train.py 文件源码

def train_model_CV(slices_images,slices_labels,slice_number,f):
    '''
    Training model using cross-validation passing the slices manually

    Parameters
    ----------
    slices_images: list of numpy.array
    slices_labels: list of numpy.array

    Output
    ----------
    Write in a results file the mean of the accuracy in the test set
    '''

    #images,labels,list_of_images = reorderRandomly(images,labels,list_of_images)
    '''
    for i in range(len(labels)):
        if labels[i] == "AD":
            labels[i] = 0
        else:
            labels[i] = 1

    slices_images = [images[i::5] for i in range(5)]
    slices_list_of_images = [list_of_images[i::5] for i in range(5)]
    slices_labels = [labels[i::5] for i in range(5)]

    print(slices_list_of_images)
    '''
    values_acc = []
    for i in range(5):
        model = create_model()
        X_test = slices_images[i]
        Y_test = slices_labels[i]
        X_train = [item
                    for s in slices_images if s is not X_test
                    for item in s]
        Y_train = [item
                    for s in slices_labels if s is not Y_test
                    for item in s]

        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)
        Y_test = to_categorical(Y_test)
        history = model.fit(X_train,Y_train,epochs=70,batch_size=5,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)
    f.write(("The mean of all the test values for the slice %g is: %g \n"%(slice_number,mean)))