theano – print value of TensorVariable
Question:
How can I print the numerical value of a theano TensorVariable?
I’m new to theano, so please be patient 🙂
I have a function where I get y as a parameter.
Now I want to debug-print the shape of this y to the console.
Using
print y.shape
results in the console output (i was expecting numbers, i.e. (2,4,4)):
Shape.0
Or how can I print the numerical result of for example the following code (this counts how many values in y are bigger than half the maximum):
errorCount = T.sum(T.gt(T.abs_(y),T.max(y)/2.0))
errorCount should be a single number because T.sum sums up all the values.
But using
print errCount
gives me (expected something like 134):
Sum.0
Answers:
If y is a theano variable, y.shape will be a theano variable. so it is normal that
print y.shape
return:
Shape.0
If you want to evaluate the expression y.shape, you can do:
y.shape.eval()
if y.shape do not input to compute itself(it depend only on shared variable and constant). Otherwise, if y depend on the x Theano variable you can pass the inputs value like this:
y.shape.eval(x=numpy.random.rand(...))
this is the same thing for the sum. Theano graph are symbolic variable that do not do computation until you compile it with theano.function or call eval() on them.
EDIT: Per the docs, the syntax in newer versions of theano is
y.shape.eval({x: numpy.random.rand(...)})
For future readers: the previous answer is quite good.
But, I found the ‘tag.test_value’ mechanism more beneficial for debugging purposes (see theano-debug-faq):
from theano import config
from theano import tensor as T
config.compute_test_value = 'raise'
import numpy as np
#define a variable, and use the 'tag.test_value' option:
x = T.matrix('x')
x.tag.test_value = np.random.randint(100,size=(5,5))
#define how y is dependent on x:
y = x*x
#define how some other value (here 'errorCount') depends on y:
errorCount = T.sum(y)
#print the tag.test_value result for debug purposes!
errorCount.tag.test_value
For me, this is much more helpful; e.g., checking correct dimensions etc.
print Value of a Tensor Variable.
Do the following:
print tensor[dimension].eval() # this will print the content/value at that position in the Tensor
Example, for a 1 d tensor:
print tensor[0].eval()
Use theano.printing.Print to add print operator to your computational graph.
Example:
import numpy
import theano
x = theano.tensor.dvector('x')
x_printed = theano.printing.Print('this is a very important value')(x)
f = theano.function([x], x * 5)
f_with_print = theano.function([x], x_printed * 5)
#this runs the graph without any printing
assert numpy.all( f([1, 2, 3]) == [5, 10, 15])
#this runs the graph with the message, and value printed
assert numpy.all( f_with_print([1, 2, 3]) == [5, 10, 15])
Output:
this is a very important value __str__ = [ 1. 2. 3.]
Source: Theano 1.0 docs: “How do I Print an Intermediate Value in a Function?”
I found @zuuz ‘s answer is pretty helpful,
for values,
print(your_variable.tag.test_value)
for shapes it should be updated as,
print(np.shape(your_variable.tag.test_value))
How can I print the numerical value of a theano TensorVariable?
I’m new to theano, so please be patient 🙂
I have a function where I get y as a parameter.
Now I want to debug-print the shape of this y to the console.
Using
print y.shape
results in the console output (i was expecting numbers, i.e. (2,4,4)):
Shape.0
Or how can I print the numerical result of for example the following code (this counts how many values in y are bigger than half the maximum):
errorCount = T.sum(T.gt(T.abs_(y),T.max(y)/2.0))
errorCount should be a single number because T.sum sums up all the values.
But using
print errCount
gives me (expected something like 134):
Sum.0
If y is a theano variable, y.shape will be a theano variable. so it is normal that
print y.shape
return:
Shape.0
If you want to evaluate the expression y.shape, you can do:
y.shape.eval()
if y.shape do not input to compute itself(it depend only on shared variable and constant). Otherwise, if y depend on the x Theano variable you can pass the inputs value like this:
y.shape.eval(x=numpy.random.rand(...))
this is the same thing for the sum. Theano graph are symbolic variable that do not do computation until you compile it with theano.function or call eval() on them.
EDIT: Per the docs, the syntax in newer versions of theano is
y.shape.eval({x: numpy.random.rand(...)})
For future readers: the previous answer is quite good.
But, I found the ‘tag.test_value’ mechanism more beneficial for debugging purposes (see theano-debug-faq):
from theano import config
from theano import tensor as T
config.compute_test_value = 'raise'
import numpy as np
#define a variable, and use the 'tag.test_value' option:
x = T.matrix('x')
x.tag.test_value = np.random.randint(100,size=(5,5))
#define how y is dependent on x:
y = x*x
#define how some other value (here 'errorCount') depends on y:
errorCount = T.sum(y)
#print the tag.test_value result for debug purposes!
errorCount.tag.test_value
For me, this is much more helpful; e.g., checking correct dimensions etc.
print Value of a Tensor Variable.
Do the following:
print tensor[dimension].eval() # this will print the content/value at that position in the Tensor
Example, for a 1 d tensor:
print tensor[0].eval()
Use theano.printing.Print to add print operator to your computational graph.
Example:
import numpy
import theano
x = theano.tensor.dvector('x')
x_printed = theano.printing.Print('this is a very important value')(x)
f = theano.function([x], x * 5)
f_with_print = theano.function([x], x_printed * 5)
#this runs the graph without any printing
assert numpy.all( f([1, 2, 3]) == [5, 10, 15])
#this runs the graph with the message, and value printed
assert numpy.all( f_with_print([1, 2, 3]) == [5, 10, 15])
Output:
this is a very important value __str__ = [ 1. 2. 3.]
Source: Theano 1.0 docs: “How do I Print an Intermediate Value in a Function?”
I found @zuuz ‘s answer is pretty helpful,
for values,
print(your_variable.tag.test_value)
for shapes it should be updated as,
print(np.shape(your_variable.tag.test_value))