张量流标量摘要标签名称异常 [英] tensorflow scalar summary tags name exception
问题描述
我正在尝试按照 HowTo mnist 教程学习如何使用 tensorflow 摘要编写器.该教程为损失函数添加了标量摘要.我通过建立正则化项以不寻常的方式编写了一个损失函数,但我得到了这个例外:
I am trying to learn how to work with tensorflow summary writers by following the HowTo mnist tutorial. That tutorial adds a scalar summary for the loss function. I wrote a loss function in an unusual by building up a regularization term, and I get this exception:
W tensorflow/core/common_runtime/executor.cc:1027] 0x1e9ab70 Compute status: Invalid argument: tags and values not the same shape: [] != [1]
[[Node: ScalarSummary = ScalarSummary[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](ScalarSummary/tags, loss)]]
损失函数和添加摘要看起来像
The loss function and adding the summary look like
loss = tf.add(modelError, regularizationTerm, name='loss')
tf.scalar_summary(loss.op.name, loss)
如果我像这样建立正则化项
and if I build up the regularizationTerm like this
regularizationTerm = tf.Variable(tf.zeros([1], dtype=np.float32), name='regterm')
regularizationTerm += tf.mul(2.0, regA)
regularizationTerm += tf.mul(3.0, regB)
regA 和 regB 是之前定义的 tf.Variables,我得到了异常,而我像
were regA and regB are tf.Variables previously defined, I get the exception, whereas is I build it up like
regularizationTerm = tf.add(tf.mul(2.0, regA), tf.mul(3.0, regB), name='regterm')
然后就可以了.所以我想我没有正确设置名称,当我执行 += 时,我创建了一个未命名的新张量?但是为什么我不能将其添加到损失中,然后命名损失?这是我唯一要总结的事情吗?
then it works. So I guess I am not setting the name correctly, when I do the += I create a new tensor that is unamed? But why can't I add that into the loss, and then name the loss? That is the only thing I am trying to summarize?
是否有类似 += 之类的东西,我可以在其中命名输出,或者保留我正在修改的张量的名称?
Is there something like += where I can name the output, or preserve the name of the tensor I am modifying?
如果问题与其他问题有关,以下是我确定问题的简单示例:
In case the issue is related to something else, here is my simple example where I identified the problem:
import numpy as np
import tensorflow as tf
def main():
x_input = tf.placeholder(tf.float32, shape=(None, 1))
y_output = tf.placeholder(tf.float32, shape=(None, 1))
hidden_weights = tf.Variable(tf.truncated_normal([1,10], stddev=0.1), name='weights')
output_weights = tf.Variable(tf.truncated_normal([10,1], stddev=0.1), name='output')
inference = tf.matmul(tf.matmul(x_input, hidden_weights), output_weights)
regA = tf.reduce_sum(tf.pow(hidden_weights, 2))
regB = tf.reduce_sum(tf.pow(output_weights, 2))
modelError = tf.reduce_mean(tf.pow(tf.sub(inference,y_output),2), name='model-error')
fail = True
if fail:
regularizationTerm = tf.Variable(tf.zeros([1], dtype=np.float32), name='regterm')
regularizationTerm += tf.mul(2.0, regA)
regularizationTerm += tf.mul(3.0, regB)
else:
regularizationTerm = tf.add(tf.mul(2.0, regA), tf.mul(3.0, regB), name='regterm')
loss = tf.add(modelError, regularizationTerm, name='loss')
tf.scalar_summary(loss.op.name, loss)
optimizer = tf.train.GradientDescentOptimizer(0.05)
global_step = tf.Variable(0, name='global_step', trainable=False)
train_op = optimizer.minimize(loss, global_step=global_step)
summary_op = tf.merge_all_summaries()
saver = tf.train.Saver()
sess = tf.Session()
init = tf.initialize_all_variables()
sess.run(init)
summary_writer = tf.train.SummaryWriter('train_dir',
graph_def=sess.graph_def)
feed_dict = {x_input:np.ones((30,1), dtype=np.float32),
y_output:np.ones((30,1), dtype=np.float32)}
for step in xrange(1000):
_, loss_value = sess.run([train_op, loss], feed_dict=feed_dict)
if step % 100 == 0:
print( "step=%d loss=%.2f" % (step, loss_value))
summary_str = sess.run(summary_op, feed_dict=feed_dict)
summary_writer.add_summary(summary_str, step)
if __name__ == '__main__':
main()
推荐答案
TL;DR: 问题在于 tf.scalar_summary()
,而不是名称.
TL;DR: The problem is the shape of the argument to tf.scalar_summary()
, not the names.
我认为问题是与形状相关的问题,源于这一行:
I think the problem is a shape-related issue, stemming from this line:
regularizationTerm = tf.Variable(tf.zeros([1], dtype=np.float32), name='regterm')
这定义了一个变量,其形状是长度为 1 的向量.随后的 +=
运算符(它们是 tf.add()
) 和 tf.add()
计算 loss
将产生矢量形状的结果,因为 tf.add()
广播 标量参数成为向量.最后,tf.scalar_summary()
期望它的两个参数具有相同的形状——与广播 add
不同,tf.scalar_summary()
对其输入的形状不宽容.tags
输入是一个标量字符串(loss
操作的名称),而 values
输入是一个长度为 1 的向量(loss
张量).因此,您会收到您报告的错误.
This defines a variable whose shape is a vector of length 1. The subsequent +=
operators (which are syntactic sugar for tf.add()
) and the tf.add()
to compute loss
will produce vector-shaped results, because tf.add()
broadcasts the scalar argument to become a vector. Finally, tf.scalar_summary()
expects its two arguments to have the same shape—unlike the broadcasting add
, tf.scalar_summary()
is not permissive about the shapes of its inputs. The tags
input is a scalar string (the name of the loss
op) whereas the values
input is a vector of length one (the value of the loss
tensor). Therefore you get the error that you reported.
幸运的是,解决方案很简单!将 regularizationTerm
变量定义为标量,如下所示:
Fortunately, the solution is simple! Either define the regularizationTerm
variable as a scalar, like so:
# Note that `[]` is the scalar shape.
regularizationTerm = tf.Variable(tf.zeros([], dtype=np.float32), name='regterm')
...或将字符串向量(长度为 1)传递给 tf.scalar_summary()
:
...or pass a vector (of length 1) of strings to tf.scalar_summary()
:
# Wrap `loss.op.name` in a list to make it a vector.
tf.scalar_summary([loss.op.name], loss)
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