- How to handle missing values (NaN) in categorical data when using scikit-learn OneHotEncoder?
- How to get probabilities along with classification in LogisticRegression?
- How to choose the number of units for the Dense layer in the Convoluted neural network for a Image classification problem?
- How to use pydensecrf in Python3.7?
- How to set class weights in DecisionTreeClassifier for multi-class setting
- How to Extract Data from tmdB using Python
- How to add attention layer to a Bi-LSTM
- How to include SimpleImputer before CountVectorizer in a scikit-learn Pipeline?
- How to load a keras model saved as .pb
- How to train new classes on pretrained yolov4 model in darknet
- How To Import The MNIST Dataset From Local Directory Using PyTorch
- how to split up tf.data.Dataset into x_train, y_train, x_test, y_test for keras
- How to plot confusion matrix for prefetched dataset in Tensorflow
- How to Use Class Weights with Focal Loss in PyTorch for Imbalanced dataset for MultiClass Classification
- How to solve "ValueError: y should be a 1d array, got an array of shape (3, 5) instead." for naive Bayes?
- How to create image of confusion matrix in Python
- What are the numbers in torch.transforms.normalize and how to select them?
- How to assign a name for a pytorch layer?
- How to solve dist.init_process_group from hanging or deadlocks?
- How to use sample weights with tensorflow datasets?
How to reset Keras metrics?
Written by- Aionlinecourse1369 times views
In Keras, you can reset the metrics of a model by calling the reset_metrics method on the model object. Here's an example:
model = keras.Model(...)This will reset the metric values for all metrics that have been compiled into the model. Note that this does not change the list of metrics that the model is compiled with; it only resets their values. To change the list of metrics, you will need to recompile the model.
# Compile the model with some metrics
model.compile(optimizer='adam', loss='mean_squared_error', metrics=['mean_absolute_error'])
# Fit the model on some data
model.fit(x_train, y_train, epochs=10)
# Reset the metrics
model.reset_metrics()
Alternatively, you can reset the metrics for a specific epoch by passing the reset_metrics argument to the fit method. For example:
# Fit the model on some data and reset the metrics after each epoch
model.fit(x_train, y_train, epochs=10, reset_metrics=True)
This will reset the metric values after each epoch, so that the metric values are only calculated for the current epoch.