znxlwm / pytorch-mnist-celeba-cgan-cdcgan Goto Github PK

error:
RuntimeError: output with shape [1, 28, 28] doesn't match the broadcast shape [3, 28, 28]

MNIST has [1, 28, 28] and [3. 28. 28] two format. In this code,
change
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
to
transforms.Normalize([0.5], [0.5])

There are no files named "Fixed_results", it seems that some files are missed

Would much appreciate a link if so, been trying for days.

代码直接cuda，不考虑cpu的情况。而且还一堆错误，这种代码为啥

@znxlwm good job you did. And also I think the figure about the framework of cGAN and cDCGan looks very clean and beautiful, what's the software you use? thanks.

I only changed one place
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
to transforms.Normalize((0.1307,), (0.3081,))
because it causes error.
following is trainning process:
[1/50] - ptime: 29.42, loss_d: 0.855, loss_g: 1.262
[2/50] - ptime: 29.97, loss_d: 0.288, loss_g: 2.511
[3/50] - ptime: 31.54, loss_d: 0.090, loss_g: 3.608
[4/50] - ptime: 32.44, loss_d: 0.086, loss_g: 4.054
[5/50] - ptime: 34.58, loss_d: 0.070, loss_g: 4.227
[6/50] - ptime: 32.97, loss_d: 0.033, loss_g: 4.767
[7/50] - ptime: 33.55, loss_d: 0.014, loss_g: 5.374
[8/50] - ptime: 34.24, loss_d: 0.029, loss_g: 5.416
[9/50] - ptime: 34.68, loss_d: 0.008, loss_g: 5.843
[10/50] - ptime: 35.56, loss_d: 0.016, loss_g: 5.878
[11/50] - ptime: 38.06, loss_d: 0.006, loss_g: 6.242
[12/50] - ptime: 34.45, loss_d: 0.026, loss_g: 6.199
[13/50] - ptime: 32.97, loss_d: 0.005, loss_g: 6.561
[14/50] - ptime: 32.59, loss_d: 0.015, loss_g: 6.542
[15/50] - ptime: 32.66, loss_d: 0.002, loss_g: 6.981
[16/50] - ptime: 32.96, loss_d: 0.025, loss_g: 6.400
[17/50] - ptime: 33.44, loss_d: 0.019, loss_g: 6.936
[18/50] - ptime: 33.80, loss_d: 0.008, loss_g: 6.489
[19/50] - ptime: 34.30, loss_d: 0.002, loss_g: 7.097
[20/50] - ptime: 34.98, loss_d: 0.040, loss_g: 6.319
[21/50] - ptime: 34.86, loss_d: 0.003, loss_g: 6.906
[22/50] - ptime: 34.65, loss_d: 0.003, loss_g: 7.332
[23/50] - ptime: 34.74, loss_d: 0.008, loss_g: 7.447
[24/50] - ptime: 34.87, loss_d: 0.003, loss_g: 7.552
[25/50] - ptime: 34.79, loss_d: 0.001, loss_g: 7.913
[26/50] - ptime: 35.62, loss_d: 0.009, loss_g: 7.999
[27/50] - ptime: 37.91, loss_d: 0.006, loss_g: 7.409
[28/50] - ptime: 33.79, loss_d: 0.001, loss_g: 8.188
[29/50] - ptime: 32.57, loss_d: 0.001, loss_g: 8.127
learning rate change!
[30/50] - ptime: 32.99, loss_d: 0.005, loss_g: 7.739
[31/50] - ptime: 33.69, loss_d: 0.001, loss_g: 7.975
[32/50] - ptime: 34.34, loss_d: 0.001, loss_g: 8.114
[33/50] - ptime: 35.43, loss_d: 0.001, loss_g: 8.140
[34/50] - ptime: 36.85, loss_d: 0.002, loss_g: 7.763
[35/50] - ptime: 40.14, loss_d: 0.001, loss_g: 8.218
[36/50] - ptime: 44.25, loss_d: 0.001, loss_g: 8.467
[37/50] - ptime: 45.44, loss_d: 0.001, loss_g: 8.708
[38/50] - ptime: 40.60, loss_d: 0.001, loss_g: 8.666
[39/50] - ptime: 40.33, loss_d: 0.001, loss_g: 8.824
learning rate change!
[40/50] - ptime: 39.38, loss_d: 0.000, loss_g: 8.775
[41/50] - ptime: 37.45, loss_d: 0.000, loss_g: 8.842
[42/50] - ptime: 37.45, loss_d: 0.001, loss_g: 8.971
[43/50] - ptime: 38.80, loss_d: 0.001, loss_g: 8.718
[44/50] - ptime: 38.23, loss_d: 0.000, loss_g: 8.672
[45/50] - ptime: 40.54, loss_d: 0.000, loss_g: 8.708
[46/50] - ptime: 36.43, loss_d: 0.000, loss_g: 8.707
[47/50] - ptime: 34.01, loss_d: 0.000, loss_g: 8.800
[48/50] - ptime: 36.30, loss_d: 0.000, loss_g: 8.910
[49/50] - ptime: 36.61, loss_d: 0.000, loss_g: 8.990
[50/50] - ptime: 35.31, loss_d: 0.000, loss_g: 9.075
Avg one epoch ptime: 35.55, total 50 epochs ptime: 1881.31
Training finish!... save training results

Hello,

Thanks for sharing your implementation of cDCGAN.

I have tried implementing it for MNIST data set following the same steps you did in 'pytorch_MNIST_cDCGAN.py' with same parameter settings except for the batch size (I used 32 instead of 128). Unfortunately, I get unstable results for epochs larger than 5.

Based on your implementation, I cannot find something like label smoothing or arbitrary Gaussian noise addition to the discriminator input images in order to fix the over fitting issues as described here. Have you already used such stabilizing tricks and I couldn't observe in your implementation ?

With many thanks in advance

Best

I see in so much other codes what uses only Conv2D and Sigmod, but never a Conv2D, LeakyReLU and Sigmoid.

Can Anybody please help how to pass custom datasets instead of MNIST?

train_loader = torch.utils.data.DataLoader( datasets.MNIST('data', train=True, download=False, transform=transform), batch_size=batch_size, shuffle=True)

ValueError: Using a target size (torch.Size([128])) that is different to the input size (torch.Size([128, 6, 6])) is deprecated. Please ensure they have the same size.

I notice that you use "y_ = (torch.rand(mini_batch, 1) * 10).type(torch.LongTensor).squeeze()" in line 235,why not use the real y_ from input image?