I created a single linear layer model with identity matrix as the weights. But by passing the random input values to the model. the output and input values are not same after 3 to 4 decimal points of float type . But incase of integers the values are same.

**Output:**

The input and output have to be same. But you can see that its not the same when i print them.

**Code used:**

```
from multiprocessing import reduction
import torch
from torchvision import datasets, models, transforms
from torchsummary import summary
import os
import argparse
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
channel=1
length=1
bredth=2
torch.set_printoptions(threshold=10000,precision=16)
size_=channel*length*bredth
tinymodel = torch.nn.Sequential(torch.nn.Linear(size_, size_, bias=False))
print('The model:----------')
print(tinymodel)
tinymodel.to(device)
weights=torch.eye(size_)
print('Identity matrix:-----------')
print(weights)
weights=weights.int()
print(weights)
print(type(weights))
print(weights.shape)
print('\n\nModel params:-------')
for param in tinymodel.parameters():
print(param.shape)
with torch.no_grad():
tinymodel[0].weight.data.copy_(weights)
for param in tinymodel.parameters():
print(param.shape)
print(param)
tinymodel.to(device)
input_val=torch.rand([channel, length, bredth], dtype=torch.float, requires_grad=False).cuda()
input_val=torch.reshape(input_val,(-1,))
print("\nInput")
print(input_val.shape)
print(input_val)
# input_val=torch.Tensor([234,244]).to(device) // Testing with integer inputs while size_=2
result=tinymodel(input_val)
print("\nOutput")
print(result.shape)
print(result)
loss=torch.nn.MSELoss(reduction='sum')
loss_val=loss(result,input_val)
print("/n",loss_val)
```

Can anyone explain why is this happening?