Debugging Theano: FAQ and Troubleshooting

There are many kinds of bugs that might come up in a computer program. This page is structured as a FAQ. It provides recipes to tackle common problems, and introduces some of the tools that we use to find problems in our own Theano code, and even (it happens) in Theano’s internals, in Using DebugMode.

Isolating the Problem/Testing Theano Compiler

You can run your Theano function in a DebugMode. This tests the Theano optimizations and helps to find where NaN, inf and other problems come from.

Interpreting Error Messages

Even in its default configuration, Theano tries to display useful error messages. Consider the following faulty code.

import numpy as np
import theano
import theano.tensor as tt

x = tt.vector()
y = tt.vector()
z = x + x
z = z + y
f = theano.function([x, y], z)
f(np.ones((2,)), np.ones((3,)))

Running the code above we see:

Traceback (most recent call last):
ValueError: Input dimension mis-match. (input[0].shape[0] = 3, input[1].shape[0] = 2)
Apply node that caused the error: Elemwise{add,no_inplace}(<TensorType(float64, vector)>, <TensorType(float64, vector)>, <TensorType(float64, vector)>)
Inputs types: [TensorType(float64, vector), TensorType(float64, vector), TensorType(float64, vector)]
Inputs shapes: [(3,), (2,), (2,)]
Inputs strides: [(8,), (8,), (8,)]
Inputs scalar values: ['not scalar', 'not scalar', 'not scalar']

HINT: Re-running with most Theano optimization disabled could give you a back-traces when this node was created. This can be done with by setting the Theano flags 'optimizer=fast_compile'. If that does not work, Theano optimization can be disabled with 'optimizer=None'.
HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint of this apply node.

Arguably the most useful information is approximately half-way through the error message, where the kind of error is displayed along with its cause (ValueError: Input dimension mis-match. (input[0].shape[0] = 3, input[1].shape[0] = 2). Below it, some other information is given, such as the apply node that caused the error, as well as the input types, shapes, strides and scalar values.

The two hints can also be helpful when debugging. Using the theano flag optimizer=fast_compile or optimizer=None can often tell you the faulty line, while exception_verbosity=high will display a debugprint of the apply node. Using these hints, the end of the error message becomes :

Backtrace when the node is created:
  File "", line 8, in <module>
    z = z + y

Debugprint of the apply node:
Elemwise{add,no_inplace} [id A] <TensorType(float64, vector)> ''
 |Elemwise{add,no_inplace} [id B] <TensorType(float64, vector)> ''
 | |<TensorType(float64, vector)> [id C] <TensorType(float64, vector)>
 | |<TensorType(float64, vector)> [id C] <TensorType(float64, vector)>
 |<TensorType(float64, vector)> [id D] <TensorType(float64, vector)>

We can here see that the error can be traced back to the line z = z + y. For this example, using optimizer=fast_compile worked. If it did not, you could set optimizer=None or use test values.

Using Test Values

As of v.0.4.0, Theano has a new mechanism by which graphs are executed on-the-fly, before a theano.function is ever compiled. Since optimizations haven’t been applied at this stage, it is easier for the user to locate the source of some bug. This functionality is enabled through the config flag theano.config.compute_test_value. Its use is best shown through the following example. Here, we use exception_verbosity=high and optimizer=fast_compile, which would not tell you the line at fault. optimizer=None would and it could therefore be used instead of test values.

import numpy
import theano
import theano.tensor as tt

# compute_test_value is 'off' by default, meaning this feature is inactive
theano.config.compute_test_value = 'off' # Use 'warn' to activate this feature

# configure shared variables
W1val = numpy.random.rand(2, 10, 10).astype(theano.config.floatX)
W1 = theano.shared(W1val, 'W1')
W2val = numpy.random.rand(15, 20).astype(theano.config.floatX)
W2 = theano.shared(W2val, 'W2')

# input which will be of shape (5,10)
x  = tt.matrix('x')
# provide Theano with a default test-value
#x.tag.test_value = numpy.random.rand(5, 10)

# transform the shared variable in some way. Theano does not
# know off hand that the matrix func_of_W1 has shape (20, 10)
func_of_W1 = W1.dimshuffle(2, 0, 1).flatten(2).T

# source of error: dot product of 5x10 with 20x10
h1 =, func_of_W1)

# do more stuff
h2 =, W2.T)

# compile and call the actual function
f = theano.function([x], h2)
f(numpy.random.rand(5, 10))

Running the above code generates the following error message:

Traceback (most recent call last):
  File "", line 31, in <module>
    f(numpy.random.rand(5, 10))
  File "PATH_TO_THEANO/theano/compile/function/", line 605, in __call__
  File "PATH_TO_THEANO/theano/compile/function/", line 595, in __call__
    outputs = self.fn()
ValueError: Shape mismatch: x has 10 cols (and 5 rows) but y has 20 rows (and 10 cols)
Apply node that caused the error: Dot22(x, DimShuffle{1,0}.0)
Inputs types: [TensorType(float64, matrix), TensorType(float64, matrix)]
Inputs shapes: [(5, 10), (20, 10)]
Inputs strides: [(80, 8), (8, 160)]
Inputs scalar values: ['not scalar', 'not scalar']

Debugprint of the apply node:
Dot22 [id A] <TensorType(float64, matrix)> ''
 |x [id B] <TensorType(float64, matrix)>
 |DimShuffle{1,0} [id C] <TensorType(float64, matrix)> ''
   |Flatten{2} [id D] <TensorType(float64, matrix)> ''
     |DimShuffle{2,0,1} [id E] <TensorType(float64, 3D)> ''
       |W1 [id F] <TensorType(float64, 3D)>

HINT: Re-running with most Theano optimization disabled could give you a back-traces when this node was created. This can be done with by setting the Theano flags 'optimizer=fast_compile'. If that does not work, Theano optimization can be disabled with 'optimizer=None'.

If the above is not informative enough, by instrumenting the code ever so slightly, we can get Theano to reveal the exact source of the error.

# enable on-the-fly graph computations
theano.config.compute_test_value = 'warn'


# input which will be of shape (5, 10)
x  = tt.matrix('x')
# provide Theano with a default test-value
x.tag.test_value = numpy.random.rand(5, 10)

In the above, we are tagging the symbolic matrix x with a special test value. This allows Theano to evaluate symbolic expressions on-the-fly (by calling the perform method of each op), as they are being defined. Sources of error can thus be identified with much more precision and much earlier in the compilation pipeline. For example, running the above code yields the following error message, which properly identifies line 24 as the culprit.

Traceback (most recent call last):
  File "", line 24, in <module>
    h1 =, func_of_W1)
  File "PATH_TO_THEANO/theano/tensor/", line 4734, in dot
    return _dot(a, b)
  File "PATH_TO_THEANO/theano/graph/", line 545, in __call__
    required = thunk()
  File "PATH_TO_THEANO/theano/graph/", line 752, in rval
    r = p(n, [x[0] for x in i], o)
  File "PATH_TO_THEANO/theano/tensor/", line 4554, in perform
    z[0] = numpy.asarray(, y))
ValueError: matrices are not aligned

The compute_test_value mechanism works as follows:

  • Theano constants and shared variables are used as is. No need to instrument them.
  • A Theano variable (i.e. dmatrix, vector, etc.) should be given a special test value through the attribute tag.test_value.
  • Theano automatically instruments intermediate results. As such, any quantity derived from x will be given a tag.test_value automatically.

compute_test_value can take the following values:

  • off: Default behavior. This debugging mechanism is inactive.
  • raise: Compute test values on the fly. Any variable for which a test value is required, but not provided by the user, is treated as an error. An exception is raised accordingly.
  • warn: Idem, but a warning is issued instead of an Exception.
  • ignore: Silently ignore the computation of intermediate test values, if a variable is missing a test value.


This feature is currently incompatible with Scan and also with ops which do not implement a perform method.

It is also possible to override variables __repr__ method to have them return tag.test_value.

x = tt.scalar('x')
# Assigning test value
x.tag.test_value = 42

# Enable test value printing
theano.config.print_test_value = True

# Disable test value printing
theano.config.print_test_value = False

Running the code above returns the following output:


“How do I Print an Intermediate Value in a Function?”

Theano provides a ‘Print’ op to do this.

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])
this is a very important value __str__ = [ 1.  2.  3.]

Since Theano runs your program in a topological order, you won’t have precise control over the order in which multiple Print() ops are evaluated. For a more precise inspection of what’s being computed where, when, and how, see the discussion “How do I Step through a Compiled Function?”.


Using this Print Theano Op can prevent some Theano optimization from being applied. This can also happen with stability optimization. So if you use this Print and have nan, try to remove them to know if this is the cause or not.

“How do I Print a Graph?” (before or after compilation)

Theano provides two functions (theano.pp() and theano.printing.debugprint()) to print a graph to the terminal before or after compilation. These two functions print expression graphs in different ways: pp() is more compact and math-like, debugprint() is more verbose. Theano also provides theano.printing.pydotprint() that creates a png image of the function.

You can read about them in printing – Graph Printing and Symbolic Print Statement.

“The Function I Compiled is Too Slow, what’s up?”

First, make sure you’re running in FAST_RUN mode. Even though FAST_RUN is the default mode, insist by passing mode='FAST_RUN' to theano.function (or theano.make) or by setting config.mode to FAST_RUN.

Second, try the Theano profiling. This will tell you which Apply nodes, and which ops are eating up your CPU cycles.


  • Use the flags floatX=float32 to require type float32 instead of float64; Use the Theano constructors matrix(),vector(),… instead of dmatrix(), dvector(),… since they respectively involve the default types float32 and float64.
  • Check in the profile mode that there is no Dot op in the post-compilation graph while you are multiplying two matrices of the same type. Dot should be optimized to dot22 when the inputs are matrices and of the same type. This can still happen when using floatX=float32 when one of the inputs of the graph is of type float64.

“Why does my GPU function seem to be slow?”

When you compile a theano function, if you do not get the speedup that you expect over the CPU performance of the same code. It is oftentimes due to the fact that some Ops might be running on CPU instead GPU. If that is the case, you can use assert_no_cpu_op to check if there is a CPU Op on your computational graph. assert_no_cpu_op can take the following one of the three options:

  • warn: Raise a warning
  • pdb: Stop with a pdb in the computational graph during the compilation
  • raise: Raise an error, if there is a CPU Op in the computational graph.

It is possible to use this mode by providing the flag in THEANO_FLAGS, such as: THEANO_FLAGS="float32,device=gpu,assert_no_cpu_op='raise'" python

But note that this optimization will not catch all the CPU Ops, it might miss some Ops.

“How do I Step through a Compiled Function?”

You can use MonitorMode to inspect the inputs and outputs of each node being executed when the function is called. The code snipped below shows how to print all inputs and outputs:

from __future__ import print_function
import theano

def inspect_inputs(fgraph, i, node, fn):
    print(i, node, "input(s) value(s):", [input[0] for input in fn.inputs],

def inspect_outputs(fgraph, i, node, fn):
    print(" output(s) value(s):", [output[0] for output in fn.outputs])

x = theano.tensor.dscalar('x')
f = theano.function([x], [5 * x],
0 Elemwise{mul,no_inplace}(TensorConstant{5.0}, x) input(s) value(s): [array(5.0), array(3.0)] output(s) value(s): [array(15.0)]

When using these inspect_inputs and inspect_outputs functions with MonitorMode, you should see [potentially a lot of] printed output. Every Apply node will be printed out, along with its position in the graph, the arguments to the functions perform or c_code and the output it computed. Admittedly, this may be a huge amount of output to read through if you are using big tensors… but you can choose to add logic that would, for instance, print something out only if a certain kind of op were used, at a certain program position, or only if a particular value showed up in one of the inputs or outputs. A typical example is to detect when NaN values are added into computations, which can be achieved as follows:

import numpy

import theano

# This is the current suggested detect_nan implementation to
# show you how it work.  That way, you can modify it for your
# need.  If you want exactly this method, you can use
# ``theano.compile.monitormode.detect_nan`` that will always
# contain the current suggested version.

def detect_nan(fgraph, i, node, fn):
    for output in fn.outputs:
        if (not isinstance(output[0], numpy.random.RandomState) and
            print('*** NaN detected ***')
            print('Inputs : %s' % [input[0] for input in fn.inputs])
            print('Outputs: %s' % [output[0] for output in fn.outputs])

x = theano.tensor.dscalar('x')
f = theano.function([x], [theano.tensor.log(x) * x],
f(0)  # log(0) * 0 = -inf * 0 = NaN
*** NaN detected ***
Elemwise{Composite{(log(i0) * i0)}} [id A] ''
 |x [id B]
Inputs : [array(0.0)]
Outputs: [array(nan)]

To help understand what is happening in your graph, you can disable the local_elemwise_fusion and all inplace optimizations. The first is a speed optimization that merges elemwise operations together. This makes it harder to know which particular elemwise causes the problem. The second optimization makes some ops’ outputs overwrite their inputs. So, if an op creates a bad output, you will not be able to see the input that was overwritten in the post_func function. To disable those optimizations (with a Theano version after 0.6rc3), define the MonitorMode like this:

mode = theano.compile.MonitorMode(post_func=detect_nan).excluding(
    'local_elemwise_fusion', 'inplace')
f = theano.function([x], [theano.tensor.log(x) * x],


The Theano flags optimizer_including, optimizer_excluding and optimizer_requiring aren’t used by the MonitorMode, they are used only by the default mode. You can’t use the default mode with MonitorMode, as you need to define what you monitor.

To be sure all inputs of the node are available during the call to post_func, you must also disable the garbage collector. Otherwise, the execution of the node can garbage collect its inputs that aren’t needed anymore by the Theano function. This can be done with the Theano flag:


How to Use pdb

In the majority of cases, you won’t be executing from the interactive shell but from a set of Python scripts. In such cases, the use of the Python debugger can come in handy, especially as your models become more complex. Intermediate results don’t necessarily have a clear name and you can get exceptions which are hard to decipher, due to the “compiled” nature of the functions.

Consider this example script (“”):

import theano
import numpy
import theano.tensor as tt

a = tt.dmatrix('a')
b = tt.dmatrix('b')

f = theano.function([a, b], [a * b])

# matrices chosen so dimensions are unsuitable for multiplication
mat1 = numpy.arange(12).reshape((3, 4))
mat2 = numpy.arange(25).reshape((5, 5))

f(mat1, mat2)

This is actually so simple the debugging could be done easily, but it’s for illustrative purposes. As the matrices can’t be multiplied element-wise (unsuitable shapes), we get the following exception:

File "", line 14, in <module>
  f(mat1, mat2)
File "/u/username/Theano/theano/compile/function/", line 451, in __call__
File "/u/username/Theano/theano/graph/", line 271, in streamline_default_f
File "/u/username/Theano/theano/graph/", line 267, in streamline_default_f
File "/u/username/Theano/theano/graph/", line 1049, in execute ValueError: ('Input dimension mis-match. (input[0].shape[0] = 3, input[1].shape[0] = 5)', Elemwise{mul,no_inplace}(a, b), Elemwise{mul,no_inplace}(a, b))

The call stack contains some useful information to trace back the source of the error. There’s the script where the compiled function was called – but if you’re using (improperly parameterized) prebuilt modules, the error might originate from ops in these modules, not this script. The last line tells us about the op that caused the exception. In this case it’s a “mul” involving variables with names “a” and “b”. But suppose we instead had an intermediate result to which we hadn’t given a name.

After learning a few things about the graph structure in Theano, we can use the Python debugger to explore the graph, and then we can get runtime information about the error. Matrix dimensions, especially, are useful to pinpoint the source of the error. In the printout, there are also 2 of the 4 dimensions of the matrices involved, but for the sake of example say we’d need the other dimensions to pinpoint the error. First, we re-launch with the debugger module and run the program with “c”:

python -m pdb
> /u/username/experiments/doctmp1/<module>()
-> import theano
(Pdb) c

Then we get back the above error printout, but the interpreter breaks in that state. Useful commands here are

  • “up” and “down” (to move up and down the call stack),
  • “l” (to print code around the line in the current stack position),
  • “p variable_name” (to print the string representation of ‘variable_name’),
  • “p dir(object_name)”, using the Python dir() function to print the list of an object’s members

Here, for example, I do “up”, and a simple “l” shows me there’s a local variable “node”. This is the “node” from the computation graph, so by following the “node.inputs”, “node.owner” and “node.outputs” links I can explore around the graph.

That graph is purely symbolic (no data, just symbols to manipulate it abstractly). To get information about the actual parameters, you explore the “thunk” objects, which bind the storage for the inputs (and outputs) with the function itself (a “thunk” is a concept related to closures). Here, to get the current node’s first input’s shape, you’d therefore do “p thunk.inputs[0][0].shape”, which prints out “(3, 4)”.

Dumping a Function to help debug

If you are reading this, there is high chance that you emailed our mailing list and we asked you to read this section. This section explain how to dump all the parameter passed to theano.function(). This is useful to help us reproduce a problem during compilation and it doesn’t request you to make a self contained example.

For this to work, we need to be able to import the code for all Op in the graph. So if you create your own Op, we will need this code. Otherwise, we won’t be able to unpickle it. We already have all the Ops from Theano and Pylearn2.

# Replace this line:
# with
theano.function_dump(filename, ...)
# Where filename is a string to a file that we will write to.

Then send us filename.

Breakpoint during Theano function execution

You can set a breakpoint during the execution of a Theano function with PdbBreakpoint. PdbBreakpoint automatically detects available debuggers and uses the first available in the following order: pudb, ipdb, or pdb.