Answers for "metrics for keras model"

metrics for keras model

### Keras Regression Metrics
# Below is a list of the metrics that you can use in Keras on regression problems.

Mean Squared Error: mean_squared_error, MSE or mse
Mean Absolute Error: mean_absolute_error, MAE, mae
Mean Absolute Percentage Error: mean_absolute_percentage_error, MAPE, mape
Cosine Proximity: cosine_proximity, cosine
  
### Keras Classification Metrics
# Below is a list of the metrics that you can use in Keras on classification problems.

Binary Accuracy: binary_accuracy, acc
Categorical Accuracy: categorical_accuracy, acc
Sparse Categorical Accuracy: sparse_categorical_accuracy
Top k Categorical Accuracy: top_k_categorical_accuracy (requires you specify a k parameter)
Sparse Top k Categorical Accuracy: sparse_top_k_categorical_accuracy (requires you specify a k parameter)
  
### Keras custom metrics for rmse
from keras import backend
 
def rmse(y_true, y_pred):
	return backend.sqrt(backend.mean(backend.square(y_pred - y_true), axis=-1))