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I am training a CNN for multiclass image classification into 4 images , what accuracy metric should i use from Keras. My labels are not one hot encoded as I am trying to predict probability of different images.

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  • $\begingroup$ What do you mean that you have not one-hot encoded the labels because you want to predict the probabilities? Do you mean the outputs are probabilities instead of categories? $\endgroup$
    – Dave
    May 13, 2023 at 18:41
  • $\begingroup$ Yeah , the I want the outputs to be probabilities not a single class , I am working on an emotion classifier whose output I later want to feed to a recommendation algo , as a project , so based on the probabilities of the output emotions , I want to be able to suggest music $\endgroup$
    – zero_day
    May 13, 2023 at 18:45

2 Answers 2

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For multiclass classification you can simply use a categorical cross entropy loss function. Depending on whether or not the values are one-hot encoded you would have to use either the sparse categorical cross entropy loss or the normal categorical cross entropy loss.

Another option is f1_score which is a combination of precision_score and recall_score. Below is an implementation in keras which you can use:

from tensorflow.keras import backend as K

def f1(y_true, y_pred):    
    def recall_m(y_true, y_pred):
        TP = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
        Positives = K.sum(K.round(K.clip(y_true, 0, 1)))
        
        recall = TP / (Positives+K.epsilon())    
        return recall 
    
    
    def precision_m(y_true, y_pred):
        TP = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
        Pred_Positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
    
        precision = TP / (Pred_Positives+K.epsilon())
        return precision 
    
    precision, recall = precision_m(y_true, y_pred), recall_m(y_true, y_pred)
    
    return 2*((precision*recall)/(precision+recall+K.epsilon()))
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If you are predicting probabilities for each class, you can use the "categorical_crossentropy" loss function in Keras and set the "softmax" activation function on the output layer of your CNN.

Since your labels are not one-hot encoded, you can use the "sparse_categorical_accuracy" metric in Keras to evaluate the accuracy of your model. This metric will compare the predicted class probabilities to the actual class labels.

Here is an example code snippet:

from tensorflow import keras
model = keras.models.Sequential([
    # add your CNN layers here
    keras.layers.Dense(4, activation='softmax') # output layer with 4 classes and softmax activation
])

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['sparse_categorical_accuracy'])

Note that even though your labels are not one-hot encoded, Keras will automatically convert them to one-hot encoded format internally when using the "categorical_crossentropy" loss function.

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