Python tensor.dscalars函数代码示例

    def test_scalars(self):
        
        try:
            import theano.tensor as T
            from theano import function            
        except:
            return
        
        # Set up variables and function
        vals = [1, 2, 3, 4, 5]
        f = lambda a, b, c, d, e : a + (b * c) - d ** e

        # Set up our objects
        Cs = [ch.Ch(v) for v in vals]
        C_result = f(*Cs)

        # Set up Theano's equivalents
        Ts = T.dscalars('T1', 'T2', 'T3', 'T4', 'T5')
        TF = f(*Ts)        
        T_result = function(Ts, TF)        

        # Make sure values and derivatives are equal
        self.assertEqual(C_result.r, T_result(*vals))
        for k in range(len(vals)):
            theano_derivative = function(Ts, T.grad(TF, Ts[k]))(*vals)
            #print C_result.dr_wrt(Cs[k])
            our_derivative = C_result.dr_wrt(Cs[k])[0,0]
            #print theano_derivative, our_derivative
            self.assertEqual(theano_derivative, our_derivative)

def sample_gradient():
    print "微分"
    x, y = T.dscalars("x", "y")
    z = (x+2*y)**2
    # dz/dx
    gx = T.grad(z, x)
    fgx = theano.function([x,y], gx)
    print fgx(1.0, 1.0)
    # dz/dy
    gy = T.grad(z, y)
    fgy = theano.function([x,y], gy)
    print fgy(1.0, 1.0)
    # d{sigmoid(x)}/dx
    x = T.dscalar("x")
    sig = sigmoid(x)
    dsig = T.grad(sig, x)
    f = theano.function([x], dsig)
    print f(0.0)
    print f(1.0)
    # d{sigmoid(<x,w>)}/dx
    w = T.dscalar("w")
    sig = sigmoid(T.dot(x,w))
    dsig = T.grad(sig, x)
    f = theano.function([x, w], dsig)
    print f(1.0, 2.0)
    print f(3.0, 4.0)
    print

    def test_examples_6(self):

        from theano import Param
        x, y = T.dscalars('x', 'y')
        z = x + y
        f = function([x, Param(y, default=1)], z)
        assert f(33)    == array(34.0)
        assert f(33, 2) == array(35.0)

def defaultValue(*arg):
    x, y, w = T.dscalars('x', 'y', 'w')
    z = x + y + w
    f = th.function([x, th.In(y, value=1), th.In(w, value=2, name='wName')], z)
    if len(arg) == 3:
        print(f(arg[0], wName = arg[1], y = arg[2]))
    elif len(arg) == 2:
        print(f(arg[0], arg[1]))
    else:
        print(f(arg[0]))

 def test_examples_7(self):
     from theano import Param
     x, y, w = T.dscalars('x', 'y', 'w')
     z = (x + y) * w
     f = function([x, Param(y, default=1), Param(w, default=2, name='w_by_name')], z)
     assert f(33)                   == array(68.0)
     assert f(33, 2)                == array(70.0)
     assert f(33, 0, 1)             == array(33.0)
     assert f(33, w_by_name=1)      == array(34.0)
     assert f(33, w_by_name=1, y=0) == array(33.0)

 def test_default_values(self):
     # Check that default values are restored
     # when an exception occurs in interactive mode.
     a, b = T.dscalars('a', 'b')
     c = a + b
     func = theano.function([theano.In(a, name='first'), theano.In(b, value=1, name='second')], c)
     x = func(first=1)
     try:
         func(second=2)
     except TypeError:
         assert(func(first=1) == x)

    def test_deepcopy_trust_input(self):
        a = T.dscalar()  # the a is for 'anonymous' (un-named).
        x, s = T.dscalars('xs')

        f = function([x, In(a, value=1.0, name='a'),
                      In(s, value=0.0, update=s + a * x, mutable=True)],
                     s + a * x)
        f.trust_input = True
        try:
            g = copy.deepcopy(f)
        except NotImplementedError as e:
            if e[0].startswith('DebugMode is not picklable'):
                return
            else:
                raise
        self.assertTrue(f.trust_input is g.trust_input)
        f(np.asarray(2.))
        self.assertRaises((ValueError, AttributeError), f, 2.)
        g(np.asarray(2.))
        self.assertRaises((ValueError, AttributeError), g, 2.)

def test():
    # multiple inputs, multiple outputs
    a, b = T.dmatrices('a', 'b')
    diff = a - b
    abs_diff = T.abs_(diff)
    sqr_diff = diff ** 2
    f = function([a, b], [diff, abs_diff, sqr_diff])
    h, i, j = f([[0, 1], [2, 3]], [[4, 5], [6, 7]])

    # default value for function arguments
    a, b = T.dscalars('a', 'b')
    z = a + b
    f = function([a, Param(b, default=1)], z)
    print f(1, b=2)
    print f(1)
    print f(1, 2)

    # shared variable
    state = shared(0)
    inc = T.lscalar('inc') # state is int64 by default
    accumulator = function([inc], state, updates=[(state, state + inc)])
    print accumulator(300)
    print state.get_value()

def brachistochrone_functional():
    # define all symbols
    lx, ly = T.dscalars('lx', 'ly')
    fseq = T.dvector('fseq')
    N = fseq.size + 1
    delta_x = lx / N
    iseq = T.arange(N-1)

    # functional term
    functional_ithterm = lambda i: T.switch(T.eq(i, 0),
                                            T.sqrt(0.5*(delta_x**2+(fseq[0]-ly)**2)/(ly-0.5*(fseq[0]+ly))),
                                            T.sqrt(0.5*(delta_x**2+(fseq[i]-fseq[i-1])**2)/(ly-0.5*(fseq[i]+fseq[i-1])))
                                            )

    # defining the functions
    functional_parts, _ = theano.map(fn=lambda k: functional_ithterm(k), sequences=[iseq])
    functional = functional_parts.sum() + T.sqrt(0.5*(delta_x**2+(0-fseq[N-2])**2)/(ly-0.5*(0+fseq[N-2])))
    gfunc = T.grad(functional, fseq)

    # compile the functions
    time_fcn = theano.function(inputs=[fseq, lx, ly], outputs=functional)
    grad_time_fcn = theano.function(inputs=[fseq, lx, ly], outputs=gfunc)

    return time_fcn, grad_time_fcn

'''
Created on Jun 1, 2015

@author: xujian
'''

import theano
from theano import Param
import theano.tensor as T
from samba.dcerpc.atsvc import Third

a,b,c = T.dscalars('a','b','c')
z=(a+b)*c
f=theano.function([a,Param(b,default=0),Param(c,default=1,name="third_var")],z)
print f(1)
print f(1,2)
print f(1,third_var=2)

'''
Executing multiple functions
'''
a,b = T.dmatrices('a','b')
diff = a-b
abs_diff = abs(a-b)
diff_sq = diff**2
mult = function([a,b],[diff,abs_diff,diff_sq])
print mult([[0,1],[1,2]],[[-1,2],[5,7]])
#print pp(diff)
#print pp(abs_diff)

'''
Setting a default value for an argument
So, if arg not give, take default value; else take the given value
'''
x, y = T.dscalars("x","y")
z = x+y
add = function([x,Param(y,default=1)],z)
print add(33.0)
print add(2,6)

'''
Setting names to parameters
'''
x,y,w = T.dscalars("x","y","w")
z = (x+y)*w
add_par = function([x,Param(y,default=1),Param(w,default=2,name="debalu")],z)
print add_par(33)
print add_par(33,6,debalu=5)

def defaultValue():
    x, y, z = T.dscalars('x', 'y', 'z')
    return function([x, In(y, value=1), In(z, value=2, name='namedZ')], (x + y) * z)

def tuto():
    
    print "\nLogistic Function 1"
    print "---------------------"
    x = T.dmatrix('x')
    s = 1 / (1 + T.exp(-x))
    logistic = theano.function([x], s)
    print logistic([[0, 1], [-1, -2]])

    print "\nLogistic Function 2"
    print "---------------------"
    s2 = (1 + T.tanh(x / 2)) / 2
    logistic2 = theano.function([x], s2)
    print logistic2([[0, 1], [-1, -2]])
    
    print "\nComputing More than one Thing at the Same Time"
    print "------------------------------------------------"
    a, b = T.dmatrices('a', 'b')
    diff = a - b
    abs_diff = abs(diff)
    diff_squared = diff**2
    f = theano.function([a, b], [diff, abs_diff, diff_squared])
    print f([[1, 1], [1, 1]], [[0, 1], [2, 3]]) 
    
    print "\nSetting a Default Value for an Argument"
    print "---------------------------------------"
    x, y = T.dscalars('x', 'y')
    z = x + y
    f = function([x, In(y, value=1)], z)
    print f(33)
    print f(33, 2)
    
    print "A Real Example: Logistic Regression"
    print "-----------------------------------"
    rng = numpy.random
    N = 400                                   # training sample size
    feats = 784                               # number of input variables
    
    # generate a dataset: D = (input_values, target_class)
    D = (rng.randn(N, feats), rng.randint(size=N, low=0, high=2))
    training_steps = 10000
    
    # Declare Theano symbolic variables
    x = T.dmatrix("x")
    y = T.dvector("y")
    
    # initialize the weight vector w randomly
    #
    # this and the following bias variable b
    # are shared so they keep their values
    # between training iterations (updates)
    w = theano.shared(rng.randn(feats), name="w")
    
    # initialize the bias term
    b = theano.shared(0., name="b")
    
    print("Initial model:")
    print(w.get_value())
    print(b.get_value())
    
    # Construct Theano expression graph
    p_1 = 1 / (1 + T.exp(-T.dot(x, w) - b))   # Probability that target = 1
    prediction = p_1 > 0.5                    # The prediction thresholded
    xent = -y * T.log(p_1) - (1-y) * T.log(1-p_1) # Cross-entropy loss function
    cost = xent.mean() + 0.01 * (w ** 2).sum()# The cost to minimize
    gw, gb = T.grad(cost, [w, b])             # Compute the gradient of the cost
                                              # w.r.t weight vector w and
                                              # bias term b
                                              # (we shall return to this in a
                                              # following section of this tutorial)
    
    # Compile
    train = theano.function(
              inputs=[x,y],
              outputs=[prediction, xent],
              updates=((w, w - 0.1 * gw), (b, b - 0.1 * gb)))
    predict = theano.function(inputs=[x], outputs=prediction)
    
    # Train
    for i in range(training_steps):
        pred, err = train(D[0], D[1])
    
    print("Final model:")
    print(w.get_value())
    print(b.get_value())
    print("target values for D:")
    print(D[1])
    print("prediction on D:")
    print(predict(D[0]))

def test_1_examples_param_default():
    x, y = T.dscalars('x', 'y')
    f = theano.function([x, theano.Param(y, default=1)], x + y)
    assert f(1, 2) == 3
    assert f(1) == 2

from time import clock
from numpy import ones

from theano import Mode
from theano import function

from theano.tensor import dscalars
from theano.tensor import dmatrices

from theano.tensor import lt
from theano.tensor import mean

from theano.tensor import switch
from theano.ifelse import ifelse

a_dscalar, b_dscalar = dscalars('a', 'b')
x_dmatrix, y_dmatrix = dmatrices('x', 'y')

z_switch_dmatrix = switch(lt(a_dscalar, b_dscalar), mean(x_dmatrix), mean(y_dmatrix))
z_ifelse_dmatrix = ifelse(lt(a_dscalar, b_dscalar), mean(x_dmatrix), mean(y_dmatrix))

# Both ops build a condition over symbolic variables. IfElse takes a boolean condition and two variables as inputs.
# Switch evaluates both output variables, ifelse is lazy and only evaluates one variable with respect to the condition.
# Unless linker='vm' or linker='cvm' are used, ifelse will compute both variables and take the same computation time as switch.
f_switch = function([a_dscalar, b_dscalar, x_dmatrix, y_dmatrix], z_switch_dmatrix, mode=Mode(linker='vm'))
f_ifelse = function([a_dscalar, b_dscalar, x_dmatrix, y_dmatrix], z_ifelse_dmatrix, mode=Mode(linker='vm'))

var1 = 0.
var2 = 1.
big_mat1 = ones((10000, 1000))
big_mat2 = ones((10000, 1000))