DOC fixed Python 3 compatibility issues in Tutorial and Library Reference

doc/library/compile/io.txt

@@ -90,7 +90,7 @@ Since we provided a ``value`` for ``s`` and ``x``, we can call it with just a va
 
 >>> inc(5)         # update s with 10+3*5
 []
->>> print inc[s]
+>>> print(inc[s])
 25.0
 
 The effect of this call is to increment the storage associated to ``s`` in ``inc`` by 15.
@@ -100,9 +100,9 @@ If we pass two arguments to ``inc``, then we override the value associated to
 
 >>> inc(3, 4)      # update s with 25 + 3*4
 []
->>> print inc[s]
+>>> print(inc[s])
 37.0
->>> print inc[x]   # the override value of 4 was only temporary
+>>> print(inc[x])   # the override value of 4 was only temporary
 3.0
 
 If we pass three arguments to ``inc``, then we override the value associated
@@ -111,7 +111,7 @@ Since ``s``'s value is updated on every call, the old value of ``s`` will be ign
 
 >>> inc(3, 4, 7)      # update s with 7 + 3*4
 []
->>> print inc[s]
+>>> print(inc[s])
 19.0
 
 We can also assign to ``inc[s]`` directly:

doc/library/config.txt

@@ -35,7 +35,7 @@ variables, type this from the command-line:
 
 .. code-block:: bash
 
-    python -c 'import theano; print theano.config' | less
+    python -c 'import theano; print(theano.config)' | less
 
 Environment Variables
 =====================
@@ -98,7 +98,7 @@ import theano and print the config variable, as in:
 
 .. code-block:: bash
 
-    python -c 'import theano; print theano.config' | less
+    python -c 'import theano; print(theano.config)' | less
 
 .. attribute:: device
 
@@ -525,7 +525,7 @@ import theano and print the config variable, as in:
     This is a Python format string that specifies the subdirectory
     of ``config.base_compiledir`` in which to store platform-dependent
     compiled modules. To see a list of all available substitution keys,
-    run ``python -c "import theano; print theano.config"``, and look
+    run ``python -c "import theano; print(theano.config)"``, and look
     for compiledir_format.
 
     This flag's value cannot be modified during the program execution.

doc/library/scan.txt

@@ -24,7 +24,7 @@ More precisely, if *A* is a tensor you want to compute
 .. code-block:: python
 
   result = 1
-  for i in xrange(k):
+  for i in range(k):
       result = result * A
 
 There are three things here that we need to handle: the initial value
@@ -57,8 +57,8 @@ The equivalent Theano code would be:
   # compiled function that returns A**k
   power = theano.function(inputs=[A,k], outputs=final_result, updates=updates)
 
-  print power(range(10),2)
-  print power(range(10),4)
+  print(power(range(10),2))
+  print(power(range(10),4))
 
 .. testoutput::
 
@@ -121,8 +121,8 @@ from a list of its coefficients:
     # Test
     test_coefficients = numpy.asarray([1, 0, 2], dtype=numpy.float32)
     test_value = 3
-    print calculate_polynomial(test_coefficients, test_value)
-    print 1.0 * (3 ** 0) + 0.0 * (3 ** 1) + 2.0 * (3 ** 2)
+    print(calculate_polynomial(test_coefficients, test_value))
+    print(1.0 * (3 ** 0) + 0.0 * (3 ** 1) + 2.0 * (3 ** 2))
 
 .. testoutput::
 
@@ -513,7 +513,7 @@ value ``max_value``.
 
     f = theano.function([max_value], values)
 
-    print f(45)
+    print(f(45))
 
 .. testoutput::
 

doc/tutorial/adding.txt

@@ -97,7 +97,7 @@ The second step is to combine *x* and *y* into their sum *z*:
 function to pretty-print out the computation associated to *z*.
 
 >>> from theano import pp
->>> print pp(z)
+>>> print(pp(z))
 (x + y)
 
 

doc/tutorial/aliasing.txt

@@ -279,22 +279,24 @@ For GPU graphs, this borrowing can have a major speed impact.  See the following
                  Out(sandbox.cuda.basic_ops.gpu_from_host(tensor.exp(x)),
                      borrow=True))
    t0 = time.time()
-   for i in xrange(iters):
+   for i in range(iters):
        r = f1()
    t1 = time.time()
    no_borrow = t1 - t0
    t0 = time.time()
-   for i in xrange(iters):
+   for i in range(iters):
        r = f2()
    t1 = time.time()
-   print 'Looping', iters, 'times took', no_borrow, 'seconds without borrow',
-   print 'and', t1 - t0, 'seconds with borrow.'
+   print(
+       "Looping %s times took %s seconds without borrow "
+       "and %s seconds with borrow" % (iters, no_borrow, (t1 - t0))
+   )
    if numpy.any([isinstance(x.op, tensor.Elemwise) and
                  ('Gpu' not in type(x.op).__name__)
                  for x in f1.maker.fgraph.toposort()]):
-       print 'Used the cpu'
+       print('Used the cpu')
    else:
-       print 'Used the gpu'
+       print('Used the gpu')
 
 Which produces this output:
 

doc/tutorial/conditions.txt

@@ -43,14 +43,14 @@ IfElse vs Switch
    n_times = 10
 
    tic = time.clock()
-   for i in xrange(n_times):
+   for i in range(n_times):
        f_switch(val1, val2, big_mat1, big_mat2)
-   print 'time spent evaluating both values %f sec' % (time.clock() - tic)
+   print('time spent evaluating both values %f sec' % (time.clock() - tic))
 
    tic = time.clock()
-   for i in xrange(n_times):
+   for i in range(n_times):
        f_lazyifelse(val1, val2, big_mat1, big_mat2)
-   print 'time spent evaluating one value %f sec' % (time.clock() - tic)
+   print('time spent evaluating one value %f sec' % (time.clock() - tic))
 
 .. testoutput::
    :hide:

doc/tutorial/debug_faq.txt

@@ -328,13 +328,15 @@ shows how to print all inputs and outputs:
 
 .. testcode::
 
+    from __future__ import print_function
     import theano
 
     def inspect_inputs(i, node, fn):
-        print i, node, "input(s) value(s):", [input[0] for input in fn.inputs],
+        print(i, node, "input(s) value(s):", [input[0] for input in fn.inputs],
+              end='')
 
     def inspect_outputs(i, node, fn):
-        print "output(s) value(s):", [output[0] for output in fn.outputs]
+        print("output(s) value(s):", [output[0] for output in fn.outputs])
 
     x = theano.tensor.dscalar('x')
     f = theano.function([x], [5 * x],
@@ -376,10 +378,10 @@ can be achieved as follows:
         for output in fn.outputs:
             if (not isinstance(output[0], numpy.random.RandomState) and
                 numpy.isnan(output[0]).any()):
-                print '*** NaN detected ***'
+                print('*** NaN detected ***')
                 theano.printing.debugprint(node)
-                print 'Inputs : %s' % [input[0] for input in fn.inputs]
-                print 'Outputs: %s' % [output[0] for output in fn.outputs]
+                print('Inputs : %s' % [input[0] for input in fn.inputs])
+                print('Outputs: %s' % [output[0] for output in fn.outputs])
                 break
 
     x = theano.tensor.dscalar('x')

doc/tutorial/loop.txt

@@ -277,7 +277,7 @@ The full documentation can be found in the library: :ref:`Scan <lib_scan>`.
   x = np.eye(5, dtype=theano.config.floatX)[0]
   w = np.eye(5, 3, dtype=theano.config.floatX)
   w[2] = np.ones((3), dtype=theano.config.floatX)
-  print compute_jac_t(w, x)[0]
+  print(compute_jac_t(w, x)[0])
 
   # compare with numpy
   print(((1 - np.tanh(x.dot(w)) ** 2) * w).T)
@@ -412,7 +412,7 @@ Note that if you want to use a random variable ``d`` that will not be updated th
                                        outputs=polynomial)
 
   test_coeff = numpy.asarray([1, 0, 2], dtype=numpy.float32)
-  print calculate_polynomial(test_coeff, 3)
+  print(calculate_polynomial(test_coeff, 3))
 
 .. testoutput::
 

doc/tutorial/modes.txt

@@ -31,7 +31,7 @@ variables, type this from the command-line:
 
 .. code-block:: bash
 
-    python -c 'import theano; print theano.config' | less
+    python -c 'import theano; print(theano.config)' | less
 
 
 For more detail, see :ref:`Configuration <libdoc_config>` in the library.

doc/tutorial/sparse.txt

@@ -138,11 +138,11 @@ a ``csr`` one.
 >>> y = sparse.CSR(data, indices, indptr, shape)
 >>> f = theano.function([x], y)
 >>> a = sp.csc_matrix(np.asarray([[0, 1, 1], [0, 0, 0], [1, 0, 0]]))
->>> print a.toarray()
+>>> print(a.toarray())
 [[0 1 1]
  [0 0 0]
  [1 0 0]]
->>> print f(a).toarray()
+>>> print(f(a).toarray())
 [[0 0 1]
  [1 0 0]
  [1 0 0]]
@@ -165,11 +165,11 @@ provide a structured gradient. More explication below.
 >>> y = sparse.structured_add(x, 2)
 >>> f = theano.function([x], y)
 >>> a = sp.csc_matrix(np.asarray([[0, 0, -1], [0, -2, 1], [3, 0, 0]], dtype='float32'))
->>> print a.toarray()
+>>> print(a.toarray())
 [[ 0.  0. -1.]
  [ 0. -2.  1.]
  [ 3.  0.  0.]]
->>> print f(a).toarray()
+>>> print(f(a).toarray())
 [[ 0.  0.  1.]
  [ 0.  0.  3.]
  [ 5.  0.  0.]]

doc/tutorial/symbolic_graphs.txt

@@ -158,7 +158,7 @@ as we apply it. Consider the following example of optimization:
 >>> a = theano.tensor.vector("a")      # declare symbolic variable
 >>> b = a + a ** 10                    # build symbolic expression
 >>> f = theano.function([a], b)        # compile function
->>> print f([0, 1, 2])                 # prints `array([0,2,1026])`
+>>> print(f([0, 1, 2]))                # prints `array([0,2,1026])`
 [    0.     2.  1026.]
 >>> theano.printing.pydotprint(b, outfile="./pics/symbolic_graph_unopt.png", var_with_name_simple=True)  # doctest: +SKIP
 The output file is available at ./pics/symbolic_graph_unopt.png

doc/tutorial/using_gpu.txt

@@ -48,7 +48,7 @@ file and run it.
     f = function([], T.exp(x))
     print(f.maker.fgraph.toposort())
     t0 = time.time()
-    for i in xrange(iters):
+    for i in range(iters):
         r = f()
     t1 = time.time()
     print("Looping %d times took %f seconds" % (iters, t1 - t0))
@@ -124,7 +124,7 @@ after the ``T.exp(x)`` is replaced by a GPU version of ``exp()``.
     f = function([], sandbox.cuda.basic_ops.gpu_from_host(T.exp(x)))
     print(f.maker.fgraph.toposort())
     t0 = time.time()
-    for i in xrange(iters):
+    for i in range(iters):
         r = f()
     t1 = time.time()
     print("Looping %d times took %f seconds" % (iters, t1 - t0))
@@ -405,7 +405,7 @@ into a file and run it.
   f = function([], tensor.exp(x))
   print(f.maker.fgraph.toposort())
   t0 = time.time()
-  for i in xrange(iters):
+  for i in range(iters):
       r = f()
   t1 = time.time()
   print("Looping %d times took %f seconds" % (iters, t1 - t0))
@@ -473,7 +473,7 @@ the GPU object directly.  The following code is modifed to do just that.
   f = function([], sandbox.gpuarray.basic_ops.gpu_from_host(tensor.exp(x)))
   print(f.maker.fgraph.toposort())
   t0 = time.time()
-  for i in xrange(iters):
+  for i in range(iters):
       r = f()
   t1 = time.time()
   print("Looping %d times took %f seconds" % (iters, t1 - t0))