用theano实现绘制ResNet的ROC曲线

首先，我们需要训练一个ResNet模型并得到其预测结果。然后，我们可以使用sklearn库中的roc_curve函数来计算ROC曲线，并使用matplotlib库来绘制ROC曲线。

下面是一个使用theano库实现绘制ResNet的ROC曲线的示例代码：

import numpy as np
import theano
import theano.tensor as T
import lasagne
from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt

# 定义ResNet模型
def build_resnet(input_var=None):
    l_in = lasagne.layers.InputLayer(shape=(None, 3, 32, 32), input_var=input_var)
    l_conv1 = lasagne.layers.Conv2DLayer(l_in, num_filters=16, filter_size=(3, 3), stride=(1, 1),
                                         pad='same', nonlinearity=lasagne.nonlinearities.rectify,
                                         W=lasagne.init.HeNormal(gain='relu'))

    l_bn1 = lasagne.layers.BatchNormLayer(l_conv1)
    l_conv2 = lasagne.layers.Conv2DLayer(l_bn1, num_filters=16, filter_size=(3, 3), stride=(1, 1),
                                         pad='same', nonlinearity=lasagne.nonlinearities.rectify,
                                         W=lasagne.init.HeNormal(gain='relu'))
    l_bn2 = lasagne.layers.BatchNormLayer(l_conv2)
    l_pool1 = lasagne.layers.Pool2DLayer(l_bn2, pool_size=(2, 2), mode='max')

    l_res1 = lasagne.layers.ConcatLayer([l_pool1, l_pool1])
    l_conv3 = lasagne.layers.Conv2DLayer(l_res1, num_filters=32, filter_size=(3, 3), stride=(1, 1),
                                         pad='same', nonlinearity=lasagne.nonlinearities.rectify,
                                         W=lasagne.init.HeNormal(gain='relu'))
    l_bn3 = lasagne.layers.BatchNormLayer(l_conv3)
    l_conv4 = lasagne.layers.Conv2DLayer(l_bn3, num_filters=32, filter_size=(3, 3), stride=(1, 1),
                                         pad='same', nonlinearity=lasagne.nonlinearities.rectify,
                                         W=lasagne.init.HeNormal(gain='relu'))
    l_bn4 = lasagne.layers.BatchNormLayer(l_conv4)
    l_pool2 = lasagne.layers.Pool2DLayer(l_bn4, pool_size=(2, 2), mode='max')

    l_res2 = lasagne.layers.ConcatLayer([l_pool2, l_pool2])
    l_conv5 = lasagne.layers.Conv2DLayer(l_res2, num_filters=64, filter_size=(3, 3), stride=(1, 1),
                                         pad='same', nonlinearity=lasagne.nonlinearities.rectify,
                                         W=lasagne.init.HeNormal(gain='relu'))
    l_bn5 = lasagne.layers.BatchNormLayer(l_conv5)
    l_conv6 = lasagne.layers.Conv2DLayer(l_bn5, num_filters=64, filter_size=(3, 3), stride=(1, 1),
                                         pad='same', nonlinearity=lasagne.nonlinearities.rectify,
                                         W=lasagne.init.HeNormal(gain='relu'))
    l_bn6 = lasagne.layers.BatchNormLayer(l_conv6)
    l_pool3 = lasagne.layers.Pool2DLayer(l_bn6, pool_size=(2, 2), mode='max')

    l_flat = lasagne.layers.FlattenLayer(l_pool3)
    l_hidden = lasagne.layers.DenseLayer(l_flat, num_units=128, nonlinearity=lasagne.nonlinearities.rectify,
                                          W=lasagne.init.HeNormal(gain='relu'))
    l_drop = lasagne.layers.DropoutLayer(l_hidden, p=0.5)
    l_out = lasagne.layers.DenseLayer(l_drop, num_units=10, nonlinearity=lasagne.nonlinearities.softmax)

    return l_out

# 加载数据集
def load_dataset():
    from six.moves import urllib
    def download(filename, source='http://yann.lecun.com/exdb/mnist/'):
        print("Downloading %s" % filename)
        urllib.request.urlretrieve(source + filename, filename)
    import gzip
    def load_mnist_images(filename):
        if not os.path.exists(filename):
            download(filename)
        with gzip.open(filename, 'rb') as f:
            data = np.frombuffer(f.read(), np.uint8, offset=16)
        data = data.reshape(-1,1,28,28)
        return data / np.float32(256)
    def load_mnist_labels(filename):
        if not os.path.exists(filename):
            download(filename)
        with gzip.open(filename, 'rb') as f:
            data = np.frombuffer(f.read(), np.uint8, offset=8)
        return data

    X_train = load_mnist_images('train-images-idx3-ubyte.gz')
    y_train = load_mnist_labels('train-labels-idx1-ubyte.gz')
    X_test = load_mnist_images('t10k-images-idx3-ubyte.gz')
    y_test = load_mnist_labels('t10k-labels-idx1-ubyte.gz')

    return X_train, y_train, X_test, y_test

# 训练ResNet模型
def train_resnet():
    X_train, y_train, X_test, y_test = load_dataset()

    input_var = T.tensor4('inputs')
    target_var = T.ivector('targets')

    network = build_resnet(input_var)

    prediction = lasagne.layers.get_output(network)
    loss = lasagne.objectives.categorical_crossentropy(prediction, target_var)
    loss = loss.mean()

    params = lasagne.layers.get_all_params(network, trainable=True)
    updates = lasagne.updates.nesterov_momentum(loss, params, learning_rate=0.01, momentum=0.9)

    test_prediction = lasagne.layers.get_output(network, deterministic=True)
    test_loss = lasagne.objectives.categorical_crossentropy(test_prediction, target_var)
    test_loss = test_loss.mean()

    test_acc = T.mean(T.eq(T.argmax(test_prediction, axis=1), target_var), dtype=theano.config.floatX)

    train_fn = theano.function([input_var, target_var], loss, updates=updates)
    val_fn = theano.function([input_var, target_var], [test_loss, test_acc])
    predict_fn = theano.function([input_var], test_prediction)

    print("Starting training...")
    for epoch in range(10):
        train_err = 0
        train_batches = 0
        for batch in iterate_minibatches(X_train, y_train, 500, shuffle=True):
            inputs, targets = batch
            train_err += train_fn(inputs, targets)
            train_batches += 1

        val_err = 0
        val_acc = 0
        val_batches = 0
        for batch in iterate_minibatches(X_test, y_test, 500, shuffle=False):
            inputs, targets = batch
            err, acc = val_fn(inputs, targets)
            val_err += err
            val_acc += acc
            val_batches += 1

        print("Epoch {} of 10".format(epoch+1))
        print("  training loss:\t\t{:.6f}".format(train_err / train_batches))
        print("  validation loss:\t\t{:.6f}".format(val_err / val_batches))
        print("  validation accuracy:\t\t{:.2f} %".format(val_acc / val_batches * 100))

    # 得到测试集上的预测结果
    y_pred = predict_fn(X_test)

    return y_test, y_pred

# 计算并绘制ROC曲线
def plot_roc(y_true, y_pred):
    class_names = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
    n_classes = len(class_names)

    # 计算ROC曲线和AUC值
    fpr = dict()
    tpr = dict()
    roc_auc = dict()
    for i in range(n_classes):
        fpr[i], tpr[i], _ = roc_curve(y_true[:, i], y_pred[:, i])
        roc_auc[i] = auc(fpr[i], tpr[i])

    # 绘制ROC曲线
    plt.figure(figsize=(8, 6))
    for i in range(n_classes):
        plt.plot(fpr[i], tpr[i], label='ROC curve of class %s (area = %.2f)' % (class_names[i], roc_auc[i]))

    plt.plot([0, 1], [0, 1], 'k--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('ROC curves for ResNet model')
    plt.legend(loc="lower right")
    plt.show()

# 主函数
def main():
    y_true, y_pred = train_resnet()
    plot_roc(y_true, y_pred)

if __name__ == '__main__':
    main()

需要注意的是，上面的代码仅提供了一个示例，因此可能需要根据实际情况进行修改和调整。