train.py 文件源码

def train_LOO(images,labels):
    '''
    Training model using LOO validation using all the images

    Parameters
    ----------
    images: list of numpy.array
    labels: list of numpy.array

    Output
    ----------
    Print the mean of the LOO validation
    '''
    values_acc = []
    for i in range(len(labels)):
        if labels[i] == "AD":
            labels[i] = 0
        else:
            labels[i] = 1
    print("The lenght of images is "+str(len(images)))
    for i in range(len(images)):
        print("We are in the fold "+str(i))
        model = create_model()
        X_test = []
        Y_test = []
        X_test.append(images[i])
        Y_test.append(labels[i])
        X_train = []
        Y_train = []
        for j in range(len(images)):
            if j != i:
                X_train.append(images[j])
                Y_train.append(labels[j])


        X_train = np.array(X_train)
        Y_train = np.array(Y_train)
        X_test = np.array(X_test)
        Y_test = np.array(Y_test)
        from keras.utils.np_utils import to_categorical
        Y_train = to_categorical(Y_train,2)
        Y_test = to_categorical(Y_test,2)


        history = model.fit(X_train,Y_train,epochs=70,batch_size=10,verbose=0)
        test_loss = model.evaluate(X_test,Y_test)
        print("Loss and accuracy in the test set: Loss %g, Accuracy %g"%(test_loss[0],test_loss[1]))
        values_acc.append(test_loss[1])

    mean = calculate_mean(values_acc)
    print("The mean of all the test values is: %g"%mean)