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：
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<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> |
我們希望獲取第一個卷積層的權重weight，該怎么辦呢？？
在訓練時，這些可訓練的變量會被tensorflow保存在 tf.trainable_variables() 中，于是我們就可以通過打印 tf.trainable_variables() 來獲取該卷積層的名稱（或者你也可以自己根據scope來看出來該變量的name ），然后利用tf.get_default_grap().get_tensor_by_name 來獲取該變量。
舉個簡單的例子：
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<span style="font-size:14px;">import tensorflow as tf</span><span style="font-size:14px;">with tf.variable_scope("generate"):</span><span style="font-size:14px;"> with tf.variable_scope("resnet_stack"):</span><span style="font-size:14px;"> #簡單起見，這里沒有用第三方庫來說明，</span><span style="font-size:14px;"> bias = tf.Variable(0.0,name="bias")</span><span style="font-size:14px;"> weight = tf.Variable(0.0,name="weight")</span><span style="font-size:14px;"></span><span style="font-size:14px;">for tv in tf.trainable_variables():</span><span style="font-size:14px;"> print (tv.name)</span><span style="font-size:14px;"></span><span style="font-size:14px;">b = tf.get_default_graph().get_tensor_by_name("generate/resnet_stack/bias:0")</span><span style="font-size:14px;">w = tf.get_default_graph().get_tensor_by_name("generate/resnet_stack/weight:0")</span><span style="font-size:14px;"></span><span style="font-size:14px;">with tf.Session() as sess:</span><span style="font-size:14px;"> tf.global_variables_initializer().run()</span><span style="font-size:14px;"> print(sess.run(b))</span><span style="font-size:14px;"> print(sess.run(w))</span> |
結果如下：
以上這篇tensorflow 獲取變量&打印權值的實例講解就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支持服務器之家。
原文鏈接：https://blog.csdn.net/cassiePython/article/details/79179044
