李宏毅（2020）作业9：无监督学习降维、聚类、自编码_clustering李宏毅-程序员宅基地
技术标签： 聚类 机器学习 无监督学习 自编码器 pca降维
数据集
作业
任务1
请至少使用两种方法 (autoencoder 架构、optimizer、data preprocessing、后续降维方法、clustering 算法等等) 来改进 baseline code 的 accuracy。
- 记录改进前、后的 accuracy 分别为多少。
- 使用改进前、后的方法，分别将 val data 的降维结果 (embedding) 与他们对应的 label 画出来。
任务2
使用你 accuracy 最高的 autoencoder，从 trainX 中，取出 index 1, 2, 3, 6, 7, 9 这 6 张图片。
- 画出他们的原图以及 reconstruct 之后的图片。
任务3
在 autoencoder 的训练过程中，至少挑选 10 个 checkpoints。
- 请用 model 的 reconstruction error (用所有的 trainX 计算 MSE) 和 val accuracy 对那些 checkpoints 作图。
数据
请同学以 np.load() 读入资料，valX.npy 和 valY.npy 只用来检验我们的训练效果，不能用来训练。
trainX.npy
- 里面总共有 8500 张 RGB 图片，大小都是 32 * 32 * 3
- shape 为 (8500, 32, 32, 3)
valX.npy
- 请不要用来训练
- 里面总共有 500 张 RGB 图片，大小都是 32 * 32 * 3
- shape 为 (500, 32, 32, 3)
valY.npy
- 请不要用来训练
- 对应 valX.npy 的 label
- shape为 (500,)
下载数据集
创建 checkpoints文件夹
#!gdown --id '1BZb2AqOHHaad7Mo82St1qTBaXo_xtcUc' --output trainX.npy
# !gdown --id '152NKCpj8S_zuIx3bQy0NN5oqpvBjdPIq' --output valX.npy
# !gdown --id '1_hRGsFtm5KEazUg2ZvPZcuNScGF-ANh4' --output valY.npy
!mkdir checkpoints
!ls
mkdir: 无法创建目录"checkpoints": 文件已存在
checkpoints trainX.npy
p1_baseline.png valX.npy
prediction.csv valY.npy
prediction_invert.csv 李宏毅机器学习2020-作业9：无监督学习.ipynb
准备训练数据
定义我们的 preprocess：将图片的数值介于 0~255 的 int 线性转为 -1～1 的 float。
import numpy as np
def preprocess(image_list):
""" Normalize Image and Permute (N,H,W,C) to (N,C,H,W)
Args:
image_list: List of images (9000, 32, 32, 3)
Returns:
image_list: List of images (9000, 3, 32, 32)
"""
image_list = np.array(image_list)
image_list = np.transpose(image_list, (0, 3, 1, 2))
image_list = (image_list / 255.0) * 2 - 1
image_list = image_list.astype(np.float32)
return image_list
自定义Dataset
from torch.utils.data import Dataset
class Image_Dataset(Dataset):
def __init__(self, image_list):
self.image_list = image_list
def __len__(self):
return len(self.image_list)
def __getitem__(self, idx):
images = self.image_list[idx]
return images
将训练资料读入，并且 preprocess。之后我们将 preprocess 完的训练资料变成我们需要的 dataset。请同学不要使用 valX 和 valY 来训练。
from torch.utils.data import DataLoader
trainX = np.load('trainX.npy')
trainX_preprocessed = preprocess(trainX)
img_dataset = Image_Dataset(trainX_preprocessed)
一些工具函数
这边提供一些有用的 functions。一个是计算 model 参数量的（report 会用到），另一个是固定训练的随机种子（以便 reproduce）。
import random
import torch
def count_parameters(model, only_trainable=False):
if only_trainable:
return sum(p.numel() for p in model.parameters() if p.requires_grad)
else:
return sum(p.numel() for p in model.parameters())
def same_seeds(seed):
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed) # if you are using multi-GPU.
np.random.seed(seed) # Numpy module.
random.seed(seed) # Python random module.
torch.backends.cudnn.benchmark = False #不做网络加速
torch.backends.cudnn.deterministic = True #每次返回的卷积算法固定
模型
定义我们的 baseline autoencoder
ConvTranspose2d-逆卷积
关于模型的改进，我只是加深了一层encoder和decoder，效果会变好，参数的调整，只有epoch改为了1000
import torch.nn as nn
class AE(nn.Module):
def __init__(self):
super(AE, self).__init__()
self.encoder = nn.Sequential(
nn.Conv2d(3, 64, 3, stride=1, padding=1),
nn.ReLU(True),
nn.MaxPool2d(2),
nn.Conv2d(64, 128, 3, stride=1, padding=1),
nn.ReLU(True),
nn.MaxPool2d(2),
nn.Conv2d(128, 256, 3, stride=1, padding=1),
nn.ReLU(True),
nn.MaxPool2d(2),
nn.Conv2d(256, 512, 3, stride=1, padding=1),
nn.ReLU(True),
nn.MaxPool2d(2)
)
self.decoder = nn.Sequential(
nn.ConvTranspose2d(512, 256, 3, stride=1),
nn.ReLU(True),
nn.ConvTranspose2d(256, 128, 5, stride=1),
nn.ReLU(True),
nn.ConvTranspose2d(128, 64, 9, stride=1),
nn.ReLU(True),
nn.ConvTranspose2d(64, 3, 17, stride=1),
nn.Tanh()
)
def forward(self, x):
x1 = self.encoder(x)
x = self.decoder(x1)
return x1, x
!nvidia-smi
Thu Nov 4 17:03:39 2021
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.91.03 Driver Version: 460.91.03 CUDA Version: 11.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 GeForce RTX 3090 Off | 00000000:1A:00.0 Off | N/A |
| 57% 70C P2 325W / 350W | 8107MiB / 24268MiB | 91% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 1 GeForce RTX 3090 Off | 00000000:68:00.0 Off | N/A |
| 0% 29C P8 25W / 350W | 299MiB / 24265MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 11145 C python 8103MiB |
| 1 N/A N/A 2432 G /usr/lib/xorg/Xorg 14MiB |
| 1 N/A N/A 4006 G /usr/bin/gnome-shell 17MiB |
| 1 N/A N/A 4984 G /usr/lib/xorg/Xorg 70MiB |
| 1 N/A N/A 5058 G /usr/lib/xorg/Xorg 18MiB |
| 1 N/A N/A 5233 G /usr/bin/gnome-shell 100MiB |
| 1 N/A N/A 5384 G /usr/bin/gnome-shell 36MiB |
| 1 N/A N/A 6548 G ...2179,14311511775341437302 36MiB |
+-----------------------------------------------------------------------------+
-----------------------------+
训练
这个部分就是主要的训练阶段。我们先将准备好的 dataset 当作参数喂给 dataloader。将 dataloader、model、loss criterion、optimizer 都准备好之后，就可以开始训练。训练完成后，我们会将 model 存下来。
import torch
from torch import optim
same_seeds(0)
model = AE().cuda()
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-5, weight_decay=1e-5)
model.train()
n_epoch = 1000
# 准备 dataloader, model, loss criterion 和 optimizer
img_dataloader = DataLoader(img_dataset, batch_size=64, shuffle=True)
epoch_loss = 0
# 主要的训练过程
for epoch in range(n_epoch):
epoch_loss = 0
for data in img_dataloader:
img = data
img = img.cuda()
output1, output = model(img)
loss = criterion(output, img)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (epoch+1) % 10 == 0:
torch.save(model.state_dict(), './checkpoints/checkpoint_{}.pth'.format(epoch+1))
epoch_loss += loss.item()
print('epoch [{}/{}], loss:{:.5f}'.format(epoch+1, n_epoch, epoch_loss))
# 训练完成后存储 model
torch.save(model.state_dict(), './checkpoints/last_checkpoint.pth')
epoch [1/1000], loss:30.54165
epoch [2/1000], loss:26.34405
epoch [3/1000], loss:21.83250
epoch [4/1000], loss:19.13653
epoch [5/1000], loss:16.89123
epoch [6/1000], loss:15.81137
epoch [7/1000], loss:15.24495
epoch [8/1000], loss:14.82142
epoch [9/1000], loss:14.43517
epoch [10/1000], loss:14.08439
epoch [11/1000], loss:13.73920
epoch [12/1000], loss:13.40639
epoch [13/1000], loss:13.08327
epoch [14/1000], loss:12.66554
epoch [15/1000], loss:12.26715
epoch [16/1000], loss:11.93717
epoch [17/1000], loss:11.67487
epoch [18/1000], loss:11.45737
epoch [19/1000], loss:11.28208
epoch [20/1000], loss:11.08628
epoch [21/1000], loss:10.94622
epoch [22/1000], loss:10.80847
epoch [23/1000], loss:10.70417
epoch [24/1000], loss:10.58255
epoch [25/1000], loss:10.48495
epoch [26/1000], loss:10.39527
epoch [27/1000], loss:10.30006
epoch [28/1000], loss:10.20910
epoch [29/1000], loss:10.13124
epoch [30/1000], loss:10.04456
epoch [31/1000], loss:9.96836
epoch [32/1000], loss:9.88246
epoch [33/1000], loss:9.81235
epoch [34/1000], loss:9.72425
epoch [35/1000], loss:9.65545
epoch [36/1000], loss:9.57657
epoch [37/1000], loss:9.51310
epoch [38/1000], loss:9.45421
epoch [39/1000], loss:9.38250
epoch [40/1000], loss:9.31712
epoch [41/1000], loss:9.25833
epoch [42/1000], loss:9.20196
epoch [43/1000], loss:9.14868
epoch [44/1000], loss:9.08939
epoch [45/1000], loss:9.02597
epoch [46/1000], loss:8.95911
epoch [47/1000], loss:8.91480
epoch [48/1000], loss:8.86116
epoch [49/1000], loss:8.79443
epoch [50/1000], loss:8.73779
epoch [51/1000], loss:8.68570
epoch [52/1000], loss:8.62910
epoch [53/1000], loss:8.57338
epoch [54/1000], loss:8.53807
epoch [55/1000], loss:8.48156
epoch [56/1000], loss:8.43463
epoch [57/1000], loss:8.39641
epoch [58/1000], loss:8.34074
epoch [59/1000], loss:8.30465
epoch [60/1000], loss:8.27341
epoch [61/1000], loss:8.23230
epoch [62/1000], loss:8.18089
epoch [63/1000], loss:8.15129
epoch [64/1000], loss:8.11520
epoch [65/1000], loss:8.07959
epoch [66/1000], loss:8.04687
epoch [67/1000], loss:8.02380
epoch [68/1000], loss:7.98933
epoch [69/1000], loss:7.95649
epoch [70/1000], loss:7.92910
epoch [71/1000], loss:7.88972
epoch [72/1000], loss:7.85813
epoch [73/1000], loss:7.82851
epoch [74/1000], loss:7.81065
epoch [75/1000], loss:7.78497
epoch [76/1000], loss:7.73110
epoch [77/1000], loss:7.71461
epoch [78/1000], loss:7.68887
epoch [79/1000], loss:7.65523
epoch [80/1000], loss:7.63705
epoch [81/1000], loss:7.61096
epoch [82/1000], loss:7.57877
epoch [83/1000], loss:7.54703
epoch [84/1000], loss:7.52961
epoch [85/1000], loss:7.48876
epoch [86/1000], loss:7.46642
epoch [87/1000], loss:7.43804
epoch [88/1000], loss:7.41458
epoch [89/1000], loss:7.38298
epoch [90/1000], loss:7.38157
epoch [91/1000], loss:7.34053
epoch [92/1000], loss:7.32307
epoch [93/1000], loss:7.28897
epoch [94/1000], loss:7.27476
epoch [95/1000], loss:7.25432
epoch [96/1000], loss:7.23210
epoch [97/1000], loss:7.20764
epoch [98/1000], loss:7.17726
epoch [99/1000], loss:7.16785
epoch [100/1000], loss:7.14477
epoch [101/1000], loss:7.12776
epoch [102/1000], loss:7.10490
epoch [103/1000], loss:7.08108
epoch [104/1000], loss:7.06430
epoch [105/1000], loss:7.04382
epoch [106/1000], loss:7.01336
epoch [107/1000], loss:7.00099
epoch [108/1000], loss:6.97758
epoch [109/1000], loss:6.95376
epoch [110/1000], loss:6.94354
epoch [111/1000], loss:6.91744
epoch [112/1000], loss:6.91015
epoch [113/1000], loss:6.88055
epoch [114/1000], loss:6.86521
epoch [115/1000], loss:6.84671
epoch [116/1000], loss:6.82973
epoch [117/1000], loss:6.80817
epoch [118/1000], loss:6.78769
epoch [119/1000], loss:6.77140
epoch [120/1000], loss:6.76178
epoch [121/1000], loss:6.74296
epoch [122/1000], loss:6.71641
epoch [123/1000], loss:6.69564
epoch [124/1000], loss:6.67923
epoch [125/1000], loss:6.66339
epoch [126/1000], loss:6.64667
epoch [127/1000], loss:6.62993
epoch [128/1000], loss:6.60127
epoch [129/1000], loss:6.58229
epoch [130/1000], loss:6.57563
epoch [131/1000], loss:6.55139
epoch [132/1000], loss:6.53123
epoch [133/1000], loss:6.51448
epoch [134/1000], loss:6.49753
epoch [135/1000], loss:6.46827
epoch [136/1000], loss:6.45886
epoch [137/1000], loss:6.43451
epoch [138/1000], loss:6.41819
epoch [139/1000], loss:6.39429
epoch [140/1000], loss:6.38479
epoch [141/1000], loss:6.36964
epoch [142/1000], loss:6.34008
epoch [143/1000], loss:6.32599
epoch [144/1000], loss:6.30631
epoch [145/1000], loss:6.29071
epoch [146/1000], loss:6.27065
epoch [147/1000], loss:6.25629
epoch [148/1000], loss:6.23477
epoch [149/1000], loss:6.22027
epoch [150/1000], loss:6.20892
epoch [151/1000], loss:6.18379
epoch [152/1000], loss:6.16717
epoch [153/1000], loss:6.15294
epoch [154/1000], loss:6.13922
epoch [155/1000], loss:6.12273
epoch [156/1000], loss:6.09983
epoch [157/1000], loss:6.09613
epoch [158/1000], loss:6.08098
epoch [159/1000], loss:6.06648
epoch [160/1000], loss:6.05687
epoch [161/1000], loss:6.03163
epoch [162/1000], loss:6.00917
epoch [163/1000], loss:6.00572
epoch [164/1000], loss:5.99157
epoch [165/1000], loss:5.97707
epoch [166/1000], loss:5.96627
epoch [167/1000], loss:5.96171
epoch [168/1000], loss:5.93227
epoch [169/1000], loss:5.92656
epoch [170/1000], loss:5.92673
epoch [171/1000], loss:5.90135
epoch [172/1000], loss:5.89017
epoch [173/1000], loss:5.87263
epoch [174/1000], loss:5.86483
epoch [175/1000], loss:5.85099
epoch [176/1000], loss:5.83615
epoch [177/1000], loss:5.83101
epoch [178/1000], loss:5.82030
epoch [179/1000], loss:5.82544
epoch [180/1000], loss:5.78977
epoch [181/1000], loss:5.78293
epoch [182/1000], loss:5.77460
epoch [183/1000], loss:5.76192
epoch [184/1000], loss:5.75049
epoch [185/1000], loss:5.74188
epoch [186/1000], loss:5.73882
epoch [187/1000], loss:5.72205
epoch [188/1000], loss:5.70864
epoch [189/1000], loss:5.70273
epoch [190/1000], loss:5.69353
epoch [191/1000], loss:5.68343
epoch [192/1000], loss:5.67216
epoch [193/1000], loss:5.66239
epoch [194/1000], loss:5.65125
epoch [195/1000], loss:5.63932
epoch [196/1000], loss:5.63388
epoch [197/1000], loss:5.62116
epoch [198/1000], loss:5.61385
epoch [199/1000], loss:5.61483
epoch [200/1000], loss:5.59609
epoch [201/1000], loss:5.57955
epoch [202/1000], loss:5.57469
epoch [203/1000], loss:5.56383
epoch [204/1000], loss:5.55489
epoch [205/1000], loss:5.54320
epoch [206/1000], loss:5.52971
epoch [207/1000], loss:5.53083
epoch [208/1000], loss:5.51958
epoch [209/1000], loss:5.50594
epoch [210/1000], loss:5.50194
epoch [211/1000], loss:5.49632
epoch [212/1000], loss:5.47642
epoch [213/1000], loss:5.47105
epoch [214/1000], loss:5.46103
epoch [215/1000], loss:5.45136
epoch [216/1000], loss:5.44927
epoch [217/1000], loss:5.43372
epoch [218/1000], loss:5.43229
epoch [219/1000], loss:5.41293
epoch [220/1000], loss:5.40677
epoch [221/1000], loss:5.39713
epoch [222/1000], loss:5.39402
epoch [223/1000], loss:5.38856
epoch [224/1000], loss:5.37551
epoch [225/1000], loss:5.36045
epoch [226/1000], loss:5.35389
epoch [227/1000], loss:5.34672
epoch [228/1000], loss:5.33802
epoch [229/1000], loss:5.33105
epoch [230/1000], loss:5.32277
epoch [231/1000], loss:5.30828
epoch [232/1000], loss:5.29910
epoch [233/1000], loss:5.29399
epoch [234/1000], loss:5.28984
epoch [235/1000], loss:5.27597
epoch [236/1000], loss:5.26934
epoch [237/1000], loss:5.26663
epoch [238/1000], loss:5.25943
epoch [239/1000], loss:5.24395
epoch [240/1000], loss:5.24214
epoch [241/1000], loss:5.23017
epoch [242/1000], loss:5.21525
epoch [243/1000], loss:5.21001
epoch [244/1000], loss:5.20533
epoch [245/1000], loss:5.19778
epoch [246/1000], loss:5.19444
epoch [247/1000], loss:5.17834
epoch [248/1000], loss:5.17032
epoch [249/1000], loss:5.16573
epoch [250/1000], loss:5.16030
epoch [251/1000], loss:5.15691
epoch [252/1000], loss:5.14337
epoch [253/1000], loss:5.13357
epoch [254/1000], loss:5.12614
epoch [255/1000], loss:5.12397
epoch [256/1000], loss:5.11111
epoch [257/1000], loss:5.09905
epoch [258/1000], loss:5.09718
epoch [259/1000], loss:5.09271
epoch [260/1000], loss:5.08443
epoch [261/1000], loss:5.07630
epoch [262/1000], loss:5.06473
epoch [263/1000], loss:5.06329
epoch [264/1000], loss:5.05452
epoch [265/1000], loss:5.04306
epoch [266/1000], loss:5.04899
epoch [267/1000], loss:5.03139
epoch [268/1000], loss:5.02383
epoch [269/1000], loss:5.01982
epoch [270/1000], loss:5.01273
epoch [271/1000], loss:5.00642
epoch [272/1000], loss:4.99454
epoch [273/1000], loss:4.99690
epoch [274/1000], loss:4.98375
epoch [275/1000], loss:4.98370
epoch [276/1000], loss:4.96812
epoch [277/1000], loss:4.96210
epoch [278/1000], loss:4.96167
epoch [279/1000], loss:4.94264
epoch [280/1000], loss:4.94708
epoch [281/1000], loss:4.93381
epoch [282/1000], loss:4.92656
epoch [283/1000], loss:4.92751
epoch [284/1000], loss:4.91519
epoch [285/1000], loss:4.90649
epoch [286/1000], loss:4.90130
epoch [287/1000], loss:4.89965
epoch [288/1000], loss:4.88647
epoch [289/1000], loss:4.88522
epoch [290/1000], loss:4.87119
epoch [291/1000], loss:4.86967
epoch [292/1000], loss:4.86545
epoch [293/1000], loss:4.85670
epoch [294/1000], loss:4.84635
epoch [295/1000], loss:4.84253
epoch [296/1000], loss:4.84705
epoch [297/1000], loss:4.82709
epoch [298/1000], loss:4.82251
epoch [299/1000], loss:4.81915
epoch [300/1000], loss:4.81493
epoch [301/1000], loss:4.80140
epoch [302/1000], loss:4.79302
epoch [303/1000], loss:4.79099
epoch [304/1000], loss:4.78271
epoch [305/1000], loss:4.77509
epoch [306/1000], loss:4.76755
epoch [307/1000], loss:4.76485
epoch [308/1000], loss:4.76169
epoch [309/1000], loss:4.75328
epoch [310/1000], loss:4.74254
epoch [311/1000], loss:4.74224
epoch [312/1000], loss:4.74067
epoch [313/1000], loss:4.72933
epoch [314/1000], loss:4.71486
epoch [315/1000], loss:4.71784
epoch [316/1000], loss:4.70222
epoch [317/1000], loss:4.70290
epoch [318/1000], loss:4.69542
epoch [319/1000], loss:4.69025
epoch [320/1000], loss:4.68246
epoch [321/1000], loss:4.67295
epoch [322/1000], loss:4.67523
epoch [323/1000], loss:4.67207
epoch [324/1000], loss:4.66636
epoch [325/1000], loss:4.64616
epoch [326/1000], loss:4.64512
epoch [327/1000], loss:4.64286
epoch [328/1000], loss:4.63428
epoch [329/1000], loss:4.62759
epoch [330/1000], loss:4.62275
epoch [331/1000], loss:4.61570
epoch [332/1000], loss:4.61228
epoch [333/1000], loss:4.60109
epoch [334/1000], loss:4.60413
epoch [335/1000], loss:4.58950
epoch [336/1000], loss:4.59071
epoch [337/1000], loss:4.58295
epoch [338/1000], loss:4.57782
epoch [339/1000], loss:4.57129
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epoch [899/1000], loss:2.95213
epoch [900/1000], loss:2.94890
epoch [901/1000], loss:2.94738
epoch [902/1000], loss:2.94390
epoch [903/1000], loss:2.94118
epoch [904/1000], loss:2.94426
epoch [905/1000], loss:2.94239
epoch [906/1000], loss:2.93883
epoch [907/1000], loss:2.93823
epoch [908/1000], loss:2.93640
epoch [909/1000], loss:2.93234
epoch [910/1000], loss:2.93235
epoch [911/1000], loss:2.92981
epoch [912/1000], loss:2.93039
epoch [913/1000], loss:2.93373
epoch [914/1000], loss:2.92795
epoch [915/1000], loss:2.92420
epoch [916/1000], loss:2.92136
epoch [917/1000], loss:2.91813
epoch [918/1000], loss:2.91754
epoch [919/1000], loss:2.91795
epoch [920/1000], loss:2.91643
epoch [921/1000], loss:2.91321
epoch [922/1000], loss:2.91369
epoch [923/1000], loss:2.91094
epoch [924/1000], loss:2.91049
epoch [925/1000], loss:2.90867
epoch [926/1000], loss:2.90595
epoch [927/1000], loss:2.90455
epoch [928/1000], loss:2.90523
epoch [929/1000], loss:2.90355
epoch [930/1000], loss:2.90085
epoch [931/1000], loss:2.89791
epoch [932/1000], loss:2.89439
epoch [933/1000], loss:2.89587
epoch [934/1000], loss:2.89358
epoch [935/1000], loss:2.89229
epoch [936/1000], loss:2.88939
epoch [937/1000], loss:2.89070
epoch [938/1000], loss:2.88834
epoch [939/1000], loss:2.88700
epoch [940/1000], loss:2.88633
epoch [941/1000], loss:2.88195
epoch [942/1000], loss:2.88308
epoch [943/1000], loss:2.87824
epoch [944/1000], loss:2.87709
epoch [945/1000], loss:2.87709
epoch [946/1000], loss:2.87699
epoch [947/1000], loss:2.87330
epoch [948/1000], loss:2.87141
epoch [949/1000], loss:2.87136
epoch [950/1000], loss:2.86982
epoch [951/1000], loss:2.86829
epoch [952/1000], loss:2.86615
epoch [953/1000], loss:2.86325
epoch [954/1000], loss:2.86094
epoch [955/1000], loss:2.86219
epoch [956/1000], loss:2.85894
epoch [957/1000], loss:2.86180
epoch [958/1000], loss:2.85887
epoch [959/1000], loss:2.85384
epoch [960/1000], loss:2.85410
epoch [961/1000], loss:2.85243
epoch [962/1000], loss:2.85051
epoch [963/1000], loss:2.84668
epoch [964/1000], loss:2.84494
epoch [965/1000], loss:2.84352
epoch [966/1000], loss:2.84500
epoch [967/1000], loss:2.84642
epoch [968/1000], loss:2.83922
epoch [969/1000], loss:2.83965
epoch [970/1000], loss:2.84072
epoch [971/1000], loss:2.83823
epoch [972/1000], loss:2.83543
epoch [973/1000], loss:2.83415
epoch [974/1000], loss:2.83639
epoch [975/1000], loss:2.82995
epoch [976/1000], loss:2.82914
epoch [977/1000], loss:2.82669
epoch [978/1000], loss:2.83094
epoch [979/1000], loss:2.82190
epoch [980/1000], loss:2.82548
epoch [981/1000], loss:2.82011
epoch [982/1000], loss:2.82137
epoch [983/1000], loss:2.81966
epoch [984/1000], loss:2.81743
epoch [985/1000], loss:2.81949
epoch [986/1000], loss:2.81346
epoch [987/1000], loss:2.81393
epoch [988/1000], loss:2.81204
epoch [989/1000], loss:2.81101
epoch [990/1000], loss:2.81068
epoch [991/1000], loss:2.80631
epoch [992/1000], loss:2.80828
epoch [993/1000], loss:2.80407
epoch [994/1000], loss:2.80417
epoch [995/1000], loss:2.80385
epoch [996/1000], loss:2.80122
epoch [997/1000], loss:2.80091
epoch [998/1000], loss:2.79750
epoch [999/1000], loss:2.79585
epoch [1000/1000], loss:2.79409
降维和聚类
import numpy as np
def cal_acc(gt, pred):
""" Computes categorization accuracy of our task.
Args:
gt: Ground truth labels (9000, )
pred: Predicted labels (9000, )
Returns:
acc: Accuracy (0~1 scalar)
"""
# Calculate Correct predictions
correct = np.sum(gt == pred)
acc = correct / gt.shape[0]
# 因为是binary unsupervised clustering，因此取max（acc，1-acc）# 因为我们只在乎有没有成功将图片分成两群
return max(acc, 1-acc)
import matplotlib.pyplot as plt
def plot_scatter(feat, label, savefig=None):
""" Plot Scatter Image.
Args:
feat: the (x, y) coordinate of clustering result, shape: (9000, 2)
label: ground truth label of image (0/1), shape: (9000,)
Returns:
None
"""
X = feat[:, 0]
Y = feat[:, 1]
plt.scatter(X, Y, c = label)
plt.legend(loc='best')
if savefig is not None:
plt.savefig(savefig)
plt.show()
return
接着我们使用训练好的 model，来预测 testing data 的类别。
由于 testing data 与 training data 一样，因此我们使用同样的 dataset 来实作 dataloader。与 training 不同的地方在于 shuffle 这个参数值在这边是 False。
准备好 model 与 dataloader，我们就可以进行预测了。
我们只需要 encoder 的结果（latents），利用 latents 进行 clustering 之后，就可以分类了。
import torch
from sklearn.decomposition import KernelPCA
#主成分分析（PCA）
#主成分分析（Principal Component Analysis）是目前为止最流行的降维算法。首先它找到接近数据集分布的超平面，然后将所有的数据都投影到这个超平面上。
#保留最大方差的超平面
# kPCA 是无监督学习算法，因此没有明显的性能指标可以帮助我们选择最佳的核和超参数值。不过，降维通常是监督学习任务（例如分类）的准备步骤.
from sklearn.manifold import TSNE
from sklearn.cluster import MiniBatchKMeans
def inference(X, model, batch_size=256):
X = preprocess(X)
dataset = Image_Dataset(X)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False)
latents = []
for i, x in enumerate(dataloader):
#数据格式转换,以及取出相应格式的数据
x = torch.FloatTensor(x)
vec, img = model(x.cuda())
if i == 0:
#view()函数的功能根reshape类似，用来转换size大小。
#x = x.view(batchsize, -1)中batchsize指转换后有几行，而-1指在不告诉函数有多少列的情况下，根据原tensor数据和batchsize自动分配列数。
latents = vec.view(img.size()[0], -1).cpu().detach().numpy()
else:
latents = np.concatenate((latents, vec.view(img.size()[0], -1).cpu().detach().numpy()), axis = 0)
#在零轴方向上合并
print('Latents Shape:', latents.shape)
return latents
def predict(latents):
# First Dimension Reduction
#这里用到的rbf核函数
transformer = KernelPCA(n_components=200, kernel='rbf', n_jobs=-1)
#n_components:
#意义：PCA算法中所要保留的主成分个数n，也即保留下来的特征个数n
#n_jobs:int型变量，并行运行的个数。
#-1:使用所有CPU. n_jobs<-1时，使用(n_cpus+1+n_jobs)个CPU
#transform函数是一定可以替换为fit_transform函数的
#fit_transform函数不能替换为transform函数！
#fit前缀只是方便后面API调用.
kpca = transformer.fit_transform(latents)
print('First Reduction Shape:', kpca.shape)
# # Second Dimesnion Reduction
X_embedded = TSNE(n_components=2).fit_transform(kpca)
print('Second Reduction Shape:', X_embedded.shape)
# Clustering
#n_cluster:类中心的个数，默认为8
#random_state:参数为int,RandomState instance or None.用来设置生成随机数的方式
pred = MiniBatchKMeans(n_clusters=2, random_state=0).fit(X_embedded)
pred = [int(i) for i in pred.labels_]
pred = np.array(pred)
return pred, X_embedded
def invert(pred):
return np.abs(1-pred)
def save_prediction(pred, out_csv='prediction.csv'):
with open(out_csv, 'w') as f:
f.write('id,label\n')
for i, p in enumerate(pred):
f.write(f'{
i},{
p}\n')
print(f'Save prediction to {
out_csv}.')
# load model
model = AE().cuda()
model.load_state_dict(torch.load('./checkpoints/last_checkpoint.pth'))
model.eval()
# 准备 data
trainX = np.load('trainX.npy')
# 预测答案
latents = inference(X=trainX, model=model)
pred, X_embedded = predict(latents)
# 將预测結果存檔，上上传 kaggle
save_prediction(pred, 'prediction.csv')
# 由于是unsupervised的二分类问题，我们只在乎有没有成功将图片分成两群
# 如果上面的档案上传kaggle后正确率不足0.5，只要将label反过来就行了
save_prediction(invert(pred), 'prediction_invert.csv')
Latents Shape: (8500, 2048)
First Reduction Shape: (8500, 200)
Second Reduction Shape: (8500, 2)
Save prediction to prediction.csv.
Save prediction to prediction_invert.csv.
问题1（作图）
将 val data 的降维结果 (embedding) 与他们对应的 label 画出来。
valX = np.load('valX.npy')
valY = np.load('valY.npy')
# ==============================================
# 我们示范basline model的作图，
# report请同学另外还要再画一张improved model的图。
# ==============================================
model.load_state_dict(torch.load('./checkpoints/last_checkpoint.pth'))
model.eval()
latents = inference(valX, model)
pred_from_latent, emb_from_latent = predict(latents)
acc_latent = cal_acc(valY, pred_from_latent)
print('The clustering accuracy is:', acc_latent)
print('The clustering result:')
plot_scatter(emb_from_latent, valY, savefig='p1_baseline.png')
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
No handles with labels found to put in legend.
Second Reduction Shape: (500, 2)
The clustering accuracy is: 0.75
The clustering result:
问题2
使用你 test accuracy 最高的 autoencoder，从 trainX 中，取出 index 1, 2, 3, 6, 7, 9 这 6 张图片 画出他们的原图以及 reconstruct 之后的图片
import matplotlib.pyplot as plt
import numpy as np
# 画出原图
plt.figure(figsize=(10,4))
indexes = [1,2,3,6,7,9]
imgs = trainX[indexes,]
for i, img in enumerate(imgs):
plt.subplot(2, 6, i+1, xticks=[], yticks=[])
plt.imshow(img)
# 画出 reconstruct 的图
inp = torch.Tensor(trainX_preprocessed[indexes,]).cuda()
latents, recs = model(inp)
recs = ((recs+1)/2 ).cpu().detach().numpy()
recs = recs.transpose(0, 2, 3, 1)
for i, img in enumerate(recs):
plt.subplot(2, 6, 6+i+1, xticks=[], yticks=[])
plt.imshow(img)
plt.tight_layout()
问题3
在 autoencoder 的训练过程中，至少挑选 10 个 checkpoints 请用 model 的 train reconstruction error 对 val accuracy 作图 简单说明你观察到的现象
import os
import glob
checkpoints_list = sorted(glob.glob('checkpoints/checkpoint_*.pth'), key= lambda x: int(os.path.splitext(os.path.basename(x))[0].split('_')[-1]))
print(checkpoints_list)
# load data
dataset = Image_Dataset(trainX_preprocessed)
dataloader = DataLoader(dataset, batch_size=64, shuffle=False)
points = []
with torch.no_grad():
for i, checkpoint in enumerate(checkpoints_list):
print('[{}/{}] {}'.format(i+1, len(checkpoints_list), checkpoint))
model.load_state_dict(torch.load(checkpoint))
model.eval()
err = 0
n = 0
for x in dataloader:
x = x.cuda()
_, rec = model(x)
err += torch.nn.MSELoss(reduction='sum')(x, rec).item()
n += x.flatten().size(0)
print('Reconstruction error (MSE):', err/n)
latents = inference(X=valX, model=model)
pred, X_embedded = predict(latents)
acc = cal_acc(valY, pred)
print('Accuracy:', acc)
points.append((err/n, acc))
['checkpoints/checkpoint_10.pth', 'checkpoints/checkpoint_20.pth', 'checkpoints/checkpoint_30.pth', 'checkpoints/checkpoint_40.pth', 'checkpoints/checkpoint_50.pth', 'checkpoints/checkpoint_60.pth', 'checkpoints/checkpoint_70.pth', 'checkpoints/checkpoint_80.pth', 'checkpoints/checkpoint_90.pth', 'checkpoints/checkpoint_100.pth', 'checkpoints/checkpoint_110.pth', 'checkpoints/checkpoint_120.pth', 'checkpoints/checkpoint_130.pth', 'checkpoints/checkpoint_140.pth', 'checkpoints/checkpoint_150.pth', 'checkpoints/checkpoint_160.pth', 'checkpoints/checkpoint_170.pth', 'checkpoints/checkpoint_180.pth', 'checkpoints/checkpoint_190.pth', 'checkpoints/checkpoint_200.pth', 'checkpoints/checkpoint_210.pth', 'checkpoints/checkpoint_220.pth', 'checkpoints/checkpoint_230.pth', 'checkpoints/checkpoint_240.pth', 'checkpoints/checkpoint_250.pth', 'checkpoints/checkpoint_260.pth', 'checkpoints/checkpoint_270.pth', 'checkpoints/checkpoint_280.pth', 'checkpoints/checkpoint_290.pth', 'checkpoints/checkpoint_300.pth', 'checkpoints/checkpoint_310.pth', 'checkpoints/checkpoint_320.pth', 'checkpoints/checkpoint_330.pth', 'checkpoints/checkpoint_340.pth', 'checkpoints/checkpoint_350.pth', 'checkpoints/checkpoint_360.pth', 'checkpoints/checkpoint_370.pth', 'checkpoints/checkpoint_380.pth', 'checkpoints/checkpoint_390.pth', 'checkpoints/checkpoint_400.pth', 'checkpoints/checkpoint_410.pth', 'checkpoints/checkpoint_420.pth', 'checkpoints/checkpoint_430.pth', 'checkpoints/checkpoint_440.pth', 'checkpoints/checkpoint_450.pth', 'checkpoints/checkpoint_460.pth', 'checkpoints/checkpoint_470.pth', 'checkpoints/checkpoint_480.pth', 'checkpoints/checkpoint_490.pth', 'checkpoints/checkpoint_500.pth', 'checkpoints/checkpoint_510.pth', 'checkpoints/checkpoint_520.pth', 'checkpoints/checkpoint_530.pth', 'checkpoints/checkpoint_540.pth', 'checkpoints/checkpoint_550.pth', 'checkpoints/checkpoint_560.pth', 'checkpoints/checkpoint_570.pth', 'checkpoints/checkpoint_580.pth', 'checkpoints/checkpoint_590.pth', 'checkpoints/checkpoint_600.pth', 'checkpoints/checkpoint_610.pth', 'checkpoints/checkpoint_620.pth', 'checkpoints/checkpoint_630.pth', 'checkpoints/checkpoint_640.pth', 'checkpoints/checkpoint_650.pth', 'checkpoints/checkpoint_660.pth', 'checkpoints/checkpoint_670.pth', 'checkpoints/checkpoint_680.pth', 'checkpoints/checkpoint_690.pth', 'checkpoints/checkpoint_700.pth', 'checkpoints/checkpoint_710.pth', 'checkpoints/checkpoint_720.pth', 'checkpoints/checkpoint_730.pth', 'checkpoints/checkpoint_740.pth', 'checkpoints/checkpoint_750.pth', 'checkpoints/checkpoint_760.pth', 'checkpoints/checkpoint_770.pth', 'checkpoints/checkpoint_780.pth', 'checkpoints/checkpoint_790.pth', 'checkpoints/checkpoint_800.pth', 'checkpoints/checkpoint_810.pth', 'checkpoints/checkpoint_820.pth', 'checkpoints/checkpoint_830.pth', 'checkpoints/checkpoint_840.pth', 'checkpoints/checkpoint_850.pth', 'checkpoints/checkpoint_860.pth', 'checkpoints/checkpoint_870.pth', 'checkpoints/checkpoint_880.pth', 'checkpoints/checkpoint_890.pth', 'checkpoints/checkpoint_900.pth', 'checkpoints/checkpoint_910.pth', 'checkpoints/checkpoint_920.pth', 'checkpoints/checkpoint_930.pth', 'checkpoints/checkpoint_940.pth', 'checkpoints/checkpoint_950.pth', 'checkpoints/checkpoint_960.pth', 'checkpoints/checkpoint_970.pth', 'checkpoints/checkpoint_980.pth', 'checkpoints/checkpoint_990.pth', 'checkpoints/checkpoint_1000.pth']
[1/100] checkpoints/checkpoint_10.pth
Reconstruction error (MSE): 0.10465650191961551
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.56
[2/100] checkpoints/checkpoint_20.pth
Reconstruction error (MSE): 0.08282024884691426
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.564
[3/100] checkpoints/checkpoint_30.pth
Reconstruction error (MSE): 0.07529751972123688
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.552
[4/100] checkpoints/checkpoint_40.pth
Reconstruction error (MSE): 0.06996455570295745
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.528
[5/100] checkpoints/checkpoint_50.pth
Reconstruction error (MSE): 0.06556768215403837
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.53
[6/100] checkpoints/checkpoint_60.pth
Reconstruction error (MSE): 0.0623151860704609
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.524
[7/100] checkpoints/checkpoint_70.pth
Reconstruction error (MSE): 0.05941213181439568
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.536
[8/100] checkpoints/checkpoint_80.pth
Reconstruction error (MSE): 0.057350127874636184
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.52
[9/100] checkpoints/checkpoint_90.pth
Reconstruction error (MSE): 0.05522508699753705
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.516
[10/100] checkpoints/checkpoint_100.pth
Reconstruction error (MSE): 0.05384457483478621
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.53
[11/100] checkpoints/checkpoint_110.pth
Reconstruction error (MSE): 0.05195554022695504
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.5
[12/100] checkpoints/checkpoint_120.pth
Reconstruction error (MSE): 0.05074959627787272
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.524
[13/100] checkpoints/checkpoint_130.pth
Reconstruction error (MSE): 0.04992709094402837
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.514
[14/100] checkpoints/checkpoint_140.pth
Reconstruction error (MSE): 0.04817914684146058
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.502
[15/100] checkpoints/checkpoint_150.pth
Reconstruction error (MSE): 0.04657277587815827
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.512
[16/100] checkpoints/checkpoint_160.pth
Reconstruction error (MSE): 0.045626810316945994
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.504
[17/100] checkpoints/checkpoint_170.pth
Reconstruction error (MSE): 0.04440261214387183
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.548
[18/100] checkpoints/checkpoint_180.pth
Reconstruction error (MSE): 0.04345548491384469
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.53
[19/100] checkpoints/checkpoint_190.pth
Reconstruction error (MSE): 0.04282478637321323
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.518
[20/100] checkpoints/checkpoint_200.pth
Reconstruction error (MSE): 0.042173867076051
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.522
[21/100] checkpoints/checkpoint_210.pth
Reconstruction error (MSE): 0.041361579988517014
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.504
[22/100] checkpoints/checkpoint_220.pth
Reconstruction error (MSE): 0.040615920683916874
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.522
[23/100] checkpoints/checkpoint_230.pth
Reconstruction error (MSE): 0.039873808785980826
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.522
[24/100] checkpoints/checkpoint_240.pth
Reconstruction error (MSE): 0.03932966136932373
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.506
[25/100] checkpoints/checkpoint_250.pth
Reconstruction error (MSE): 0.038771576432620775
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.506
[26/100] checkpoints/checkpoint_260.pth
Reconstruction error (MSE): 0.0381339080099966
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.504
[27/100] checkpoints/checkpoint_270.pth
Reconstruction error (MSE): 0.03751208638209923
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.504
[28/100] checkpoints/checkpoint_280.pth
Reconstruction error (MSE): 0.037052626366708794
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.506
[29/100] checkpoints/checkpoint_290.pth
Reconstruction error (MSE): 0.03666375287373861
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.528
[30/100] checkpoints/checkpoint_300.pth
Reconstruction error (MSE): 0.03611967169069776
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.506
[31/100] checkpoints/checkpoint_310.pth
Reconstruction error (MSE): 0.03564991631227381
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.518
[32/100] checkpoints/checkpoint_320.pth
Reconstruction error (MSE): 0.035199516689076144
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.514
[33/100] checkpoints/checkpoint_330.pth
Reconstruction error (MSE): 0.034691137108148314
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.524
[34/100] checkpoints/checkpoint_340.pth
Reconstruction error (MSE): 0.03432022960513246
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.51
[35/100] checkpoints/checkpoint_350.pth
Reconstruction error (MSE): 0.033855246824376725
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.52
[36/100] checkpoints/checkpoint_360.pth
Reconstruction error (MSE): 0.03339220189113243
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.53
[37/100] checkpoints/checkpoint_370.pth
Reconstruction error (MSE): 0.03329624884736304
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.502
[38/100] checkpoints/checkpoint_380.pth
Reconstruction error (MSE): 0.03264928217495189
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.53
[39/100] checkpoints/checkpoint_390.pth
Reconstruction error (MSE): 0.03237577991859586
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.534
[40/100] checkpoints/checkpoint_400.pth
Reconstruction error (MSE): 0.03208851829229617
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.514
[41/100] checkpoints/checkpoint_410.pth
Reconstruction error (MSE): 0.0316866933037253
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.506
[42/100] checkpoints/checkpoint_420.pth
Reconstruction error (MSE): 0.031364078933117434
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.51
[43/100] checkpoints/checkpoint_430.pth
Reconstruction error (MSE): 0.031136608348173254
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.506
[44/100] checkpoints/checkpoint_440.pth
Reconstruction error (MSE): 0.0309632776297775
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.528
[45/100] checkpoints/checkpoint_450.pth
Reconstruction error (MSE): 0.030496950392629587
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.534
[46/100] checkpoints/checkpoint_460.pth
Reconstruction error (MSE): 0.030128193126005284
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.51
[47/100] checkpoints/checkpoint_470.pth
Reconstruction error (MSE): 0.029998875262690527
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.506
[48/100] checkpoints/checkpoint_480.pth
Reconstruction error (MSE): 0.029572404412662283
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.51
[49/100] checkpoints/checkpoint_490.pth
Reconstruction error (MSE): 0.02939370559243595
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.508
[50/100] checkpoints/checkpoint_500.pth
Reconstruction error (MSE): 0.02911538221321854
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.554
[51/100] checkpoints/checkpoint_510.pth
Reconstruction error (MSE): 0.02889633548960966
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.546
[52/100] checkpoints/checkpoint_520.pth
Reconstruction error (MSE): 0.02860628096262614
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.648
[53/100] checkpoints/checkpoint_530.pth
Reconstruction error (MSE): 0.028405724600249645
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.54
[54/100] checkpoints/checkpoint_540.pth
Reconstruction error (MSE): 0.028084655219433353
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.506
[55/100] checkpoints/checkpoint_550.pth
Reconstruction error (MSE): 0.02798689774905934
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.538
[56/100] checkpoints/checkpoint_560.pth
Reconstruction error (MSE): 0.027731095856311276
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.552
[57/100] checkpoints/checkpoint_570.pth
Reconstruction error (MSE): 0.027528591081207875
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.522
[58/100] checkpoints/checkpoint_580.pth
Reconstruction error (MSE): 0.02748092877631094
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.516
[59/100] checkpoints/checkpoint_590.pth
Reconstruction error (MSE): 0.027148202690423704
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.536
[60/100] checkpoints/checkpoint_600.pth
Reconstruction error (MSE): 0.02693716204400156
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.538
[61/100] checkpoints/checkpoint_610.pth
Reconstruction error (MSE): 0.02663602849548938
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.656
[62/100] checkpoints/checkpoint_620.pth
Reconstruction error (MSE): 0.026486863996468338
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.51
[63/100] checkpoints/checkpoint_630.pth
Reconstruction error (MSE): 0.026279585034239526
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.518
[64/100] checkpoints/checkpoint_640.pth
Reconstruction error (MSE): 0.02615043982337503
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.61
[65/100] checkpoints/checkpoint_650.pth
Reconstruction error (MSE): 0.025924385631785674
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.534
[66/100] checkpoints/checkpoint_660.pth
Reconstruction error (MSE): 0.025687772582559023
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.59
[67/100] checkpoints/checkpoint_670.pth
Reconstruction error (MSE): 0.025555453281776577
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.628
[68/100] checkpoints/checkpoint_680.pth
Reconstruction error (MSE): 0.025691534911884983
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.648
[69/100] checkpoints/checkpoint_690.pth
Reconstruction error (MSE): 0.025101487271925984
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.708
[70/100] checkpoints/checkpoint_700.pth
Reconstruction error (MSE): 0.02504801980186911
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.732
[71/100] checkpoints/checkpoint_710.pth
Reconstruction error (MSE): 0.02484492769428328
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.752
[72/100] checkpoints/checkpoint_720.pth
Reconstruction error (MSE): 0.02478704075719796
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.76
[73/100] checkpoints/checkpoint_730.pth
Reconstruction error (MSE): 0.02446424291648117
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.608
[74/100] checkpoints/checkpoint_740.pth
Reconstruction error (MSE): 0.024349503433003145
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.508
[75/100] checkpoints/checkpoint_750.pth
Reconstruction error (MSE): 0.02417324640236649
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.764
[76/100] checkpoints/checkpoint_760.pth
Reconstruction error (MSE): 0.024010706882850796
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.726
[77/100] checkpoints/checkpoint_770.pth
Reconstruction error (MSE): 0.02394120900771197
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.762
[78/100] checkpoints/checkpoint_780.pth
Reconstruction error (MSE): 0.023713757514953613
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.55
[79/100] checkpoints/checkpoint_790.pth
Reconstruction error (MSE): 0.02374166191325468
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.554
[80/100] checkpoints/checkpoint_800.pth
Reconstruction error (MSE): 0.023461397339315977
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.64
[81/100] checkpoints/checkpoint_810.pth
Reconstruction error (MSE): 0.023291605500613943
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.756
[82/100] checkpoints/checkpoint_820.pth
Reconstruction error (MSE): 0.023138159677094105
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.526
[83/100] checkpoints/checkpoint_830.pth
Reconstruction error (MSE): 0.02306466459760479
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.792
[84/100] checkpoints/checkpoint_840.pth
Reconstruction error (MSE): 0.022922015835257138
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.664
[85/100] checkpoints/checkpoint_850.pth
Reconstruction error (MSE): 0.022727084767584706
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.67
[86/100] checkpoints/checkpoint_860.pth
Reconstruction error (MSE): 0.022709223756603166
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.76
[87/100] checkpoints/checkpoint_870.pth
Reconstruction error (MSE): 0.022506213861353257
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.542
[88/100] checkpoints/checkpoint_880.pth
Reconstruction error (MSE): 0.022330569921755323
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.682
[89/100] checkpoints/checkpoint_890.pth
Reconstruction error (MSE): 0.022259797694636325
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.792
[90/100] checkpoints/checkpoint_900.pth
Reconstruction error (MSE): 0.022161509541904226
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.534
[91/100] checkpoints/checkpoint_910.pth
Reconstruction error (MSE): 0.022015575642679253
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.712
[92/100] checkpoints/checkpoint_920.pth
Reconstruction error (MSE): 0.021944920754900166
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.758
[93/100] checkpoints/checkpoint_930.pth
Reconstruction error (MSE): 0.021774335898605047
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.59
[94/100] checkpoints/checkpoint_940.pth
Reconstruction error (MSE): 0.021657160151238534
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.76
[95/100] checkpoints/checkpoint_950.pth
Reconstruction error (MSE): 0.021555810619803037
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.698
[96/100] checkpoints/checkpoint_960.pth
Reconstruction error (MSE): 0.021441521494996313
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.528
[97/100] checkpoints/checkpoint_970.pth
Reconstruction error (MSE): 0.02138799679513071
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.802
[98/100] checkpoints/checkpoint_980.pth
Reconstruction error (MSE): 0.021166577629014558
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.508
[99/100] checkpoints/checkpoint_990.pth
Reconstruction error (MSE): 0.02112917330685784
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.736
[100/100] checkpoints/checkpoint_1000.pth
Reconstruction error (MSE): 0.02094145799150654
Latents Shape: (500, 2048)
First Reduction Shape: (500, 200)
Second Reduction Shape: (500, 2)
Accuracy: 0.554
ps = list(zip(*points))
plt.figure(figsize=(6,6))
plt.subplot(211, title='Reconstruction error (MSE)').plot(ps[0])
plt.subplot(212, title='Accuracy (val)').plot(ps[1])
plt.show()
精度抖动相当剧烈，无监督果然难train
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