train.py 文件源码

def train_LOO_pacient(images,labels,names):
    '''
    Training model using LOO validation by pacient

    Parameters
    ----------
    images: list of numpy.array. images of the pacients
    labels: list of numpy.array. labels of the different images
    names: list of numpy.array. name of the pacients
    Output
    ----------
    Print the mean of the LOO validation
    '''
    print("Training LOO with pacient name")
    values_acc = []
    already_tested = []
    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)):
        model = create_model()
        X_test = []
        Y_test = []
        # If we haven't tested that pacient already
        # we insert it to the test set and also
        # all the images with the same name
        if(not(names[i] in already_tested)):
            already_tested.append(names[i])
            X_test.append(images[i])
            Y_test.append(labels[i])
            for j in range(len(images)):
                if j!=i and names[j] == names[i]:
                    X_test.append(images[j])
                    Y_test.append(labels[j])
            X_train = []
            Y_train = []
            for j in range(len(images)):
                if names[j] != names[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)
            print("The len of the training set is: "+str(len(X_train)))
            print("The len of the test set is: "+str(len(X_test)))

            history = model.fit(X_train,Y_train,epochs=70,batch_size=10)
            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)