Some tutorials and conclusions about deep learning framework ---- 【pytorch】

============改变数组的维度==================
已知reshape函数可以有一维数组形成多维数组
ravel函数可以展平数组
b.ravel()
flatten()函数也可以实现同样的功能
区别：ravel只提供视图view，而flatten分配内存存储

重塑：

用元祖设置维度
>>> b.shape=(4,2,3)
>>> b
array(［[ 0, 1, 2],
        [ 3, 4, 5］,

       ［ 6, 7, 8],
        [ 9, 10, 11］,

       ［12, 13, 14],
        [15, 16, 17］,

       ［18, 19, 20],
        [21, 22, 23］])

转置：
>>> b
array(［0, 1],
       [2, 3］)
>>> b.transpose()
array(［0, 2],
       [1, 3］)

=============数组的组合==============
>>> a
array(［0, 1, 2],
       [3, 4, 5],
       [6, 7, 8］)
>>> b = a*2
>>> b
array(［ 0, 2, 4],
       [ 6, 8, 10],
       [12, 14, 16］)

1.水平组合
>>> np.hstack((a,b))
array(［ 0, 1, 2, 0, 2, 4],
       [ 3, 4, 5, 6, 8, 10],
       [ 6, 7, 8, 12, 14, 16］)
>>> np.concatenate((a,b),axis=1)
array(［ 0, 1, 2, 0, 2, 4],
       [ 3, 4, 5, 6, 8, 10],
       [ 6, 7, 8, 12, 14, 16］)

2.垂直组合
>>> np.vstack((a,b))
array(［ 0, 1, 2],
       [ 3, 4, 5],
       [ 6, 7, 8],
       [ 0, 2, 4],
       [ 6, 8, 10],
       [12, 14, 16］)
>>> np.concatenate((a,b),axis=0)
array(［ 0, 1, 2],
       [ 3, 4, 5],
       [ 6, 7, 8],
       [ 0, 2, 4],
       [ 6, 8, 10],
       [12, 14, 16］)

3.深度组合：沿着纵轴方向组合
>>> np.dstack((a,b))
array(［[ 0, 0],
        [ 1, 2],
        [ 2, 4］,

       ［ 3, 6],
        [ 4, 8],
        [ 5, 10］,

       ［ 6, 12],
        [ 7, 14],
        [ 8, 16］])

4.列组合column_stack()
一维数组：按列方向组合
二维数组：同hstack一样

5.行组合row_stack()
以为数组：按行方向组合
二维数组：和vstack一样

6.==用来比较两个数组
>>> a==b
array(［ True, False, False],
       [False, False, False],
       [False, False, False］, dtype=bool)
#True那个因为都是0啊

==================数组的分割===============
>>> a
array(［0, 1, 2],
       [3, 4, 5],
       [6, 7, 8］)
>>> b = a*2
>>> b
array(［ 0, 2, 4],
       [ 6, 8, 10],
       [12, 14, 16］)

1.水平分割（难道不是垂直分割？？？）
>>> np.hsplit(a,3)
[array(［0],
       [3],
       [6］),
 array(［1],
       [4],
       [7］), 
array(［2],
       [5],
       [8］)]
split(a,3,axis=1)同理达到目的

2.垂直分割
>>> np.vsplit(a,3)
[array(［0, 1, 2］), array(［3, 4, 5］), array(［6, 7, 8］)]

split(a,3,axis=0)同理达到目的

3.深度分割
某三维数组：：：
>>> d = np.arange(27).reshape(3,3,3)
>>> d
array(［[ 0, 1, 2],
        [ 3, 4, 5],
        [ 6, 7, 8］,

       ［ 9, 10, 11],
        [12, 13, 14],
        [15, 16, 17］,

       ［18, 19, 20],
        [21, 22, 23],
        [24, 25, 26］])

深度分割后（即按照深度的方向分割）
注意：dsplite只对3维以上数组起作用
raise ValueError('dsplit only works on arrays of 3 or more dimensions')
ValueError: dsplit only works on arrays of 3 or more dimensions

>>> np.dsplit(d,3)
[array(［[ 0],
        [ 3],
        [ 6］,

       ［ 9],
        [12],
        [15］,

       ［18],
        [21],
        [24］]), array(［[ 1],
        [ 4],
        [ 7］,

       ［10],
        [13],
        [16］,

       ［19],
        [22],
        [25］]), array(［[ 2],
        [ 5],
        [ 8］,

       ［11],
        [14],
        [17］,

       ［20],
        [23],
        [26］])]

===================数组的属性=================
>>> a.shape #数组维度
(3, 3)
>>> a.dtype #元素类型
dtype('int32')
>>> a.size #数组元素个数
9
>>> a.itemsize #元素占用字节数
4
>>> a.nbytes #整个数组占用存储空间=itemsize*size
36
>>> a.T #转置=transpose
array(［0, 3, 6],
       [1, 4, 7],
       [2, 5, 8］)

torch.save(model.state_dict(), filename)

sudo add-apt-repository ppa:fkrull/deadsnakes
sudo apt-get update
sudo apt-get install python3.5

def tensor_load_rgbimage(filename, size=None, scale=None):
    img = Image.open(filename)
    if size is not None:
        img = img.resize((size, size), Image.ANTIALIAS)
    elif scale is not None:
        img = img.resize((int(img.size[0] / scale), int(img.size[1] / scale)), Image.ANTIALIAS)
    img = np.array(img).transpose(2, 0, 1)
    img = torch.from_numpy(img).float()
    return img


def tensor_save_rgbimage(tensor, filename, cuda=False):
    if cuda:
        img = tensor.clone().cpu().clamp(0, 255).numpy()
    else:
        img = tensor.clone().clamp(0, 255).numpy()
    img = img.transpose(1, 2, 0).astype('uint8')
    img = Image.fromarray(img)
    img.save(filename)

########################## save log files #############################################
logfile_path = './log_files_AAE_2017.10.08.16:20.txt'
fobj=open(logfile_path,'a')
fobj.writelines(['Epoch: %d Niter:%d Loss_VAE: %.4f Loss_D: %.4f Loss_D_noise: %.4f Loss_G: %.4f D(x): %.4f D(G(z)): %.4f / %.4f \n'
% (EEEPoch, total_epoch, VAEerr.data[0], errD_noise.data[0], errD.data[0], total_errG.data[0], D_x, D_G_z1, D_G_z2)])
fobj.close()
# print('==>> saving txt files ... Done!')

###########################	save checkpoints ###########################	
if epoch%opt.saveInt == 0 and epoch!=0:
torch.save(netG.state_dict(), '%s/netG_epoch_%d.pth' % (opt.outf, epoch))
# torch.save(netD.state_dict(), '%s/netD_epoch_%d.pth' % (opt.outf, epoch))
# torch.save(netD_gaussian.state_dict(), '%s/netD_Z_epoch_%d.pth' % (opt.outf, epoch))


# ###########################	save middle images into folders	###########################	
# img_index = EEEPoch + index_batch + epoch 
# if epoch % 10 == 0:
# vutils.save_image(real_cpu, '%s/real_samples.png' % img_index,
# normalize=True)
# fake = netG.decoder(fixed_noise)
# vutils.save_image(fake.data,
# '%s/fake_samples_epoch_%03d.png' % (img_index, img_index),
# normalize=True)