コード例 #1
0
ファイル: graph.py プロジェクト: Sandy4321/parser-model
b2 = TT.dvector()

from theano.tensor.nnet import crossentropy_softmax_argmax_1hot_with_bias
from theano.compile.function_module import function

xw1 = theano.dot(w1.T, x.T).T
h = ACTIVATION_FUNCTION(xw1 + b1)

if HLAYERS == 2:
    xwh = theano.dot(wh.T, h.T).T
    h = ACTIVATION_FUNCTION(xwh + bh)

#zero = tensor.zeros_like(x[0,:])

if HYPERPARAMETERS["locally normalize"]:
    (kl, softmax, argmax) = crossentropy_softmax_argmax_1hot_with_bias(theano.dot(h, w2), b2, targety)
else:
    prey = theano.dot(h, w2) + b2
    softmax = nnet.sigmoid(prey)
    kl = -TT.mean(TT.sum(targety * TT.log(softmax) + (1 - targety) * TT.log(1 - softmax), axis=1), axis=0)
    argmax = TT.argmax(softmax)

if HLAYERS == 2:
    validatefn = function([x, targety, w1, b1, wh, bh, w2, b2], [kl, softmax, argmax, xw1, xwh], mode=COMPILE_MODE)
    (gw1, gb1, gwh, gbh, gw2, gb2) = TT.grad(kl, [w1, b1, wh, bh, w2, b2])
    trainfn = function([x, targety, w1, b1, wh, bh, w2, b2], [kl, softmax, argmax, xw1, xwh, theano.compile.io.Out(gw1, borrow = True), gb1, gwh, gbh, gw2, gb2], mode=COMPILE_MODE)
else:
    validatefn = function([x, targety, w1, b1, w2, b2], [kl, softmax, argmax, xw1], mode=COMPILE_MODE)
    (gw1, gb1, gw2, gb2) = TT.grad(kl, [w1, b1, w2, b2])
    trainfn = function([x, targety, w1, b1, w2, b2], [kl, softmax, argmax, xw1, theano.compile.io.Out(gw1, borrow = True), gb1, gw2, gb2], mode=COMPILE_MODE)
コード例 #2
0
xw1 = theano.dot(w1.T, x.T).T
h = ACTIVATION_FUNCTION(xw1 + b1)

if HLAYERS == 2:
    xwh = theano.dot(wh.T, h.T).T
    h = ACTIVATION_FUNCTION(xwh + bh)

#zero = tensor.zeros_like(x[0,:])
(kl, softmax,
 argmax) = crossentropy_softmax_argmax_1hot_with_bias(theano.dot(h, w2), b2,
                                                      targety)

if HLAYERS == 2:
    validatefn = function([x, targety, w1, b1, wh, bh, w2, b2],
                          [kl, softmax, argmax, xw1, xwh],
                          mode=COMPILE_MODE)
    (gw1, gb1, gwh, gbh, gw2, gb2) = TT.grad(kl, [w1, b1, wh, bh, w2, b2])
    trainfn = function([x, targety, w1, b1, wh, bh, w2, b2], [
        kl, softmax, argmax, xw1, xwh,
        theano.compile.io.Out(gw1, borrow=True), gb1, gwh, gbh, gw2, gb2
    ],
                       mode=COMPILE_MODE)
else:
    validatefn = function([x, targety, w1, b1, w2, b2],
                          [kl, softmax, argmax, xw1],
                          mode=COMPILE_MODE)
    (gw1, gb1, gw2, gb2) = TT.grad(kl, [w1, b1, w2, b2])
    trainfn = function([x, targety, w1, b1, w2, b2], [
        kl, softmax, argmax, xw1,
        theano.compile.io.Out(gw1, borrow=True), gb1, gw2, gb2
コード例 #3
0
ファイル: train.py プロジェクト: Sandy4321/parser-model
b1R = TT.dvector('b1')
w2R = TT.dmatrix('w2')
b2R = TT.dvector('b2')

import pylearn.algorithms.cost as cost
from theano.compile.function_module import function

#xw1R = theano.dot(w1R.T, xR.T).T
xw1R = TS.structured_dot(w1R.T, xR.T).T
#print w1R.type
#print xR.type

hR = ACTIVATION_FUNCTION(xw1R + b1R)
yR = nnet.sigmoid(theano.dot(hR, w2R).T + b2R)
loss = cost.KL_divergence(targR, yR)
fn = function([xR, targR, w1R, b1R, w2R, b2R], [yR, loss], mode=COMPILE_MODE)
(gw1, gb1, gw2, gb2) = TT.grad(loss, [w1R, b1R, w2R, b2R])
trainfn = function([xR, targR, w1R, b1R, w2R, b2R], [yR, loss, theano.compile.io.Out(gw1, borrow = True), gb1, gw2, gb2, hR], mode=COMPILE_MODE)
#print type(hR), type(yR)

print "TRAINING"
nex = xinstances.shape[0]
for epoch in range(EPOCHS):
    print "Epoch #", epoch
    for j in range(nex):
        #print "Example #", j
        x = xinstances[j,:]
#        #print "x", x.todense()
#        #print x.indices
        targety = targets[j,:]
        #print "target y", targety