
.. _tutorial_printing_drawing:

==============================
Printing/Drawing Theano graphs
==============================

.. TODO: repair the defective links in the next paragraph

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

Consider again the logistic regression but notice the additional printing instuctions. 
The following output depicts the pre- and post- compilation graphs.

.. code-block:: python
    
    import numpy
    import theano
    import theano.tensor as T
    rng = numpy.random

    N = 400
    feats = 784
    D = (rng.randn(N, feats).astype(theano.config.floatX),
    rng.randint(size=N,low=0, high=2).astype(theano.config.floatX))
    training_steps = 10000

    # Declare Theano symbolic variables
    x = T.matrix("x")
    y = T.vector("y")
    w = theano.shared(rng.randn(feats).astype(theano.config.floatX), name="w")
    b = theano.shared(numpy.asarray(0., dtype=theano.config.floatX), name="b")
    x.tag.test_value = D[0]
    y.tag.test_value = D[1]
    #print "Initial model:"
    #print w.get_value(), b.get_value()


    # Construct Theano expression graph
    p_1 = 1 / (1 + T.exp(-T.dot(x, w) - b)) # Probabily of having a one
    prediction = p_1 > 0.5 # The prediction that is done: 0 or 1
    xent = -y * T.log(p_1) - (1 - y) * T.log(1 - p_1) # Cross-entropy
    cost = xent.mean() + 0.01 * (w ** 2).sum() # The cost to optimize
    gw,gb = T.grad(cost, [w, b])

    # Compile expressions to functions
    train = theano.function(
                inputs=[x, y],
                outputs=[prediction, xent],
                updates={w: w - 0.01 * gw, b: b - 0.01 * gb},
                name="train")
    predict = theano.function(inputs=[x], outputs=prediction,
                name="predict")

    if any( [x.op.__class__.__name__=='Gemv' for x in
    train.maker.fgraph.toposort()]):
        print 'Used the cpu'
    elif any( [x.op.__class__.__name__=='GpuGemm' for x in
    train.maker.fgraph.toposort()]):
        print 'Used the gpu'
    else:
        print 'ERROR, not able to tell if theano used the cpu or the gpu'
        print train.maker.fgraph.toposort()


    for i in range(training_steps):
        pred, err = train(D[0], D[1])
    #print "Final model:"
    #print w.get_value(), b.get_value()

    print "target values for D"
    print D[1]

    print "prediction on D"
    print predict(D[0])


    # Print the picture graphs
    # after compilation
    theano.printing.pydotprint(predict,
                               outfile="pics/logreg_pydotprint_predic.png",
                               var_with_name_simple=True)
    # before compilation
    theano.printing.pydotprint_variables(prediction,
                               outfile="pics/logreg_pydotprint_prediction.png",
                               var_with_name_simple=True)
    theano.printing.pydotprint(train,
                               outfile="pics/logreg_pydotprint_train.png",
                               var_with_name_simple=True)


Pretty Printing
===============

``theano.printing.pprint(variable)``

>>> theano.printing.pprint(prediction)  # (pre-compilation)
gt((TensorConstant{1} / (TensorConstant{1} + exp(((-(x \\dot w)) - b)))),TensorConstant{0.5})


Debug Printing
==============

``theano.printing.debugprint({fct, variable, list of variables})``

>>> theano.printing.debugprint(prediction)  # (pre-compilation)
Elemwise{gt,no_inplace} [@181772236] ''
 |Elemwise{true_div,no_inplace} [@181746668] ''
 | |InplaceDimShuffle{x} [@181746412] ''
 | | |TensorConstant{1} [@181745836]
 | |Elemwise{add,no_inplace} [@181745644] ''
 | | |InplaceDimShuffle{x} [@181745420] ''
 | | | |TensorConstant{1} [@181744844]
 | | |Elemwise{exp,no_inplace} [@181744652] ''
 | | | |Elemwise{sub,no_inplace} [@181744012] ''
 | | | | |Elemwise{neg,no_inplace} [@181730764] ''
 | | | | | |dot [@181729676] ''
 | | | | | | |x [@181563948]
 | | | | | | |w [@181729964]
 | | | | |InplaceDimShuffle{x} [@181743788] ''
 | | | | | |b [@181730156]
 |InplaceDimShuffle{x} [@181771788] ''
 | |TensorConstant{0.5} [@181771148]
>>> theano.printing.debugprint(predict)  # (post-compilation)
Elemwise{Composite{neg,{sub,{{scalar_sigmoid,GT},neg}}}} [@183160204] ''   2
 |dot [@183018796] ''   1
 | |x [@183000780]
 | |w [@183000812]
 |InplaceDimShuffle{x} [@183133580] ''   0
 | |b [@183000876]
 |TensorConstant{[ 0.5]} [@183084108]


Picture Printing
================

>>> theano.printing.pydotprint_variables(prediction)  # (pre-compilation)

.. image:: ../hpcs2011_tutorial/pics/logreg_pydotprint_prediction.png
   :width: 800 px

Notice that ``pydotprint()`` requires *Graphviz* and Python's ``pydot``.

>>> theano.printing.pydotprint(predict)  # (post-compilation)

.. image:: ../hpcs2011_tutorial/pics/logreg_pydotprint_predic.png
   :width: 800 px

>>> theano.printing.pydotprint(train) # This is a small train example!

.. image:: ../hpcs2011_tutorial/pics/logreg_pydotprint_train.png
   :width: 1500 px

