Introduction to TensorFlow Metrics
The following article provides an outline for TensorFlow Metrics. The Keras is the library available in deep learning, which is a subtopic of machine learning and consists of many other sub-libraries such as tensorflow and Theano. These are used in Artificial intelligence and robotics as this technology uses algorithms developed based on the patterns in which the human brain works and is capable of self-learning. Tensorflow keras is one of the most popular and highly progressing fields in technology right now as it possesses the potential to change the future of technology.
In this article, we will look at the metrics of Keras TensorFlow, classes, and functions available in TensorFlow and learn about the classification metrics along with the implementation of metrics TensorFlow with an example.
What are TensorFlow metrics?
Tensorflow metrics are nothing but the functions and classes which help in calculating and analyzing the estimation of the performance of your TensorFlow model. The output evaluated from the metric functions cannot be used for training the model. Other than that, the behavior of the metric functions is quite similar to that of loss functions. We can even use the loss function while considering it as a metric. The TensorFlow tf.keras.namespace is the public application programming interface.
There is a list of functions and classes available in tensorflow that can be used to judge the performance of your application. These metrics help in monitoring how you train your model. We can specify all the parameters and arguments required and mention the names of functions required or their aliases while you run the compile() function inside your model. For example, while using the fit() function for fitting in the model, you should mention the metrics that will help you monitor your model along with the optimizer and loss function.
TensorFlow metrics Classes
The list of all the available classes in tensorflow metrics are listed below –
- Class Poisson – The calculation of the metrics of Poisson between the range of y_pred and y_true can be made using this class.
- Class Metric – All the metrics’ states and logic are encapsulated within the class metric.
- Class MeanTensor – We can calculate the weighted or regular means element-wise by using this class for the specified tensor models.
- Class Recall – According to the labels mentioned, the recall of computation of the precision corresponding to the made predictions can be achieved by using this class.
- Class Precision – According to the labels mentioned, the computation of the precision corresponding to the made predictions can be achieved using this class.
- Class PrecisionAtRecall – The calculation of the best prediction when the scenario is such that the recall is greater than or equal to the specified value can be achieved by using this.
- Class sparseTopKCategoricalAccuracy – When analyzing the top k predictions, this class helps determine the probability of getting an integer over there.
- Class SensitivityAtSpecificity – Whenever we have the specificity value greater than or equal to the specified value, this class will help calculate the best sensitivity.
- Class RootMeanSquareError – This class will help you to calculate the effective root mean in the squared error metric, where the range will be considered from y_true to y_pred parameters.
- Class SparseCategoricalAccuracy – This class helps calculate the probability of getting an integer label that matches the predictions.
- Class SparseCategoricalCrossentropy – This helps compute the cross-entropy metric between the range of labels and predictions mentioned.
- Class Sum – When the list of values is specified, this class helps you calculate the weighted sum.
- Class TrueNegatives – This class calculates the count of the true negatives present.
- Class SpecificityAtSensitivity – Computation of the specificity can be done by using this class where the condition that sensitivity should be greater than or equal to the specified value.
- Class SquaredHinge – The value of the squared hinge can be calculated using this metric class that considers the range between y_true and y_pred values.
- Class TruePositives – This class calculates the count of several true positives.
TensorFlow Metrics Functions
The list functions available in Tensorflow are as listed below in table –
|MSE (…)||The computation of mean square error while considering the range of labels to the specified predictions.|
|MAE (…)||Mean Absolute Error can be calculated between the specified range of labels and the predictions.|
|KLD (…)||The Kullback Leibler divergence loss value can be estimated by using this function which considers the range between y
_true and y_pred.
|MAPE (…)||Mean Absolute Percentage error can be calculated using this function that considers the y_pred and y_true range for calculation.|
|MSLE (…)||Mean Squared Logarithmic error can be estimated by using this function which considers the range between y
_true and y_pred.
|Categorical_accuracy (…)||The probability of calculating how often the value of predictions matches with the one-hot labels can be calculated using this function.|
|Binary_accuracy (…)||The probability of matching the value of predictions with binary labels can be calculated using this function.|
|Categorical_crossentropy (…)||The loss of categorical cross-entropy can be calculated by using this function.|
|Binary_crossentropy (…)||The computation of loss of binary cross-entropy can be done by using this function.|
|Get (…)||Using this function, we can retrieve the value of keras metrics such as an instance of Function/ Metric class.|
|kl_divergence (…)||This function is used for calculating the kullback Leibler loss of divergence while considering the range between y_true and y_pred.|
|Deserialize (…)||The process of deserializing a function or class into its serialized version can be done using this function.|
|Hinge (…)||The hinge loss can be calculated using this function that considers the range of y_true to y_pred.|
|Kld (…)||This function is used for calculating the kullback Leibler loss of divergence while considering the range between y_true and y_pred.|
Besides the functions mentioned above, there are many other functions for calculating mean and logging-related functionalities. But, again, you can refer to this official link for complete guidance.
TensorFlow’s most important classification metrics include precision, recall, accuracy, and F1 score. Precision differs from the recall only in some of the specific scenarios. The ROC curve stands for Receiver Operating Characteristic, and the decision threshold also plays a key role in classification metrics. Evaluating true and false negatives and true and false positives is also important.
TensorFlow Metrics Examples
Let us consider one example –
We will follow the steps of sequence preparation, creating the model, training it, plotting its various metrics values, and displaying all of them. Our program will be –
from numpy import array
from keras.educba_Models import Sequential
from keras.layers import Dense
from matplotlib import educba_python_plotting
sampleEducbaSequence = array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0])
educba_Model = Sequential()
educba_Model.compile(loss='mse', optimizer='adam', metrics=['mse', 'mae', 'mape', 'cosine'])
model_history = educba_Model.fit(sampleEducbaSequence, sampleEducbaSequence, epochs=500, batch_size=len(sampleEducbaSequence), verbose=2)
The output of executing the above program gives the following output –
Various functions and classes are available for calculating and estimating the tensorflow metrics. This article discusses some key classification metrics that affect the application’s performance.
This is a guide to TensorFlow Metrics. Here we discuss the Introduction, What are TensorFlow metrics? Examples with code implementation. You may also have a look at the following articles to learn more –