Keras: predict_proba (), predict () and predict_classes ()

During the prediction phase of my keras model, when I print the predicted values ​​and classes, I am given different probabilities in pred_proba () and pred (). Also, the output for class_prediction () does not match the probabilities. Sample code and sample printout below:

Code:

p = pd.read_csv("test.csv", header=None)
p = np.reshape(p.values, (50, seq_length))
for i in range(len(p)):
    p[i] = scaler.fit_transform(p[i])

p = np.reshape(p, (50, seq_length, 1))
model.predict(p, batch_size=50)
model.predict_classes(p, batch_size=50)
model.predict_on_batch(p)
model.predict_proba(p, batch_size=50)
for i in zip(model.predict_proba(p, batch_size=50), model.predict_classes(p, batch_size=50), model.predict(p, batch_size=50)):
    print("model.predict_proba", "--", i[0], "model.predict","--", i[2], "predict_clases", "--", i[1])

      

        
        
        
      

    

Sample output with different predict_proba()

andpredict()

model.predict_proba -- [ 0.18768159  0.81231844] model.predict -- [ 0.18982948  0.81017047] predict_classes -- 1
model.predict_proba -- [ 0.55918539  0.4408147 ] model.predict -- [ 0.78916383  0.2108362 ] predict_classes -- 1

      

        
        
        
      

    

The latter is also the case where the highest probability is index = 0, but the predicted class is 1

Below is another example. Highest probability for index = 0, but predicted class is 1

model.predict_proba -- [ 0.55918539  0.4408147 ] model.predict -- [ 0.78916383  0.2108362 ] predict_classes -- 1  

      

        
        
        
      

    

But most of the cases as shown below, where pred_proba () and pred () show the same values, and the predicted class matches the index correctly with the highest probability

model.predict_proba -- [ 0.39802018  0.60197979] model.predict -- [ 0.39802018  0.60197979] predict_classes -- 1 

      

        
        
        
      

    

Maybe I don't understand how to read this? Thanks to