In this article, we will discuss the examples of this **Valueerror: negative dimensions are not allowed** error, its causes, and provide working solutions to help you resolve it.

## What is ValueError: Negative dimensions are not allowed?

The **ValueError Negative dimensions are not allowed** is a common error that occurs in different programming languages, including Python, when trying to work with arrays, matrices, or tensors.

This error occurs when we attempt to specify an object with negative dimensions, which is not allowed in most programming contexts.

## How to Reproduce the Valueerror?

Here’s an example program where a ValueError with the message “**Negative dimensions are not allowed**” could occur:

**import numpy as np
# Attempting to create a numpy array with negative dimensions
negative_dimensions_sample = np.zeros((-5, 9))
**

**Output:**

**Traceback (most recent call last):File “C:\Users\Dell\PycharmProjects\Python-Code-Example\main.py”, line 4, innegative_dimensions_sample = np.zeros((-5, 9))ValueError: negative dimensions are not allowed**

In this example, we are attempting to make a NumPy array using the **np.zeros()** function.

The array is asserted to have two dimensions, with** -5** rows and **9 **columns.

However, defining a negative dimensions is not allowed, and it results in a ValueError being raised with the message “**Negative dimensions are not allowed**“.

This error shown that the dimensions given for the array are invalid and need to be positive values.

## Causes of ValueError Negative dimensions are not allowed

Understanding the causes behind the ValueError is important for fixing this error completely.

Some common causes are the following:

**Misunderstanding the Reshaping Process****Inconsistent Dimension Calculations****Invalid Input Data**

Now that we have identify the causes of this value error, let’s move on to the solutions.

## How to Solve the ValueError: Negative dimensions are not allowed?

Fixing the **ValueError Negative dimensions are not allowed** needs to understand the root cause and apply the proper solutions.

Here are some effective solutions you need to follow:

### Solution 1: Update the Dimensions to Positive Values

To solve the valueerror in the example code where negative dimensions are used to create a NumPy array, you can update the dimensions to positive values.

Here’s the updated code for the previous example:

**import numpy as np
# Creating a numpy array with positive dimensions
positive_dimensions_example = np.zeros((5, 9))
print(positive_dimensions_example)**

Output:

**[[0. 0. 0. 0. 0. 0. 0. 0. 0.][0. 0. 0. 0. 0. 0. 0. 0. 0.][0. 0. 0. 0. 0. 0. 0. 0. 0.][0. 0. 0. 0. 0. 0. 0. 0. 0.][0. 0. 0. 0. 0. 0. 0. 0. 0.]]**

In this example code, the **np.zeros()** function is used to make a NumPy array with dimensions of 2 rows and 3 columns.

By defining the positive values for the dimensions, the code will run without raising a ValueError.

The resulting array will be printed, illustrating that the issue has been resolved.

**Solution 2: Provide Valid Dimensions**

To fix this value error, make sure that you provide valid dimensions when reshaping arrays, matrices, or tensors.

Avoid using negative values and calculate the dimensions accurately based on your requirements.

For example:

**import numpy as np
arr = np.array([[1, 2], [3, 4]])
reshaped_arr = np.reshape(arr, (4, 1))
**

**Output:**

**[[1 2][3 4]]**

In this example, we provide valid dimensions (4, 1) for reshaping the array. This resolves the ValueError and successfully reshapes the array.

## Solution 3: Validate Input Data

Another way is to validate the input data before trying any reshaping operations.

Make sure that the input data has the expected shape and dimensions, and handle any unexpected or invalid data easily.

**import tensorflow as tf
input_data_example = tf.constant([1, 2, 3, 4])
if input_data_example .shape[0] != input_data_example .shape[1]:
print("Invalid input data dimensions. Reshaping is not possible.")
else:
reshaped_data = tf.reshape(input_data_example , (2, 2))
**

In this example, we check if the input data has equal dimensions before reshaping it.

When the dimensions are not consistent, we handle the situation by displaying an appropriate error message.

## FAQs

**Why am I getting the ValueError: Negative dimensions are not allowed when reshaping my NumPy array?**

This error occurs if we provide negative values for the dimensions while reshaping the array. Make sure to provide valid and positive dimensions to avoid this error.

**How can I determine the correct dimensions for reshaping my data?**

The correct dimensions depend on your desired outcome and the structure of your data.

It is important to understand the data shape and the desired shape after reshaping. You can use functions like numpy.shape, numpy.ndarray.shape

## Conclusion

In conclusion, the **ValueError: Negative dimensions are not allowed** is a common error encountered when working with arrays, matrices, or tensors in various programming languages.

By understanding the causes and applying the solutions provided in this article, you can resolve this error and write more quality and error-free program.