我决定更新张量板，因为它没有显示图表， 在图形面板上，我只能看到一个没有错误消息的空白页面。 现在我已经更新了图表，这是我的张量板显示的唯一内容。现在我看不到标量或直方图。我有：
No scalar data was found.
消息，直方图等也一样。
这是我的代码的相关部分：
def train_model(self):
with tf.Session(graph=self.graph) as session:
session.run(tf.global_variables_initializer())#Now all variables should be initialized.
print("Uninitialized variables: ", session.run(tf.report_uninitialized_variables())) #Just to check, should print nothing
self.train_writer = tf.summary.FileWriter(self.default_folder+"/logs/train", graph=session.graph)
self.test_writer = tf.summary.FileWriter(self.default_folder+"/logs/test", graph=session.graph)
print("Training for ", self.n_steps)
for step in range(self.n_steps):
feed_train = self._create_feed_dict(self.X_train,step)
feed_test = self._create_feed_dict(self.X_test,step)
session.run(self.optimizer, feed_dict = {self.v_clamp: feed_train})
#Get the loss in tensorborad
loss_train = session.run(self.summary_loss, feed_dict = {self.v_clamp: feed_train})
self.train_writer.add_summary(loss_train, step)
self.train_writer.flush()
loss_test = session.run(self.summary_loss, feed_dict = {self.v_clamp: feed_test})
self.test_writer.add_summary(loss_test, step)
self.test_writer.flush()
if (step%1000)==0:
histograms = session.run(self.merged, feed_dict = {self.v_clamp: feed_train})
self.train_writer.add_summary(histograms, step)
self.train.flush()
self.saver.save(session, self.default_folder + self.model_name, global_step = step)
print(step)
我在build_model方法中创建了这些变量。
def _build_model():
...
with tf.variable_scope("hidden_layer_1"):
self.W = tf.get_variable(name = "w", dtype = tf.float32, shape=[self.n_visibles, self.n_hidden], initializer=tf.random_uniform_initializer(maxval=0.01,minval=-0.01))
hist1=tf.summary.histogram("hidden_layer_1" + '/weights', self.W)
self.bh = tf.get_variable(name = "b_h", dtype = tf.float32, shape=[self.n_hidden], initializer=tf.random_uniform_initializer(maxval=0.01,minval=-0.01))
hist2=tf.summary.histogram("hidden_layer_1" + '/biases', self.bh)
if self.hidden_activation == 'sigma':
self.h_clamp = tf.nn.sigmoid(tf.matmul(self.v_clamp,self.W) + self.bh)
elif self.hidden_activation == 'relu':
self.h_clamp = tf.nn.relu(tf.matmul(self.v_clamp,self.W) + self.bh)
elif self.hidden_activation == 'tanh':
self.h_clamp = tf.nn.tanh(tf.matmul(self.v_clamp,self.W) + self.bh)
hist3=tf.summary.histogram("hidden_layer_1" + '/h', self.h_clamp)
...
#Get loss in tensorboard
with tf.variable_scope("compute_loss"):
self.loss_function = tf.sqrt(tf.reduce_mean(tf.square(self.v_clamp - v_free)))
self.summary_loss = tf.summary.scalar("loss", self.loss_function) #this is what we need to run!
self.merged = tf.summary.merge([hist1,hist2,hist3])
#Object to save variable weights and more
self.saver = tf.train.Saver()
我的default_folder是＆＃34; ./ models / GSSU-RBM /＆＃34;。我的model_name是＆＃34; myRBM＆＃34;。
我像这样执行tensorboard：
tensorboard --logdir "C:\Users\isaac\Desktop\TFM\models\GSSU-RBM"
我收到以下消息：
Exception in thread Reloader:
Traceback (most recent call last):
File "c:\users\isaac\anaconda3\envs\tensorflow-gpu\lib\threading.py", line 914, in _bootstrap_inner
self.run()
File "c:\users\isaac\anaconda3\envs\tensorflow-gpu\lib\threading.py", line 862, in run
self._target(*self._args, **self._kwargs)
File "c:\users\isaac\anaconda3\envs\tensorflow-gpu\lib\site-packages\tensorboard\backend\application.py", line 361, in _reload_forever
reload_multiplexer(multiplexer, path_to_run)
File "c:\users\isaac\anaconda3\envs\tensorflow-gpu\lib\site-packages\tensorboard\backend\application.py", line 335, in reload_multiplexer
multiplexer.Reload()
File "c:\users\isaac\anaconda3\envs\tensorflow-gpu\lib\site-packages\tensorboard\backend\event_processing\plugin_event_multiplexer.py", line 195, in Reload
accumulator.Reload()
File "c:\users\isaac\anaconda3\envs\tensorflow-gpu\lib\site-packages\tensorboard\backend\event_processing\plugin_event_accumulator.py", line 186, in Reload
self._ProcessEvent(event)
File "c:\users\isaac\anaconda3\envs\tensorflow-gpu\lib\site-packages\tensorboard\backend\event_processing\plugin_event_accumulator.py", line 332, in _ProcessEvent
value = data_compat.migrate_value(value)
File "c:\users\isaac\anaconda3\envs\tensorflow-gpu\lib\site-packages\tensorboard\data_compat.py", line 57, in migrate_value
return handler(value) if handler else value
File "c:\users\isaac\anaconda3\envs\tensorflow-gpu\lib\site-packages\tensorboard\data_compat.py", line 106, in _migrate_scalar_value
tensor_proto = tf.make_tensor_proto(scalar_value)
AttributeError: module 'tensorflow' has no attribute 'make_tensor_proto'
BTW我在Windows 10上使用tensorflow-gpu运行，在anaconda上的jupyter笔记本上运行。我通过conda安装了像pip install tensorboard和tensorflow-gpu这样的张量板。
编辑：此外，我的--inspect编辑
做：tensorboard --inspect --logdir "C:\Users\isaac\Desktop\TFM\models\GSSU-RBM"
我明白了：
These tags are in C:\Users\isaac\Desktop\TFM\models\GSSU-RBM\logs\test:
audio -
histograms -
images -
scalars
compute_loss/loss
tensor -
======================================================================
Event statistics for C:\Users\isaac\Desktop\TFM\models\GSSU-RBM\logs\test:
audio -
graph
first_step 0
last_step 0
max_step 0
min_step 0
num_steps 1
outoforder_steps []
histograms -
images -
scalars
first_step 0
last_step 84487
max_step 84487
min_step 0
num_steps 84488
outoforder_steps []
sessionlog:checkpoint -
sessionlog:start -
sessionlog:stop -
tensor -
======================================================================
These tags are in C:\Users\isaac\Desktop\TFM\models\GSSU-RBM\logs\train:
audio -
histograms
hidden_layer_1/hidden_layer_1/biases
hidden_layer_1/hidden_layer_1/h
hidden_layer_1/hidden_layer_1/weights
images -
scalars
compute_loss/loss
tensor -
======================================================================
Event statistics for C:\Users\isaac\Desktop\TFM\models\GSSU-RBM\logs\train:
audio -
graph
first_step 0
last_step 0
max_step 0
min_step 0
num_steps 1
outoforder_steps []
histograms
first_step 0
last_step 84000
max_step 84000
min_step 0
num_steps 85
outoforder_steps []
images -
scalars
first_step 0
last_step 84599
max_step 84599
min_step 0
num_steps 84600
outoforder_steps []
sessionlog:checkpoint -
sessionlog:start -
sessionlog:stop -
tensor -
======================================================================
答案 0 :(得分：2)
解决！如果有人处于相同的情况，解决方案是卸载conda tensorflow并通过pip安装它。 它后来给你一个错误请求特定的cuda和cudnn。 一旦你安装这两个它应该工作。