i use python3.6 and pytorch1.5.1, but the program show error, i dont know what envirement did u use

[nmvbxcz]

`D:\anaconda3\envs\pytorch\python.exe C:/Users/Lenovo/PycharmProjects/ladder/ladder/ladder.py --batch 100 --epochs 20 --noise_std 0.2 --data_dir ..\utils\data

BATCH SIZE: 100
EPOCHS: 20
RANDOM SEED: 42
NOISE STD: 0.2
LR DECAY EPOCH: 15
CUDA: False

Loading Data

========NETWORK=======
Ladder(
(se): StackedEncoders(
(encoders): Sequential(
(encoder_0): Encoder(
(linear): Linear(in_features=784, out_features=1000, bias=False)
(bn_normalize_clean): BatchNorm1d(1000, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(bn_normalize): BatchNorm1d(1000, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(activation): ReLU()
)
(encoder_1): Encoder(
(linear): Linear(in_features=1000, out_features=500, bias=False)
(bn_normalize_clean): BatchNorm1d(500, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(bn_normalize): BatchNorm1d(500, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(activation): ReLU()
)
(encoder_2): Encoder(
(linear): Linear(in_features=500, out_features=250, bias=False)
(bn_normalize_clean): BatchNorm1d(250, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(bn_normalize): BatchNorm1d(250, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(activation): ReLU()
)
(encoder_3): Encoder(
(linear): Linear(in_features=250, out_features=250, bias=False)
(bn_normalize_clean): BatchNorm1d(250, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(bn_normalize): BatchNorm1d(250, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(activation): ReLU()
)
(encoder_4): Encoder(
(linear): Linear(in_features=250, out_features=250, bias=False)
(bn_normalize_clean): BatchNorm1d(250, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(bn_normalize): BatchNorm1d(250, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(activation): ReLU()
)
(encoder_5): Encoder(
(linear): Linear(in_features=250, out_features=10, bias=False)
(bn_normalize_clean): BatchNorm1d(10, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(bn_normalize): BatchNorm1d(10, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(activation): Softmax(dim=None)
)
)
)
(de): StackedDecoders(
(bn_u_top): BatchNorm1d(10, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
(decoders): Sequential(
(decoder_0): Decoder(
(V): Linear(in_features=10, out_features=250, bias=False)
(bn_normalize): BatchNorm1d(250, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
)
(decoder_1): Decoder(
(V): Linear(in_features=250, out_features=250, bias=False)
(bn_normalize): BatchNorm1d(250, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
)
(decoder_2): Decoder(
(V): Linear(in_features=250, out_features=250, bias=False)
(bn_normalize): BatchNorm1d(250, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
)
(decoder_3): Decoder(
(V): Linear(in_features=250, out_features=500, bias=False)
(bn_normalize): BatchNorm1d(500, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
)
(decoder_4): Decoder(
(V): Linear(in_features=500, out_features=1000, bias=False)
(bn_normalize): BatchNorm1d(1000, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
)
(decoder_5): Decoder(
(V): Linear(in_features=1000, out_features=784, bias=False)
(bn_normalize): BatchNorm1d(784, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
)
)
(bottom_decoder): Decoder()
)
(bn_image): BatchNorm1d(784, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
)

==UNSUPERVISED-COSTS==
[0.1, 0.1, 0.1, 0.1, 0.1, 10.0, 1000.0]

=====================
TRAINING

C:\Users\Lenovo\PycharmProjects\ladder\ladder\encoder.py:92: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
h = self.activation(z)
C:\Users\Lenovo\PycharmProjects\ladder\ladder\encoder.py:74: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
h = self.activation(z_gb)
Traceback (most recent call last):
File "C:/Users/Lenovo/PycharmProjects/ladder/ladder/ladder.py", line 282, in
main()
File "C:/Users/Lenovo/PycharmProjects/ladder/ladder/ladder.py", line 248, in main
bn_hat_z_layers_unlabelled = ladder.decoder_bn_hat_z_layers(hat_z_layers_unlabelled, z_pre_layers_unlabelled)
File "C:/Users/Lenovo/PycharmProjects/ladder/ladder/ladder.py", line 50, in decoder_bn_hat_z_layers
return self.de.bn_hat_z_layers(hat_z_layers, z_pre_layers)
File "C:\Users\Lenovo\PycharmProjects\ladder\ladder\decoder.py", line 150, in bn_hat_z_layers
hat_z_normalized = torch.div(hat_z - ones.mm(mean), ones.mm(torch.sqrt(var + 1e-10)))
IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)

Process finished with exit code 1
`

[999Yy999]

try to add a dimension of 'mean' tensor which from [10] to [1,10]