1. 神经网络
① 把网络结构放在Sequential里面，好处就是代码写起来比较简介、易懂。
② 可以根据神经网络每层的尺寸，根据下图的公式计算出神经网络中的参数。
2. 搭建神经网络
import torch
import torchvision
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear
class Tudui(nn.Module):
def __init__(self):
super(Tudui, self).__init__()
self.conv1 = Conv2d(3,32,5,padding=2)
self.maxpool1 = MaxPool2d(2)
self.cov2 = Conv2d(32,32,5,padding=2)
self.maxpool2 = MaxPool2d(2)
self.conv3 = Conv2d(32,64,5,padding=2)
self.maxpool3 = MaxPool2d(2)
self.flatten = Flatten()
self.linear1 = Linear(1024,64)
self.Linear2 = Linear(64,10)
def forward(self, x):
x = self.conv1(x)
x = self.maxpool1(x)
x = self.conv2(x)
x = self.maxpool2(x)
x = self.conv3(x)
x = self.maxpool3(x)
x = self.flatten(x)
x = self.linear1(x)
x = self.Linear2(x)
return x
tudui = Tudui()
print(tudui)
Tudui(
(conv1): Conv2d(3, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(maxpool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(cov2): Conv2d(32, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(maxpool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(conv3): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(maxpool3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(flatten): Flatten(start_dim=1, end_dim=-1)
(linear1): Linear(in_features=1024, out_features=64, bias=True)
(Linear2): Linear(in_features=64, out_features=10, bias=True)
)
3. 神经网络输入数据
import torch
import torchvision
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear
class Tudui(nn.Module):
def __init__(self):
super(Tudui, self).__init__()
self.conv1 = Conv2d(3,32,5,padding=2)
self.maxpool1 = MaxPool2d(2)
self.conv2 = Conv2d(32,32,5,padding=2)
self.maxpool2 = MaxPool2d(2)
self.conv3 = Conv2d(32,64,5,padding=2)
self.maxpool3 = MaxPool2d(2)
self.flatten = Flatten()
self.linear1 = Linear(1024,64)
self.Linear2 = Linear(64,10)
def forward(self, x):
x = self.conv1(x)
x = self.maxpool1(x)
x = self.conv2(x)
x = self.maxpool2(x)
x = self.conv3(x)
x = self.maxpool3(x)
x = self.flatten(x)
x = self.linear1(x)
x = self.Linear2(x)
return x
tudui = Tudui()
input = torch.ones((64,3,32,32))
output = tudui(input)
print(output.shape)
torch.Size([64, 10])
4. Sequential神经网络
import torch
import torchvision
from torch import nn
from torch.nn import MaxPool2d, Conv2d, Linear, Flatten
dataset = torchvision.datasets.CIFAR10(root="./dataset", train=False, transform=torchvision.transforms.ToTensor(),
download=False)
dataloader = torch.utils.data.DataLoader(dataset, shuffle=True, batch_size=64)
class Tudui(nn.Module):
def __init__(self):
super(Tudui, self).__init__()
self.model1 = nn.Sequential(
Conv2d(in_channels=3, out_channels=32, kernel_size=5, padding=2),
MaxPool2d(kernel_size=2),
Conv2d(in_channels=32, out_channels=32, kernel_size=5, padding=2),
MaxPool2d(kernel_size=2),
Flatten(),
Linear(in_features=2048, out_features=64),
Linear(in_features=64, out_features=10)
)
def forward(self, x):
x = self.model1(x)
return x
tudui = Tudui()
for data in dataloader:
imgs, targets = data
output = tudui(imgs)
# print("output shape {}".format(output.shape))
print("output is {}".format(output))
output is tensor([[-2.3615e-02, 1.8250e-03, -7.8551e-02, 1.2691e-01, -1.5526e-01,
7.7614e-02, -6.8493e-02, 4.0825e-02, 9.0246e-02, 3.9827e-02],
[-6.1182e-02, 7.0736e-03, -8.9508e-02, 1.0367e-01, -8.7010e-02,
5.1265e-02, -2.7388e-03, 9.1466e-03, -2.2674e-02, 3.0224e-02],
[-6.4400e-02, -3.6798e-03, -9.4247e-02, 1.1550e-01, -1.0292e-01,
2.2583e-02, -2.1646e-02, -8.4335e-03, 1.4409e-02, 2.1647e-02],
[ 1.5610e-02, 3.1449e-04, -9.5202e-02, 1.0084e-01, -1.2478e-01,
7.0675e-02, -5.9286e-02, 2.5851e-02, 4.0245e-02, 6.1655e-02],
[-4.6673e-02, -9.7266e-03, -6.9526e-02, 1.2682e-01, -1.1653e-01,
7.1982e-02, -5.3568e-02, 1.7379e-02, 6.2401e-02, 4.7139e-02],
[-4.1664e-02, -1.1452e-02, -5.1683e-02, 1.3005e-01, -1.2339e-01,
9.6499e-03, -8.0074e-02, 6.5803e-02, 6.9826e-02, 1.9303e-02],
[-3.4755e-02, -4.4054e-02, -6.2755e-02, 1.0650e-01, -1.3098e-01,
5.0111e-02, -7.4378e-02, -4.0802e-02, -3.1260e-02, 4.4909e-02],
[-1.3223e-02, -2.0264e-02, -9.4805e-02, 1.4009e-01, -1.4072e-01,
5.7015e-02, -5.9366e-02, -6.8289e-03, 5.6033e-02, 6.1831e-02],
[-7.5300e-02, -2.9962e-02, -5.7633e-02, 1.2907e-01, -1.2816e-01,
1.5127e-02, -6.4247e-02, -1.7496e-02, 3.7334e-03, 4.1407e-02],
[-2.3666e-02, -9.8894e-03, -5.6945e-02, 1.3378e-01, -1.4308e-01,
3.1430e-02, -8.6617e-02, 3.9363e-02, 4.1120e-02, 5.3902e-02],
[-1.4798e-02, -7.1488e-03, -8.2792e-02, 1.1393e-01, -1.3065e-01,
4.1990e-02, -6.7315e-02, 1.7343e-02, 4.9975e-02, 2.7557e-02],
[-2.8248e-02, -9.9576e-03, -8.0721e-02, 1.3636e-01, -1.4339e-01,
3.8339e-02, -6.2476e-02, 2.5220e-02, 5.7065e-02, 3.1379e-02],
[-7.0002e-02, 1.9104e-02, -9.6102e-02, 1.4530e-01, -8.5652e-02,
9.0018e-02, 2.0184e-02, 3.1397e-02, 3.4451e-03, 2.4169e-02],
[-5.3675e-02, -1.2565e-02, -6.4912e-02, 1.4213e-01, -1.3661e-01,
5.7003e-02, -6.1474e-02, 5.2025e-02, 4.4845e-02, 5.2094e-02],
[-9.5288e-02, -2.9256e-03, -6.3647e-02, 1.2873e-01, -1.1294e-01,
6.5400e-02, -6.2999e-03, 2.4702e-02, -1.3582e-02, 3.9842e-02],
[-5.7253e-02, 3.8234e-03, -9.7886e-02, 1.2850e-01, -1.0765e-01,
4.6879e-02, -1.5504e-02, 6.3958e-03, 3.7386e-03, 2.0155e-02],
[-3.8005e-02, 9.9999e-04, -9.5126e-02, 1.1605e-01, -9.9510e-02,
9.4036e-03, -4.8355e-02, 4.0768e-03, 2.7601e-02, 1.5325e-02],
[-1.5584e-02, -2.3432e-02, -5.9569e-02, 1.6696e-01, -1.4426e-01,
1.8599e-02, -1.0751e-01, 2.5691e-02, 2.3649e-02, 5.0499e-02],
[-7.8040e-03, -3.1917e-02, -9.3790e-02, 1.3450e-01, -1.2356e-01,
1.2086e-02, -7.0096e-02, 1.5479e-02, 2.5654e-02, -6.4263e-03],
[-4.2162e-02, -3.0667e-02, -9.3001e-02, 1.1821e-01, -1.2764e-01,
3.8395e-02, -5.2159e-02, -1.9626e-02, 1.0861e-02, 3.1258e-02],
[-1.9780e-02, -3.8435e-04, -6.4193e-02, 1.3056e-01, -1.5289e-01,
8.6184e-02, -2.8410e-02, 1.1764e-02, 6.7643e-02, 8.2983e-02],
[-5.2773e-02, 5.8871e-03, -1.0690e-01, 1.0912e-01, -7.8524e-02,
3.3645e-02, -2.6665e-02, -2.1264e-03, 3.5745e-02, 5.4903e-05],
[-5.3793e-02, 2.6279e-03, -6.3686e-02, 1.3576e-01, -1.3113e-01,
4.9796e-02, -4.9896e-02, 2.4388e-02, 2.1727e-02, 4.7059e-02],
[ 2.2799e-04, -3.6371e-03, -9.6604e-02, 1.2646e-01, -1.3482e-01,
4.1129e-02, -6.0263e-02, 1.7525e-03, 5.7424e-02, 2.1721e-02],
[-7.4307e-02, -2.0304e-02, -9.4244e-02, 1.0157e-01, -9.3050e-02,
1.9982e-02, 1.4244e-03, -1.4214e-02, -4.3063e-02, 2.8154e-02],
[-4.1353e-02, -2.1676e-03, -8.0401e-02, 1.1395e-01, -9.6345e-02,
1.0295e-02, -3.5654e-02, 1.6936e-02, 1.4784e-02, 3.7693e-02],
[-5.3108e-02, 2.9761e-03, -8.8528e-02, 1.1426e-01, -1.1037e-01,
7.6206e-02, -9.3686e-03, 1.1555e-02, 1.7274e-02, 4.5665e-02],
[-4.5105e-02, 1.9513e-02, -6.8260e-02, 9.8222e-02, -1.1694e-01,
9.9301e-02, -1.0400e-02, 4.5562e-02, 5.6156e-02, 9.8495e-02],
[-6.9877e-02, 6.3312e-03, -8.5599e-02, 1.1048e-01, -9.0688e-02,
5.7754e-02, -2.5171e-03, 1.0758e-02, -2.3661e-02, 4.3083e-02],
[-5.9384e-02, 7.3605e-04, -5.1319e-02, 1.2200e-01, -1.3301e-01,
2.2527e-02, -6.4096e-02, 2.0918e-02, 3.6655e-02, 4.4539e-02],
[ 1.0336e-02, -3.2013e-02, -4.1449e-02, 1.2199e-01, -1.8572e-01,
5.9653e-02, -1.1291e-01, 3.6900e-02, 1.1096e-01, 8.3072e-02],
[-7.9116e-02, -1.3616e-02, -8.8112e-02, 9.7858e-02, -9.0502e-02,
4.3219e-02, -3.0628e-02, 1.0607e-02, 1.7241e-02, 1.9210e-02],
[-4.2547e-02, 1.4505e-02, -9.3859e-02, 1.1234e-01, -1.5127e-01,
4.0333e-02, -6.0704e-02, 3.7587e-02, 1.0553e-01, 7.5193e-02],
[ 3.2890e-02, -1.1473e-02, -6.4170e-02, 1.3770e-01, -1.7194e-01,
2.4003e-03, -1.2616e-01, 2.3754e-02, 1.4159e-01, 2.8525e-02],
[-4.3070e-03, 7.4132e-04, -1.1588e-01, 1.3319e-01, -1.0016e-01,
6.2998e-02, -2.7203e-02, 2.1123e-02, 3.8526e-02, 3.0377e-02],
[-2.6196e-02, -7.4863e-03, -7.1357e-02, 1.3534e-01, -1.5458e-01,
2.4679e-02, -7.7664e-02, 1.4389e-02, 5.8542e-02, 5.9445e-02],
[-7.0221e-02, -1.5888e-02, -3.7366e-02, 7.1467e-02, -1.5059e-01,
1.0658e-03, -8.4952e-02, 1.3381e-02, 8.5288e-03, 6.1961e-02],
[-7.2554e-02, -1.4130e-02, -8.6299e-02, 1.0370e-01, -8.9900e-02,
6.5269e-02, 1.1490e-02, 3.6128e-02, -1.8944e-02, 5.1369e-02],
[-4.6528e-02, 3.1540e-04, -8.3351e-02, 1.3788e-01, -1.4122e-01,
5.4197e-02, -5.9223e-02, 1.1399e-02, 3.3112e-02, 3.4940e-02],
[-5.6136e-02, -1.7781e-02, -9.3526e-02, 1.3945e-01, -1.1470e-01,
6.0145e-02, -8.3547e-03, -1.3112e-02, -1.8558e-03, -2.3190e-03],
[-7.0860e-02, -4.8134e-03, -7.2432e-02, 1.1727e-01, -1.1366e-01,
3.0761e-02, -3.0471e-02, -2.3686e-02, -1.8740e-02, 4.6468e-02],
[-4.0819e-02, -7.0537e-03, -9.8158e-02, 1.0776e-01, -1.2250e-01,
1.8186e-02, -5.3161e-02, 2.0710e-02, 2.8961e-02, 3.8512e-02],
[-4.5672e-02, -5.2776e-02, -8.5304e-02, 1.1047e-01, -1.2507e-01,
3.4772e-02, -5.3520e-02, -3.0086e-02, -2.5309e-02, 4.0357e-02],
[ 1.8747e-03, -1.2271e-02, -6.6618e-02, 1.2992e-01, -1.6625e-01,
3.3430e-02, -1.1483e-01, 3.1296e-02, 1.0192e-01, 4.2795e-02],
[-4.1783e-02, -1.7836e-02, -5.0829e-02, 1.3556e-01, -1.6577e-01,
3.2344e-02, -1.0459e-01, 2.0434e-02, 6.8270e-02, 3.1450e-02],
[-2.7503e-02, -1.2992e-02, -7.0351e-02, 1.3401e-01, -1.4173e-01,
3.9915e-02, -5.0505e-02, 2.6135e-02, 3.6766e-02, 3.2178e-02],
[-7.2584e-02, -4.5547e-02, -4.6548e-02, 9.7393e-02, -1.2752e-01,
7.4835e-02, -4.4088e-02, -6.1300e-03, 5.8323e-02, 3.6059e-02],
[-4.7841e-02, 3.3392e-03, -4.4590e-02, 1.3841e-01, -1.1356e-01,
3.8561e-02, -4.0708e-02, 8.5809e-03, 2.3774e-02, 3.3308e-02],
[-3.7696e-02, -7.2928e-03, -8.4875e-02, 9.9994e-02, -1.0863e-01,
5.4534e-02, -3.5170e-02, 2.0363e-02, 1.6977e-02, 5.5492e-02],
[-4.5441e-02, -2.0799e-02, -5.2572e-02, 1.2355e-01, -1.4230e-01,
2.8047e-02, -7.1577e-02, 3.5649e-02, 3.6925e-02, 3.2124e-02],
[-1.7192e-02, -7.4935e-03, -8.6443e-02, 1.1857e-01, -1.1042e-01,
6.9302e-02, -4.6578e-02, 3.1787e-02, 2.3118e-02, 6.8480e-02],
[-7.0082e-02, 1.7739e-03, -7.3155e-02, 1.1367e-01, -1.2026e-01,
4.5055e-02, -4.9076e-02, 2.6385e-02, 1.8727e-02, 2.2943e-02],
[-1.6301e-02, -7.8896e-03, -7.7346e-02, 1.1155e-01, -1.3790e-01,
4.1550e-02, -8.0114e-02, 2.1943e-02, 4.8703e-02, 6.4513e-02],
[-3.5971e-02, -7.5133e-03, -6.9455e-02, 1.1012e-01, -1.3869e-01,
4.3932e-02, -6.9878e-02, 2.4907e-02, 5.3119e-02, 4.1208e-02],
[-1.6986e-02, -2.5996e-02, -6.9294e-02, 1.0568e-01, -1.3901e-01,
1.3198e-03, -1.1303e-01, 4.7393e-03, 1.2941e-01, 3.2106e-02],
[-6.7890e-02, -6.5649e-03, -6.5851e-02, 1.4036e-01, -1.1302e-01,
-2.4363e-02, -7.4180e-02, -2.0661e-02, 2.7968e-02, -2.3933e-02],
[-1.2204e-03, -2.7933e-02, -1.6114e-01, 1.6136e-01, -7.8734e-02,
-6.2795e-03, -2.5899e-02, -4.2182e-02, -2.1026e-02, -2.9459e-02],
[-3.8823e-02, 1.1573e-02, -6.7227e-02, 1.3061e-01, -1.2088e-01,
3.9839e-02, -3.4606e-02, 1.0478e-02, 3.9676e-02, 4.3093e-02],
[-6.5854e-02, 3.8803e-03, -4.1160e-02, 1.6040e-01, -1.3805e-01,
5.7103e-02, -4.5153e-02, 1.4298e-02, 7.3263e-02, 5.7349e-02],
[-4.6866e-02, -5.1631e-02, -7.2332e-02, 1.3644e-01, -1.3193e-01,
-1.9418e-04, -1.0241e-01, -1.6400e-02, -2.8736e-02, 1.0323e-02],
[-4.0617e-02, 1.6285e-03, -8.5676e-02, 1.0748e-01, -1.2412e-01,
6.4405e-02, -5.2963e-02, 7.3182e-03, 3.7644e-02, 5.1876e-02],
[-3.5717e-02, -3.3270e-03, -7.2937e-02, 1.2173e-01, -1.0499e-01,
3.2307e-02, -6.8859e-02, 1.2477e-02, 2.4989e-02, 3.3591e-02],
[-7.3010e-02, -1.8299e-02, -6.0515e-02, 1.1862e-01, -1.1703e-01,
4.7759e-02, -3.7457e-02, -2.8958e-02, -2.2176e-02, 2.6583e-02],
[-2.9753e-02, -1.5972e-02, -6.4001e-02, 1.1483e-01, -1.1549e-01,
6.2850e-02, -5.8441e-02, 1.3931e-02, 3.2057e-02, 2.8651e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-4.7398e-02, -1.1309e-02, -7.4044e-02, 1.1302e-01, -1.3328e-01,
3.5848e-02, -5.4265e-02, 2.0749e-02, 2.0069e-02, 2.2057e-02],
[-6.7815e-02, 3.3837e-02, -6.5425e-02, 1.1104e-01, -1.0327e-01,
7.4544e-02, -6.4879e-04, 2.0691e-02, 2.0254e-02, 4.6374e-02],
[-6.0335e-02, -4.2719e-02, -8.4978e-02, 1.2141e-01, -1.2913e-01,
1.7510e-02, -8.2481e-02, 3.3006e-02, 4.3995e-03, 3.5876e-02],
[-4.3635e-02, 4.5917e-03, -4.8063e-02, 5.2709e-02, -1.0516e-01,
3.2265e-02, -4.8177e-02, 2.1187e-02, 1.9437e-02, 9.2307e-02],
[-3.1431e-02, -2.0747e-03, -6.1969e-02, 7.5407e-02, -1.4066e-01,
-1.9062e-02, -1.1545e-01, -4.2117e-02, 7.1261e-02, 4.0969e-02],
[-6.0823e-02, -2.5206e-02, -7.3578e-02, 7.6934e-02, -1.3461e-01,
7.2250e-02, -6.4396e-02, 3.1516e-02, 3.6806e-02, 7.2099e-02],
[-4.6422e-02, -3.8501e-03, -1.0546e-01, 1.2237e-01, -1.0372e-01,
6.3652e-02, -2.8218e-02, 4.2488e-03, 3.2400e-02, 2.9348e-02],
[-5.2666e-02, 1.6764e-02, -7.2109e-02, 1.1464e-01, -1.1756e-01,
6.3130e-02, -4.1540e-02, 1.5678e-02, 1.0756e-02, 5.6350e-02],
[-4.9287e-02, -1.5795e-02, -8.9394e-02, 8.0870e-02, -9.0198e-02,
5.0821e-02, -3.4355e-02, 3.9153e-02, 1.7960e-02, 7.0281e-02],
[ 6.7330e-03, -1.9674e-02, -7.7350e-02, 1.4598e-01, -1.6910e-01,
3.2269e-02, -1.0405e-01, 2.5208e-02, 9.1950e-02, 3.6595e-02],
[-6.6010e-02, -2.2454e-03, -7.3368e-02, 1.1664e-01, -7.5045e-02,
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3.3315e-02, -6.0683e-02, 1.0907e-02, 2.6953e-02, 2.2711e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-8.4058e-02, -6.0017e-03, -6.0104e-02, 8.7489e-02, -1.1325e-01,
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4.2119e-02, -1.1685e-01, 5.0934e-02, 1.0860e-01, 4.9207e-02],
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1.3358e-01, 4.5838e-03, 4.7698e-02, 6.6976e-02, 1.0042e-01],
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1.0538e-02, -1.3685e-01, 2.6192e-02, 6.9645e-02, 7.1369e-02],
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3.8609e-02, -4.8082e-02, 1.3201e-02, 4.4708e-02, 1.8575e-02],
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1.0396e-02, -1.6581e-01, 1.4894e-02, 1.3447e-01, 5.7009e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-4.4604e-02, -7.5065e-03, -1.0173e-01, 1.0945e-01, -1.2974e-01,
7.4130e-02, 1.2317e-04, 1.5891e-02, 3.6280e-02, 5.4280e-02],
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[-3.9666e-02, -2.0712e-02, -6.9106e-02, 1.0672e-01, -1.2197e-01,
3.1198e-02, -7.3250e-02, 1.5164e-03, 4.7670e-02, 3.4678e-02],
[-8.5810e-02, 1.6854e-02, -3.0884e-02, 1.2554e-01, -1.2776e-01,
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grad_fn=<AddmmBackward>)
output is tensor([[-1.7552e-02, 1.2251e-02, -6.8708e-02, 1.7154e-01, -1.4430e-01,
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grad_fn=<AddmmBackward>)
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7.1534e-02, 2.2077e-02, 1.1600e-04, -8.2313e-02, 5.9690e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-2.0461e-02, 6.1364e-03, -8.4840e-02, 1.0085e-01, -7.6649e-02,
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[-4.1812e-02, -4.7080e-03, -7.5112e-02, 1.0261e-01, -1.2470e-01,
1.5219e-02, -6.0024e-02, 7.0480e-03, 3.1876e-02, 4.3407e-02],
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2.1980e-02, -1.0784e-01, 2.3826e-02, 9.1374e-02, 3.1649e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-4.6766e-02, 1.4404e-03, -6.1306e-02, 1.2643e-01, -1.3546e-01,
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1.1174e-02, -7.3810e-02, 1.4971e-02, 9.1333e-02, 5.8643e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-7.1223e-02, 4.3875e-03, -8.1770e-02, 1.0815e-01, -1.0810e-01,
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7.7039e-02, -1.1534e-01, 8.1892e-03, 1.2375e-01, 6.5964e-02],
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7.4776e-02, 6.1869e-03, -2.3133e-02, 3.6349e-03, 6.3971e-02],
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1.6648e-02, -3.0244e-02, 1.6118e-02, 3.1027e-02, -1.1119e-04],
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7.1170e-02, -9.4264e-02, 5.7524e-02, 7.8564e-02, 5.9934e-02],
[ 7.4407e-03, -3.5014e-02, -6.6167e-02, 1.0569e-01, -1.5531e-01,
6.8534e-03, -1.0908e-01, 3.1324e-02, 7.3680e-02, 3.4080e-02],
[-1.6630e-02, -2.1861e-02, -1.3978e-01, 1.1575e-01, -9.6756e-02,
2.2837e-02, -3.4529e-02, -1.9270e-02, 3.0438e-02, 2.2087e-02],
[-4.3703e-02, 3.3525e-04, -8.7931e-02, 1.0940e-01, -1.0357e-01,
4.2782e-02, -2.9669e-02, 2.0882e-03, 1.7571e-02, 2.1180e-02],
[-1.9651e-02, 4.5300e-04, -9.5625e-02, 1.0786e-01, -1.4381e-01,
3.6401e-02, -4.2573e-02, 1.9766e-02, 5.7928e-02, 5.4488e-02],
[-5.4001e-02, -1.3805e-03, -5.2858e-02, 1.1461e-01, -8.7476e-02,
3.2590e-02, -5.6672e-02, -2.1622e-02, 4.9910e-03, 3.6346e-02],
[ 7.6258e-03, -4.3573e-02, -1.1018e-01, 1.2497e-01, -1.4998e-01,
3.5175e-02, -9.3903e-02, 1.8482e-02, 8.4953e-02, 6.0302e-02],
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5.0892e-02, -1.1232e-01, 3.0239e-03, 8.5562e-02, 8.0315e-02],
[-8.1182e-02, -9.8426e-03, -9.4976e-02, 1.1000e-01, -7.9868e-02,
1.9522e-02, -1.9668e-02, -4.2982e-02, -5.9058e-02, 6.1037e-03]],
grad_fn=<AddmmBackward>)
output is tensor([[-0.0631, -0.0079, -0.0709, 0.1315, -0.1102, 0.0786, -0.0161, 0.0293,
0.0193, 0.0386],
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0.0529, 0.0484],
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0.0250, 0.0487],
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grad_fn=<AddmmBackward>)
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6.3278e-02, -9.3015e-02, 4.2281e-02, 1.0433e-01, 4.2521e-02],
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4.4608e-02, -6.4498e-02, 2.6146e-02, 4.9637e-02, 2.2978e-02],
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3.0628e-02, -5.1729e-02, 1.9070e-02, 6.4306e-02, 4.0910e-02],
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4.3282e-02, -8.6019e-02, 8.3832e-05, 4.4711e-02, 1.9530e-02],
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1.8060e-02, -6.2128e-02, -4.1245e-02, 3.5088e-02, -1.6559e-02],
[-2.5695e-02, -1.1127e-02, -5.5483e-02, 1.6230e-01, -1.4025e-01,
3.7755e-02, -8.0109e-02, 3.1369e-02, 5.7008e-02, 5.5065e-02],
[ 3.0204e-02, -4.2184e-02, -8.3736e-02, 2.1217e-01, -1.7006e-01,
1.8424e-02, -9.9174e-02, 4.5931e-02, 8.4018e-02, 1.7163e-03],
[ 2.2498e-02, -2.7468e-02, -9.2678e-02, 1.3089e-01, -1.5260e-01,
8.7840e-02, -6.4407e-02, 2.5897e-02, 1.1675e-01, 7.2705e-02],
[-2.5745e-02, -8.2801e-03, -6.5581e-02, 1.3969e-01, -1.1591e-01,
6.1593e-02, -3.8124e-02, -4.5453e-03, 3.2459e-02, 3.2052e-02],
[-2.8744e-03, -1.8499e-02, -8.4864e-02, 1.2656e-01, -1.2784e-01,
4.5473e-02, -5.5406e-02, 2.1555e-02, 7.1301e-02, 6.2381e-03]],
grad_fn=<AddmmBackward>)
output is tensor([[-2.0724e-02, -2.1420e-02, -9.0713e-02, 1.1515e-01, -1.3835e-01,
2.4225e-02, -8.2385e-02, -2.8107e-03, 1.1107e-02, 1.9815e-02],
[-6.4036e-02, 2.6733e-05, -1.0601e-01, 1.3672e-01, -1.1544e-01,
1.8739e-02, -4.8349e-02, 2.8695e-02, 4.1325e-04, 3.0906e-02],
[-4.0911e-02, -1.6972e-02, -1.0094e-01, 1.6681e-01, -1.0121e-01,
5.3716e-02, 4.3163e-03, -3.6824e-02, -1.2688e-02, 1.2368e-02],
[-6.2299e-02, 7.9320e-03, -8.9208e-02, 1.2019e-01, -8.3019e-02,
6.4541e-02, -1.7043e-04, 1.4275e-03, -8.0214e-03, 3.5194e-02],
[-4.3683e-02, -8.6668e-03, -9.2554e-02, 1.1306e-01, -1.1769e-01,
3.2667e-02, -5.3011e-02, -6.6432e-03, 8.1904e-03, 6.1243e-03],
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7.6251e-02, 1.0000e-02, 2.2582e-02, -1.7942e-02, 3.4939e-02],
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2.2875e-02, -9.7411e-02, 3.3814e-02, 7.1815e-02, 5.3928e-02],
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grad_fn=<AddmmBackward>)
output is tensor([[ 1.3925e-02, 3.4791e-02, -6.3905e-02, 1.0218e-01, -1.3399e-01,
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grad_fn=<AddmmBackward>)
output is tensor([[-4.7896e-02, -2.0379e-02, -6.6143e-02, 1.1557e-01, -1.3415e-01,
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4.9424e-02, -1.2971e-02, -4.7132e-03, -1.6124e-03, 1.9573e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-2.9744e-02, -1.8991e-02, -8.5336e-02, 1.2646e-01, -1.4658e-01,
4.5963e-02, -7.4124e-02, 2.6661e-02, 5.4258e-02, 7.0668e-03],
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[-4.1340e-02, -3.7091e-02, -7.0295e-02, 1.3774e-01, -1.3881e-01,
4.4240e-02, -8.9993e-02, -7.9696e-04, 4.5528e-02, -9.6368e-03],
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3.6053e-02, -5.2726e-02, 1.0295e-03, 8.7866e-02, 6.2652e-02],
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5.6378e-02, -2.9318e-02, 2.3672e-04, -1.0221e-02, 2.2889e-02],
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2.5127e-02, -1.0867e-01, 5.2181e-03, 4.0632e-02, 5.7880e-02],
[-3.7123e-02, 4.2859e-03, -5.7836e-02, 1.1540e-01, -1.4853e-01,
4.5755e-02, -6.9434e-02, 1.9601e-02, 3.1702e-02, 5.1361e-02],
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[-5.2063e-02, 4.1937e-03, -8.3682e-02, 1.1514e-01, -1.0929e-01,
5.8683e-02, -2.8554e-02, 1.6320e-02, 1.4048e-02, 4.2414e-02],
[-5.3414e-03, 1.3219e-02, -1.1919e-01, 1.1517e-01, -6.6963e-02,
6.0911e-02, 4.7714e-03, 2.0159e-02, 4.4093e-02, 1.2005e-02],
[-1.6755e-02, 1.5062e-02, -5.0313e-02, 1.3493e-01, -1.5319e-01,
8.9462e-02, -5.1672e-02, 4.0506e-02, 9.0102e-02, 9.1374e-02],
[-8.0468e-02, 1.3409e-03, -6.0952e-02, 6.9940e-02, -1.2069e-01,
8.2001e-02, -3.4445e-02, 3.1219e-02, -1.1778e-02, 8.4515e-02],
[-4.1948e-02, 1.4301e-02, -1.0134e-01, 1.0836e-01, -9.4465e-02,
6.0664e-02, -1.7771e-02, 1.1062e-02, 3.3256e-02, 4.4633e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-6.1467e-02, -2.4932e-02, -9.0099e-02, 9.1710e-02, -1.1731e-01,
4.2195e-02, -4.0771e-02, -5.5547e-03, 2.5136e-02, 3.7947e-02],
[-8.2666e-04, -1.9612e-02, -6.0640e-02, 1.3440e-01, -1.4838e-01,
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[-4.1226e-02, 1.8556e-02, -7.9227e-02, 1.2877e-01, -1.1344e-01,
3.8084e-02, -3.9106e-02, 1.1971e-02, 4.4659e-02, 3.3660e-02],
[-2.9395e-02, -1.8206e-02, -5.4401e-02, 1.0614e-01, -1.2594e-01,
7.3553e-02, -2.2951e-02, -1.0003e-02, 2.5592e-02, 3.5919e-02],
[-1.3598e-02, -2.5444e-02, -9.6731e-02, 1.3223e-01, -1.2632e-01,
3.3633e-02, -6.7965e-02, 5.5537e-02, 6.3682e-02, 2.9118e-02],
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5.4462e-03, -1.1182e-01, 5.5540e-02, 8.4305e-02, 2.2869e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-4.1961e-02, 7.4336e-03, -7.2281e-02, 1.2922e-01, -1.1291e-01,
3.9683e-02, -4.7668e-02, 2.8478e-02, 2.9524e-02, 4.7286e-02],
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[-4.5739e-02, -5.1888e-03, -6.8279e-02, 1.2649e-01, -1.2361e-01,
6.3130e-02, -4.8179e-02, 9.0618e-03, 3.9372e-02, 6.0988e-02],
[ 1.9784e-02, -3.4297e-02, -7.1130e-02, 1.5085e-01, -1.6406e-01,
2.0843e-03, -1.4912e-01, 2.7062e-02, 1.1661e-01, 2.3086e-02],
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grad_fn=<AddmmBackward>)
output is tensor([[-5.4794e-02, -2.6544e-02, -8.3410e-02, 9.8730e-02, -1.1600e-01,
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grad_fn=<AddmmBackward>)
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grad_fn=<AddmmBackward>)
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[ 9.6820e-04, -2.8329e-03, -9.8962e-02, 7.9076e-02, -1.4461e-01,
5.3192e-02, -4.6798e-02, 1.5843e-02, 6.0383e-02, 6.5530e-02],
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3.6859e-02, -6.1981e-02, 1.2491e-02, 4.5634e-02, 4.7315e-03]],
grad_fn=<AddmmBackward>)
output is tensor([[-2.2576e-02, -1.0446e-03, -8.8249e-02, 9.4295e-02, -1.4153e-01,
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[-3.8133e-02, -1.0261e-02, -7.6899e-02, 1.3095e-01, -1.3800e-01,
1.1373e-02, -8.1280e-02, 1.2850e-03, 6.7408e-03, 5.5517e-02],
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grad_fn=<AddmmBackward>)
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grad_fn=<AddmmBackward>)
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grad_fn=<AddmmBackward>)
output is tensor([[-1.9780e-02, -2.3824e-03, -6.5035e-02, 1.2498e-01, -1.2834e-01,
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grad_fn=<AddmmBackward>)
output is tensor([[ 6.8702e-03, 1.0238e-02, -6.0100e-02, 1.0238e-01, -7.3555e-02,
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grad_fn=<AddmmBackward>)
output is tensor([[ 2.8915e-03, -1.8061e-02, -7.9662e-02, 1.2641e-01, -1.5487e-01,
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6.9685e-02, -3.9212e-02, 3.3716e-02, 4.7363e-02, 7.2749e-02],
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5.2046e-02, -6.5152e-02, 5.1556e-02, 6.7464e-02, 5.5603e-02],
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4.4182e-02, -2.8419e-02, -7.3536e-04, -5.8117e-03, 2.2079e-02],
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7.6955e-02, -4.7215e-02, 2.8660e-03, 2.9686e-02, 4.3101e-02],
[ 3.8242e-02, -4.7980e-02, -6.0667e-02, 1.2172e-01, -1.9121e-01,
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[-7.2447e-02, 1.3352e-02, -1.0295e-01, 1.1028e-01, -8.5970e-02,
6.8003e-02, 1.4869e-02, 1.0920e-02, -1.1692e-02, 2.9391e-02],
[-4.7528e-02, -8.5699e-03, -8.2229e-02, 1.1257e-01, -1.0240e-01,
4.9704e-02, -2.5846e-02, 5.4186e-03, 1.1150e-02, 4.3930e-02],
[ 2.3106e-02, 1.2668e-03, -7.9079e-02, 1.4745e-01, -1.6480e-01,
9.4420e-02, -9.1931e-02, 5.7727e-02, 1.3623e-01, 6.2435e-02],
[-3.3721e-02, -1.5059e-02, -5.8492e-02, 1.2786e-01, -1.5705e-01,
2.4048e-02, -9.2048e-02, 1.6094e-02, 7.4013e-02, 4.4329e-02],
[-1.4092e-02, -4.2199e-02, -8.0875e-02, 1.2824e-01, -1.8909e-01,
-2.1539e-03, -1.4973e-01, -1.1273e-02, 8.1999e-02, 8.2417e-03],
[-3.4214e-02, 1.3217e-03, -9.8580e-02, 1.0746e-01, -1.3611e-01,
3.5254e-02, -4.7834e-02, -7.6881e-04, 2.1494e-02, 3.6355e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-2.5373e-02, -7.9482e-03, -7.6570e-02, 1.2195e-01, -1.3001e-01,
4.9403e-02, -7.5007e-02, 1.4399e-02, 6.2122e-02, 2.6325e-02],
[-6.2021e-02, -2.5096e-02, -1.0392e-01, 1.3789e-01, -1.2303e-01,
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[-2.9716e-03, -3.1578e-02, -8.1211e-02, 1.3638e-01, -1.4453e-01,
1.3162e-02, -9.2474e-02, 5.6149e-03, 4.8267e-02, 7.3540e-03],
[-1.9718e-02, -3.2162e-02, -5.3305e-02, 1.1473e-01, -1.5357e-01,
-2.7881e-03, -1.2409e-01, 5.0255e-03, 1.0072e-01, 5.4393e-02],
[-4.4147e-02, -2.6965e-02, -6.9190e-02, 1.5707e-01, -1.3163e-01,
6.4677e-02, -5.5656e-02, 7.5489e-04, 6.1139e-02, 1.8014e-02],
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1.1198e-01, -5.5895e-02, 5.6924e-02, 4.4565e-02, 7.7557e-02],
[ 1.6346e-02, -1.0784e-02, -7.7092e-02, 1.4481e-01, -1.6522e-01,
4.7417e-02, -1.0405e-01, 2.6102e-02, 8.5910e-02, 5.3883e-02],
[-4.5278e-02, 2.4679e-02, -7.5274e-02, 1.0941e-01, -6.7805e-02,
3.3175e-02, 5.7202e-03, 1.0315e-02, 1.3062e-02, 5.9057e-02],
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[-4.9192e-02, 1.5865e-02, -8.1046e-02, 1.1710e-01, -1.3349e-01,
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7.3241e-02, -3.9767e-02, 2.9830e-02, -1.4074e-02, 6.0359e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-5.3619e-02, 1.0698e-03, -6.0350e-02, 1.0718e-01, -1.0565e-01,
5.3899e-02, -5.6008e-02, 2.6832e-02, 4.6955e-02, 5.3654e-02],
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[-5.4726e-02, 5.8292e-03, -7.5460e-02, 1.2487e-01, -9.7110e-02,
5.8588e-02, -3.0999e-02, 8.5052e-03, 2.9058e-02, 3.3674e-02],
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grad_fn=<AddmmBackward>)
output is tensor([[-8.0759e-02, -1.7341e-02, -6.2015e-02, 1.1839e-01, -1.0981e-01,
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grad_fn=<AddmmBackward>)
output is tensor([[-1.3720e-02, -1.8452e-02, -9.7438e-02, 1.7180e-01, -1.2139e-01,
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6.7659e-02, -6.3285e-02, 5.7843e-02, 8.7638e-02, 5.5464e-02],
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6.4040e-02, -3.9931e-02, 4.5464e-02, 7.6340e-02, 6.2346e-02],
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3.5552e-02, -2.1944e-02, 2.2375e-03, -4.9751e-02, 1.4899e-02],
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8.3992e-02, -2.0912e-02, -1.9209e-02, 7.4056e-02, 5.6351e-02],
[-4.3469e-02, 1.1073e-02, -8.2357e-02, 1.0721e-01, -1.1910e-01,
6.3480e-02, -3.2345e-02, 3.0247e-02, 2.3824e-02, 6.1560e-02],
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[-4.2962e-02, -1.3525e-02, -5.8196e-02, 1.5587e-01, -1.5321e-01,
7.1191e-02, -7.3553e-02, 5.2588e-02, 5.4555e-02, 4.6182e-02],
[-3.9428e-02, 6.3777e-05, -8.4449e-02, 1.1441e-01, -1.2174e-01,
1.5739e-02, -5.0991e-02, -8.2013e-04, 4.5165e-02, 2.5026e-02],
[-1.1508e-02, -5.3677e-02, -7.8377e-02, 1.3090e-01, -1.2692e-01,
-4.1607e-03, -8.7853e-02, -1.2338e-02, 1.3273e-02, -1.5363e-02],
[-2.3646e-02, -3.2286e-02, -7.3430e-02, 1.4761e-01, -1.3508e-01,
1.9024e-02, -6.7688e-02, -6.2948e-03, 3.1943e-02, 3.4992e-02],
[-7.2523e-02, -2.2418e-02, -4.8565e-02, 1.2367e-01, -1.3762e-01,
-3.3110e-03, -7.5524e-02, 3.9912e-03, 2.1199e-02, 1.9632e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-3.4669e-02, 2.8934e-02, -7.5095e-02, 8.8826e-02, -1.1470e-01,
7.3451e-02, -4.1954e-02, 3.3993e-02, 4.4119e-02, 3.8912e-02],
[-3.3027e-02, -6.0118e-04, -8.8491e-02, 9.7681e-02, -1.3128e-01,
4.5528e-02, -6.4751e-02, 9.4433e-03, 6.1151e-02, 6.8370e-02],
[-3.3812e-02, -8.4274e-03, -1.0134e-01, 1.1266e-01, -1.2546e-01,
6.4235e-02, -3.0211e-02, -7.4062e-04, 2.4213e-03, 3.5779e-02],
[-6.7716e-02, -5.7993e-03, -7.4011e-02, 9.0470e-02, -1.2633e-01,
4.3718e-02, -6.4991e-02, -1.3530e-02, -1.5956e-02, 6.6412e-02],
[-7.5814e-02, 1.3744e-02, -1.0341e-01, 1.0828e-01, -7.5308e-02,
8.5290e-02, 3.5139e-02, 1.8996e-02, -6.0967e-02, 5.6241e-02],
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[-1.7796e-02, 2.1064e-02, -8.5131e-02, 1.0745e-01, -1.1916e-01,
4.6069e-02, -5.3266e-02, 3.5715e-02, 6.2693e-02, 4.5118e-02],
[-4.9019e-02, -1.9368e-02, -6.9082e-02, 1.2374e-01, -1.2487e-01,
2.0535e-02, -5.7163e-02, 2.6055e-02, -3.8763e-03, 1.3595e-02],
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3.0921e-02, -4.7969e-02, 5.6891e-03, 6.4132e-02, 6.3421e-02],
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grad_fn=<AddmmBackward>)
output is tensor([[ 7.9848e-04, -1.5800e-02, -6.3472e-02, 9.4273e-02, -1.4996e-01,
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6.5924e-02, -2.8651e-02, 1.0033e-02, 1.9079e-02, 2.1109e-02],
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[-8.6720e-02, 1.2454e-02, -7.5234e-02, 1.2030e-01, -8.9989e-02,
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[-6.4675e-02, 7.1694e-04, -7.5965e-02, 1.0438e-01, -1.0279e-01,
1.7972e-02, -6.7058e-02, -3.2568e-02, 9.3346e-03, 4.0081e-02],
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[-7.8540e-02, -1.0288e-02, -7.5527e-02, 1.2780e-01, -8.7492e-02,
4.3614e-02, -2.9846e-02, -1.0893e-02, -2.9684e-02, 1.2486e-02],
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[-6.1282e-02, -3.6193e-02, -6.7393e-02, 1.0487e-01, -1.5206e-01,
2.6094e-02, -8.3777e-02, -2.7716e-02, 2.7653e-02, 1.3851e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-6.7223e-02, -1.2310e-02, -6.4341e-02, 1.2517e-01, -1.0209e-01,
2.0644e-02, -4.2471e-02, -4.7670e-03, -2.3305e-02, 8.9561e-03],
[-1.1796e-01, 1.9254e-02, -5.5398e-02, 1.0839e-01, -8.4119e-02,
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[-7.4590e-02, -1.6855e-02, -1.1283e-01, 1.1148e-01, -1.0961e-01,
3.7994e-02, -2.0072e-03, 2.3863e-02, -2.1888e-02, 3.1123e-02],
[-3.1634e-02, 3.4749e-03, -9.3700e-02, 9.1204e-02, -1.0116e-01,
7.2462e-02, -3.4817e-02, 1.8894e-02, 1.8595e-02, 5.4635e-02],
[-5.1390e-02, -3.1156e-02, -5.1378e-02, 1.0957e-01, -1.4233e-01,
5.2207e-02, -7.6062e-02, 1.8696e-02, 4.0756e-02, 4.4538e-02],
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6.7022e-02, -7.8311e-02, 1.5340e-03, 3.9587e-02, 8.1208e-02],
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8.3714e-02, -2.5043e-02, 3.8879e-02, 2.1401e-02, 4.7109e-02],
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4.3320e-02, -8.1876e-02, 6.2322e-02, 8.9725e-02, 8.7677e-02],
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[-4.4406e-02, 1.6578e-02, -8.7205e-02, 1.0624e-01, -1.1400e-01,
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3.4661e-02, -9.7434e-02, 2.7038e-02, 7.3427e-02, 4.0774e-02],
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5.1366e-02, -2.4848e-02, -4.4236e-02, 4.7140e-05, 2.4810e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-2.3869e-02, -4.1821e-02, -4.6095e-02, 1.2229e-01, -1.8222e-01,
1.6185e-02, -1.0597e-01, 3.3051e-02, 8.3294e-02, 8.6682e-03],
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[ 4.8053e-02, -6.7195e-02, -7.7320e-02, 1.4645e-01, -1.6742e-01,
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grad_fn=<AddmmBackward>)
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grad_fn=<AddmmBackward>)
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6.2701e-02, -4.4409e-02, 2.8221e-02, 2.5329e-02, 5.7392e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-1.3781e-02, -2.3579e-02, -9.8295e-02, 1.7094e-01, -1.2363e-01,
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[-3.7616e-02, 1.6232e-02, -6.9846e-02, 1.4348e-01, -1.1652e-01,
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[ 1.1616e-02, -3.9258e-04, -8.0047e-02, 1.5853e-01, -1.4724e-01,
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4.7631e-02, -4.3441e-02, 1.1108e-02, 2.7844e-02, 3.8693e-02],
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[ 2.5449e-02, -1.4087e-02, -7.8667e-02, 1.0464e-01, -1.6379e-01,
5.6291e-02, -1.0104e-01, 3.8083e-02, 1.1744e-01, 5.6997e-02],
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4.0225e-02, -3.7622e-02, 1.4871e-02, 2.8997e-02, 1.2724e-02],
[-6.5924e-02, -3.1127e-02, -5.2709e-02, 7.8283e-02, -1.5612e-01,
7.5134e-02, -6.5686e-02, 1.9169e-02, 2.5154e-02, 7.0524e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-4.2644e-02, 1.4302e-03, -9.6907e-02, 9.1643e-02, -8.2687e-02,
8.9775e-02, -1.3907e-02, 3.8142e-02, 4.1279e-03, 8.7089e-02],
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[-2.3124e-02, 6.7300e-03, -8.2615e-02, 1.3087e-01, -1.1416e-01,
6.3998e-02, -3.6939e-02, 2.8998e-02, 3.0850e-02, 4.4168e-02],
[-1.0271e-02, -3.1769e-02, -9.9314e-02, 1.3741e-01, -1.3062e-01,
4.8500e-02, -7.0009e-02, -7.1167e-03, 3.8264e-02, 1.4968e-02],
[-3.4828e-02, -7.3568e-03, -8.0231e-02, 1.1997e-01, -1.3752e-01,
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3.4796e-02, -5.4025e-02, 1.8900e-02, 1.4064e-02, 4.7462e-02],
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[-3.3557e-02, -1.8622e-02, -9.7121e-02, 1.1486e-01, -1.1883e-01,
1.7638e-02, -3.2181e-02, -1.4788e-02, 1.5478e-02, -3.2359e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-6.0885e-02, -2.2728e-04, -6.8230e-02, 1.2755e-01, -1.0946e-01,
5.7307e-02, -2.5547e-02, 2.3508e-02, 2.8075e-03, 5.9228e-02],
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[-2.2500e-02, -1.5972e-02, -7.6686e-02, 1.4574e-01, -1.1692e-01,
6.6468e-02, -3.1293e-02, -3.3104e-03, 2.8236e-02, 3.6231e-02],
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5.9911e-02, -2.5692e-02, 6.0552e-02, 7.2390e-03, 3.1328e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-7.6137e-02, 1.0163e-02, -9.9031e-02, 8.0870e-02, -9.4897e-02,
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[-2.6491e-02, -7.8330e-03, -7.6124e-02, 1.3454e-01, -1.4101e-01,
3.3539e-02, -7.7395e-02, 2.0409e-02, 6.3002e-02, 2.7561e-02],
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grad_fn=<AddmmBackward>)
output is tensor([[-1.0879e-02, -8.4554e-03, -9.7167e-02, 1.4487e-01, -1.1550e-01,
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grad_fn=<AddmmBackward>)
output is tensor([[ 1.5944e-02, -2.1696e-02, -7.8542e-02, 1.0675e-01, -1.7624e-01,
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grad_fn=<AddmmBackward>)
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1.0820e-01, -5.5227e-02, 4.2928e-02, 1.0041e-01, 8.2816e-02],
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4.8730e-02, -3.3310e-02, 2.2989e-03, 5.2115e-03, 2.2917e-02],
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8.4526e-02, -4.5657e-02, 2.0524e-02, 6.0874e-02, 6.8211e-02],
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1.6192e-02, -1.0181e-02, -7.1046e-03, -5.6479e-02, 1.8877e-02],
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4.5341e-02, -4.5278e-02, 9.7772e-03, 8.2764e-02, 4.7472e-02],
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3.5486e-02, -6.8004e-02, 1.2777e-02, 3.3065e-02, 1.8770e-02],
[-6.1109e-02, 5.0827e-04, -8.1373e-02, 1.0698e-01, -1.0781e-01,
8.0409e-02, -2.2652e-02, 3.2924e-02, 2.7992e-02, 6.4543e-02],
[ 1.4519e-02, -2.5662e-02, -5.8135e-02, 1.6801e-01, -1.5889e-01,
-3.5855e-03, -9.9115e-02, -8.4477e-03, 4.4793e-02, -1.0420e-02],
[-9.6560e-03, -3.3887e-02, -6.0851e-02, 1.6078e-01, -1.6291e-01,
3.5059e-02, -1.0787e-01, 2.1876e-02, 9.9206e-02, 2.8733e-02],
[-3.9726e-02, -1.4623e-03, -9.6224e-02, 1.3813e-01, -7.0191e-02,
1.1777e-02, -1.3306e-02, -3.4860e-03, 1.4566e-03, 5.6024e-03],
[-7.6667e-02, -1.5974e-05, -4.8080e-02, 1.2331e-01, -1.0452e-01,
1.6038e-02, -3.8263e-02, -4.6975e-04, -2.6216e-02, 1.8730e-02],
[-2.9644e-02, 1.8837e-02, -1.1403e-01, 1.5244e-01, -1.0236e-01,
8.5104e-02, 4.8646e-03, 1.4927e-02, 4.8133e-02, 4.8495e-02],
[-9.0611e-02, -2.0972e-02, -7.5705e-02, 1.2007e-01, -1.0800e-01,
2.0906e-02, -1.7152e-02, -1.2016e-02, 1.5024e-02, 1.0441e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-7.4344e-03, -3.0623e-03, -1.1664e-01, 1.3000e-01, -1.2149e-01,
5.3370e-02, -3.6632e-02, 1.8278e-02, 5.5979e-02, 3.5298e-02],
[-4.6401e-02, -1.6507e-02, -1.0912e-01, 9.8567e-02, -1.0947e-01,
5.5487e-02, -2.4112e-02, 1.6405e-02, 1.2029e-02, 6.1075e-02],
[ 6.7760e-03, -1.9287e-02, -4.8621e-02, 2.0232e-01, -2.0514e-01,
-1.0033e-02, -1.8366e-01, 3.3149e-02, 1.4076e-01, 1.7813e-02],
[-1.7225e-03, 2.1292e-03, -7.9558e-02, 1.1088e-01, -1.2313e-01,
8.5226e-03, -7.9068e-02, -4.0792e-03, 8.9948e-02, 2.7463e-02],
[-4.6193e-02, 1.4958e-02, -7.4487e-02, 1.1812e-01, -9.7637e-02,
6.4561e-02, -1.8279e-02, 2.4994e-02, 4.3062e-02, 1.6804e-02],
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[-8.4337e-02, 2.1308e-02, -7.5198e-02, 1.0343e-01, -6.6705e-02,
7.6755e-02, 1.3287e-02, 2.3740e-02, 2.5458e-03, 3.8230e-02],
[-2.8771e-02, -2.4938e-02, -8.0913e-02, 1.5900e-01, -1.4919e-01,
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grad_fn=<AddmmBackward>)
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grad_fn=<AddmmBackward>)
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[ 2.2866e-02, -7.7136e-03, -6.4798e-02, 1.4866e-01, -1.5675e-01,
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[-2.3340e-02, -2.9747e-03, -4.4383e-02, 1.4153e-01, -1.7010e-01,
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[-4.0858e-02, -1.9756e-02, -6.3269e-02, 1.4140e-01, -1.2642e-01,
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[-2.7902e-02, -2.3607e-02, -8.3225e-02, 1.0082e-01, -1.2694e-01,
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[-6.2028e-02, 6.2612e-03, -6.7123e-02, 1.1861e-01, -1.3014e-01,
5.0784e-02, -4.2423e-02, 3.2746e-02, 2.3503e-02, 4.5201e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[ 2.3120e-02, -6.4674e-03, -1.0354e-01, 1.3456e-01, -1.2527e-01,
4.5953e-02, -6.2323e-02, 7.4757e-03, 5.4908e-02, 1.6269e-02],
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4.4123e-02, -5.4841e-02, 1.3343e-02, 2.3071e-02, 7.4158e-02],
[-1.7273e-02, 3.8403e-03, -7.6337e-02, 1.3236e-01, -1.2316e-01,
2.7321e-02, -5.6784e-02, 4.5121e-03, 3.2108e-02, 2.2832e-02],
[-3.9825e-02, 1.5125e-02, -9.9718e-02, 1.1717e-01, -1.0642e-01,
6.8810e-02, -2.0108e-02, 3.0969e-02, 5.2033e-02, 6.5330e-02],
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grad_fn=<AddmmBackward>)
output is tensor([[ 1.1597e-03, -6.6959e-02, -5.6005e-02, 1.6165e-01, -1.6889e-01,
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grad_fn=<AddmmBackward>)
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grad_fn=<AddmmBackward>)
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6.2624e-02, -3.6978e-03, -2.8254e-03, 1.1594e-02, 5.0646e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[ 5.6734e-02, -1.3497e-02, -6.8867e-02, 1.1673e-01, -1.7277e-01,
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6.6097e-02, 9.8109e-03, -1.0851e-02, -2.5431e-02, 4.8684e-02],
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grad_fn=<AddmmBackward>)
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[-3.4573e-02, 2.1688e-02, -9.1859e-02, 1.0714e-01, -8.8086e-02,
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6.3755e-02, -3.8526e-02, 1.5129e-03, 2.2163e-02, 4.6398e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-5.1483e-02, 1.7898e-04, -5.5502e-02, 1.2821e-01, -1.2138e-01,
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grad_fn=<AddmmBackward>)
output is tensor([[-1.0531e-02, -7.9825e-03, -5.8910e-02, 1.3447e-01, -1.7315e-01,
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grad_fn=<AddmmBackward>)
output is tensor([[-2.8816e-03, -2.1620e-02, -7.6418e-02, 9.3671e-02, -1.4987e-01,
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4.9144e-02, -6.2355e-02, 1.5721e-03, 1.2258e-02, 4.2003e-02],
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7.2605e-02, -1.5214e-02, 1.6675e-02, 5.0981e-02, 5.9203e-02],
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4.2451e-02, -4.3544e-02, 1.4273e-02, 5.8799e-02, 1.4151e-02],
[ 2.1485e-02, -1.6835e-02, -9.6822e-02, 9.8737e-02, -1.3717e-01,
7.5212e-04, -9.5367e-02, 3.2056e-03, 8.0735e-02, 1.6696e-02],
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2.4630e-02, -1.0005e-01, 1.0131e-02, 4.3638e-02, 5.3844e-02],
[-4.7980e-02, 1.2408e-02, -7.9407e-02, 1.4907e-01, -1.1149e-01,
6.3392e-02, -3.5437e-02, 3.3626e-02, 2.9813e-02, 4.4972e-02],
[-5.0075e-02, 1.4407e-02, -7.9661e-02, 8.0073e-02, -1.0996e-01,
3.9145e-02, -3.6941e-02, -4.4684e-03, 1.8341e-02, 5.6154e-02],
[-4.9746e-02, -1.4671e-02, -6.5893e-02, 1.1268e-01, -1.2376e-01,
2.5103e-02, -6.0952e-02, -1.0103e-02, 4.0882e-02, 2.7895e-02],
[-4.3064e-02, -3.4499e-02, -8.1691e-02, 1.5844e-01, -1.5535e-01,
8.1376e-03, -9.4318e-02, 9.2571e-03, 2.7216e-02, -1.3181e-02],
[-2.4926e-02, 3.7189e-04, -3.1210e-02, 8.8770e-02, -1.3593e-01,
8.0590e-03, -1.1775e-01, 2.7286e-02, 1.0563e-01, 6.8094e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-5.1253e-02, -7.1495e-03, -8.2274e-02, 8.3687e-02, -1.2098e-01,
3.9002e-02, -5.8069e-02, -2.3763e-03, 1.8455e-02, 5.2719e-02],
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[-2.2389e-02, -1.4613e-02, -7.8178e-02, 1.2840e-01, -1.2138e-01,
1.7825e-02, -7.4100e-02, 1.3462e-02, 5.0271e-02, 1.9516e-02],
[-7.2685e-02, 9.1301e-03, -1.0185e-01, 1.1280e-01, -8.7898e-02,
6.2707e-02, 7.6065e-03, 2.5183e-02, -1.3814e-02, 5.3073e-02],
[ 3.0523e-02, -7.8846e-03, -1.0747e-01, 1.0621e-01, -1.4662e-01,
1.3238e-02, -1.1229e-01, -4.9241e-02, 9.3509e-02, -5.4002e-03],
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5.1065e-02, -8.0260e-02, 1.9876e-02, 5.0419e-02, 4.8558e-02],
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grad_fn=<AddmmBackward>)
output is tensor([[-4.7313e-02, 6.8050e-04, -5.4462e-02, 1.4546e-01, -1.2918e-01,
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6.5162e-03, -1.0819e-01, 9.9782e-03, 6.1888e-02, 3.0261e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-3.6636e-03, -6.9303e-03, -8.2248e-02, 1.1322e-01, -1.1654e-01,
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[-2.4608e-02, 8.8076e-03, -9.0630e-02, 1.0739e-01, -1.1046e-01,
5.8423e-02, -5.2733e-02, 2.2832e-02, 4.9765e-02, 4.8263e-02],
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4.4234e-02, -4.5054e-02, -1.0312e-03, 4.5716e-02, 4.1605e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-2.5312e-02, -3.0715e-02, -9.8654e-02, 1.5344e-01, -1.4269e-01,
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[-7.3383e-02, 1.4224e-02, -9.0522e-02, 1.2085e-01, -8.1238e-02,
7.7756e-02, 1.7335e-03, 1.5714e-02, -1.1237e-02, 2.5701e-02],
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2.7602e-03, -1.2305e-01, 5.9897e-03, 7.7873e-02, 4.9479e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-5.5510e-02, 9.6211e-04, -7.9951e-02, 1.0665e-01, -1.0985e-01,
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[ 1.2315e-02, -6.0796e-03, -5.3681e-02, 1.4653e-01, -1.6783e-01,
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2.5394e-02, -4.1208e-02, 4.1829e-02, 5.4750e-02, 2.2931e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-2.0591e-02, -1.6367e-02, -6.4859e-02, 1.3488e-01, -1.4718e-01,
1.5540e-02, -8.4741e-02, -1.9272e-03, 3.2474e-02, 8.7027e-03],
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[-7.1233e-03, -5.4569e-03, -9.2265e-02, 1.5489e-01, -1.5068e-01,
7.8812e-02, -4.3553e-02, 4.4230e-02, 6.5259e-02, 8.9954e-02],
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5.4909e-02, -6.0885e-02, 3.4030e-02, 3.3704e-02, 6.1474e-02],
[-7.3419e-02, 3.3564e-03, -6.4551e-02, 1.0759e-01, -9.3949e-02,
1.2647e-01, 1.5322e-02, 3.6500e-02, 9.2021e-03, 5.9323e-02],
[-4.8135e-02, -3.4548e-02, -6.7569e-02, 1.2696e-01, -1.2228e-01,
2.2545e-02, -6.4470e-02, -3.5645e-02, 3.5525e-02, 6.5245e-05],
[-5.1367e-02, -5.4839e-03, -7.2721e-02, 1.2496e-01, -1.2390e-01,
8.4013e-02, -4.5905e-02, 4.9246e-02, 5.0431e-02, 7.0042e-02],
[ 1.5127e-02, 4.6144e-03, -7.7278e-02, 1.5126e-01, -1.5515e-01,
6.6875e-02, -7.7702e-02, 1.3755e-02, 1.1421e-01, 5.8176e-02],
[-4.4299e-02, -2.3803e-02, -8.3413e-02, 1.4454e-01, -1.1115e-01,
2.0417e-02, -3.7193e-02, -3.3045e-02, -4.0821e-02, 1.3411e-02],
[-6.7443e-02, 4.1101e-04, -6.1127e-02, 1.1533e-01, -1.0873e-01,
7.2877e-02, -2.3848e-02, -7.0450e-03, -1.0125e-04, 3.0220e-02],
[-3.9038e-02, -1.9612e-02, -1.1293e-01, 9.8456e-02, -1.0576e-01,
1.3073e-02, -6.8324e-02, 5.4101e-03, 5.5351e-02, 3.0498e-02],
[-4.7568e-02, -6.5576e-02, -8.8403e-02, 1.4651e-01, -1.3198e-01,
8.4979e-03, -6.0827e-02, 2.6309e-02, 2.0009e-02, -2.9346e-02],
[ 2.3141e-03, 8.3614e-03, -4.3304e-02, 1.1271e-01, -1.5107e-01,
5.6458e-02, -1.0900e-01, 3.3794e-02, 1.2213e-01, 6.1900e-02],
[-9.0200e-02, -2.3492e-02, -5.7125e-02, 1.1927e-01, -8.8512e-02,
6.4982e-03, -3.8413e-02, -9.3604e-03, 5.9381e-03, 2.4350e-03],
[-1.4736e-02, -1.8632e-03, -7.3981e-02, 8.7445e-02, -1.2180e-01,
5.0241e-02, -3.4169e-02, -2.9033e-03, 5.2707e-02, 4.9606e-02],
[ 7.5091e-03, -3.0277e-02, -6.8050e-02, 1.5315e-01, -1.8450e-01,
4.1286e-02, -1.0855e-01, 3.5101e-03, 9.3021e-02, 1.8607e-02],
[ 2.3085e-02, -5.3362e-02, -9.0930e-02, 1.7525e-01, -1.5886e-01,
4.9181e-03, -1.3134e-01, 1.6742e-02, 5.0975e-02, 1.6304e-02],
[ 7.6169e-03, -2.4752e-02, -1.0192e-01, 1.5229e-01, -1.5048e-01,
9.4831e-02, -3.7559e-02, 2.2861e-02, 1.1362e-01, 4.4936e-02],
[-2.3415e-02, -1.5786e-02, -7.0270e-02, 1.0988e-01, -1.2632e-01,
1.1330e-02, -8.6824e-02, 2.2655e-02, 6.9032e-02, 3.5452e-02],
[-5.8027e-02, -1.1562e-02, -8.6932e-02, 1.2518e-01, -1.0114e-01,
8.5318e-02, 1.9589e-02, -2.6425e-03, -4.9138e-02, 5.1864e-02],
[ 9.2869e-04, -1.7520e-02, -9.4640e-02, 1.2283e-01, -1.1889e-01,
6.5803e-02, -8.9037e-02, 2.5630e-02, 3.9540e-02, 3.5927e-02],
[-5.7933e-02, 4.7676e-03, -9.5378e-02, 8.9141e-02, -1.0847e-01,
4.9174e-02, -3.0331e-02, -1.3931e-02, 1.9852e-02, 2.8068e-02],
[-1.8516e-02, 1.7881e-03, -9.0949e-02, 8.6276e-02, -1.1444e-01,
5.5298e-02, -4.9872e-02, 2.4069e-02, 2.6287e-02, 6.8376e-02],
[-3.7867e-02, -3.9755e-03, -6.6025e-02, 1.1808e-01, -1.3899e-01,
4.5684e-02, -6.8182e-02, 3.0673e-02, 3.5237e-02, 4.8517e-02],
[-5.0327e-02, 5.7608e-03, -8.7435e-02, 1.1149e-01, -1.1677e-01,
4.9008e-02, -1.5364e-02, 2.0783e-02, -7.7574e-03, 2.7453e-02],
[-2.1624e-02, 8.5519e-04, -7.6788e-02, 1.3298e-01, -1.2433e-01,
7.4109e-02, -4.0837e-02, 4.3517e-02, 3.6429e-02, 6.9267e-02],
[-1.2964e-02, -6.8964e-02, -7.4428e-02, 1.1791e-01, -1.4938e-01,
-2.2393e-02, -1.1701e-01, 1.1996e-02, 1.8843e-02, -2.0437e-02],
[-2.6827e-02, -9.3519e-03, -8.6732e-02, 1.1049e-01, -1.2225e-01,
3.7751e-02, -3.9267e-02, 1.5189e-02, 1.4735e-02, 4.0967e-02],
[-8.8281e-02, -1.2716e-02, -5.2090e-02, 1.3643e-01, -1.1096e-01,
1.8677e-02, -3.2463e-02, 2.1814e-02, 7.0439e-03, 3.0338e-02],
[-4.9747e-02, 2.4986e-02, -5.1595e-02, 1.6217e-01, -1.1433e-01,
6.5097e-02, -1.7532e-02, 5.8065e-02, 4.2474e-02, 8.9708e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-6.3764e-02, -1.7701e-02, -6.1773e-02, 1.1683e-01, -1.2389e-01,
-4.7209e-02, -9.5763e-02, -5.9007e-03, 1.2011e-02, -1.8543e-02],
[-5.7347e-02, -6.4775e-03, -8.3617e-02, 1.0929e-01, -1.1649e-01,
3.1687e-02, -6.4165e-02, -6.6365e-03, 2.9312e-02, 3.1383e-02],
[-2.2072e-02, 2.2322e-03, -1.0191e-01, 1.0540e-01, -1.1610e-01,
3.8816e-02, -5.5473e-02, 1.5857e-02, 4.6146e-02, 1.8496e-02],
[-3.3111e-02, -3.6766e-02, -9.5980e-02, 1.0596e-01, -1.2872e-01,
2.9730e-02, -7.4184e-02, -2.1391e-02, -1.5785e-02, 1.5160e-02],
[ 5.0103e-03, -3.0258e-02, -6.4041e-02, 1.3279e-01, -1.7462e-01,
9.4059e-03, -1.3354e-01, 1.9254e-02, 9.5959e-02, 4.7168e-02],
[-5.2915e-02, -1.8980e-02, -7.6718e-02, 1.4716e-01, -7.2801e-02,
4.1405e-02, -3.4514e-02, -1.1587e-02, -4.2900e-02, -1.6698e-02],
[-1.6239e-02, 9.6048e-04, -4.9967e-02, 1.1732e-01, -1.4163e-01,
1.9528e-02, -1.0656e-01, 2.4444e-02, 1.0117e-01, 4.0472e-02],
[-1.6204e-03, -3.8402e-03, -1.1817e-01, 1.1588e-01, -1.3890e-01,
9.4909e-02, -1.8551e-02, 3.0855e-03, 5.3305e-02, 7.1978e-02],
[-6.2166e-02, 1.7873e-02, -7.5637e-02, 1.2048e-01, -1.0103e-01,
7.1407e-02, -9.5350e-03, 2.2740e-02, -9.7720e-03, 5.2705e-02],
[-2.9408e-02, -1.6107e-02, -8.6463e-02, 1.2632e-01, -1.1752e-01,
6.0720e-02, -4.9306e-02, 1.7668e-02, 3.7590e-02, 3.7846e-02],
[-1.5034e-03, 5.6067e-03, -8.3637e-02, 1.6507e-01, -1.5042e-01,
8.1913e-02, -5.8808e-02, 3.7199e-02, 7.4796e-02, 4.4082e-02],
[-7.6539e-02, 1.0180e-02, -6.0200e-02, 1.1827e-01, -1.2424e-01,
3.0809e-02, -3.8196e-02, 1.8370e-02, 2.5976e-02, 3.8676e-02],
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1.6409e-02, -4.9471e-02, 3.6446e-03, -5.6166e-03, 1.7649e-02],
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2.0728e-02, -1.0233e-01, 2.2331e-02, 2.6680e-02, -1.9289e-02],
[-3.7350e-02, -2.0944e-02, -7.0457e-02, 1.0522e-01, -1.3038e-01,
5.8578e-02, -5.6340e-02, -2.9345e-02, 3.4782e-02, 6.2511e-02],
[-1.9382e-02, -5.7019e-05, -7.7170e-02, 8.6323e-02, -1.5826e-01,
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[-5.3130e-02, -3.2692e-02, -7.6445e-02, 1.2076e-01, -1.3200e-01,
3.5526e-02, -8.6451e-02, 1.3194e-02, 4.5883e-03, 4.5237e-03],
[-6.4936e-03, -1.2350e-02, -8.9140e-02, 1.0384e-01, -1.3494e-01,
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[-3.9236e-02, 7.3585e-03, -6.3238e-02, 1.0584e-01, -1.5336e-01,
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[ 1.2535e-02, -4.0371e-03, -6.3361e-02, 1.4114e-01, -1.4253e-01,
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[ 1.9935e-03, 1.5839e-02, -7.5136e-02, 9.9024e-02, -1.4044e-01,
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[-4.3347e-02, 1.4284e-03, -8.5171e-02, 7.1129e-02, -1.1778e-01,
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[-1.1258e-01, 9.0467e-03, -2.6752e-02, 6.6011e-02, -1.3382e-01,
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[-8.7729e-02, -1.5560e-02, -1.1345e-01, 1.1669e-01, -9.7125e-02,
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[-5.4546e-02, -1.9306e-02, -1.0871e-01, 1.1811e-01, -8.4032e-02,
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[-3.7875e-02, -8.5988e-03, -8.8608e-02, 1.1987e-01, -1.2057e-01,
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grad_fn=<AddmmBackward>)
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3.0737e-02, -7.1327e-02, 1.5871e-02, 4.9510e-02, 1.3730e-02],
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6.0976e-03, -2.1279e-02, 1.4966e-03, -2.2271e-03, 1.1003e-02],
[-7.2227e-02, -2.9859e-02, -8.4205e-02, 1.3414e-01, -9.9809e-02,
1.1458e-02, -6.3918e-02, -4.8982e-02, 1.3965e-02, -1.0095e-03],
[-6.8540e-02, 5.9774e-03, -6.7127e-02, 1.1534e-01, -1.2568e-01,
7.4821e-02, -2.3942e-02, 2.8771e-02, -4.4647e-02, 5.6042e-02],
[-4.1800e-02, -4.1347e-02, -9.1245e-02, 1.2751e-01, -7.3984e-02,
5.4498e-02, -3.0835e-02, 1.0100e-02, 1.6429e-02, 4.7489e-03],
[-7.9995e-03, -2.7967e-03, -7.6885e-02, 1.1495e-01, -1.2717e-01,
6.6192e-02, -6.9019e-02, 3.0069e-02, 6.5465e-02, 6.6737e-02],
[-3.8203e-02, -2.7607e-02, -7.3403e-02, 1.5835e-01, -1.5246e-01,
2.4127e-02, -9.6390e-02, 2.5422e-02, 1.1245e-02, 7.0491e-02],
[-4.0004e-02, -9.0012e-03, -6.8847e-02, 1.4850e-01, -1.3655e-01,
3.8872e-02, -5.9555e-02, 2.4771e-02, 3.5466e-02, 3.9463e-02],
[-7.6592e-02, 1.6613e-02, -6.8447e-02, 9.7314e-02, -8.7862e-02,
9.4206e-02, 2.0215e-02, -9.1788e-03, -6.4037e-03, 5.8267e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-2.5429e-02, 3.0424e-03, -4.5169e-02, 1.6255e-01, -1.4902e-01,
7.0496e-03, -8.2492e-02, 1.0896e-02, 7.3528e-02, 4.0422e-02],
[ 4.5510e-03, 5.3284e-04, -7.8653e-02, 1.3432e-01, -1.2965e-01,
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[-3.3416e-02, -1.4368e-02, -7.9761e-02, 1.1176e-01, -1.6157e-01,
5.3070e-02, -6.4135e-02, 2.1267e-02, 5.0708e-02, 3.9946e-02],
[-1.2212e-01, -3.4504e-02, -3.7971e-02, 9.3053e-02, -1.1240e-01,
4.3279e-02, -5.7999e-02, 1.3490e-02, 1.8246e-02, 2.1396e-02],
[-9.4232e-03, -6.5756e-03, -5.7934e-02, 1.2224e-01, -1.4573e-01,
9.0548e-02, -7.2224e-02, 3.2618e-02, 9.4634e-02, 6.7292e-02],
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5.8497e-02, -6.3315e-02, 3.4986e-02, 9.0945e-02, 8.4227e-02],
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grad_fn=<AddmmBackward>)
output is tensor([[-3.9326e-02, -1.8429e-02, -6.0344e-02, 1.1425e-01, -1.5054e-01,
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grad_fn=<AddmmBackward>)
output is tensor([[-1.7436e-02, -3.6351e-03, -7.8689e-02, 1.0458e-01, -1.0287e-01,
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8.8844e-03, -6.5895e-02, -1.0685e-02, 3.4939e-02, 2.2986e-02],
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1.0018e-01, -4.3745e-02, 3.0639e-02, 9.2984e-02, 5.8740e-02],
[-8.6646e-02, 1.2572e-02, -9.5772e-02, 1.0235e-01, -8.0753e-02,
8.0696e-02, 3.9492e-03, 1.7240e-02, -3.8988e-02, 4.8101e-02],
[ 2.4040e-02, -6.6258e-02, -1.0868e-01, 9.3898e-02, -1.5811e-01,
6.4636e-02, -1.0739e-01, 1.4617e-02, 6.9821e-02, 4.2586e-03],
[-4.7782e-02, -2.0485e-02, -6.8333e-02, 1.5956e-01, -1.2697e-01,
4.5816e-02, -4.4258e-02, 7.7798e-03, 1.2290e-02, 2.3181e-02],
[-2.7368e-02, 5.5374e-03, -6.8039e-02, 8.3572e-02, -1.1255e-01,
4.7698e-02, -5.4495e-02, 1.9580e-02, 1.2044e-02, 6.4603e-02],
[-4.1301e-02, -2.0564e-02, -7.9905e-02, 1.3839e-01, -1.3845e-01,
4.7762e-02, -6.8067e-02, 1.6663e-02, 5.6715e-02, 1.5100e-02],
[-4.1192e-02, -1.3028e-02, -7.6569e-02, 1.3965e-01, -1.0678e-01,
4.5385e-02, -6.5895e-02, 3.1631e-02, 5.6874e-02, 2.4320e-02],
[-4.1415e-02, -4.4557e-02, -2.4105e-02, 1.5322e-01, -1.4947e-01,
8.8266e-03, -8.4506e-02, 1.3293e-02, 5.2329e-02, 1.7847e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-2.7525e-03, -3.3639e-02, -7.6609e-02, 1.1730e-01, -1.4849e-01,
2.0936e-02, -1.1824e-01, 2.1354e-02, 1.1072e-01, 1.7470e-02],
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[-6.3398e-02, -2.5847e-02, -1.0550e-01, 1.1018e-01, -1.2846e-01,
6.2285e-02, -2.9023e-02, -1.8483e-02, 8.9095e-03, 3.3376e-02],
[ 6.4787e-04, -1.4112e-02, -1.0273e-01, 1.4750e-01, -1.4704e-01,
7.1400e-02, -4.7237e-02, -9.2128e-04, 7.7049e-02, 1.4079e-02],
[-5.1388e-02, 1.6193e-03, -7.1668e-02, 1.2933e-01, -1.2049e-01,
1.8529e-02, -5.9405e-02, 6.3275e-03, 6.0481e-02, 4.0016e-04],
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1.3822e-02, -1.2475e-01, 1.9342e-02, 9.5291e-02, 9.4852e-03],
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7.1056e-02, 9.8989e-03, 5.1151e-03, 3.6553e-02, 6.9539e-02],
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4.8434e-02, 1.7166e-02, 8.1020e-03, -2.2079e-02, 7.1727e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-2.6713e-02, -5.0716e-03, -8.5714e-02, 1.0419e-01, -1.1848e-01,
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[-8.5078e-03, -2.2326e-02, -8.2459e-02, 1.2882e-01, -1.6630e-01,
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grad_fn=<AddmmBackward>)
output is tensor([[-5.8277e-02, -6.4402e-02, -7.9435e-02, 1.2193e-01, -2.0003e-01,
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grad_fn=<AddmmBackward>)
output is tensor([[-6.9100e-03, -3.9424e-04, -4.9460e-02, 1.1066e-01, -1.2873e-01,
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[-7.6379e-02, 1.1525e-02, -9.0147e-02, 1.2558e-01, -8.8310e-02,
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6.8136e-02, -6.2347e-02, 7.5056e-02, 8.9338e-02, 5.7624e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-5.2964e-02, -1.5774e-03, -7.5063e-02, 1.1760e-01, -1.2586e-01,
3.4189e-02, -3.1263e-02, 1.9799e-02, 3.1960e-02, 3.8186e-02],
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[-7.6096e-02, -1.2747e-02, -7.2314e-02, 7.8108e-02, -1.0304e-01,
5.2276e-02, -1.9717e-03, 2.2779e-02, 1.6549e-02, 5.2659e-02],
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6.6002e-02, -3.7514e-02, -8.1231e-03, 4.7894e-02, 1.4598e-02],
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[-7.4318e-02, -5.1009e-04, -7.5670e-02, 1.2214e-01, -1.0068e-01,
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6.4405e-02, 2.2677e-02, -1.0942e-02, -3.9915e-02, 2.2502e-02],
[ 1.2349e-02, 6.6504e-03, -7.2576e-02, 1.2404e-01, -1.0954e-01,
3.0390e-02, -6.8308e-02, 5.6132e-02, 7.8482e-02, 2.3476e-02],
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1.2902e-01, -7.6453e-02, 4.3111e-02, 8.8393e-02, 6.8937e-02],
[-9.3548e-03, 2.2574e-02, -6.2382e-02, 1.0712e-01, -1.0204e-01,
6.6662e-02, -6.8339e-02, 4.4604e-03, 4.9209e-02, 6.3255e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-2.5906e-02, -5.8305e-03, -5.8504e-02, 1.4455e-01, -1.6976e-01,
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1.1405e-01, -3.0472e-02, 5.9739e-02, 7.7037e-02, 5.4375e-02],
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grad_fn=<AddmmBackward>)
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grad_fn=<AddmmBackward>)
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grad_fn=<AddmmBackward>)
output is tensor([[-5.4288e-02, 3.1084e-02, -7.1155e-02, 1.1937e-01, -1.0410e-01,
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grad_fn=<AddmmBackward>)
output is tensor([[-2.2815e-02, 7.4533e-03, -8.6957e-02, 1.3055e-01, -9.2295e-02,
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grad_fn=<AddmmBackward>)
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[ 8.3844e-04, 2.3579e-02, -7.5199e-02, 1.6975e-01, -1.5365e-01,
1.1796e-01, -4.2477e-02, 7.6032e-02, 1.2101e-01, 8.7363e-02],
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1.3499e-02, -5.1336e-02, 4.0339e-03, 2.2898e-02, 1.0595e-03]],
grad_fn=<AddmmBackward>)
output is tensor([[-3.9417e-03, 1.4134e-02, -8.9242e-02, 1.5011e-01, -1.1594e-01,
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[-5.6530e-02, -6.4483e-03, -6.4865e-02, 1.1640e-01, -1.0240e-01,
3.8832e-02, -4.1118e-02, -1.1599e-02, 5.2162e-02, 4.2766e-02],
[-6.9787e-02, 3.0725e-03, -1.0334e-01, 1.1522e-01, -8.3254e-02,
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grad_fn=<AddmmBackward>)
output is tensor([[-3.7247e-02, 6.7514e-04, -7.8634e-02, 8.3659e-02, -1.2092e-01,
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grad_fn=<AddmmBackward>)
output is tensor([[-1.8225e-02, -2.6480e-03, -5.4358e-02, 1.4016e-01, -1.5149e-01,
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[-1.1696e-01, 5.6979e-03, -8.0270e-02, 9.4856e-02, -6.4379e-02,
8.7028e-02, 3.5610e-02, 1.1494e-02, -6.2507e-02, 5.7381e-02],
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1.4992e-02, -1.0867e-01, 3.9224e-02, 6.5393e-02, 5.6316e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-3.9200e-02, -2.9664e-03, -9.3039e-02, 1.2705e-01, -9.8107e-02,
3.6094e-02, -3.0353e-02, -5.6026e-03, 2.0948e-02, 2.6030e-02],
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6.1487e-02, -6.8163e-02, 6.1863e-02, 1.1496e-01, 7.2726e-02],
[-1.5907e-02, -4.3529e-03, -1.0761e-01, 1.2016e-01, -1.2995e-01,
4.7414e-02, -9.2239e-02, 2.2827e-02, 1.2094e-01, 2.9867e-02],
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6.9745e-02, 1.2679e-02, 5.0628e-03, -3.1215e-02, 2.9184e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-4.5328e-02, 7.5597e-03, -1.0474e-01, 9.4094e-02, -8.4045e-02,
6.2916e-02, -5.6981e-03, 1.5050e-02, -1.9152e-02, 4.2468e-02],
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1.9605e-02, -9.5051e-02, 1.0157e-02, 4.7389e-02, -1.5421e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-1.7010e-02, -1.4230e-02, -6.1670e-02, 1.7756e-01, -1.9171e-01,
7.5269e-02, -8.9593e-02, 4.2041e-02, 8.7121e-02, 5.9676e-02],
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2.2112e-03, -9.8088e-02, 3.5418e-02, 9.4081e-02, 8.0109e-02],
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6.1016e-02, -1.7381e-02, 1.4474e-02, 2.7605e-02, 1.5577e-02],
[-4.1701e-02, 1.2632e-04, -7.6232e-02, 1.6349e-01, -1.1421e-01,
4.5511e-02, -3.0856e-02, 1.5397e-02, 9.0820e-02, 3.6625e-02],
[-6.0090e-02, -9.7549e-03, -8.5732e-02, 1.4343e-01, -1.0534e-01,
4.4167e-02, -2.3994e-02, -2.2093e-03, -8.0479e-03, 9.9330e-03],
[-2.1301e-02, -6.9943e-04, -9.4219e-02, 1.6088e-01, -1.2271e-01,
7.5824e-02, -3.7252e-02, 1.3531e-02, 7.1632e-02, 6.9005e-02],
[-2.1442e-02, 2.2594e-02, -6.0994e-02, 1.1813e-01, -1.1695e-01,
1.1212e-02, -8.0729e-02, 3.1763e-02, 6.6751e-02, 4.7103e-02],
[-2.3398e-02, -1.7879e-02, -6.1721e-02, 1.3735e-01, -1.4877e-01,
4.9195e-02, -1.0709e-01, 1.7657e-02, 4.6644e-02, 2.6364e-02],
[-6.1908e-02, -2.3039e-02, -7.3054e-02, 1.1617e-01, -1.3911e-01,
4.6114e-02, -5.9996e-02, -1.6803e-02, -1.4721e-02, 2.9801e-02],
[-1.7127e-02, 1.4300e-02, -6.8809e-02, 1.3017e-01, -1.2529e-01,
7.3214e-02, -3.4881e-02, 3.4218e-02, 6.8611e-02, 7.3153e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-3.9468e-02, -2.1891e-02, -7.2171e-02, 1.3709e-01, -1.1794e-01,
9.8241e-02, -2.8273e-02, 2.1077e-02, 3.6586e-02, 5.9620e-02],
[-2.7931e-02, -1.4704e-03, -9.8421e-02, 1.2212e-01, -1.0113e-01,
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[-1.2643e-02, -2.8253e-02, -8.7678e-02, 1.3492e-01, -1.3849e-01,
3.3448e-02, -9.2012e-02, 1.2696e-02, 8.2000e-02, 5.9392e-03],
[-5.8335e-02, 2.2393e-05, -8.5251e-02, 1.2322e-01, -1.0116e-01,
5.8769e-02, -1.6723e-02, 2.4359e-03, 3.4510e-03, 2.9792e-02],
[-3.3300e-02, -7.4891e-03, -9.8319e-02, 6.5316e-02, -1.3266e-01,
2.1124e-02, -5.4030e-02, 1.9662e-02, 5.4583e-02, 7.0700e-02],
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3.8201e-02, -5.9155e-02, 7.0831e-03, 7.3269e-02, 5.1922e-02],
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5.7886e-02, -1.9856e-02, 1.6157e-02, -6.4868e-03, 2.8583e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-5.2850e-02, -5.3663e-03, -7.9433e-02, 9.3892e-02, -9.3542e-02,
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[-2.0440e-02, -2.5351e-02, -8.9721e-02, 1.4730e-01, -1.1632e-01,
1.4715e-02, -9.0996e-02, 1.9691e-02, 3.0345e-02, 1.8056e-02],
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grad_fn=<AddmmBackward>)
output is tensor([[-5.8576e-02, -2.0823e-02, -6.8298e-02, 1.0916e-01, -1.4686e-01,
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grad_fn=<AddmmBackward>)
output is tensor([[-9.3030e-02, 1.5726e-02, -7.1502e-02, 1.0017e-01, -8.8561e-02,
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4.5135e-02, -5.6523e-02, 2.8401e-03, 4.1168e-02, 2.9879e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-7.1038e-02, 4.1334e-03, -5.8767e-02, 1.0570e-01, -1.3316e-01,
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grad_fn=<AddmmBackward>)
output is tensor([[ 2.0828e-03, -8.7166e-03, -9.5211e-02, 1.1245e-01, -1.3373e-01,
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grad_fn=<AddmmBackward>)
output is tensor([[-5.3166e-02, -1.3802e-02, -6.0701e-02, 1.5195e-01, -1.4813e-01,
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grad_fn=<AddmmBackward>)
output is tensor([[-2.0326e-02, -1.1156e-02, -8.0365e-02, 1.4111e-01, -1.1074e-01,
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[-4.8019e-02, 1.0637e-02, -9.1387e-02, 1.3688e-01, -1.0900e-01,
5.5500e-02, -3.3409e-02, 5.7024e-03, 3.0454e-02, 3.9181e-02],
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4.2986e-02, -8.9934e-02, 2.4847e-02, 9.5418e-02, 6.2446e-02],
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8.1685e-02, 2.2679e-03, 2.9650e-02, -1.3597e-02, 3.3976e-02],
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[-7.7547e-02, -2.6344e-02, -7.0349e-02, 9.0048e-02, -1.0714e-01,
6.9679e-02, -4.0870e-02, 1.0203e-02, 3.0948e-02, 5.0912e-02],
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5.1861e-02, -6.0610e-02, 2.1693e-02, 3.8948e-02, 3.8166e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-3.5610e-02, -9.8718e-03, -5.6914e-02, 1.2368e-01, -1.4473e-01,
1.3742e-02, -8.2393e-02, 3.8380e-02, 5.2110e-02, 3.1322e-02],
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2.2050e-02, -9.8955e-02, 2.4341e-02, 7.1517e-02, 3.2105e-02],
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grad_fn=<AddmmBackward>)
output is tensor([[-6.5202e-02, 2.0820e-02, -5.2632e-02, 1.0472e-01, -1.0679e-01,
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[-3.3478e-02, -1.1190e-02, -1.0305e-01, 1.4733e-01, -1.2438e-01,
4.2026e-02, -4.9440e-02, 2.4616e-02, 3.3157e-02, 2.3316e-02],
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2.9750e-03, -4.9654e-02, 5.5564e-03, -7.2547e-03, 4.7930e-03],
[-2.8353e-02, 1.9218e-02, -7.2797e-02, 1.3916e-01, -1.1105e-01,
8.4832e-02, -1.3494e-02, 8.7241e-03, 6.3325e-02, 7.0823e-02],
[ 4.3717e-02, -2.3810e-02, -6.0718e-02, 1.6342e-01, -2.2079e-01,
4.6768e-02, -1.3595e-01, 4.4429e-02, 1.4324e-01, 5.2727e-02],
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5.2370e-02, -6.6781e-02, 2.4626e-02, 1.5064e-02, 2.5097e-02],
[ 1.2053e-02, -4.6178e-02, -9.1353e-02, 1.7842e-01, -1.3854e-01,
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[ 1.7074e-02, -3.0808e-02, -6.0708e-02, 1.6688e-01, -1.8801e-01,
1.6705e-02, -1.3982e-01, 4.3471e-02, 8.9844e-02, 4.2304e-02],
[-8.0012e-02, 9.5451e-03, -8.0625e-02, 1.0563e-01, -8.5947e-02,
5.8368e-02, 2.0640e-03, -2.8559e-03, -2.5063e-02, 2.8870e-02],
[-1.0127e-01, -1.6337e-02, -6.0184e-02, 1.1574e-01, -1.0417e-01,
4.3696e-02, -4.9410e-02, 2.2508e-02, -6.9975e-03, 3.8419e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-2.1247e-02, -3.3295e-02, -3.9573e-02, 1.4196e-01, -1.7743e-01,
1.2177e-02, -1.3303e-01, -3.4283e-03, 8.8979e-02, 1.8612e-02],
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8.6647e-02, -3.0383e-02, 2.6410e-02, 3.9532e-02, 6.0431e-02],
[-1.2037e-02, -8.1865e-03, -8.5305e-02, 1.1985e-01, -1.3224e-01,
2.0908e-02, -7.1054e-02, 2.0384e-02, 1.0729e-01, 2.4560e-02],
[-3.3728e-02, 5.0090e-04, -7.3156e-02, 1.4914e-01, -1.4051e-01,
5.1755e-02, -6.0437e-02, -1.8369e-03, 8.2834e-02, 3.3254e-02],
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[-3.2430e-02, 7.8724e-04, -6.0784e-02, 1.2739e-01, -1.2286e-01,
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6.3352e-02, -1.4566e-02, -1.9073e-02, 2.6043e-02, -6.1151e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-3.5192e-02, 6.0942e-03, -9.9837e-02, 1.2470e-01, -1.1106e-01,
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8.3525e-02, -5.6929e-02, 4.2778e-02, 7.6713e-02, 7.7519e-02],
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grad_fn=<AddmmBackward>)
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grad_fn=<AddmmBackward>)
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7.7685e-02, -5.2894e-02, 1.8917e-02, 6.5182e-02, 7.9267e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[ 1.1010e-02, -2.9759e-03, -8.3695e-02, 8.9578e-02, -1.4571e-01,
4.5741e-02, -9.5681e-02, 2.4558e-02, 8.6570e-02, 6.3629e-02],
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[-3.1856e-02, -1.5258e-02, -1.0359e-01, 1.2970e-01, -1.2281e-01,
2.2434e-02, -5.7323e-02, -1.0344e-02, 2.5528e-02, 2.4973e-02],
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grad_fn=<AddmmBackward>)
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grad_fn=<AddmmBackward>)
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grad_fn=<AddmmBackward>)
output is tensor([[-2.2952e-02, -6.9260e-03, -8.8036e-02, 1.5411e-01, -1.3824e-01,
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grad_fn=<AddmmBackward>)
output is tensor([[-5.1154e-02, -5.1544e-03, -1.1276e-01, 1.1369e-01, -8.5130e-02,
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grad_fn=<AddmmBackward>)
output is tensor([[-5.9113e-02, -2.8741e-03, -7.5665e-02, 1.3314e-01, -9.4410e-02,
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3.6514e-02, -3.0409e-02, 1.0717e-02, 2.5997e-02, 4.6661e-02],
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4.2995e-02, -4.2065e-02, 6.2647e-03, 3.8898e-03, 4.9300e-02],
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2.8203e-02, -6.5340e-02, 9.7371e-03, 2.9645e-02, 5.5865e-02],
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4.5581e-03, -1.6863e-01, 1.4368e-02, 1.3176e-01, 2.8942e-02],
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1.0396e-02, -6.0584e-02, 1.8527e-03, 3.7626e-02, 1.4975e-02],
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6.9783e-02, -4.4385e-02, 5.4106e-02, 3.6047e-02, 7.6010e-02],
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3.5614e-02, -4.1278e-02, -6.2782e-03, 2.4544e-02, 2.4698e-02],
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4.8290e-02, -5.1747e-02, 1.9981e-02, 5.1231e-02, 2.1966e-02],
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5.4484e-02, -2.3975e-02, 2.4581e-02, 3.1334e-02, 5.9113e-02],
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4.3858e-02, -7.6945e-02, 2.3228e-02, 1.0225e-01, 2.5733e-02],
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3.4073e-02, -4.5731e-02, 1.5999e-02, 1.3549e-03, 2.3504e-02],
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6.2262e-02, -1.0872e-01, 2.4318e-02, 8.8555e-02, 5.7141e-02],
[-6.6557e-02, -3.8542e-04, -9.5505e-02, 1.1362e-01, -1.0910e-01,
5.6324e-02, -1.8502e-02, 1.3009e-02, -6.8479e-03, 3.6141e-02],
[-5.8933e-02, 3.5456e-03, -1.0365e-01, 1.0002e-01, -1.0062e-01,
5.4737e-02, -2.0893e-02, 1.0962e-02, -1.5450e-02, 4.4527e-02],
[-2.0842e-02, -7.0543e-04, -7.2138e-02, 1.2777e-01, -1.3449e-01,
5.3071e-02, -5.0821e-02, 1.9066e-02, 4.7990e-02, 3.4275e-02],
[-2.4446e-02, -3.6289e-02, -6.3430e-02, 1.3546e-01, -1.5694e-01,
1.3240e-02, -8.7995e-02, 1.1429e-02, 6.7705e-02, 1.9979e-02],
[-6.7622e-02, 3.6561e-03, -9.2819e-02, 9.6443e-02, -1.0601e-01,
8.8856e-02, -1.1446e-02, 8.9627e-03, 1.1574e-02, 4.7423e-02],
[-3.2707e-02, -1.8583e-02, -8.7481e-02, 1.4821e-01, -1.3514e-01,
1.4612e-02, -6.9276e-02, 1.9726e-02, 5.2247e-02, 3.8307e-02],
[-5.4248e-02, -1.5509e-02, -7.9784e-02, 1.1650e-01, -1.0024e-01,
3.0240e-02, -2.6823e-02, 1.5259e-02, 1.1696e-02, 2.7997e-02],
[-4.8652e-02, 8.9306e-03, -8.9216e-02, 1.2378e-01, -1.1131e-01,
5.3469e-02, -2.8711e-02, 2.1149e-02, 2.5537e-02, 3.9452e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[ 0.0120, 0.0028, -0.0905, 0.1370, -0.1415, 0.0602, -0.0761, 0.0363,
0.0784, 0.0522],
[-0.1058, -0.0232, -0.0727, 0.1171, -0.1384, 0.0290, -0.0703, -0.0477,
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0.0541, 0.0925],
[-0.0461, -0.0271, -0.1185, 0.1511, -0.0801, 0.0592, -0.0244, -0.0138,
0.0366, 0.0247],
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-0.0288, 0.0180],
[ 0.0137, -0.0025, -0.1077, 0.1222, -0.1272, 0.0632, -0.0554, 0.0264,
0.0722, 0.0376],
[-0.0844, 0.0030, -0.0403, 0.1153, -0.1212, 0.0674, -0.0541, 0.0031,
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[-0.0389, -0.0317, -0.1035, 0.1201, -0.1430, 0.0323, -0.0254, -0.0107,
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0.0506, 0.0566],
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0.1222, 0.0699],
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0.0394, 0.0955],
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0.0230, 0.0621],
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0.0144, 0.0489],
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[-5.0214e-02, 2.4706e-04, -9.5196e-02, 1.2020e-01, -1.0313e-01,
5.2744e-02, 1.3840e-03, 7.3908e-03, -1.7795e-02, 4.4963e-02],
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4.2555e-02, -4.2819e-02, -2.4409e-02, -1.2500e-02, 1.6843e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-6.3050e-02, -7.0737e-03, -7.2246e-02, 1.3561e-01, -1.2051e-01,
5.4440e-02, -4.7050e-02, 1.9668e-02, 5.6281e-03, 3.9695e-02],
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5.8822e-02, -6.6425e-02, 4.6330e-02, 5.3508e-02, 5.6819e-02],
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6.7078e-02, -3.7676e-02, 6.4734e-03, -1.1778e-02, 5.2331e-02],
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0.1278, 0.0927],
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[ 0.0294, -0.0268, -0.0694, 0.1757, -0.1783, 0.0745, -0.1100, 0.0481,
0.1154, 0.0381],
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[ 0.0054, -0.0372, -0.0759, 0.1300, -0.1620, 0.0072, -0.1026, 0.0595,
0.0759, 0.0264]], grad_fn=<AddmmBackward>)
output is tensor([[-4.4728e-02, -4.5111e-03, -5.5698e-02, 8.8670e-02, -1.0813e-01,
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[-3.6903e-02, -1.2766e-02, -7.7011e-02, 1.1787e-01, -1.3242e-01,
3.4217e-02, -6.4358e-02, 1.8760e-02, 3.1720e-02, 3.1956e-02],
[-5.3388e-02, -3.1050e-02, -6.3126e-02, 1.2380e-01, -1.5555e-01,
2.0281e-02, -7.8243e-02, 3.8046e-03, 3.4644e-02, 4.9506e-02],
[-4.8449e-02, -1.5720e-02, -4.2740e-02, 9.0341e-02, -1.6573e-01,
6.5261e-02, -8.2102e-02, 3.1788e-02, 3.8138e-02, 8.7939e-02],
[-3.0930e-02, -4.6772e-03, -7.3508e-02, 1.2644e-01, -1.0309e-01,
2.4534e-02, -6.7648e-02, -3.5854e-02, 7.0771e-02, 2.3170e-02]],
grad_fn=<AddmmBackward>)
output is tensor([[-0.0333, -0.0095, -0.0864, 0.1225, -0.1241, 0.0391, -0.0425, 0.0031,
0.0398, 0.0174],
[-0.0959, 0.0069, -0.0633, 0.0653, -0.1031, 0.0724, -0.0173, 0.0096,
-0.0203, 0.0901],
[-0.0631, -0.0210, -0.0361, 0.0906, -0.1231, 0.0246, -0.0540, 0.0200,
0.0196, 0.0143],
[-0.0177, -0.0516, -0.0979, 0.1329, -0.1607, -0.0010, -0.0983, -0.0306,
0.0724, -0.0065],
[-0.0239, 0.0138, -0.0650, 0.0853, -0.1413, 0.0482, -0.0919, 0.0418,
0.0730, 0.0641],
[-0.0056, -0.0008, -0.0736, 0.1310, -0.1247, 0.0233, -0.0637, 0.0539,
0.0591, 0.0477],
[-0.0365, -0.0137, -0.0745, 0.0863, -0.1188, 0.0695, -0.0355, 0.0190,
0.0159, 0.0593],
[-0.0544, -0.0566, -0.0474, 0.1312, -0.1558, -0.0300, -0.1180, -0.0212,
0.0420, -0.0127],
[-0.0375, -0.0016, -0.0746, 0.1221, -0.1063, 0.0934, -0.0139, 0.0089,
0.0319, 0.0584],
[-0.0545, 0.0033, -0.0695, 0.1364, -0.1198, 0.0582, -0.0421, 0.0220,
0.0632, 0.0346],
[ 0.0226, -0.0143, -0.0926, 0.1309, -0.1366, 0.0660, -0.0766, -0.0256,
0.0943, 0.0512],
[-0.0347, -0.0161, -0.0856, 0.1014, -0.0987, 0.0866, -0.0154, 0.0159,
0.0241, 0.0815],
[-0.1150, -0.0070, -0.0614, 0.0824, -0.0836, 0.0518, 0.0053, -0.0022,
-0.0519, 0.0541],
[-0.0528, -0.0206, -0.0536, 0.1391, -0.1080, 0.0671, -0.0365, 0.0057,
0.0196, 0.0398],
[-0.0247, -0.0275, -0.0894, 0.1103, -0.1113, 0.0600, -0.0380, 0.0120,
0.0383, 0.0319],
[-0.0591, -0.0040, -0.0544, 0.1088, -0.1388, 0.0103, -0.0674, -0.0218,
0.0467, 0.0279]], grad_fn=<AddmmBackward>)
import torch
import torchvision
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential
class Tudui(nn.Module):
def __init__(self):
super(Tudui, self).__init__()
self.model1 = Sequential(
Conv2d(3,32,5,padding=2),
MaxPool2d(2),
Conv2d(32,32,5,padding=2),
MaxPool2d(2),
Conv2d(32,64,5,padding=2),
MaxPool2d(2),
Flatten(),
Linear(1024,64),
Linear(64,10)
)
def forward(self, x):
x = self.model1(x)
return x
tudui = Tudui()
input = torch.ones((64,3,32,32))
output = tudui(input)
print(output.shape)
torch.Size([64, 10])
4. Tensorboard显示网络
3
import torch
import torchvision
from torch import nn
from torch.nn import MaxPool2d, Conv2d, Linear, Flatten
from torch.utils.tensorboard import SummaryWriter
dataset = torchvision.datasets.CIFAR10(root="./dataset", train=False, transform=torchvision.transforms.ToTensor(),
download=False)
dataloader = torch.utils.data.DataLoader(dataset, shuffle=True, batch_size=64)
class Tudui(nn.Module):
def __init__(self):
super(Tudui, self).__init__()
self.model1 = nn.Sequential(
Conv2d(in_channels=3, out_channels=32, kernel_size=5, padding=2),
MaxPool2d(kernel_size=2),
Conv2d(in_channels=32, out_channels=32, kernel_size=5, padding=2),
MaxPool2d(kernel_size=2),
Flatten(),
Linear(in_features=2048, out_features=64),
Linear(in_features=64, out_features=10)
)
def forward(self, x):
x = self.model1(x)
return x
tudui = Tudui()
writer = SummaryWriter("logs/add_graph")
for data in dataloader:
imgs, targets = data
writer.add_graph(tudui, imgs)
writer.close()
import torch
import torchvision
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
dataset = torchvision.datasets.CIFAR10("./dataset",train=False,transform=torchvision.transforms.ToTensor(),download=True)
dataloader = DataLoader(dataset, batch_size=64,drop_last=True)
class Tudui(nn.Module):
def __init__(self):
super(Tudui, self).__init__()
self.model1 = Sequential(
Conv2d(3,32,5,padding=2),
MaxPool2d(2),
Conv2d(32,32,5,padding=2),
MaxPool2d(2),
Conv2d(32,64,5,padding=2),
MaxPool2d(2),
Flatten(),
Linear(1024,64),
Linear(64,10)
)
def forward(self, x):
x = self.model1(x)
return x
tudui = Tudui()
writer = SummaryWriter("logs")
tudui = Tudui()
input = torch.ones((64,3,32,32))
output = tudui(input)
print(output.shape)
writer.add_graph(tudui, input)
writer.close()
Files already downloaded and verified
torch.Size([64, 10])
① 在 Anaconda 终端里面，激活py3.6.3环境，再输入 tensorboard --logdir=C:\Users\wangy\Desktop\03CV\logs 命令，将网址赋值浏览器的网址栏，回车，即可查看tensorboard显示日志情况。