TensorFlow Mean Squared Error

In this Python tutorial, we will learn how to find the mean squared error in Python TensorFlow. Additionally, we will cover the following topics.

TensorFlow mean squared error
TensorFlow metrices mean squared error
TensorFlow mean squared logarithmic error
TensorFlow weighted mean squared error
TensorFlow root mean squared error
TensorFlow reduce mean squared error
TensorFlow calculate mean squared error
Mean pairwise squared error tensorflow

Table of Contents

TensorFlow mean squared error

In this section, we will discuss how to find the mean squared error in Python TensorFlow.
To perform this particular task, we are going to use the tf.compat.v1.losses.mean_squared_error() function and this function is used to insert a sum of squares from given labels and prediction.

Syntax:

Let’s have a look at the Syntax and understand the working of tf.compat.v1.losses.mean_squared_error() function in Python TensorFlow.

tf.compat.v1.losses.mean_squared_error
                                      (
                                       labels,
                                       prediction,
                                       weights=1.0,
                                       scope=None,
                                       loss_collection=tf.graphkeys.Losses,
                                       reduction=Reduction.SUM_BY_NONZERO_WEIGHTS
                                      )

It consists of a few parameters
- labels: This parameter indicates the output tensor and it is similar to the prediction parameter.
- prediction: This parameter specifies the prediction of output.
- weights: By default, it takes 1.0 value and it specifies rank will be 0 or similar rank as a label and it is also an optional parameter.
- scope: By default, it takes none value and indicates the scope of the operation which we can perform in the loss function.
- loss_collection: This parameter specifies the collection which we want to insert into the loss function and by default it takes tf.graph.keys.losses().

Example:

Let’s take an example and check how to find the mean squared error in Python TensorFlow.

Source Code:

import tensorflow as tf
tf.compat.v1.disable_eager_execution()
new_true = [15, 56, 77]
new_val_predict = [89, 26, 108]
weights = [45, 189, 0.34]

result=tf.compat.v1.losses.mean_squared_error(labels=new_true,predictions=new_val_predict,weights=weights)
with tf.compat.v1.Session() as val:
    new_output=val.run(result)
    print(new_output)

In the above code, we have imported the TensorFlow library and then initialized the prediction and the values of the weight.

After that, we have used the tf.compat.v1.losses.mean_squared_error() function and within this function, we have assigned prediction and weights as an argument. Once you will execute this code the output displays the mean squared error.

Here is the implementation of the following given code.

Read: TensorFlow Tensor to numpy

TensorFlow metrices mean squared error

In this Program, we will discuss how to find the metrics mean squared error in Python TensorFlow.
To perform this particular task, we are going to use the tf.compat.v1.metrices.mean_squared_error() function and this function is used to calculate the mean squared error between the given prediction and labels in this example, we will consider the label as ‘new_true’ variable and the prediction as ‘new_val_predict’.

Syntax:

Here is the Syntax of tf.compat.v1.metrices.mean_squared_error() function.

tf.compat.v1.metrics.mean_squared_error
                                       (
                                        labels,
                                        predictions,
                                        weights=None,
                                        metrices_collections=None,
                                        update_collections=None,
                                        name=None
                                       )

It consists of a few parameters
- labels: This parameter indicates the tensor of a similar shape as we have mentioned in the prediction.
- prediction: This parameter specifies the shape of a tensor.
- weights: By default, it takes none value and it is an optional parameter whose rank value is 0.
- metrics collection: It is an optional list of collections that we have to add a mean squared error.
- name: By default, it takes none value and it is an optional scope name.

Example:

Let’s take an example and check how to find the metrices mean squared error in Python.

Source Code:

import tensorflow as tf
tf.compat.v1.disable_eager_execution()
new_true = [15, 56, 77]
new_val_predict = [89, 26, 108]


result=tf.compat.v1.metrics.mean_squared_error(labels=new_true,predictions=new_val_predict)
print(result)

After creating the labels and prediction values we have used the tf.compat.v1.metrices.mean_squared_error() function and within this function, we have passed the labels and prediction as an argument.

Here is the Screenshot of the following given code.

Read: TensorFlow get shape

TensorFlow mean squared logarithmic error

Here we are going to discuss how to find the mean squared logarithmic error in Python TensorFlow.
To do this task we are going to use the tf.Keras.losses.MeanSquaredLogarithmic() function and this function is used to calculate the difference between y_pred and y_true value in mean squared logarithmic error.

Syntax:

Here is the Syntax of tf.Keras.losses.MeanSquaredLogarithmic Error in Python TensorFlow.

tf.keras.losses.MeanSquaredLogarithmicError
                                           (
                                            reduction=losses_utils.ReductionV2.AUTO,
                                            name='mean_squared_logarithmic_error'
                                           )

It consists of a few parameters
- reduction: By default it takes AUTO value that specifies the reduction option and it determines the usage of contxt.
- name: This parameter indicates the name of the operation.

Example:

Let’s take an example and check how to find the mean squared logarithmic error in Python TensorFlow.

Source Code:

import tensorflow as tf

y_true = [[12., 8.], [3., 2.]]
y_pred = [[13., 12.], [4., 2.]]

new_result = tf.keras.losses.MeanSquaredLogarithmicError()
new_result(y_true, y_pred)

In the following given code we have imported the TensorFlow library and then declares two variables ‘y_true’ and ‘y_pred’. After that, we have used the tf.keras.losses.MeanSquaredLogarithmicError() and within this function, we have assigned these given values.

Here is the Screenshot of the following given code.

TensorFlow mean squared logarithmic error

Read: Python TensorFlow reduce_sum

TensorFlow weighted mean squared error

In this section, we will discuss how to calculate the weighted of mean squared error in Python TensorFlow.
To perform this particular task we are going to use the tf.keras.losses.MeanSquaredError() function and this function will help the user to generate the mean of squares errors between the prediction and labels values.
In this example, we have mentioned the label and prediction in the form of lists’ y_true’ and ‘new_val_predict’.

Syntax:

Here is the Syntax of tf.Keras.losses.MeanSquaredError in Python TensorFlow

tf.keras.losses.MeanSquaredError
                               (
                                reduction=losses_utils.ReductionV2.AUTO,
                                name='mean_squared_error'
                               )

Example:

import tensorflow as tf
tf.compat.v1.disable_eager_execution()
y_true = [0, 1],[1,1]
new_val_predict = [0,0],[1,0]


result=tf.keras.losses.MeanSquaredError()
result(y_true, new_val_predict)

After creating the variables we have used the tf.Keras.losses.MeanSquaredError() function and within this function we have assigned ‘y_true’ and ‘new_val_predict’ value.

You can refer to the below Screenshot.

Read: Python TensorFlow reduce_mean

TensorFlow root mean squared error

In this section, we will discuss how to calculate the root mean squared error in Python TensorFlow.
To perform this particular task we are going to use the tf.compat.v1.metrices.root_mean_squared_error() function and this function is used to generate the root mean squared error into prediction and label parameters.

Syntax:

Let’s have a look at the Syntax and understand the working of tf.compat.v1.metrices.root_mean_squared_error() function in Python TensorFlow.

tf.compat.v1.metrices.root_mean_squared_error
                                            (
                                             labels,
                                             predictions,
                                             weights=None,
                                             metrices_collection=None
                                             update_collections=None,
                                             name=None
                                            )

It consists of a few parameters
- labels: This parameter indicates the tensor of the similar shape as we have mentioned in prediction.
- Prediction: This parameter specifies the shape of a tensor.
- weights: By default it takes none value.

Example:

Let’s take an example and check how to find the root mean squared error in Python TensorFlow.

Source Code:

import tensorflow as tf
tf.compat.v1.disable_eager_execution()
new_true = [65, 289, 198]
new_val_predict = [567, 118, 290]
weights = [345, 93, 45]

result=tf.compat.v1.metrics.root_mean_squared_error(labels=new_true,predictions=new_val_predict,weights=weights)
print(result)

Here is the Screenshot of the following given code.

Read: Python TensorFlow random uniform

TensorFlow reduce mean squared error

In this section, we will discuss how to reduce mean squared error in Python TensorFlow.
In this example, we have used the tf.compat.v1.losses.mean_squared_error() function and within this function we have passed the tensor 1 and tensor 2 values.
Once you will execute this code the output displays the mean value of the given tensors.

Example:

Let’s take an example and check how to reduce mean squared error in Python TensorFlow.

Source Code:

import tensorflow as tf

new_tens1 = tf.constant([[15.2, 2.8, 13.4],
                      [15.3,17.8,13.1]])
new_tens2 = tf.constant([[14.4, 13.2, 17.9],
                      [14.2,17.3,13.6]])


new_output=tf.compat.v1.losses.mean_squared_error(new_tens1,new_tens2)
print("Reduce mean squared error:",new_output)

Here is the Screenshot of the following given code.

Read: Python TensorFlow one_hot

TensorFlow calculate mean squared error

Here we are going to discuss how to calculate the mean squared error in Python TensorFlow.

The mean squared error is used to calculate the difference between the estimated values and predicted values of the given variables.

Mean Square Error Formula:

MSE = (1/n)∑i=1n(Yi-Yi)²

Defining the variables:

∑: The summation indicates the addition of all the values that are known to estimate the error.
n: This variable specifies the sample size.
yi: The actual or we can say the observed value of the variable.

Now we will take some random values and measure the difference between each pair of the actual and the predicted values. Next, we will square off those difference values and then obtain the mean value.

Example:

Let’s take an example and calculate the mean squared error by mathematically way.

Suppose we have the following set of numbers

Now we will calculate the Mean squared error

Now we will calculate the mean squared error by using the TensorFlow method

Source Code:

import tensorflow as tf
 
y_true = [3,4,6]
new_val_predict = [2.5,5,6]
 
result=tf.keras.losses.MeanSquaredError()
result(y_true, new_val_predict)

Here is the implementation of the following given code

Read: Module ‘TensorFlow’ has no attribute ‘session’

Mean pairwise squared error tensorflow

In this section, we will discuss how to find mean pairwise squared error tensorflow in Python.
To do this task we are going to use the tf.compat.v1.losses.mean_pairwise_squared_error() function and this function is used to insert a pairwise squared error in Python TensorFlow.

Syntax:

Let’s have a look at the Syntax and understand the working of tf.compat.v1.losses.mean_pairwise_squared_error() function.

tf.compat.v1.losses.mean_pairwise_squared_error
                                               (
                                                labels,
                                                predictions,
                                                weights=1.0,
                                                scope=None,
                                                loss_collection=tf.GraphKeys.LOSSES
                                               )

Example:

import tensorflow as tf
y_true = [2,5,7]
new_val_predict = [3,2,1]
 
result=tf.compat.v1.losses.mean_pairwise_squared_error(y_true, new_val_predict)
print(result)

Here is the execution of the following given code

mean pairwise squared error tensorflow — mean pairwise squared error TensorFlow

Also, take a look at some more TensorFlow tutorials.

So, in this Python tutorial, we have learned how to find the mean squared error in Python TensorFlow. Additionally, we have covered the following topics.

TensorFlow mean squared error
TensorFlow metrices mean squared error
TensorFlow mean squared logarithmic error
TensorFlow weighted mean squared error
TensorFlow root mean squared error
TensorFlow reduce mean squared error
TensorFlow calculate mean squared error
mean pairwise squared error tensorflow

Bijay Kumar

I am Bijay Kumar, a Microsoft MVP in SharePoint. Apart from SharePoint, I started working on Python, Machine learning, and artificial intelligence for the last 5 years. During this time I got expertise in various Python libraries also like Tkinter, Pandas, NumPy, Turtle, Django, Matplotlib, Tensorflow, Scipy, Scikit-Learn, etc… for various clients in the United States, Canada, the United Kingdom, Australia, New Zealand, etc. Check out my profile.

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