在使用tensorflow中，我們常常需要獲取某個變量的值，比如：打印某一層的權重，通常我們可以直接利用變量的name屬性來獲取，但是當我們利用一些第三方的庫來構造神經網絡的layer時，存在一種情況：就是我們自己無法定義該層的變量，因為是自動進行定義的。

比如用tensorflow的slim庫時：

<span style="font-size:14px;">def resnet_stack(images, output_shape, hparams, scope=None):</span><span style="font-size:14px;"> """Create a resnet style transfer block.</span><span style="font-size:14px;"></span><span style="font-size:14px;"> Args:</span><span style="font-size:14px;"> images: [batch-size, height, width, channels] image tensor to feed as input</span><span style="font-size:14px;"> output_shape: output image shape in form [height, width, channels]</span><span style="font-size:14px;"> hparams: hparams objects</span><span style="font-size:14px;"> scope: Variable scope</span><span style="font-size:14px;"></span><span style="font-size:14px;"> Returns:</span><span style="font-size:14px;"> Images after processing with resnet blocks.</span><span style="font-size:14px;"> """</span><span style="font-size:14px;"> end_points = {}</span><span style="font-size:14px;"> if hparams.noise_channel:</span><span style="font-size:14px;"> # separate the noise for visualization</span><span style="font-size:14px;"> end_points['noise'] = images[:, :, :, -1]</span><span style="font-size:14px;"> assert images.shape.as_list()[1:3] == output_shape[0:2]</span><span style="font-size:14px;"></span><span style="font-size:14px;"> with tf.variable_scope(scope, 'resnet_style_transfer', [images]):</span><span style="font-size:14px;"> with slim.arg_scope(</span><span style="font-size:14px;">  [slim.conv2d],</span><span style="font-size:14px;">  normalizer_fn=slim.batch_norm,</span><span style="font-size:14px;">  kernel_size=[hparams.generator_kernel_size] * 2,</span><span style="font-size:14px;">  stride=1):</span><span style="font-size:14px;">  net = slim.conv2d(</span><span style="font-size:14px;">   images,</span><span style="font-size:14px;">   hparams.resnet_filters,</span><span style="font-size:14px;">   normalizer_fn=None,</span><span style="font-size:14px;">   activation_fn=tf.nn.relu)</span><span style="font-size:14px;">  for block in range(hparams.resnet_blocks):</span><span style="font-size:14px;">  net = resnet_block(net, hparams)</span><span style="font-size:14px;">  end_points['resnet_block_{}'.format(block)] = net</span><span style="font-size:14px;"></span><span style="font-size:14px;">  net = slim.conv2d(</span><span style="font-size:14px;">   net,</span><span style="font-size:14px;">   output_shape[-1],</span><span style="font-size:14px;">   kernel_size=[1, 1],</span><span style="font-size:14px;">   normalizer_fn=None,</span><span style="font-size:14px;">   activation_fn=tf.nn.tanh,</span><span style="font-size:14px;">   scope='conv_out')</span><span style="font-size:14px;">  end_points['transferred_images'] = net</span><span style="font-size:14px;"> return net, end_points</span>