test_set_y=测试集
请有人给我代码，构造这个数据集，以便创建我自己的？
我使用的DBN示例需要这种格式的数据，我不知道如何做到这一点。
如果有人知道怎么解决这个问题，请告诉我。
这是我的代码：from datetime import datetime
import time
import os
from pprint import pprint
import numpy as np
import gzip, cPickle
import theano.tensor as T
from theano import function
os.system("cls")
filename = "completeData.txt"
f = open(filename,"r")
X = []
Y = []
for line in f:
line = line.strip('\n')
b = line.split(';')
b[0] = float(b[0])
b[1] = float(b[1])
b[2] = float(b[2])
b[3] = float(b[3])
b[4] = float(b[4])
b[5] = float(b[5])
b[6] = float(b[6])
b[7] = float(b[7])
b[8] = float(b[8])
b[9] = float(b[9])
b[10] = float(b[10])
b[11] = float(b[11])
b[12] = float(b[12])
b[13] = float(b[13])
b[14] = float(b[14])
b[15] = float(b[15])
b[17] = int(b[17])
X.append(b[:16])
Y.append(b[17])
Len = len(X);
X = np.asmatrix(X)
Y = np.asarray(Y)
sizes = [0.8, 0.1, 0.1]
arr_index = int(sizes[0]*Len)
arr_index2_start = arr_index + 1
arr_index2_end = arr_index + int(sizes[1]*Len)
arr_index3_start = arr_index2_start + 1
"""
train_set_x = np.array(X[:arr_index])
train_set_y = np.array(Y[:arr_index])
val_set_x = np.array(X[arr_index2_start:arr_index2_end])
val_set_y = np.array(Y[arr_index2_start:arr_index2_end])
test_set_x = np.array(X[arr_index3_start:])
test_set_y = np.array(X[arr_index3_start:])
train_set = train_set_x, train_set_y
val_set = val_set_x, val_set_y
test_set = test_set_x, test_set_y
"""
x = T.dmatrix('x')
z = x
t_mat = function([x],z)
y = T.dvector('y')
k = y
t_vec = function([y],k)
train_set_x = t_mat(X[:arr_index].T)
train_set_y = t_vec(Y[:arr_index])
val_set_x = t_mat(X[arr_index2_start:arr_index2_end].T)
val_set_y = t_vec(Y[arr_index2_start:arr_index2_end])
test_set_x = t_mat(X[arr_index3_start:].T)
test_set_y = t_vec(Y[arr_index3_start:])
train_set = train_set_x, train_set_y
val_set = val_set_x, val_set_y
test_set = test_set_x, test_set_y
dataset = [train_set, val_set, test_set]
f = gzip.open('..\..\..\data\dex.pkl.gz','wb')
cPickle.dump(dataset, f, protocol=-1)
f.close()
pprint(train_set_x.shape)
print('Finished\n')