TFlearn中的variance_scaling_initializer，版本控制

Question

我试图运行这个 model 来构建 RestNet model：

#Creating residual architecture
 n = 5  #[3]
 net = tflearn.input_data(shape=[None, 48, 48, 1], 
 data_preprocessing=img_prep, data_augmentation=img_aug)
 net = tflearn.conv_2d(net, nb_filter=16, filter_size=3, 
 regularizer='L2', weight_decay=0.0001)
 net = tflearn.residual_block(net, n, 16)
 net = tflearn.residual_block(net, 1, 32, downsample=True)
 net = tflearn.residual_block(net, n-1, 32)
 net = tflearn.residual_block(net, 1, 64, downsample=True)
 net = tflearn.residual_block(net, n-1, 64)

 # Normalizing the previous batches by converting activation 
  function close to 0 and loss function close to 1
 net = tflearn.batch_normalization(net)
 net = tflearn.activation(net, 'relu')
 net = tflearn.global_avg_pool(net)

 # Creating final layer
 net = tflearn.fully_connected(net, 7, activation='softmax')
 mom = tflearn.Momentum(learning_rate=0.1, lr_decay=0.0001, 
 decay_step=32000, staircase=True, momentum=0.9)
 net = tflearn.regression(net, optimizer=mom,
                     loss='categorical_crossentropy')

然而不知何故我在这里遇到了这个问题'variance_scaling_initializer' 不支持，请在网上更新 TensorFlow net = tflearn.residual_block(net, n, 16)

这很奇怪，因为我通过在 Google Colab 中运行我的代码来使用最新的 tflearn 版本 0.5.0。 请问我的假设是否正确（最新版本）以及如何解决此问题