Tensorboard嵌入不顯示張量？

Question

Tensorboard通過使用tf.train.Saver()提供了張量變量嵌入可視化。下面是一個工作示例（從this answer）

import os 
import tensorflow as tf 
from tensorflow.examples.tutorials.mnist import input_data 
from tensorflow.contrib.tensorboard.plugins import projector 


LOG_DIR = '/tmp/emb_logs/' 
metadata = os.path.join(LOG_DIR, 'metadata.tsv') 

mnist = input_data.read_data_sets('MNIST_data') 

#Variables 
images = tf.Variable(mnist.test.images, name='images') 
weights=tf.Variable(tf.random_normal([3,3,1,16])) 
biases=tf.Variable(tf.zeros([16])) 

#Tensor from the variables 
x = tf.reshape(images, shape=[-1, 28, 28, 1]) 
conv_layer=tf.nn.conv2d(x, weights, [1,1,1,1], padding="SAME") 
conv_layer=tf.add(conv_layer, biases) 
y = tf.reshape(conv_layer, shape=[-1, 28*28*16]) 


with open(metadata, 'wb') as metadata_file: 
    for row in mnist.test.labels: 
     metadata_file.write('%d 
' % row) 

with tf.Session() as sess: 
    saver = tf.train.Saver([images]) 

    sess.run(images.initializer) 
    saver.save(sess, os.path.join(LOG_DIR, 'images.ckpt')) 

    config = projector.ProjectorConfig() 
    # One can add multiple embeddings. 
    embedding = config.embeddings.add() 
    embedding.tensor_name = images.name 
    # Link this tensor to its metadata file (e.g. labels). 
    embedding.metadata_path = metadata 
    # Saves a config file that TensorBoard will read during startup. 
    projector.visualize_embeddings(tf.summary.FileWriter(LOG_DIR), config) 

如何可以可視化的嵌入從tensorflow張量，如在上面的代碼y？

只需使用

saver = tf.train.Saver([y]) 

更換

saver = tf.train.Saver([images]) 

不起作用，因爲有下列錯誤：

474   var = ops.convert_to_tensor(var, as_ref=True) 
475   if not BaseSaverBuilder._IsVariable(var): 
476   raise TypeError("Variable to save is not a Variable: %s" % var) 
477   name = var.op.name 
478   if name in names_to_saveables: 

TypeError: Variable to save is not a Variable: Tensor("Reshape_11:0", shape=(10000, 12544), dtype=float32) 

有誰知道的替代方法生成tensorboard嵌入的可視化一個tf.tensor？

Answer 1

您可以創建一個新變量，您可以將值分配給y。

y_var = tf.Variable(tf.shape(y)) 
saver = tf.train.Saver([y_var]) 
assign_op = y_var.assign(y) 
... 
# Training 
sess.run((loss, assign_op)) 
saver.save(...) 

Answer 2

tf.summary.tensor_summary將保存任意張量的摘要。