In you are running a program, encountering errors is a common issue. One of the often error encountered is the** RuntimeError: mat1 and mat2 shapes cannot be multiplied**.

This error message typically occur if we are attempting to perform matrix multiplication in certain programming languages, such as Python.

In this article, we will discuss this error, its causes, and provide solutions to help you prevent it.

So, let’s resolve the problem of the “**mat1 and mat2 shapes cannot be multiplied**” error.

## What is Matrix Multipliction?

Before we explain the error, let’s take a moment to know the concept of matrix multiplication.

In mathematics, matrices are rectangular arrays of numbers or symbols arranged in rows and columns.

Multiplying matrices involves combining their corresponding elements in a precise way to produce a resulting matrix.

## Why this Error Occur?

The “**RuntimeError: mat1 and mat2 shapes cannot be multiplied**” error because we attempt to multiply two matrices with incompatible shapes.

In matrix multiplication, the number of columns in the first matrix (mat1) should match the number of rows in the second matrix (mat2) for the operation to be valid.

This error can be caused by several case, including:

- Matrices with incompatible dimensions
- Incorrect dimensions passed as arguments
- Incorrect matrix multiplication operation
- Broadcasting issues

## How to Fix the Error mat1 and mat2 shapes cannot be multiplied?

The following are the solutions to solve the error **runtimeerror mat1 and mat2 shapes cannot be multiplied**.

### Solution 1: Check the Dimensions of the Matrices

To fix this error, you need to make sure that the dimensions of the matrices are compatible for multiplication.

Here’s a step-by-step process to fix the error:

- Check the number of columns in mat1 and the number of rows in mat2.
- If the dimensions are not compatible, modify the matrices or redefine them to align the dimensions correctly.
- Remember that the number of columns in mat1 should be equal to the number of rows in mat2 for the multiplication operation to be valid.

For example:

```
def check_matrix_dimensions(matrix1, matrix2):
rows1, cols1 = len(matrix1), len(matrix1[0])
rows2, cols2 = len(matrix2), len(matrix2[0])
if rows1 == rows2 and cols1 == cols2:
print("The matrices have the same dimensions.")
else:
print("The matrices have different dimensions.")
# Example usage
matrix_a = [[1, 2], [3, 4]]
matrix_b = [[5, 6], [7, 8]]
matrix_c = [[1, 2, 3], [4, 5, 6]]
# The matrices have the same dimensions.
check_matrix_dimensions(matrix_a, matrix_b)
# The matrices have different dimensions.
check_matrix_dimensions(matrix_a, matrix_c)
```

### Solution 2: Check the Correct Dimensions are Passed as Arguments

To fix this error, check the code where the multiplication operation is performed and make sure that the correct dimensions are passed as arguments.

Double-check the matrices being multiplied are assigned in the appropriate dimensions and are passed in the correct order.

For example:

```
def multiply_matrices(matrix1, matrix2):
rows1, cols1 = len(matrix1), len(matrix1[0])
rows2, cols2 = len(matrix2), len(matrix2[0])
if cols1 != rows2:
raise ValueError("The number of columns in matrix1 must be equal to the number of rows in matrix2.")
# Perform matrix multiplication
result = [[0] * cols2 for _ in range(rows1)]
for i in range(rows1):
for j in range(cols2):
for k in range(cols1):
result[i][j] += matrix1[i][k] * matrix2[k][j]
return result
# Test case 1: Correct dimensions
matrix1 = [[1, 2], [3, 4]]
matrix2 = [[5, 6], [7, 8]]
result = multiply_matrices(matrix1, matrix2)
print("Result of multiplication:")
for row in result:
print(row)
# Output:
# Result of multiplication:
# [19, 22]
# [43, 50]
# Test case 2: Incorrect dimensions
matrix3 = [[1, 2, 3], [4, 5, 6]]
matrix4 = [[7, 8], [9, 10]]
try:
result = multiply_matrices(matrix3, matrix4)
except ValueError as e:
print("Error:", e)
```

Output:

**Result of multiplication:[19, 22][43, 50]Error: The number of columns in matrix1 must be equal to the number of rows in matrix2.**

### Solution 3: Use appropriate broadcasting techniques

If your programming language or library supports broadcasting, make sure to handle it correctly.

You should understand the broadcasting rules and make sure that the matrices’ shapes align with the requirements.

This will enable you to perform operations on arrays of different shapes without causing the “RuntimeError” error.

For example:

```
import numpy as np
# Create two arrays with different shapes
a = np.array([1, 2, 3])
b = np.array([[4], [5], [6]])
# Perform broadcasting to add the arrays
result = a + b
print(result)
```

Output:

**[[5 6 7][6 7 8][7 8 9]]**

### Solution 4: Double-check matrix multiplication operations

Always double-check your matrix multiplication operations. Check that you are using the correct multiplication operator and applying it correctly.

Carefully review your code to avoid any accidental mistakes or incorrect operations that may cause to the error.

For example:

```
import numpy as np
# Define two matrices
matrix_A = np.array([[1, 2],
[3, 4]])
matrix_B = np.array([[5, 6],
[7, 8]])
# Perform matrix multiplication
result = np.dot(matrix_A, matrix_B)
# Double-check the result using element-wise multiplication
double_check = np.multiply(matrix_A, matrix_B)
# Print the original result and the double-checked result
print("Original Result:")
print(result)
print("Double-Checked Result:")
print(double_check)
```

Output:

**Original Result:[[19 22][43 50]]Double-Checked Result:[[ 5 12][21 32]]**

### Solution 5: Check Data Integrity

Check the data you are working with. Make sure there are no missing values, like **NaNs**, or unexpected characters within the matrices. Sometimes, even a small anomaly can lead to incompatible matrix shapes.

## Additional Resources

The following resources can hep you to understand more about runtimeerrors:

- Runtimeerror: address already in use
- asyncio.run cannot be called from a running event loop
- Runtimeerror: distributed package doesn’t have nccl built in
- Runtimeerror: cudnn error: cudnn_status_internal_error

## Conclusion

The “**RuntimeError: mat1 and mat2 shapes cannot be multiplied**” error occurs if we are attempting to multiply matrices with incompatible shapes.

By following the solutions outlined in this article, Checking the Dimensions of the Matrices, Checking the Correct Dimensions are Passed as Arguments, Use appropriate broadcasting techniques you can prevent this error from occurring in your code.

## FAQs

**What does the “RuntimeError: mat1 and mat2 shapes cannot be multiplied” mean?**

This error shows that the multiplication operation between two matrices is not possible due to incompatible dimensions.

**Why is broadcasting important in matrix operations?**

Broadcasting allows operations between arrays of different shapes, enhancing flexibility in matrix operations.

It enables performing operations on arrays with different shapes by automatically aligning dimensions

**Are there any libraries or tools that can help with matrix operations?**

Yes, several popular libraries like NumPy, TensorFlow, and PyTorch provide comprehensive support for matrix operations.