#encoding:UTF-8
__author__ = 'auroua'
import theano_test.tensor as T
from theano_test import function
import theano_test
import pydot
print pydot.find_graphviz()
x = T.dmatrix('x')
y = x*2
print type(y.owner)
print y.owner.op.name
print len(y.owner.inputs)
print type(y.owner.inputs[1].owner)
#apply nodes are those that define which computations the graph does
# When compiling a Theano function, what you give to the theano.function is actually a graph
# (starting from the output variables you can traverse the graph up to the input variables).
# While this graph structure shows how to compute the output from the input,
# it also offers the possibility to improve the way this computation is carried out.
a = T.vector('a')
b = a+a**10
fab = function([a],b)
print fab([0,1,2])
theano_test.printing.pydotprint(b, outfile="/home/auroua/symbolic_graph_unopt.png", var_with_name_simple=True)
theano_test.printing.pydotprint(fab, outfile="/home/auroua/symbolic_graph_opt.png", var_with_name_simple=True)
#encoding:UTF-8
__author__ = 'auroua'
import theano_test
from theano_test import pp
from theano_test import function
import theano_test.tensor as T
x = T.dscalar('x')
y = x**2
gy = T.grad(y,x)
f = function([x],y)
print f(4)
x2 = T.dmatrix('x2')
s = T.sum(1/(1+T.exp(-x2)))
gs = T.grad(s,x2)
dlogistic = function([x2],gs)
print dlogistic([[0,1],[-1,-2]])
x3 = T.dvector('x3')
y3 = x3**2
J,updates = theano_test.scan(lambda i,y,x:T.grad(y[i],x),sequences=T.arange(y3.shape[0]),non_sequences=[y3,x3])
f = function([x3],J,updates=updates)
print f([4,4])
x4 = T.dvector('x4')
y4 = x4**2
cost = y4.sum()
gy4 = T.grad(cost,x4)
H,updates2 = theano_test.scan(lambda i,gy,x4:T.grad(gy[i],x4),sequences=T.arange(gy4.shape[0]),non_sequences=[gy4,x4])
__author__ = "auroua"
import theano_test.tensor as T
from theano_test import function
from theano_test import pp
x = T.dscalar("x")
y = T.dscalar("y")
z = x + y
f = function([x, y], z)
f(2, 3)
z.eval({x: 16.3, y: 14.3})
print z
print pp(z)
xm = T.dmatrix("xm")
ym = T.dmatrix("ym")
zm = xm + ym
f2 = function((xm, ym), zm)
f2(np.array([[1, 2], [2, 3]]), np.array([[3, 4], [4, 5]]))
xv = T.dvector("xv")
yv = T.dvector("yv")
zv = xv ** 2 + yv ** 2 + 2 * xv * yv
fv = function((xv, yv), zv)
print pp(zv)
print fv([1, 2], [3, 4])