The **RuntimeError: Expected object of scalar type Double but got scalar type Float **error in **Pytorch** occurs when a function or operation was expecting a scalar value of type double (a **64-bit floating point number**) for a tensor. But, it received a scalar value of type float (a **32-bit floating point number**) as input.

To **fix** the **RuntimeError: Expected object of scalar type Double but got scalar type Float** error, the input value should be cast to double, or the function should be modified to support float inputs.

To **convert** your **tensor** to **float64**, you can use the **torch.double()** method.

To **convert** your **tensor** to **float32**, you can use the **torch.float()** method.

You can also use the** .to()** method to convert the tensor to the desired data type.

```
import torch
# Create a tensor
main_tensor = torch.randn(2, 2)
double_tensor = main_tensor.double()
float_tensor = main_tensor.float()
print(main_tensor)
print(double_tensor)
print(float_tensor)
```

**Output**

```
tensor([[-1.0531, 0.6726],
[ 0.6947, -0.3040]])
tensor([[-1.0531, 0.6726],
[ 0.6947, -0.3040]], dtype=torch.float64)
tensor([[-1.0531, 0.6726],
[ 0.6947, -0.3040]])
```

You can see from the output that we converted a normal tensor to type **float32** and **float64** tensors.

When working with either **float32** or **float64** data type, ensure you are not using both simultaneously.

It is always better to use the **float64** data type for more accuracy, and if the input is in the **float32** data type, you can convert it to the **float64** data type.

**PyTorch** has three main different data types that can represent floating point numbers.

**torch.float32**or**torch.float****torch.float64**or**torch.double****torch.bfloat16**

**What is torch.float32?**

The **torch.float32** is a **32-bit floating point number**, also known as a **“single precision”** float. It takes up less memory and is faster to perform calculations, but it may not be as accurate as a **torch.float64**.

```
import torch
# Create a float32 tensor
main_tensor = torch.randn(2, 2, dtype=torch.float32)
print(main_tensor)
```

**Output**

```
tensor([[0.5858, 1.0883],
[0.6733, 0.4548]])
```

**What is torch.float64?**

The **torch.float64** is a **64-bit floating point number**, also known as a **“double precision”** float. It takes up more memory and is slower to perform calculations, but it is more accurate than a **torch.float32**.

```
import torch
# Create a float64 tensor
main_tensor = torch.randn(2, 2, dtype=torch.float64)
print(main_tensor)
```

**Output**

```
tensor([[-0.8973, 1.3078],
[-0.9175, -1.3086]], dtype=torch.float64)
```

The **double** **data** **type** has a **64-bit floating point number.**

The **float** **data** **type** has a **32-bit floating point number**.

If you pass the **32-bit floating point number** instead of a **64-bit floating number**, it will throw a **RuntimeError**.

I hope this solution resolves your issue!

Krunal Lathiya is a seasoned Computer Science expert with over eight years in the tech industry. He boasts deep knowledge in Data Science and Machine Learning. Versed in Python, JavaScript, PHP, R, and Golang. Skilled in frameworks like Angular and React and platforms such as Node.js. His expertise spans both front-end and back-end development. His proficiency in the Python language stands as a testament to his versatility and commitment to the craft.